title: Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue

abstract: Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community's ability to develop better theories, as well as good off the shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic independent way. Our results show that our theoretically motivated features achieve 66% accuracy, an improvement over a unigram baseline of an absolute 6%.


title: Description-oriented community detection using exhaustive subgroup discovery

abstract: Communities can intuitively be defined as subsets of nodes of a graph with a dense structure in the corresponding subgraph. However, for mining such communities usually only structural aspects are taken into account. Typically, no concise nor easily interpretable community description is provided.For tackling this issue, this paper focuses on description-oriented community detection using subgroup discovery. In order to provide both structurally valid and interpretable communities we utilize the graph structure as well as additional descriptive features of the graph's nodes. A descriptive community pattern built upon these features then describes and identifies a community, i.e., a set of nodes, and vice versa. Essentially, we mine patterns in the "description space" characterizing interesting sets of nodes (i.e., subgroups) in the "graph space"; the interestingness of a community is evaluated by a selectable quality measure.We aim at identifying communities according to standard community quality measures, while providing characteristic descriptions of these communities at the same time. For this task, we propose several optimistic estimates of standard community quality functions to be used for efficient pruning of the search space in an exhaustive branch-and-bound algorithm. We demonstrate our approach in an evaluation using five real-world data sets, obtained from three different social media applications.


title: A piezoelectric energy harvester with vortex induced vibration

abstract: This paper presents a piezoelectric energy harvester (PEH) which can convert fluid kinetic energy into electricity with vortex induced vibration (VIV). The PEH is composed by a piezoelectric cantilever beam and a cylinder extension. Dynamic response and energy harvesting of the PEH are studied numerically. It is found that maximum output power can be obtained with optimal resistance and the more power output, the stronger elimination of vibration amplitude. Vibration frequency makes greater contribution on power output at smaller cylinder diameter and decreases with increasing of cylinder diameter. Vibration amplitude plays main role on output power and increases first until reaching peak point with increasing of cylinder diameter. Maximum output power can be obtained with reasonable cylinder diameter.


title: Adapting Mask-RCNN for Automatic Nucleus Segmentation

abstract: Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for a variety of cells acquired under a variety of conditions.


title: On and Off-Blockchain Enforcement Of Smart Contracts

abstract: Emerging blockchain technology is a promising platform for implementing smart contracts. But there is a large class of applications, where blockchain is inadequate due to performance, scalability, and consistency requirements, and also due to language expressiveness and cost issues that are hard to solve. In this paper we explain that in some situations a centralised approach that does not rely on blockchain is a better alternative due to its simplicity, scalability, and performance. We suggest that in applications where decentralisation and transparency are essential, developers can advantageously combine the two approaches into hybrid solutions where some operations are enforced by enforcers deployed on–blockchains and the rest by enforcers deployed on trusted third parties.


title: Corporate governance and stock liquidity dimensions: Panel evidence from pure order-driven Australian market

abstract: Our newly constructed index of corporate governance quality (CGQ) provides comprehensive and robust evidence for the association between CGQ and stock liquidity in the pure order-driven Australia market. By using a large sample of 1207 firms from 2001 to 2013, we find a significant positive relationship between CGQ and stock liquidity, suggesting that better governed firms have greatly improved stock liquidity. Specifically, we find that better governed firms have a lower trading cost, smaller price impacts of trade, and higher trading speed. Moreover, the empirical results reveal that CGQ improves stock liquidity because it is associated with higher information disclosure.


title: A rapid miniprep for the isolation of total DNA fromAgrobacterium tumefaciens

abstract: A procedure which avoids the use of phenol-chloroform and RNAase for the isolation of total DNA fromA. tumefaciens is described. Specific precipitation of protein by 2.5 M ammonium acetate is employed and much of the RNA is removed by an isopropanol precipition step. The procedure yields easily restrictable, good quality DNA and is probably applicable to other Gramnegative bacteria.


COCOTS: a COTS software integration cost model - model overview and preliminary data findings


title: Sensor fusion: A review of methods and applications

abstract: This paper aims to present a brief overview of the development of sensor fusion in various application in recent years, and to understand the challenges and ability of sensor fusion. Various algorithms that are typically employed are covered to comprehend the complexity of usage in different scenarios.


title: User simulation for spoken dialogue systems: Learning and evaluation

abstract: We propose the “advanced” n-grams as a new technique for simulating user behaviour in spoken dialogue systems, and we compare it with two methods used in our prior work, i.e. linear feature combination and “normal” n-grams. All methods operate on the intention level and can incorporate speech recognition and understanding errors. In the linear feature combination model user actions (lists of 〈 speech act, task 〉 pairs) are selected, based on features of the current dialogue state which encodes the whole history of the dialogue. The user simulation based on “normal” n-grams treats a dialogue as a sequence of lists of 〈 speech act, task 〉 pairs. Here the length of the history considered is restricted by the order of the n-gram. The “advanced” n-grams are a variation of the normal ngrams, where user actions are conditioned not only on speech acts and tasks but also on the current status of the tasks, i.e. whether the information needed by the application (in our case flight booking) has been provided and confirmed by the user. This captures elements of goal-directed user behaviour. All models were trained and evaluated on the COMMUNICATOR corpus, to which we added annotations for user actions and dialogue context. We then evaluate how closely the synthetic responses resemble the real user responses by comparing the user response generated by each user simulation model in a given dialogue context (taken from the annotated corpus) with the actual user response. We propose the expected accuracy, expected precision, and expected recall evaluation metrics as opposed to standard precision and recall used in prior work. We also discuss why they are more appropriate metrics for evaluating user simulation models compared to their standard counterparts. The advanced n-grams produce higher scores than the normal n-grams for small values of n, which proves their strength when little amount of data is available to train larger ngrams. The linear model produces the best expected accuracy but with respect to expected precision and expected recall it is outperformed by the large n-grams even though it is trained using more information. As a task-based evaluation, we also run each of the user simulation models against a system policy trained on the same corpus. Here the linear feature combination model outperforms the other methods and the advanced n-grams outperform the normal ngrams for all values of n, which again shows their potential. We also calculate the perplexity of the different user models.


title: Latent Dirichlet Co-Clustering

abstract: We present a generative model for simultaneously clustering documents and terms. Our model is a four-level hierarchical Bayesian model, in which each document is modeled as a random mixture of document topics , where each topic is a distribution over some segments of the text. Each of these segments in the document can be modeled as a mixture of word topics where each topic is a distribution over words. We present efficient approximate inference techniques based on Markov Chain Monte Carlo method and a moment-matching algorithm for empirical Bayes parameter estimation. We report results in document modeling, document and term clustering, comparing to other topic models, Clustering and Co-Clustering algorithms including latent Dirichlet allocation (LDA), model-based overlapping clustering (MOC), model-based overlapping co-clustering (MOCC) and information-theoretic co-clustering (ITCC).


title: An Inference Approach to Basic Level of Categorization

abstract: Humans understand the world by classifying objects into an appropriate level of categories. This process is often automatic and subconscious. Psychologists and linguists call it as Basic-level Categorization (BLC). BLC can benefit lots of applications such as knowledge panel, advertising and recommendation. However, how to quantify basic-level concepts is still an open problem. Recently, much work focuses on constructing knowledge bases or semantic networks from web scale text corpora, which makes it possible for the first time to analyze computational approaches for deriving BLC. In this paper, we introduce a method based on typicality and PMI for BLC. We compare it with a few existing measures such as NPMI and commute time to understand its essence, and conduct extensive experiments to show the effectiveness of our approach. We also give a real application example to show how BLC can help sponsored search.


title: A review of Nyström methods for large-scale machine learning

abstract: Nystrom methods are state-of-the-art techniques for large scale machine learning.Both the standard and enhanced Nystrom methods are reviewed.Different sampling methods are also reviewed and compared.Typical machine learning applications are summarized.Interesting open problems are discussed. Generating a low-rank matrix approximation is very important in large-scale machine learning applications. The standard Nystrom method is one of the state-of-the-art techniques to generate such an approximation. It has got rapid developments since being applied to Gaussian process regression. Several enhanced Nystrom methods such as ensemble Nystrom, modified Nystrom and SS-Nystrom have been proposed. In addition, many sampling methods have been developed. In this paper, we review the Nystrom methods for large-scale machine learning. First, we introduce various Nystrom methods. Second, we review different sampling methods for the Nystrom methods and summarize them from the perspectives of both theoretical analysis and practical performance. Then, we list several typical machine learning applications that utilize the Nystrom methods. Finally, we make our conclusions after discussing some open machine learning problems related to Nystrom methods.


title: Low Birth Weight Is Associated With Increased Sympathetic Activity Dependence on Genetic Factors

abstract: Background— Low birth weight may be associated with high blood pressure in later life through genetic factors, an association that may be explained by alterations in sympathetic and parasympathetic activity. We examined the association of birth weight with cardiac pre-ejection period and respiratory sinus arrhythmia (indicators of cardiac sympathetic and parasympathetic activity, respectively) and with blood pressure in 53 dizygotic and 61 monozygotic adolescent twin pairs. Methods and Results— Birth weight of the twins was obtained from the mothers. Pre-ejection period and respiratory sinus arrhythmia were measured with electrocardiography and impedance cardiography at rest, during a reaction time task, and during a mental arithmetic task. In the overall sample, lower birth weight was significantly associated with shorter pre-ejection period at rest, during the reaction time task, and during the mental arithmetic task (P=0.0001, P<0.0001, and P=0.0001, respectively) and with larger pre-ejection period re...


