title: Strategies for ensuring trustworthiness in qualitative research projects

abstract: Although many critics are reluctant to accept the trustworthiness of qualitative research, frameworks for ensuring rigour in this form of work have been in existence for many years. Guba’s constructs, in particular, have won considerable favour and form the focus of this paper. Here researchers seek to satisfy four criteria. In addressing credibility, investigators attempt to demonstrate that a true picture of the phenomenon under scrutiny is being presented. To allow transferability, they provide sufficient detail of the context of the fieldwork for a reader to be able to decide whether the prevailing environment is similar to another situation with which he or she is familiar and whether the findings can justifiably be applied to the other setting. The meeting of the dependability criterion is difficult in qualitative work, although researchers should at least strive to enable a future investigator to repeat the study. Finally, to achieve confirmability, researchers must take steps to demonstrate that findings emerge from the data and not their own predispositions. The paper concludes by suggesting that it is the responsibility of research methods teachers to ensure that this or a comparable model for ensuring trustworthiness is followed by students undertaking a qualitative inquiry.


title: EXAM TIMETABLING PROBLEM USING GENETIC ALGORITHM

abstract: Timetabling is an extremely difficult problem which is an element of the wider-field of scheduling. Scheduling is generally defined as the problem of the allocation of resources over time to perform a set of tasks and is NP-hard problems. Scheduling exam timetables in departments of college/university is a complex problem. This is usually done by hand taking several days or weeks of iterative repair after feedback from staff and students. This paper proposed an algorithm to solve the problem of exam timetabling, which is a good candidate to generate a timetable using genetic algorithm (GA). This paper details the implementation of a computer program which employs GA for an optimal solution of solving a timetable problem and generate exam timetable by using real student data from engineering department courses at ICT.


title: Grid-interfaced solar PV powered electric vehicle battery system with novel adaptive digital control algorithm

abstract: This study deals with the control of a three-phase grid-tied solar photovoltaic (PV) powered electric vehicle battery (EVB) system. The perturb and observe technique is used to obtain the peak power of a solar PV array. The EVB is connected through a DC–DC buck–boost converter at the DC link of voltage source converter (VSC), which is charged in base load periods and discharged at high load periods. This converter also maintains the DC link voltage to its maximum power point tracking and the EVB capacity is reduced in comparison to a system, where it is directly assimilated at the DC link. An adaptive recursive digital filter is used in an economical and efficient single-stage grid-connected solar PV-EVB system for the evaluation of the fundamental current constituent of non-linear load current, subsequently the grid current is sinusoidal and has unity power factor. The system with recursive filter control is adaptable at solar insolation variation and load change. VSC works as an active power filter under no solar power generation and compensates the load reactive power. The operation of scheme is validated with test results on a prototype developed in steady-state and dynamic conditions.


title: Hybridisation among groupers (genus Cephalopholis) at the eastern Indian Ocean suture zone: taxonomic and evolutionary implications

abstract: Hybridisation is a significant evolutionary process that until recently was considered rare in the marine environment. A suture zone in the eastern Indian Ocean is home to numerous hybridising sister species, providing an ideal opportunity to determine how hybridisation affects speciation and biodiversity in coral reef fishes. At this location, hybridisation between two grouper (Epinephelidae) species: Cephalopholis urodeta (Pacific Ocean) and C. nigripinnis (Indian Ocean) was investigated to determine the genetic basis of hybridisation and to compare the ecology and life history of hybrids and their parent species. This approach aimed to provide insights into the taxonomic and evolutionary consequences of hybridisation. Despite clear phenotypic differences, multiple molecular markers revealed hybrids, and their parent species were genetically homogenous within and (thousands of kilometres) outside of the hybrid zone. Hybrids were at least as fit as their parent species (in terms of growth, reproduction, and abundance) and were observed in a broad range of intermediate phenotypes. The two species appear to be interbreeding at Christmas Island due to inherent biological and ecological compatibilities, and the lack of genetic structure may be explained by three potential scenarios: (1) hybridisation and introgression; (2) discordance between morphology and genetics; and (3) incomplete lineage sorting. Further molecular analyses are necessary to discriminate these scenarios. Regardless of which applies, C. urodeta and C. nigripinnis are unlikely to evolve in reproductive isolation as they cohabit where they are common (Christmas Island) and will source congeneric mates where they are rare (Cocos Keeling Islands). Our results add to the growing body of evidence that hybridisation among coral reef fishes is a dynamic evolutionary factor.


title: Information-Centric Wireless Networks with Mobile Edge Computing

abstract: In order to better accommodate the dramatically increasing demand for data caching and computing services, storage and computation capabilities should be endowed to some of the intermediate nodes within the network. In this paper, we design a novel virtualized heterogeneous networks framework aiming at enabling content caching and computing. With the virtualization of the whole system, the communication, computing and caching resources can be shared among all users associated with different virtual service providers. We formulate the virtual resource allocation strategy as a joint optimization problem, where the gains of not only virtualization but also caching and computing are taken into consideration in the proposed architecture. In addition, a distributed algorithm based on alternating direction method of multipliers is adopted to solve the formulated problem, in order to reduce the computational complexity and signaling overhead. Finally, extensive simulations are presented to show the effectiveness of the proposed scheme under different system parameters.


title: Pommerman: A Multi-Agent Playground

abstract: We present Pommerman, a multi-agent environment based on the classic console game Bomberman. Pommerman consists of a set of scenarios, each having at least four players and containing both cooperative and competitive aspects. We believe that success in Pommerman will require a diverse set of tools and methods, including planning, opponent/teammate modeling, game theory, and communication, and consequently can serve well as a multi-agent benchmark. To date, we have already hosted one competition, and our next one will be featured in the NIPS 2018 competition track.


title: Literal and Metaphorical Senses in Compositional Distributional Semantic Models

abstract: Metaphorical expressions are pervasive in natural language and pose a substantial challenge for computational semantics. The inherent compositionality of metaphor makes it an important test case for compositional distributional semantic models (CDSMs). This paper is the first to investigate whether metaphorical composition warrants a distinct treatment in the CDSM framework. We propose a method to learn metaphors as linear transformations in a vector space and find that, across a variety of semantic domains, explicitly modeling metaphor improves the resulting semantic representations. We then use these representations in a metaphor identification task, achieving a high performance of 0.82 in terms of F-score.


title: Semantic segmentation using regions and parts

abstract: We address the problem of segmenting and recognizing objects in real world images, focusing on challenging articulated categories such as humans and other animals. For this purpose, we propose a novel design for region-based object detectors that integrates efficiently top-down information from scanning-windows part models and global appearance cues. Our detectors produce class-specific scores for bottom-up regions, and then aggregate the votes of multiple overlapping candidates through pixel classification. We evaluate our approach on the PASCAL segmentation challenge, and report competitive performance with respect to current leading techniques. On VOC2010, our method obtains the best results in 6/20 categories and the highest performance on articulated objects.


title: A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

abstract: Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.


title: The four-domain architecture: an approach to support enterprise architecture design

abstract: Managing an enterprise architecture is a challenging task. While careful planning typically goes into its design, an enterprise architecture actually emerges as a result of implementing individual projects. It is this de facto architecture, not the conceptual one, that provides the capabilities for executing business strategies, and understanding this emergent architecture is of paramount importance. In this paper, we present the Four-Domain Architecture (FDA), which integrates business process, information, knowledge, and elements pertaining to infrastructure and organization. The FDA approach can help guide the development of both the conceptual and emergent architecture. The FDA helps an enterprise in the definition, design, and creation of a set of tools and methods to support frameworks such as the Zachman framework.