title: A Scheme for the Generation of Strong ICMetrics Based Session Key Pairs for Secure Embedded System Applications

abstract: This paper presents a scheme for the generation of strong session based ICMetrics key pairs for security critical embedded system applications. ICMetrics generates the security attributes of the sensor node based on measurable hardware and software characteristics of the integrated circuit. In the proposed scheme a random session ID is assigned by a trusted party to each participating network entity, which remains valid for a communication session. Our work is based on the design of a key derivation function that uses an ICMetrics secret key and a session token assigned by the trusted party to derive strong cryptographic key pairs for each entity. These session tokens also serve the purpose of identification/authentication between the trusted parties and the respective nodes in each network. The main strength of our proposed scheme rests on the randomness feature incorporated via the random session ID's, which makes the generated strong private/public key pair highly resistant against exhaustive search and rainbow table attacks. Our proposed approach makes use of key stretching using random session tokens via SHA-2 and performs multiple iterations of the proposed key derivation function to generate strong high entropy session key pairs of sufficient length. The randomness of the assigned ID's and the session based communication hinders the attacker's ability to launch various sorts of cryptanalytic attacks, thereby making the generated keys very high in entropy. The randomness feature has also been very carefully tuned according to the construction principles of ICMetrics, so that it doesn't affect the original ICMetrics data. The second part of the proposed scheme generates a corresponding public session key by computing the Hermite Normal Form of the high entropy private session key.


title: Object discovery in 3D scenes via shape analysis

abstract: We present a method for discovering object models from 3D meshes of indoor environments. Our algorithm first decomposes the scene into a set of candidate mesh segments and then ranks each segment according to its “objectness” - a quality that distinguishes objects from clutter. To do so, we propose five intrinsic shape measures: compactness, symmetry, smoothness, and local and global convexity. We additionally propose a recurrence measure, codifying the intuition that frequently occurring geometries are more likely to correspond to complete objects. We evaluate our method in both supervised and unsupervised regimes on a dataset of 58 indoor scenes collected using an Open Source implementation of Kinect Fusion [1]. We show that our approach can reliably and efficiently distinguish objects from clutter, with Average Precision score of .92. We make our dataset available to the public.


title: Experimental Analysis of Mode Switching Techniques in Touch-based User Interfaces

abstract: This paper presents the results of a 36 participant empirical comparison of touch mode-switching. Six techniques are evaluated, spanning current and future techniques: long press, non-dominant hand, two-fingers, hard press, knuckle, and thumb-on-finger. Two poses are controlled for, seated with the tablet on a desk and standing with the tablet held on the forearm. Findings indicate pose has no effect on mode switching time and little effect on error rate; using two-fingers is fastest while long press is much slower; non-preferred hand and thumb-on-finger also rate highly in subjective scores. The experiment protocol is based on Li et al.'s pen mode-switching study, enabling a comparison of touch and pen mode switching. Among the common techniques, the non-dominant hand is faster than pressure with touch, whereas no significant difference had been found for pen. Our work addresses the lack of empirical evidence comparing touch mode-switching techniques and provides guidance to practitioners when choosing techniques and to researchers when designing new mode-switching methods.


title: Content-based audio segmentation using support vector machines

abstract: Audio exists at everywhere, but is often out-of-order. It is necessary to arrange them into regularized classes in order to use them more easily. It is also useful, especially in video content analysis, to segment an audio stream according to audio types. In this paper, we present our work in applying support vector machines (SVMs) in audio segmentation and classification. Five audio classes are considered: silence, music, background sound, pure speech, and non-pure speech which includes speech over music and speech over noise. A SVM learns optimal class boundaries from training data to best separate between two classes. A sound clip is segmented by classifying each sub-clip of one second into one of these five classes. Experiments on a database composed of clips of 14870 seconds in total length show that the average accuracy rate for the SVM method is much better than that of the traditional Euclidean distance based (nearest neighbor) method.


title: A Two-Phase Weighted Collaborative Representation for 3D partial face recognition with single sample

abstract: 3D face recognition with the availability of only partial data (missing parts, occlusions and data corruptions) and single training sample is a highly challenging task. This paper presents an efficient 3D face recognition approach to address this challenge. We represent a facial scan with a set of local Keypoint-based Multiple Triangle Statistics (KMTS), which is robust to partial facial data, large facial expressions and pose variations. To address the single sample problem, we then propose a Two-Phase Weighted Collaborative Representation Classification (TPWCRC) framework. A class-based probability estimation is first calculated based on the extracted local descriptors as a prior knowledge. The resulting class-based probability estimation is then incorporated into the proposed classification framework as a locality constraint to further enhance its discriminating power. Experimental results on six challenging 3D facial datasets show that the proposed KMTS-TPWCRC framework achieves promising results for human face recognition with missing parts, occlusions, data corruptions, expressions and pose variations. HighlightsNovel Keypoint-based Multiple Triangle Statistics (KMTS) are proposed for 3D face representation.The proposed local descriptor is robust to partial facial data and expression/pose variations.A Two-Phase Weighted Collaborative Representation Classification (TPWCRC) framework is used to perform face recognition.The proposed classification framework can effectively address the single sample problem.State-of-the-art performance on six challenging datasets with high efficiency is achieved.


Query Intent Detection Based on Query Log Mining.


title: Document Ranking and the Vector-Space Model

abstract: Efficient and effective text retrieval techniques are critical in managing the increasing amount of textual information available in electronic form. Yet text retrieval is a daunting task because it is difficult to extract the semantics of natural language texts. Many problems must be resolved before natural language processing techniques can be effectively applied to a large collection of texts. Most existing text retrieval techniques rely on indexing keywords. Unfortunately, keywords or index terms alone cannot adequately capture the document contents, resulting in poor retrieval performance. Yet keyword indexing is widely used in commercial systems because it is still the most viable way by far to process large amounts of text. Using several simplifications of the vector-space model for text retrieval queries, the authors seek the optimal balance between processing efficiency and retrieval effectiveness as expressed in relevant document rankings.


title: Materials and Processing for Gate Dielectrics on Silicon Carbide (SiC) Surface

abstract: Dielectrics are the materials that do not conduct current in the presence of an electric field. The applications of this material in semiconductor industries are very broad in various ca‐ pacities. Nowadays, extensive research and development (R&D) are in progress to grow high quality high-k gate dielectrics on semiconductors surface. Applications include extend‐ ing the limit of transistor gate capacitance beyond that of ultra thin silicon dioxide (SiO2) and to improve the gate dielectric reliability in wide band gap semiconductor devices. SiO2 is one of the best gate dielectric, which is continuously investigated rigorously since long lime back for silicon based metal-oxide-semiconductor (MOS) device technology. The ther‐ mally grown SiO2 offers several key advantages in microelectronics device processing in‐ cluding thermodynamically and electrically stable high quality interface state density as well as better electric insulation properties. New materials are endlessly researched to fulfill the limitation of silicon across a wide spectrum of industrial applications.


title: Topographic mapping using radar interferometry: processing techniques

abstract: A new processing algorithm for the NASA JPL TOPSAR topographic radar mapper is described. It incorporates extensive motion compensation features as well as accurate three-dimensional target location algorithm. The processor applies an algorithm to resolving the absolute phase ambiguity. This allows rectified height maps to be generated without any use of ground reference points. The processor was tested using data acquired with extreme aircraft motion so that performance could be evaluated under adverse conditions. The topographic maps generated by the radar were compared to digital elevation models (DEMs) derived using conventional optical stereo techniques. In one region, the RMS elevation deviations measured were less than the specified DEM accuracy, and, in the region covered by the more accurate DEM, errors varied from 2.2 m RMS in relatively flat terrain up to 5.0 m in mountainous area. The RMS difference between radar and DEM elevation over the 6.5-km by 22-km area covered by the more accurate DEM was 3.6 m. >


title: An Efficient Density Based Incremental Clustering Algorithm in Data Warehousing Environment

abstract: Data Warehouses are a good source of data for downstream data mining applications. New data arrives in data warehouses during the periodic refresh cycles. Appending of data on existing data requires that all patterns discovered earlier using various data mining algorithms are updated with each refresh. In this paper, we present an incremental density based clustering algorithm. Incremental DBSCAN is an existing incremental algorithm in which data can be added/deleted to/from existing clusters, one point at a time. Our algorithm is capable of adding points in bulk to existing set of clusters. In this new algorithm, the data points to be added are first clustered using the DBSCAN algorithm and then these new clusters are merged with existing clusters, to come up with the modified set of clusters. That is, we add the clusters incrementally rather than adding points incrementally. It is found that the proposed incremental clustering algorithm produces the same clusters as obtained by Incremental DBSCAN. We have used R*-trees as the data structure to hold the multidimensional data that we need to cluster. One of the major advantages of the proposed approach is that it allows us to see the clustering patterns of the new data along with the existing clustering patterns. Moreover, we can see the merged clusters as well. The proposed algorithm is capable of considerable savings, in terms of region queries performed, as compared to incremental DBSCAN. Results are presented to support the claim.