title: Voice conversion using deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks

abstract: This paper investigates the use of Deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks (DBLSTM-RNNs) for voice conversion. Temporal correlations across speech frames are not directly modeled in frame-based methods using conventional Deep Neural Networks (DNNs), which results in a limited quality of the converted speech. To improve the naturalness and continuity of the speech output in voice conversion, we propose a sequence-based conversion method using DBLSTM-RNNs to model not only the frame-wised relationship between the source and the target voice, but also the long-range context-dependencies in the acoustic trajectory. Experiments show that DBLSTM-RNNs outperform DNNs where Mean Opinion Scores are 3.2 and 2.3 respectively. Also, DBLSTM-RNNs without dynamic features have better performance than DNNs with dynamic features.


title: The Nonverbal Communication Functions of Emoticons in Computer-Mediated Communication

abstract: Abstract Most past studies assume that computer-mediated communication (CMC) lacks nonverbal communication cues. However, Internet users have devised and learned to use emoticons to assist their communications. This study examined emoticons as a communication tool that, although presented as verbal cues, perform nonverbal communication functions. We therefore termed emoticons quasi-nonverbal cues.


Service-oriented Cost Allocation for Business Intelligence and Analytics: Who pays for BI&A?


title: Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment

abstract: High-throughput techniques have revolutionized the study of genomics and molecular biology in recent years. These methods provide a large quantity of sequence data, and have applications in different areas of bioinformatics. One can sequence parts or whole of an organism's DNA to determine genetic information about an individual or a population, measure expression levels of different genes under different conditions, and determine binding affinity of proteins to DNA segments revealing details regarding gene regulation, at a higher resolution than before. However, different high-throughput methods that target even a single application have different underlying error models. Robust analytic pipelines are necessary to extract necessary information from the raw data. In this paper, we discuss future research directions for developing such analytics using techniques from Machine Learning and Deep Neural Networks. We focus on two applications that will affect the diagnosis and treatment of cancer.


title: Comprehensible predictive models for business processes

abstract: Predictive modeling approaches in business process management provide a way to streamline operational business processes. For instance, they can warn decision makers about undesirable events that are likely to happen in the future, giving the decision maker an opportunity to intervene. The topic is gaining momentum in process mining, a field of research that has traditionally developed tools to discover business process models from data sets of past process behavior. Predictive modeling techniques are built on top of process-discovery algorithms. As these algorithms describe business process behavior using models of formal languages (e.g., Petri nets), strong language biases are necessary in order to generate models with the limited amounts of data included in the data set. Naturally, corresponding predictive modeling techniques reflect these biases. Based on theory from grammatical inference, a field of research that is concerned with inducing language models, we design a new predictive modeling technique based on weaker biases. Fitting a probabilistic model to a data set of past behavior makes it possible to predict how currently running process instances will behave in the future. To clarify how this technique works and to facilitate its adoption, we also design a way to visualize the probabilistic models. We assess the effectiveness of the technique in an experimental evaluation with synthetic and real-world data.


Conflict resolution : a review of some non-governmental practices


title: The role of trust in OSS communities - Case Linux Kernel community

abstract: Open source software development has been the subject of interest among businesses as well as in the academic world. OSS enables many possibilities for companies but also sets new kinds of challenges. Because of the characteristics of the OSS phenomenon we propose that trust in OSS communities plays a key role in facilitating their success. Therefore, the aim of this study is to explore the factors that affect trust in OSS communities. The data is gathered by a survey aimed to Linux Kernel developers. Among other results it may be concluded that the most important factors affecting trust seem to be other developers’ skills, reputation as well as the formal and informal practices.


Rational points on, and the arithmetic of, elliptic curves: A tale of two books (and an article)


title: Emotion-based music recommendation by association discovery from film music

abstract: With the growth of digital music, the development of music recommendation is helpful for users. The existing recommendation approaches are based on the users' preference on music. However, sometimes, recommending music according to the emotion is needed. In this paper, we propose a novel model for emotion-based music recommendation, which is based on the association discovery from film music. We investigated the music feature extraction and modified the affinity graph for association discovery between emotions and music features. Experimental result shows that the proposed approach achieves 85% accuracy in average.


title: Adding structure to unstructured peer-to-peer networks: the role of overlay topology

abstract: The ''small-world'' graph structure is pervasive and is observed to arise ''without-design'' or ''naturally'' in many practical systems such as the World Wide Web. In contrast to natural systems, overlay networks provide an opportunity to design structure. We seek the advantages of designing overlay topologies with small-world properties to support file sharing in peer-to-peer networks. We focus on two metrics of performance: (a) search protocol performance, a local gain perceived directly by peer-to-peer network users and (b) network utilization, a global property that is of interest to network service providers. We propose a class of overlay topologies and show, by simulation, that a particular topology instance of this class where every node has many close neighbors and few random neighbors (i.e., a small-world graph) exhibits very good properties. In this overlay topology, the chances of locating files are high, and the nodes where these files are found are, on average, close to the query source. This improvement in search protocol performance is achieved while decreasing the traffic load on the links in the underlying network. We propose a simple greedy algorithm to construct such overlay topologies where each node operates independently and in a decentralized manner to select its neighbors.


title: A Game-Theoretic Analysis of Adversarial Classification

abstract: Attack detection is usually approached as a classification problem. However, standard classification tools often perform poorly, because an adaptive attacker can shape his attacks in response to the algorithm. This has led to the recent interest in developing methods for adversarial classification , but to the best of our knowledge, there have been a very few prior studies that take into account the attacker’s tradeoff between adapting to the classifier being used against him with his desire to maintain the efficacy of his attack. Including this effect is a key to derive solutions that perform well in practice. In this investigation, we model the interaction as a game between a defender who chooses a classifier to distinguish between attacks and normal behavior based on a set of observed features and an attacker who chooses his attack features (class 1 data). Normal behavior (class 0 data) is random and exogenous. The attacker’s objective balances the benefit from attacks and the cost of being detected while the defender’s objective balances the benefit of a correct attack detection and the cost of false alarm. We provide an efficient algorithm to compute all Nash equilibria and a compact characterization of the possible forms of a Nash equilibrium that reveals intuitive messages on how to perform classification in the presence of an attacker. We also explore qualitatively and quantitatively the impact of the non-attacker and underlying parameters on the equilibrium strategies.


title: Integration of BIM and GIS: The Development of the CityGML GeoBIM Extension

abstract: There is a growing interest in the integration of BIM and GIS. However, most of the research is focused on importing BIM data in GIS applications and vice versa. Real integration of BIM and GIS is using the strong parts of the GIS technology in BIM, and of course the strong parts from BIM technology in GIS. In this paper a mix of strong parts from both worlds is integrated in a single project. The paper describes the development of a CityGML extension called GeoBIM to get semantic IFC data into a GIS context. The conversion of IFC to CityGML (including the GeoBIM extension) is implemented in the open source Building Information Modelserver.


title: A new design approach of home automation system for patients with physical disability to reduce water wastage and power consumption using renewable energy

abstract: The paper demonstrates to design a smart home system for physically disabled persons with the purpose of reduction of water and power consumption. The physically disabled persons require special assistances from caretakers or other persons to lead their normal life and even at home, it is not convenient for them to control the house apparatus system according to their wish. In this paper, a smart home automation system is implemented which consists of automatic control of lights to turn on in presence of human and turn off rest of the time as well as Bluetooth remote controlling ability along with automatic door lock and open system, automatic Water tap, automatic water tank fill-up and Solar charging system with the solar controller to reduce the power consumption. The project has been implemented using low-cost components and based on Arduino platform with an aim to develop a system which will be effective to reduce water wastage in real time as well as the project will be able to reduce power consumption and utility bill by using renewable energy as the power source.