title: Generalized Frequency Division Multiplexing for 5th Generation Cellular Networks

abstract: Cellular systems of the fourth generation (4G) have been optimized to provide high data rates and reliable coverage to mobile users. Cellular systems of the next generation will face more diverse application requirements: the demand for higher data rates exceeds 4G capabilities; battery-driven communication sensors need ultra-low power consumption; and control applications require very short response times. We envision a unified physical layer waveform, referred to as generalized frequency division multiplexing (GFDM), to address these requirements. In this paper, we analyze the main characteristics of the proposed waveform and highlight relevant features. After introducing the principles of GFDM, this paper contributes to the following areas: 1) the means for engineering the waveform's spectral properties; 2) analytical analysis of symbol error performance over different channel models; 3) concepts for MIMO-GFDM to achieve diversity; 4) preamble-based synchronization that preserves the excellent spectral properties of the waveform; 5) bit error rate performance for channel coded GFDM transmission using iterative receivers; 6) relevant application scenarios and suitable GFDM parameterizations; and 7) GFDM proof-of-concept and implementation aspects of the prototype using hardware platforms available today. In summary, the flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks.


title: A Unified Approach to Building and Controlling Spiking Attractor Networks

abstract: Extending work in Eliasmith and Anderson (2003), we employ a general framework to construct biologically plausible simulations of the three classes of attractor networks relevant for biological systems: static (point, line, ring, and plane) attractors, cyclic attractors, and chaotic attractors. We discuss these attractors in the context of the neural systems that they have been posited to help explain: eye control, working memory, and head direction; locomotion (specifically swimming); and olfaction, respectively. We then demonstrate how to introduce control into these models. The addition of control shows how attractor networks can be used as subsystems in larger neural systems, demonstrates how a much larger class of networks can be related to attractor networks, and makes it clear how attractor networks can be exploited for various information processing tasks in neurobiological systems.


title: Internet of Bins: Trash Management in India

abstract: Trash Collection and management in residential locality is the basic considerations referred to in Smart Cities. The concept of trash collection in India is quite tedious. For efficient trash collection, we use the concept of Internet of Things (IOT) which acts as a backbone Technology. Surveillance systems can be used to improve Quality of Service in Trash collection. “This paper proposes an advanced Trash collection system with smart bins that alerts the authorised collector by sending alert messages for efficient Trash collection in Cities.” The system incorporates a data sharing model by using cloud to establish connection between truck drivers, corporation and trash cans to collect and gather waste in a profitable way. The system brings in the way of optimized way to collect things and maintain a clean and green environment.


title: Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

abstract: Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector.


title: Big data-driven optimization for mobile networks toward 5G

abstract: Big data offers a plethora of opportunities to mobile network operators for improving quality of service. This article explores various means of integrating big data analytics with network optimization toward the objective of improving the user quality of experience. We first propose a framework of Big Data-Driven (BDD) mobile network optimization. We then present the characteristics of big data that are collected not only from user equipment but also from mobile networks. Moreover, several techniques in data collection and analytics are discussed from the viewpoint of network optimization. Certain user cases on the application of the proposed framework for improving network performance are also given in order to demonstrate the feasibility of the framework. With the integration of the emerging fifth generation (5G) mobile networks with big data analytics, the quality of our daily mobile life is expected to be tremendously enhanced.


title: A frequency-shift CMOS magnetic biosensor array with single-bead sensitivity and no external magnet

abstract: Future Point-of-Care (PoC) molecular-level diagnosis requires advanced biosensing systems that can achieve high sensitivity and portability at low power consumption levels, all within a low price-tag for a variety of applications such as in-field medical diagnostics, epidemic disease control, biohazard detection, and forensic analysis. Magnetically labeled biosensors are proposed as a promising candidate to potentially eliminate or augment the optical instruments used by conventional fluorescence-based sensors. However, magnetic biosensors developed thus far require externally generated magnetic biasing fields [1–4] and/or exotic post-fabrication processes [1,2]. This limits the ultimate form-factor of the system, total power consumption, and cost. To address these impediments, we present a low-power scalable frequency-shift magnetic particle biosensor array in bulk CMOS, which provides single-bead detection sensitivity without any (electrical or permanent) external magnets.


title: Optical models for human myopic eyes

abstract: Abstract Data from the author’s investigations and other studies are used to construct refractive dependent models. These models include a gradient index lens and aspheric corneal, lens and retinal surfaces. Elements that alter with refraction are anterior corneal radius, vitreous length and retinal shape (vertex radius of curvature and asphericity) and decentration. Two versions of the models are produced, one with centred and symmetrical optical elements, and one with tilts of the lens and decentrations and tilts of the retina. The centred model predicts increase in spherical aberration in myopia. It predicts the relative change in mean sphere in the periphery between the horizontal and vertical meridians that has been observed in a recent experimental study. It overestimates peripheral astigmatism by about 50%. The decentred version has limited success in predicting changes in peripheral refraction of average eyes.


title: MKNN: Modified K-Nearest Neighbor

abstract: In this paper, a new classification method for enhancing the performance of K-Nearest Neighbor is proposed which uses robust neighbors in training data. This new classification method is called Modified K-Nearest Neighbor, MKNN. Inspired the traditional KNN algorithm, the main idea is classifying the test samples according to their neighbor tags. This method is a kind of weighted KNN so that these weights are determined using a different procedure. The procedure computes the fraction of the same labeled neighbors to the total number of neighbors. The proposed method is evaluated on five different data sets. Experiments show the excellent improvement in accuracy in comparison with KNN method.


title: Support slimming for single material based additive manufacturing

abstract: Abstract In layer-based additive manufacturing (AM), supporting structures need to be inserted to support the overhanging regions. The adding of supporting structures slows down the speed of fabrication and introduces artifacts onto the finished surface. We present an orientation-driven shape optimizer to slim down the supporting structures used in single material-based AM. The optimizer can be employed as a tool to help designers to optimize the original model to achieve a more self-supported shape, which can be used as a reference for their further design. The model to be optimized is first enclosed in a volumetric mesh, which is employed as the domain of computation. The optimizer is driven by the operations of reorientation taken on tetrahedra with ‘facing-down’ surface facets. We formulate the demand on minimizing shape variation as global rigidity energy. The local optimization problem for determining a minimal rotation is analyzed on the Gauss sphere, which leads to a closed-form solution. Moreover, we also extend our approach to create the functions of controlling the deformation and searching for optimal printing directions.


title: The Development of a Smart Taxicab Scheduling System: A Multi-source Data Fusion Perspective

abstract: Recent advances in vehicular networks, GPS and smartphone technologies have changed the paradigm of intelligent taxicab systems. Indeed, taxicab trajectories and online calling information have enabled us to provide more efficient and personalized services. However, existing approaches are not sufficient in exploiting cooperative scheduling techniques and utilizing real time calling information. To this end, in this paper, we model the time-varying regularities of traffic flows, activity ratios of passengers, and unoccupied taxicabs of road segments by mining statistical data on taxicab trajectories. Along this line, we propose a novel approach to calculate the expected revenue of possible routes for individual taxicabs while considering the influence of others, and at the same time, advance a dynamic taxicab scheduling mechanism with online taxicab calling information. Finally, we evaluate our algorithm on real-world taxicab data. Experimental results demonstrate that our approach outperforms existing alternative solutions in terms of average revenue of taxi drivers.


title: Maximal lactate steady state, critical power and EMG during cycling

abstract: We hypothesised that: (1) the maximal lactate steady state (MLSS), critical power (CP) and electromyographic fatigue threshold (EMG(FT)) occur at the same power output in cycling exercise, and (2) exercise above the power output at MLSS (P-MLSS) results in continued increases in oxygen uptake (VO(2)), blood lactate concentration ([La]) and integrated electromyogram (iEMG) with time. Eight healthy subjects [mean (SD) age 25 (3) years, body mass 72.1 (8.2) kg] performed a series of laboratory tests for the determination of MLSS, CP and EMG(FT). The CP was determined from four exhaustive trials of between 2 and 15 min duration. The MLSS was determined as the highest power output at which the increase in blood [La] was less than 1.0 mM across the last 20 min of a series of 30-min trials. The EMG(FT) was determined from four trials of 2 min duration at different power outputs. The surface electromyogram was recorded continuously from the vastus lateralis muscle. The CP was significantly higher than the P-MLSS [242 (25) vs. 222 (23) W; P<0.05], although the two variables were strongly correlated (r=0.95; P<0.01). The EMG(FT) could not be determined in 50% of the subjects. Blood [La], VO(2) and minute ventilation all increased significantly with time for exercise at power outputs above the P-MLSS. In conclusion, the P-MLSS, and not the CP, represents the upper limit of the heavy exercise domain in cycling. During exercise above the P-MLSS, there is no association between changes in iEMG and increases in VO(2) and blood [La] with time.