Alabama's turfgrass-sod industry


title: HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension

abstract: This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. In this paper, we present a neural network called Hybrid Multi-Aspects (HMA) model, which mimic the human's intuitions on dealing with the multiple-choice reading comprehension. In this model, we aim to produce the predictions in multiple aspects by calculating attention among the text, question and choices, and combine these results for final predictions. Experimental results show that our HMA model could give substantial improvements over the baseline system and got the first place on the final test set leaderboard with the accuracy of 84.13%.


title: Application of Myo Armband System to Control a Robot Interface

abstract: This paper discusses the application of myoelectric signals to control electronic devices aiming the development of a digital controlling interface with Myo Gesture Control Armband System. Through this interface it is possible to control the movement of a robot and its interaction with the environment, in this case the robot being PeopleBot, a robot designed for home necessities. Thus, allowing an assessment on the operation of controlling devices with myoelectric signals and Inertial Measurement Unit (IMU), the advantages and disadvantages of working with this technology are discussed.


title: Building effective deep neural network architectures one feature at a time

abstract: Successful training of convolutional neural networks is often associated with sufficiently deep architectures composed of high amounts of features. These networks typically rely on a variety of regularization and pruning techniques to converge to less redundant states. We introduce a novel bottom-up approach to expand representations in fixed-depth architectures. These architectures start from just a single feature per layer and greedily increase width of individual layers to attain effective representational capacities needed for a specific task. While network growth can rely on a family of metrics, we propose a computationally efficient version based on feature time evolution and demonstrate its potency in determining feature importance and a networks' effective capacity. We demonstrate how automatically expanded architectures converge to similar topologies that benefit from lesser amount of parameters or improved accuracy and exhibit systematic correspondence in representational complexity with the specified task. In contrast to conventional design patterns with a typical monotonic increase in the amount of features with increased depth, we observe that CNNs perform better when there is more learnable parameters in intermediate, with falloffs to earlier and later layers.


title: Balanced topology to cancel Tx leakage in CW radar

abstract: Balanced topology in radar front-end is presented, which can reduce Tx leakage to receiver drastically. The balanced radar front-end which is composed of a differential cross-coupled oscillator, two Lange couplers, and a Wilkinson combiner is implemented using commercially available InGaP/GaAs heterojunction bipolar transistor technology. The tuning range of voltage control oscillator is 320 MHz from 25.307 to 25.627 GHz. The peak output power is 0.8 dBm. Average Tx-to-Rx isolation is about 40.5 dB. Since the isolation of the Lange coupler is 19 dB, the additional cancellation due to balanced topology is expected to be 21.5 dB. The chip size is 1.05 /spl times/ 1.70 mm/sup 2/.


title: Learning Optimal Individualized Treatment Rules from Electronic Health Record Data

abstract: Medical research is experiencing a paradigm shift from "one-size-fits-all" strategy to a precision medicine approach where the right therapy, for the right patient, and at the right time, will be prescribed. We propose a statistical method to estimate the optimal individualized treatment rules (ITRs) that are tailored according to subject-specific features using electronic health records (EHR) data. Our approach merges statistical modeling and medical domain knowledge with machine learning algorithms to assist personalized medical decision making using EHR. We transform the estimation of optimal ITR into a classification problem and account for the non-experimental features of the EHR data and confounding by clinical indication. We create a broad range of feature variables that reflect both patient health status and healthcare data collection process. Using EHR data collected at Columbia University clinical data warehouse, we construct a decision tree for choosing the best second line therapy for treating type 2 diabetes patients.


title: Adapting a Generic Platform for Poetry Generation to Produce Spanish Poems

abstract: PoeTryMe was created as a generic system for the generation of poetry that takes into account both semantics, in the form of triplets of relations between concepts, and textual structure, in the form of a grammar of templates extracted from existing poems. It was originally instantiated to generate poetry in Portuguese. The present paper describes an effort to create a different instantiation of PoeTryMe, this time focused on the production of poems in Spanish. The instantiation effort involved the creation of a set of triplets of relations to represent the semantics of Spanish terms, the extraction of a grammar of templates for Spanish from a corpus of Spanish poetry, the application of a different tool for Spanish syllabic division, the integration of the various modules, and several experiments with the resulting system.