title: Parameterized Streaming Algorithms for Vertex Cover

abstract: As graphs continue to grow in size, we seek ways to effectively process such data at scale. The model of streaming graph processing, in which a compact summary is maintained as each edge insertion/deletion is observed, is an attractive one. However, few results are known for optimization problems over such dynamic graph streams. In this paper, we introduce a new approach to handling graph streams, by instead seeking solutions for the parameterized versions of these problems where we are given a parameter $k$ and the objective is to decide whether there is a solution bounded by $k$. By combining kernelization techniques with randomized sketch structures, we obtain the first streaming algorithms for the parameterized versions of the Vertex Cover problem. We consider the following three models for a graph stream on $n$ nodes: 1. The insertion-only model where the edges can only be added. 2. The dynamic model where edges can be both inserted and deleted. 3. The \emph{promised} dynamic model where we are guaranteed that at each timestamp there is a solution of size at most $k$. In each of these three models we are able to design parameterized streaming algorithms for the Vertex Cover problem. We are also able to show matching lower bound for the space complexity of our algorithms. (Due to the arXiv limit of 1920 characters for abstract field, please see the abstract in the paper for detailed description of our results)


title: Answering Tree Pattern Queries Using Views

abstract: We study the query answering using views (QAV) problem for tree pattern queries. Given a query and a view, the QAV problem is traditionally formulated in two ways: (i) find an equivalent rewriting of the query using only the view, or (ii) find a maximal contained rewriting using only the view. The former is appropriate for classical query optimization and was recently studied by Xu and Ozsoyoglu for tree pattern queries (TP). However, for information integration, we cannot rely on equivalent rewriting and must instead use maximal contained rewriting as shown by Halevy. Motivated by this, we study maximal contained rewriting for TP, a core subset of XPath, both in the absence and presence of a schema. In the absence of a schema, we show there are queries whose maximal contained rewriting (MCR) can only be expressed as the union of exponentially many TPs. We characterize the existence of a maximal contained rewriting and give a polynomial time algorithm for testing the existence of an MCR. We also give an algorithm for generating the MCR when one exists. We then consider QAV in the presence of a schema. We characterize the existence of a maximal contained rewriting when the schema contains no recursion or union types, and show that it consists of at most one TP. We give an efficient polynomial time algorithm for generating the maximal contained rewriting whenever it exists. Finally, we discuss QAV in the presence of recursive schemas.


title: Using natural language processing in order to create SQL queries

abstract: Using query language for dealing with databases is always a professional and complex problem. This complexity causes the userpsilas usage of data existing in database limits to use definite reports there are in some pre implemented softwares. However, you can create this opportunity that each none professional user transfers his questions and requirements to computer in natural language and derives his desired data by natural language processing. In this paper we represent a method for building a ldquonatural languages interfaces to data basesrdquo (NLIDB) system. This system prepares an ldquoexpert systemrdquo implemented in prolog which it can identify synonymous words in any language. It first parses the input sentences, and then the natural language expressions are transformed to SQL language.


title: Universal Approximation Using Feedforward Neural Networks: A Survey of Some Existing Methods, and Some New Results

abstract: In this paper, we present a review of some recent works on approximation by feedforward neural networks. A particular emphasis is placed on the computational aspects of the problem, i.e. we discuss the possibility of realizing a feedforward neural network which achieves a prescribed degree of accuracy of approximation, and the determination of the number of hidden layer neurons required to achieve this accuracy. Furthermore, a unifying framework is introduced to understand existing approaches to investigate the universal approximation problem using feedforward neural networks. Some new results are also presented. Finally, two training algorithms are introduced which can determine the weights of feedforward neural networks, with sigmoidal activation neurons, to any degree of prescribed accuracy. These training algorithms are designed so that they do not suffer from the problems of local minima which commonly affect neural network learning algorithms.


title: Latent feature learning in social media network

abstract: The current trend in social media analysis and application is to use the pre-defined features and devoted to the later model development modules to meet the end tasks. In this work, we claim that representation is critical to the end tasks and contributes much to the model development module. We provide evidence that specially learned feature well addresses the diverse, heterogeneous and collective characteristics of social media data. Therefore, we propose to transfer the focus from the model development to latent feature learning, and present a general feature learning framework based on the popular deep architecture. In particular, following the proposed framework, we design a novel relational generative deep learning model to test the idea on link analysis tasks in the social media networks. We show that the derived latent features well embed both the media content and their observed links, leading to improvement in social media tasks of user recommendation and social image annotation.


title: Support Vector Regression Machines

abstract: A new regression technique based on Vapnik's concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.


title: Image feature learning for cold start problem in display advertising

abstract: In online display advertising, state-of-the-art Click Through Rate(CTR) prediction algorithms rely heavily on historical information, and they work poorly on growing number of new ads without any historical information. This is known as the the cold start problem. For image ads, current state-of-the-art systems use handcrafted image features such as multimedia features and SIFT features to capture the attractiveness of ads. However, these handcrafted features are task dependent, inflexible and heuristic. In order to tackle the cold start problem in image display ads, we propose a new feature learning architecture to learn the most discriminative image features directly from raw pixels and user feedback in the target task. The proposed method is flexible and does not depend on human heuristic. Extensive experiments on a real world dataset with 47 billion records show that our feature learning method outperforms existing handcrafted features significantly, and it can extract discriminative and meaningful features.


title: Constructive memory: The ghosts of past and future

abstract: A memory that works by piecing together bits of the past may be better suited to simulating future events than one that is a store of perfect records.


title: and Hungarian Academy of Sciences THE X-BAR THEORY OF PHRASE STRUCTURE

abstract: X-bar theory is widely regarded as a substantive theory of phrase structure properties in natural languages. In this paper we will demonstrate that a formalization of its content reveals very little substance in its claims. We state and discuss six conditions that encapsulate the claims of X-bar theory: LEXICALITY—each nonterminal is a projection of a preterminal; SUCCESSION—each Xn + 1 dominates an Xn for all n ≥ 0; UNIFORMITY—all maximal projections have the same bar-level; MAXIMALITY—all nonheads are maximal projections; CENTRALITY—the start symbol is a maximal projection; and OPTIONALITY—all and only nonheads are optional. We then consider recent proposals to 'eliminate' base components from transformational grammars and to reinterpret X-bar theory as a set of universal constraints holding for all languages at D-structure, arguing that this strategy fails. We show that, as constraints on phrase-structure rule systems, the X-bar conditions have hardly any effect on the descriptive power of grammars, and that the principles with the most chance of making some descriptive difference are the least adhered to in practice. Finally, we reconstruct X-bar theory in a way that makes no reference to the notion of bar-level but instead makes the notion 'head of the central one.


title: Semantic Information Retrieval based on Wikipedia Taxonomy

abstract: Information retrieval is used to find a subset of relevant documents against a set of documents. Determining semantic similarity between two terms is a crucial problem in Web Mining for such applications as information retrieval systems and recommender systems. Semantic similarity refers to the sameness of two terms based on sameness of their meaning or their semantic contents. Recently many techniques have introduced measuring semantic similarity using Wikipedia, a free online encyclopedia. In this paper, a new technique of measuring semantic similarity is proposed. The proposed method uses Wikipedia as an ontology and spreading activation strategy to compute semantic similarity. The utility of the proposed system is evaluated by using the taxonomy of Wikipedia categories.


title: Identifying Well-formed Natural Language Questions

abstract: Understanding search queries is a hard problem as it involves dealing with "word salad" text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-wellformed categories and report an accuracy of 70.7% on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence models for generating questions for reading comprehension.


title: Prominent edge detection with deep metric expression and multi-scale features

abstract: Edge detection is one of today’s hottest computer vision issues with widely applications. It is beneficial for improving the capability of many vision systems, such as semantic segmentation, salient object detection and object recognition. Deep convolution neural networks (CNNs) recently have been employed to extract robust features, and have achieved a definite improvement. However, there is still a long run to study this hotspot with the main reason that CNNs-based approaches may cause the edges thicker. To address this problem, a novel semantic edge detection algorithm using multi-scale features is proposed. Our model is deep symmetrical metric learning network, which includes 3 key parts. Firstly, the deep detail layer, as a preprocessing layer and a guide module, is employed to remove some low-frequency information and still maintain the edge. Secondly, the deep encoder-decoder networks extract multi-scale features of original image, integrated for complementing information among each level feature. Finally, metric learning is introduced to generate a metric space used to predict edge result. It is easy to distinguish different categories, such as edge space and object space. Simulations and comparisons on benchmark datasets demonstrate the proposed algorithm is superior to the others through visual and quantitative evaluation, and specifically, the score of ODS reachs 0.788.