title: Exploring Cloudy Collaboration in Healthcare: An Evaluation Framework of Cloud Computing Services for Hospitals

abstract: Cloud computing (CC) is regarded as having the potential to facilitate collaboration in the hospital sector. Yet, adoption of cloud computing services (CCS) in hospitals is low. While a well-designed evaluation of CCS can promote informed adoption decision-making, research on CCS evaluation in the hospital sector is insufficient. To address this research gap, we propose an evaluation framework (EF) of CCS for hospitals. Grounded in the human, organization and technology-fit model, our EF employs six dimensions to evaluate how a CCS facilitates collaboration in hospitals. By applying our EF to 38 identified CCS for hospitals, we demonstrate its efficacy. Our research contributes to both practice and research. For practice, our EF can be used to screen available CCS for hospitals and thus expedite cloud adoption processes. For research, our EF unfolds the complexity of CC in healthcare and is of particular relevance for IS research in healthcare.


Things you wanted to know about the Latin hypercube design and were afraid to ask


title: Characteristics of the home food environment that mediate immediate and sustained increases in child fruit and vegetable consumption: mediation analysis from the Healthy Habits cluster randomised controlled trial

abstract: The home food environment can influence the development of dietary behaviours in children, and interventions that modify characteristics of the home food environment have been shown to increase children’s fruit and vegetable consumption. However to date, interventions to increase children’s fruit and vegetable consumption have generally produced only modest effects. Mediation analysis can help in the design of more efficient and effective interventions by identifying the mechanisms through which interventions have an effect. This study aimed to identify characteristics of the home food environment that mediated immediate and sustained increases in children’s fruit and vegetable consumption following the 4-week Healthy Habits telephone-based parent intervention. Analysis was conducted using 2-month (immediate) and 12-month (sustained) follow-up data from a cluster randomised control trial of a home food environment intervention to increase the fruit and vegetable consumption of preschool children. Using recursive path analysis, a series of mediation models were created to investigate the direct and indirect effects of immediate and sustained changes to characteristics of the home food environment (fruit and vegetable availability, accessibility, parent intake, parent providing behaviour, role-modelling, mealtime eating practices, child feeding strategies, and pressure to eat), on the change in children’s fruit and vegetable consumption. Of the 394 participants in the randomised trial, 357 and 329 completed the 2- and 12-month follow-up respectively. The final mediation model suggests that the effect of the intervention on the children’s fruit and vegetable consumption was mediated by parent fruit and vegetable intake and parent provision of these foods at both 2- and 12-month follow-up. Analysis of data from the Healthy Habits trial suggests that two environmental variables (parental intake and parent providing) mediate the immediate and sustained effect of the intervention, and it is recommended these variables be targeted in subsequent home food environment interventions to bring about immediate and sustained changes in child fruit and vegetable intake. ACTRN12609000820202.


title: AN IMPROVED APPROACH TO MINIMIZE CONTEXT SWITCHING IN ROUND ROBIN SCHEDULING ALGORITHM USING OPTIMIZATION TECHNIQUES

abstract: Scheduling is a fundamental operating system function. Almost all computer resources are scheduled before use. All major CPU scheduling algorithms concentrates more on to maximize CPU utilization, throughput, waiting time and turnaround time. In particularly, the performance of round robin algorithm depends heavily on the size of the time quantum. To improve the performance of CPU and to minimize the overhead on the CPU, time quantum should be large with respect to the context switch time. Otherwise, context switching will be more. In this research paper, we propose a method to minimize the context switching and to break the fixed time quantum size in round robin scheduling algorithm using optimization techniques. Both results and calculations show that, our proposed method is more efficient than the existing round robin scheduling algorithm.