title: The Why and How of Nonnegative Matrix Factorization

abstract: Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of high-dimensional data as it automatically extracts sparse and meaningful features from a set of nonnegative data vectors. We first illustrate this property of NMF on three applications, in image processing, text mining and hyperspectral imaging --this is the why. Then we address the problem of solving NMF, which is NP-hard in general. We review some standard NMF algorithms, and also present a recent subclass of NMF problems, referred to as near-separable NMF, that can be solved efficiently (that is, in polynomial time), even in the presence of noise --this is the how. Finally, we briefly describe some problems in mathematics and computer science closely related to NMF via the nonnegative rank.


title: Artificial agents among us: Should we recognize them as agents proper?

abstract: In this paper, I discuss whether in a society where the use of artificial agents is pervasive, these agents should be recognized as having rights like those we accord to group agents. This kind of recognition I understand to be at once social and legal, and I argue that in order for an artificial agent to be so recognized, it will need to meet the same basic conditions in light of which group agents are granted such recognition. I then explore the implications of granting recognition in this manner. The thesis I will be defending is that artificial agents that do meet the conditions of agency in light of which we ascribe rights to group agents should thereby be recognized as having similar rights. The reason for bringing group agents into the picture is that, like artificial agents, they are not self-evidently agents of the sort to which we would naturally ascribe rights, or at least that is what the historical record suggests if we look, for example, at what it took for corporations to gain legal status in the law as group agents entitled to rights and, consequently, as entities subject to responsibilities. This is an example of agency ascribed to a nonhuman agent, and just as a group agent can be described as nonhuman, so can an artificial agent. Therefore, if these two kinds of nonhuman agents can be shown to be sufficiently similar in relevant ways, the agency ascribed to one can also be ascribed to the other--this despite the fact that neither is human, a major impediment when it comes to recognizing an entity as an agent proper, and hence as a bearer of rights.


title: Preventing preterm births: analysis of trends and potential reductions with interventions in 39 countries with very high human development index

abstract: The authors of this study examined trends and estimate the potential reduction in preterm births for countries with very high human development index (VHHDI) if present evidence-based interventions were widely implemented. This analysis is to inform a rate reduction target for Born too soon.


title: Corporate Governance and Corporate Social Responsibility Synergies and Interrelationships

abstract: Manuscript Type: Empirical Research Questions/Issue: This paper seeks to explore the interrelationships between corporate governance (CG) and corporate social responsibility (CSR): first, theoretically, by reviewing the literature and surveying various postulations on offer; second, empirically, by investigating the conception and interpretation of this relationship in the context of a sample of firms operating in Lebanon. Accordingly, the paper seeks to highlight the increasing cross-connects or interfaces between CG and CSR, capitalizing on fresh insights from a developing country perspective. Research Findings/Results: A qualitative interpretive research methodology was adopted, drawing on in-depth interviews with the top managers of eight corporations operating in Lebanon, with the findings suggesting that the majority of managers conceive of CG as a necessary pillar for sustainable CSR. These findings are significant and interesting, implying that recent preoccupation with CG in developing countries is starting to be counterbalanced by some interest/attention to CSR, with growing appreciation of their interdependencies and the need to move beyond CG conformance toward voluntary CSR performance. Theoretical Implications: This study makes two important contributions. First, it suggests that there is a salient two-way relationship and increasing overlap between CG and CSR. While much previous literature has researched CG and CSR independently, this paper makes the case for considering them jointly and systematically. Second, the paper outlines a number of theoretical propositions that can serve as the basis for future research on the topic, particularly in developing countries, given that the data and theoretical propositions are both derived from and tailored to developing country contexts. Practical Implications: This study can potentially alert managers to the increasing overlap between the CG and CSR agendas and the need to exert diligent systematic efforts on both fronts. CG and CSR share more in common than previously assumed, and this needs to be accounted for by practitioners. The research can also alert policy makers in developing countries to the need to increase the vigilance and capacity of the regulatory and judicial systems in the context of CG reform and to increase institutional pressures, particularly of the coercive and normative variety to enhance CSR adoption.


Adapting Grammatical Error Correction Based on the Native Language of Writers with Neural Network Joint Models


title: Making Community: The Wider Role of Makerspaces in Public Life

abstract: Makerspaces public workshops where makers can share tools and knowledge are a growing resource for amateurs and professionals alike. While the role of makerspaces in innovation and peer learning is widely discussed, we attempt to look at the wider roles that makerspaces play in public life. Through site visits and interviews at makerspaces and similar facilities across the UK, we have identified additional roles that these spaces play: as social spaces, in supporting wellbeing, by serving the needs of the communities they are located in and by reaching out to excluded groups. Based on these findings, we suggest implications and future directions for both makerspace organisers and community researchers.


title: Adaptive transaction scheduling for transactional memory systems

abstract: Transactional memory systems are expected to enable parallel programming at lower programming complexity, while delivering improved performance over traditional lock-based systems. Nonetheless, there are certain situations where transactional memory systems could actually perform worse. Transactional memory systems can outperform locks only when the executing workloads contain sufficient parallelism. When the workload lacks inherent parallelism, launching excessive transactions can adversely degrade performance. These situations are likely to become dominant in future workloads when large-scale transactions are frequently executed. In this paper, we propose a new paradigm called adaptive transaction scheduling to address this issue. Based on the parallelism feedback from applications, our adaptive transaction scheduler dynamically dispatches and controls the number of concurrently executing transactions. In our case study, we show that our low-cost mechanism not only guarantees that hardware transactional memory systems perform no worse than a single global lock, but also significantly improves performance for both hardware and software transactional memory systems.


Quality of Content Ontology Design Patterns


title: Design of an IGBT-based LCL-resonant inverter for high-frequency induction heating

abstract: A power electronic inverter is developed for a high-frequency induction heating application. The application requires up to 160 kW of power at a frequency of 100 kHz. This power-frequency product represents a significant challenge for today's power semiconductor technology. Voltage source and current source inverters both using ZCS or ZVS are analyzed and compared. To attain the level of performance required, an LCL load-resonant topology is selected to enable ZVS close to the zero current crossing of the load. This mode of soft-switching is suitable to greatly reduce the IGBT losses. Inverter control is achieved via a phase locked loop (PLL). This paper presents the circuit design, modeling and control considerations.


title: Bimodal biometric system for hand shape and palmprint recognition based on SIFT sparse representation

abstract: Biometric-based hand modality is considered as one of the most popular biometric technologies especially in forensic applications. In this paper, a bimodal hand identification system was proposed based on Scale Invariant Feature Transform (SIFT) descriptors, extracted from hand shape and palmprint modalities. A local sparse representation method was adopted in order to represent images with high discrimination. Moreover, fusion was performed at feature and decision levels using a cascade fusion in order to generate the final identification rate of our bimodal system. Our experiments were applied on two hand databases: Indian Institute of Technology of Delhi (IITD) hand database and Bosphorus hand database containing, respectively, 230 and 615 subjects. The results show that the proposed method offers high accuracies compared to other popular bimodal hand biometric methods over the two hand databases. The correct identification rate reaches 99.57 % which is competitive compared to systems existing in the literature.


Multi-Grained Chinese Word Segmentation


title: Recognizing Street Lighting Poles From Mobile LiDAR Data

abstract: In this paper, a novel segmentation and recognition approach to automatically extract street lighting poles from mobile LiDAR data is proposed. First, points on or around the ground are extracted and removed through a piecewise elevation histogram segmentation method. Then, a new graph-cut-based segmentation method is introduced to extract the street lighting poles from each cluster obtained through a Euclidean distance clustering algorithm. In addition to the spatial information, the street lighting pole's shape and the point's intensity information are also considered to formulate the energy function. Finally, a Gaussian-mixture-model-based method is introduced to recognize the street lighting poles from the candidate clusters. The proposed approach is tested on several point clouds collected by different mobile LiDAR systems. Experimental results show that the proposed method is robust to noises and achieves an overall performance of 90% in terms of true positive rate.


title: A Linear GaN High Power Amplifier MMIC for Ka-Band Satellite Communications

abstract: A linear high power amplifier (HPA) monolithic microwave integrated circuit (MMIC) is designed with 0.15 $\mu\text{m}$ gallium nitride (GaN) high electron mobility transistor (HEMT) technology on silicon carbide (SiC) substrate. To keep the linear characteristics of the power stage, 2:4:8 staging ratio of $8 \times 50\;\mu\text{m}$ unit transistor is adapted for the 3-stage HPA MMIC. The MMIC delivers $\text{P}_{3\;\mathrm{dB}}$ of 39.5 dBm with a PAE of 35% at 21.5 GHz. Linear output power ( $\text{P}_{L}$ ) meeting IMD3 of −25 dBc is 37.3 dBm with an associated PAE of 29.5%. The MMIC dimensions are 3.4 mm $\times$ 2.5 mm, generating an output power density of 1049 mW/mm2.


title: Scaling Scrum in a Large Globally Distributed Organization: A Case Study

abstract: We present a case study on scaling Scrum in a large globally distributed software development project at Nokia, a global telecommunications company. We discuss how the case project scaled Scrum while growing from two collocated Scrum teams to 20 teams located in four countries and employing a total of 170 persons. Moreover, we report scaling challenges the case project faced during this 2,5 year journey. We gathered data by 19 semi-structured interviews of project personnel from two sites, interviewees comprising different roles including managers, architects, product owners, developers and testers. The project was highly successful from the business point of view, as agile enabled fast response to customer requirements. However, the project faced significant challenges in scaling Scrum despite attempts at applying the Large-scale Scrum (LeSS) framework. The organization experimented with different ways of implementing scaling practices like implementing common sprint planning meetings, Scrum-of-Scrums meetings, common demos and common retrospectives, as well as scaling the Product Owner role. We conclude the paper by reflecting on the scaling approach used in the case organization in contrast to the LeSS framework.


title: Joint Generative-Discriminative Aggregation Model for Multi-Option Crowd Labels

abstract: Although some crowdsourcing aggregation models have been introduced to aggregate noisy crowd labels, these models mostly consider single-option (i.e. discrete) crowd labels as the input variables, and are not compatible with multi-option (i.e. non-deterministic) crowd data. In this paper, we propose a novel joint generative-discriminative aggregation model, which is able to efficiently deal with both single-option and multi-option crowd labels. Considering the confidence of workers for each option as the input data, we first introduce a new discriminative aggregation model, called Constrained Weighted Majority Voting (CWMVL1), which improves the performance of majority voting method. CWMVL1 considers flexible reliability parameters for crowd workers, employs L1-norm loss function to deal with noisy crowd data, and includes optimization constraints to have probabilistic outputs. We prove that our object is convex, and derive an efficient optimization algorithm. Moreover, we integrate the discriminative CWMVL1 model with a generative model, resulting in a powerful joint aggregation model. Combination of these sub-models is obtained in a probabilistic framework rather than a heuristic way. For our joint model, we derive an efficient optimization algorithm, which alternates between updating the parameters and estimating the potential true labels. Experimental results indicate that the proposed aggregation models achieve superior or competitive results in comparison with the state-of-the-art models on single-option and multi-option crowd datasets, while having faster convergence rates and more reliable predictions.


title: #mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks

abstract: We study how users of multiple online social networks (OSNs) employ and share information by studying a common user pool that use six OSNs -- Flickr, Google+, Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical signature of users' sharing behaviour, showing how they exhibit distinct behaviorial patterns on different networks. We also examine cross-sharing (i.e., the act of user broadcasting their activity to multiple OSNs near-simultaneously), a previously-unstudied behaviour and demonstrate how certain OSNs play the roles of originating source and destination sinks.


title: Conformal Surface Parameterization for Texture Mapping

abstract: We give an explicit method for mapping any simply connected surface onto the sphere in a manner which preserves angles. This technique relies on certain conformal mappings from differential geometry. Our method provides a new way to automatically assign texture coordinates to complex undulating surfaces. We demonstrate a finite element method that can be used to apply our mapping technique to a triangulated geometric description of a surface.


title: Integration of ICT in Education: Key Challenges

abstract: In many countries, education is more than a means for enabling progress and avoiding poverty; it is also critical for the development of knowledge societies and knowledge-based economies. It has been suggested that information and communication technologies (ITCs) can play a number of roles in education. Although lessons may be learned from best practices around the world, there is no specific or best formula for determining the best level of ICTs integration in the Education. Many researchers suggested that the role of higher education institutions within the context of knowledge-based economies and globalization is to provide individuals the ability to transform information into socially beneficial knowledge, skills, and value; modernize societies and improve the standard of living [1, 2, 3]. This paper examined some key challenges faced by the higher education institutions (HEIs) in integrating information and communication technologies (ICTs) into teaching and learning. This paper argues also that the main purpose of integrating ICTs in education is to provide extra approaches that can be used to address the serious environmental, cultural and educational challenges faced by policymakers, educators, educational administrators and students in higher education. The paper concludes by providing some essential elements that can be used in integrating ICTs use in education. Keywords— Education, HEIs, ICT, ICTs integration


title: Predicting transitional interval of kidney disease stages 3 to 5 using data mining method

abstract: The number of kidney disease patients, one of the worldwide public health problems, has been increased yearly. Due to the high possibility of death within a short period of time, a patient must be hospitalized and appropriately cured since the first day of being diagnosed as stage 3. This is due to the fact that the patient's stage progression depends pretty much on medical history and treatment. Moreover, kidney dialysis for the stage-5 patient and end stage can be very costly, where few can afford this treatment, especially in Thailand. The challenging issue is to disclose patterns of transitional interval, with the possibility to delay the stage development. Therefore, the main objective of this study is to create a classification model for predicting transitional interval of Kidney disease stages 3 to 5. The existing medical records of Hemodialysis patient from Phan Hospital, Chiang Rai, Thailand, have been exploited as the case study. Decision tree, K-nearest neighbor, Naive Bayes and Artificial neural networks were used for eliciting the knowledge and creating classification model with the selected set of attributes. Based on the experiment results, the proposed classification framework is promising as a decision support tool. This can also be useful for Thai armed forces, especially since a large sum of budget and resources have been allocated to staffs and relatives with kidney disease.


title: Linearization of NTC Thermistor Characteristic Using Op-Amp Based Inverting Amplifier

abstract: A low cost linearizing circuit is developed, placing the NTC thermistor in a widely used inverting amplifier circuit using operational amplifier. The performance of the system is verified experimentally. A linearity of approximately ± 1% is achieved over 30 °C -120 °C. When used for a narrower span, a much better linearity of ± 0.5% is obtained. The gain of the arrangement can be adjusted over a wide range by simply varying the feedback resistance. The simplicity of the configuration promises a greater reliability, and also curtails the deterioration in the stability of performance, by reducing the cumulation of drifts in the different circuit components and devices.


title: Modeling and control of a Zeta converter

abstract: A Zeta converter is a fourth-order DC-DC converter made up of two inductors and two capacitors and capable of operating in either step-up or step-down mode. Compared with other converters in the same class, such as Cuk and SEPIC converters, the Zeta converter has received the least attention, and more importantly, its dynamic modeling and control have never been reported before in the literature. This paper presents dynamic modeling and control of a Zeta converter operating in Continuous Conduction Mode (CCM). The State-Space Averaging (SSA) technique is applied to find small-signal linear dynamic model of the converter and its various transfer functions. Based on the derived control-to-output transfer function, the PWM feedback controller is designed to regulate the output voltage. Results show that the converter exhibits good performance in steady state and during a step-load change.


title: Performance Indicators in Social Care for Older People

abstract: Contents: Preface The emergence of the performance review Indicators: performance and regulation Performance indicators in social care The Cheshire study The performance indicator analytical framework Constructing and using indicators in practice Implementing performance measurement Conclusions Appendix References Indexes.


title: Understanding the effective receptive field in semantic image segmentation

abstract: Deep convolutional neural networks trained with strong pixel-level supervision have recently significantly boosted the performance in semantic image segmentation. The receptive field is a crucial issue in such visual tasks, as the output must capture enough information about large objects to make a better decision. In DCNNs, the theoretical receptive field size could be very large, but the effective receptive field may be quite small. The latter is an really important factor in performance. In this work, we defined a method of measuring effective receptive field. We observed that stacking layers with large receptive field can increase the size of receptive field and increase the density of receptive field. Based on the observation, we designed a Dense Global Context Module, which makes the effective receptive field coverage larger and density higher. With the Dense Global Context Module, segmentation model reduces a large number of parameters while the performance has been substantially improved. Massive experiments proved that our Dense Global Context Module exhibits very excellent performance on the PASCAL VOC2012 and PASCAL CONTEXT data set.


title: On the Economics of Ransomware

abstract: While recognized as a theoretical and practical concept for over 20 years, only now ransomware has taken centerstage as one of the most prevalent cybercrimes. Various reports demonstrate the enormous burden placed on companies, which have to grapple with the ongoing attack waves. At the same time, our strategic understanding of the threat and the adversarial interaction between organizations and cybercriminals perpetrating ransomware attacks is lacking. ::: In this paper, we develop, to the best of our knowledge, the first game-theoretic model of the ransomware ecosystem. Our model captures a multi-stage scenario involving organizations from different industry sectors facing a sophisticated ransomware attacker. We place particular emphasis on the decision of companies to invest in backup technologies as part of a contingency plan, and the economic incentives to pay a ransom if impacted by an attack. We further study to which degree comprehensive industry-wide backup investments can serve as a deterrent for ongoing attacks.


title: ligDB—Online Query Processing Without (almost) any Storage ⇤

abstract: In the big-data era data is arriving at such a high pace and volume that data exploration and querying can only be feasible if data loading and indexing happens reasonably quick—if at all. Recent research on handling large scien- tific data suggests ignoring any database indexing or even data-loading processing steps but rather turns toward pro- cessing raw data as it is handed in by scientists, manually or by semi-automated means—if needed in multiple, itera- tive steps. In this paper, we describe the anatomy and re- search challenges of a system coined ligDB 1 that is operat- ing purely on incomplete database tables, JSON documents, or sets of SPO triplets that are being filled over time. There is no data stored per se; the only data stored is stemming from previously posed queries over the stream of arriving data; kept as long as it is used by forthcoming queries and otherwise evicted. A key point is that velocity dimension of "big data"allows queries being processed as they are posted, with higher-level queries processed on historic query results (views) and live data. Data that is not touched by any posted query is immediately discarded.


title: Integrating Memories in the Human Brain: Hippocampal-Midbrain Encoding of Overlapping Events

abstract: Summary Decisions are often guided by generalizing from past experiences. Fundamental questions remain regarding the cognitive and neural mechanisms by which generalization takes place. Prior data suggest that generalization may stem from inference-based processes at the time of generalization. By contrast, generalization may emerge from mnemonic processes occurring while premise events are encoded. Here, participants engaged in a two-phase learning and generalization task, wherein they learned a series of overlapping associations and subsequently generalized what they learned to novel stimulus combinations. Functional MRI revealed that successful generalization was associated with coupled changes in learning-phase activity in the hippocampus and midbrain (ventral tegmental area/substantia nigra). These findings provide evidence for generalization based on integrative encoding, whereby overlapping past events are integrated into a linked mnemonic representation. Hippocampal-midbrain interactions support the dynamic integration of experiences, providing a powerful mechanism for building a rich associative history that extends beyond individual events.


A Cognitive Model of Theory of Mind


title: Probabilistic FastText for Multi-Sense Word Embeddings

abstract: We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we represent each word with a Gaussian mixture density, where the mean of a mixture component is given by the sum of n-grams. This representation allows the model to share statistical strength across sub-word structures (e.g. Latin roots), producing accurate representations of rare, misspelt, or even unseen words. Moreover, each component of the mixture can capture a different word sense. Probabilistic FastText outperforms both FastText, which has no probabilistic model, and dictionary-level probabilistic embeddings, which do not incorporate subword structures, on several word-similarity benchmarks, including English RareWord and foreign language datasets. We also achieve state-of-art performance on benchmarks that measure ability to discern different meanings. Thus, the proposed model is the first to achieve multi-sense representations while having enriched semantics on rare words.


title: Semantically Selective Augmentation for Deep Compact Person Re-Identification

abstract: We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks. In particular, we propose to augment limited training data via sampling from a deep convolutional generative adversarial network (DCGAN), whose discriminator is constrained by a semantic classifier to explicitly control the domain specificity of the generation process. Thereby, we encode information in the classifier network which can be utilized to steer adversarial synthesis, and which fuels our CondenseNet ID-network training. We provide a quantitative and qualitative analysis of the approach and its variants on a number of datasets, obtaining results that outperform the state-of-the-art on the LIMA dataset for long-term monitoring in indoor living spaces.


title: Designing social presence of social actors in human computer interaction

abstract: This study examines the interaction effect between user factors and media factors on feelings of social presence which are critical in the design of virtual reality systems and human computer interfaces. Both Experiment 1 and Experiment 2 show that matching synthesized voice personality to user personality positively affects users' (especially extrovert users') feelings of social presence. Experiment 2 also reveals that users feel a stronger sense of social presence when the personality of synthesized voice matches the personality of textual content than when those two are mismatched. In both experiments, extrovert voice induces a stronger sense of presence than introvert voice. These results provide strong evidence for human's automatic social responses to artificial representations possessing humanistic properties such as language and personality. Finally, we discuss various applications of these findings in the design of human computer interfaces, as well as in the study of presence.


title: Simplicity: A unifying principle in cognitive science?

abstract: Much of perception, learning and high-level cognition involves finding patterns in data. But there are always infinitely many patterns compatible with any finite amount of data. How does the cognitive system choose ‘sensible’ patterns? A long tradition in epistemology, philosophy of science, and mathematical and computational theories of learning argues that patterns ‘should’ be chosen according to how simply they explain the data. This article reviews research exploring the idea that simplicity drives a wide range of cognitive processes. We outline mathematical theory, computational results and empirical data that underpin this viewpoint.


title: Institutionalizing enterprise resource planning in the Saudi steel industry: A punctuated socio-technical analysis

abstract: In this article, we analyze institutionalization as a process of transferring and stabilizing material artifacts and routines in the form of enterprise resource planning (ERP) systems. Although past studies have analyzed institutionalization as structuring around scripts or discourse moves, we emphasize the material role of artifacts and routines as carriers of institutional logics. In addition, insitutionalization is not linear and incremental, but goes through sudden, nonlinear disruptions. To this end, we apply punctuated socio-technical information system change (PSIC) model that draws upon Gersick's model of change to identify and trace moves that are critical during the institutionalization. The model accounts for ERP institutionalization by chronicling complex interactions between socio-technical elements in the implementation system, the work system, and organizational and environmental context which together account for the institutionalization outcome. We use the model to analyze a longitudinal case covering 11 years (1993–2004) of ERP implementation processes in a large Saudi steel firm. Our analysis shows that the proposed material and punctuated lens toward institutionalization offers rich insights how and why ERP systems become institutions and why their institutionalization is difficult and unfolds in unpredictable ways. We conclude that the normally held assumptions of successful linear and incremental adaptation to new institutional patterns logics out by ERP systems do not hold.


title: Why is working memory capacity related to matrix reasoning tasks?

abstract: One of the reasons why working memory capacity is so widely researched is its substantial relationship with fluid intelligence. Although this relationship has been found in numerous studies, researchers have been unable to provide a conclusive answer as to why the two constructs are related. In a recent study, researchers examined which attributes of Raven’s Progressive Matrices were most strongly linked with working memory capacity (Wiley, Jarosz, Cushen, & Colflesh, Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 256–263, 2011). In that study, Raven’s problems that required a novel combination of rules to solve were more strongly correlated with working memory capacity than were problems that did not. In the present study, we wanted to conceptually replicate the Wiley et al. results while controlling for a few potential confounds. Thus, we experimentally manipulated whether a problem required a novel combination of rules and found that repeated-rule-combination problems were more strongly related to working memory capacity than were novel-rule-combination problems. The relationship to other measures of fluid intelligence did not change based on whether the problem required a novel rule combination.


title: The brain’s concepts: the role of the sensory-motor system in conceptual knowledge

abstract: Concepts are the elementary units of reason and linguistic meaning. They are conventional and relatively stable. As such, they must somehow be the result of neural activity in the brain. The questions are: Where? and How? A common philosophical position is that all concepts—even concepts about action and perception—are symbolic and abstract, and therefore must be implemented outside the brain's sensory-motor system. We will argue against this position using (1) neuroscientific evidence; (2) results from neural computation; and (3) results about the nature of concepts from cognitive linguistics. We will propose that the sensory-motor system has the right kind of structure to characterise both sensory-motor and more abstract concepts. Central to this picture are the neural theory of language and the theory of cogs, according to which, brain structures in the sensory-motor regions are exploited to characterise the so-called “abstract” concepts that constitute the meanings of grammatical constructions and gen...


Social Entrepreneurship: Leadership that Facilitates Societal Transformation-An Exploratory Study


title: Accuracy and Interpretability Trade-Offs in Machine Learning Applied to Safer Gambling

abstract: Responsible gambling is an area of research and industry which seeks to understand the pathways to harm from gambling and implement programmes to reduce or prevent harm that gambling might cause. There is a growing body of research that has used gambling behavioural data to model and predict harmful gambling, and the industry is showing increasing interest in technologies that can help gambling operators to better predict harm and prevent it through appropriate interventions. However, industry surveys and feedback clearly indicate that in order to enable wider adoption of such data-driven methods, industry and policy makers require a greater understanding of how machine learning methods make these predictions. In this paper, we make use of the TREPAN algorithm for extracting decision trees from Neural Networks and Random Forests. We present the first comparative evaluation of predictive performance and tree properties for extracted trees, which is also the first comparative evaluation of knowledge extraction for safer gambling. Results indicate that TREPAN extracts better performing trees than direct learning of decision trees from the data. Overall, trees extracted with TREPAN from different models offer a good compromise between prediction accuracy and interpretability. TREPAN can produce decision trees with extended tests rules of different forms, so that interpretability depends on multiple factors. We present detailed results and a discussion of the trade-offs with regard to performance and interpretability and use in the gambling industry.


title: Unsupervised Learning of Probabilistic Concept Hierarchies

abstract: Since the field’s inception, most research in machine learning has focused on the problem of supervised induction from labeled training cases. If anything, this trend has been strengthened by the creation of data repositories that, typically, include class information. But this emphasis is misguided if we want to understand the nature of learning in intelligent agents like humans. Clearly, children acquire many concepts about the world before they learn names for them, and scientists regularly discover patterns without any clear supervision from an outside source. Even the availability of class labels in public data sets can be misleading; many such domains are medical in nature, and medical researchers first had to discover a disease before they could diagnose it for particular patients.


title: UdS-(retrain|distributional|surface): Improving POS Tagging for OOV Words in German CMC and Web Data

abstract: We present in this paper our three system submissions for the POS tagging subtask of the Empirist Shared Task: Our baseline systemUdS-retrain extends a standard training dataset with in-domain training data; UdSdistributional and UdS-surface add two different ways of handling OOV words on top of the baseline system by using either distributional information or a combination of surface similarity and language model information. We reach the best performance using the distributional model.


Comparison of motor-manual performance in quiet and noisy environments.


title: Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition

abstract: Short-term traffic volume data are generally nonstationary and often corrupted by local noise. Although the Kalman filter is suited for prediction of nonstationary variable, its prediction accuracy may be significantly affected by the noise. This paper investigates the application of the Kalman filter with discrete wavelet analysis in short-term traffic volume forecasting. Discrete wavelet decomposition and reconstruction analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data only and the prediction accuracy can be improved. Traffic volume data collected from Interstate 80 are used in this study. The test results indicate that the wavelet Kalman filter model consistently performs better than the Kalman filter model in terms of accuracy and stability.


title: A Global Snapshot of Computer Science Education in K-12 Schools

abstract: In two special issues of the ACM journal "Transactions on Computing Education" (TOCE), 14 extensive case studies about the various situations of Computer Science Education (CSE) in K-12 schools in 12 countries (respectively states) were collected. During the work at the ITiCSE 2015, we have performed a deductive qualitative text analysis on these case studies in order to extract the most useful information. As a category system, we applied some selected categories of the Darmstadt Model that was developed by the working group "Computer Science/Informatics in Secondary Schools" at the ITiCSE 2011. Based on the coding results, we summarized information about the different fields of Computing Education at schools, the intended goals and competencies, the taught content, the applied programming languages and tools and the different forms of assessment and teacher education. Despite the limitations of the analyzed articles, representing just snapshots of complex situations from the specific viewpoint of the respective authors, we were able to collect some interesting results.


title: A steerable 28 GHz array antenna using branch line coupler

abstract: This paper provides details on the design of a beam steerable array antenna for mobile applications at 28 GHz frequency band. The array is designed using a Rogers RT/duroid 5880 substrate material of 0.254 mm thickness and dielectric constant of 2.2. The designed antenna is fed by a 2 × 2 Butler matrix (BM) which also serve as a beam former to obtain the beam scanning of from −16 to 16 degrees. The 6-element array realized a peak gain of 14 dBi at the designed frequency and a wideband that cover 63 % of the frequency range. The designed antenna is applicable to 28 GHz frequency band proposed for future generation mobile communications.


title: Example-based Acquisition of Fine-grained Collocation Resources.

abstract: Comunicacio presentada a la Tenth International Conference on Language Resources and Evaluation, celebrada a Portoroz (Eslovenia) els dies 23 a 28 de maig 2016.


title: NailMelanoma In Situ: Clinical, Dermoscopic, Pathologic Clues, and Steps for Minimally Invasive Treatment

abstract: BACKGROUND ::: ::: Nail unit melanoma (NUM) is a variant of acral lentiginous melanoma. The differential diagnosis is wide but an acquired brown streak in the nail of a fair-skinned adult person must be considered a potential melanoma. Dermoscopy helps clinicians to more accurately decide if a nail apparatus biopsy is necessary. ::: ::: OBJECTIVE ::: ::: Detailed evaluation of clinical and dermoscopy features and description of conservative surgery of in situ NUM. ::: ::: METHODS ::: ::: Retrospective study of in situ NUM diagnosed and treated with conservative surgical management in the authors' center from 2008 to 2013. ::: ::: RESULTS ::: ::: Six cases of NUM were identified: 2 male and 4 female patients, age range at diagnosis of 44 to 76 years. All patients underwent complete nail unit removal with at least 6-mm security margins around the anatomic boundaries of the nail. The follow-up varies from 4 to 62 months. ::: ::: CONCLUSION ::: ::: Nail unit melanomas pose a difficult diagnostic and therapeutic challenge. Wide excision is sufficient, whereas phalanx amputation is unnecessary and associated with significant morbidity for patients with in situ or early invasive melanoma. Full-thickness skin grafting or second-intention healing after total nail unit excision is a simple procedure providing a good functional and cosmetic outcome.


title: Guarantees for Greedy Maximization of Non-submodular Functions with Applications

abstract: We investigate the performance of the standard GREEDY algorithm for cardinality constrained maximization of non-submodular nondecreasing set functions. While there are strong theoretical guarantees on the performance of GREEDY for maximizing submodular functions, there are few guarantees for non-submodular ones. However, GREEDY enjoys strong empirical performance for many important non-submodular functions, e.g., the Bayesian A-optimality objective in experimental design. We prove theoretical guarantees supporting the empirical performance. Our guarantees are characterized by a combination of the (generalized) curvature α and the sub-modularity ratio γ. In particular, we prove that GREEDY enjoys a tight approximation guarantee of 1/α (1 - e-γα) for cardinality constrained maximization. In addition, we bound the submod-ularity ratio and curvature for several important real-world objectives, including the Bayesian A-optimality objective, the determinantal function of a square submatrix and certain linear programs with combinatorial constraints. We experimentally validate our theoretical findings for both synthetic and real-world applications.


title: Local region partition for person re-identification

abstract: Due to the different posture and view angle, the image will appear some objects that do not exist in another image of the same person captured by another camera. The region covered by new items adversely improved the difficulty of person re-identification. Therefore, we named these regions as Damaged Region (DR). To overcome the influence of DR, we propose a new way to extract feature based on the local region that divides both in the horizontal and vertical directions. Before splitting the image, we enlarge it with direction to increase the useful information, potentially reducing the impact of different viewing angles. Then each divided region is a separated part, and the results of the adjacent regions will be compared. As a result the region that gets a higher score is selected as the valid one, and which gets the lower score caused by pose variation and items occlusion will be invalid. Extensive experiments carried out on three person re-identification benchmarks, including VIPeR, PRID2011, CUHK01, clearly show the significant and consistent improvements over the state-of-the-art methods.


title: Impaired antioxidant defence in guinea pig heart tissues treated with halothane

abstract: PURPOSE ::: To investigate the effects of halothane and halothane plus vitamin E treatment on myocardial free radical metabolism in guinea pigs. ::: ::: ::: METHODS ::: Four groups of seven animals were studied: control, halothane, halothane plus vitamin E and vitamin E groups. In the halothane group, halothane 1.5% in oxygen was given for 90 min over three days. In the halothane plus vitamin E group, 300 mg.kg-1.day-1 vitamin E im was started three days before the first halothane treatment and continued for three days. Following sacrifice, the hearts were assayed for superoxide dismutase (SOD), glutathione peroxidase (GSH-Px) and catalase (CAT) and malondialdehyde (MDA) level was determined. Electron spin resonance (ESR) analysis and electron microscopy (EM) were also performed. ::: ::: ::: RESULTS ::: In the halothane group, SOD activities and MDA concentrations were increased compared with control and GSH-Px and CAT activities were decreased. In the halothane plus vitamin E group, there were no differences in enzyme activity compared with halothane alone but the MDA level was decreased. In the vitamin E group, enzyme activities were increased compared with control. Mainly the CF3CHCl radical was identified by ESR analysis in heart tissues exposed to halothane and the concentration of this radical was reduced by vitamin E. Electron microscopy showed cytoplasmic vacuolisation and dilation in sarcoplasmic reticulum in the heart tissues exposed to halothane: both were prevented by vitamin E. ::: ::: ::: CONCLUSION ::: Although halothane causes impairment in enzymatic antioxidant defence potential, due to lowered GSH-Px and CAT activity, and accelerates peroxidative reactions in the tissues affected, no subcellular damage occurred. Vitamin E may protect tissues against free radical attack by scavenging toxic free radicals formed in heart tissue during halothane anaesthesia.


title: The case for episodic memory in animals

abstract: Abstract The conscious recollection of unique personal experiences in terms of their details (what), their locale (where) and temporal occurrence (when) is known as episodic memory and is thought to require a ‘self-concept’, autonoetic awareness/conciousness, and the ability to subjectively sense time. It has long been held that episodic memory is unique to humans, because it was accepted that animals lack a ‘self-concept’, ‘autonoetic awareness’, and the ability to ‘subjectively sense time’. These assumptions are now being questioned by behavioral evidence showing that various animal species indeed show behavioral manifestations of different features of episodic memory such as, e.g. ‘metacognition’, ‘conscious recollection’ of past events, ‘temporal order memory’, ‘mental time travel’ and have the capacity to remember personal experiences in terms of what happened, where and when. The aim of this review is to provide a comprehensive overview on the current progress in attempts to model different prerequisites and features of human episodic memory in animals and to identify possible neural substrates of animal episodic memory. The literature covered includes behavioral and physiological studies performed with different animal species, such as non-human primates, rodents, dolphins and birds. The search for episodic memory in animals has forced researchers to define objective behavioral criteria by which different features of episodic memory can be operationalized experimentally and assessed in both animals and humans. This is especially important because the current definition of episodic memory in terms of mentalistic constructs such as ‘self’, ‘autonoetic awareness/consciousness’, and ‘subjectively sensed time’, not only hinders animal research on the neurobiology of episodic memory but also research with healthy human subjects as well as neuropsychiatric patients with impaired language or in children with less-developed verbal abilities.


Modeling Nonlinear Oscillators via Variable-Order Fractional Operators