Seamless Robot Simulation Integration for Education : A Case Study


title: A delta-structured switched-capacitor equalizer for series-connected battery strings

abstract: A delta-structured switched-capacitor equalizer (DSSCE) is proposed to achieve the automatic any-cells-to-any-cells (AC2AC) balancing for a long battery string, leading to the fast-speed and high-efficiency balancing. Only two switches are required for each battery cell, and one capacitor is set for any two cells, materializing the delta-structured switched capacitors (SC). All MOSFETs are triggered by two complementary PWM signals, and energy can be automatically and directly exchanged among all cells without the need of cell monitoring. The proposed system has the advantages of simple control, high efficiency, and direct, simultaneous, and automatic equalization among cells. A prototype system for four LiFePO4 battery cells is set up. Experimental results demonstrate the proposed topology has an improved balancing performance with a high balancing efficiency of 94.5%.


title: Explainable artificial intelligence: A survey

abstract: In the last decade, with availability of large datasets and more computing power, machine learning systems have achieved (super)human performance in a wide variety of tasks. Examples of this rapid development can be seen in image recognition, speech analysis, strategic game planning and many more. The problem with many state-of-the-art models is a lack of transparency and interpretability. The lack of thereof is a major drawback in many applications, e.g. healthcare and finance, where rationale for model's decision is a requirement for trust. In the light of these issues, explainable artificial intelligence (XAI) has become an area of interest in research community. This paper summarizes recent developments in XAI in supervised learning, starts a discussion on its connection with artificial general intelligence, and gives proposals for further research directions.


title: H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

abstract: Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2D and 3D FCNs, serve as the back-bone in many volumetric image segmentation. However, 2D convolutions can not fully leverage the spatial information along the third dimension while 3D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2D DenseUNet for efficiently extracting intra-slice features and a 3D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We propose a new training strategy by formulating the learning process of H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion (HFF) layer. We extensively evaluated our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.


title: Blind linguistic steganalysis against translation based steganography

abstract: Translation based steganography (TBS) is a kind of relatively new and secure linguistic steganography. It takes advantage of the "noise" created by automatic translation of natural language text to encode the secret information. Up to date, there is little research on the steganalysis against this kind of linguistic steganography. In this paper, a blind steganalytic method, which is named natural frequency zoned word distribution analysis (NFZ-WDA), is presented. This method has improved on a previously proposed linguistic steganalysis method based on word distribution which is targeted for the detection of linguistic steganography like nicetext and texto. The newmethod aims to detect the application of TBS and uses none of the related information about TBS, its only used resource is a word frequency dictionary obtained from a large corpus, or a so called natural frequency dictionary, so it is totally blind. To verify the effectiveness of NFZ-WDA, two experiments with two-class and multi-class SVM classifiers respectively are carried out. The experimental results show that the steganalytic method is pretty promising.


title: Online Img2UML Repository: An Online Repository for UML Models

abstract: The Img2UML repository is a repository of UML models. A huge amount of UML models is available on the Internet – mostly in the form of images. This repository aims to offer these UML class diagram as a searchable XMI Database. The information that is in the XMI files is stored in the repository database. This repository will be useful for research as the first corpus of UML models. This repository will provide a good place for researchers and students to study and analyze UML models. This improves UML studying in research, education, and industry. In this paper, we outline the Img2UML repository, illustrate some of the research made possible, and discuss future plans.


title: Wi-fi backscatter: internet connectivity for RF-powered devices

abstract: RF-powered computers are small devices that compute and communicate using only the power that they harvest from RF signals. While existing technologies have harvested power from ambient RF sources (e.g., TV broadcasts), they require a dedicated gateway (like an RFID reader) for Internet connectivity. We present Wi-Fi Backscatter, a novel communication system that bridges RF-powered devices with the Internet. Specifically, we show that it is possible to reuse existing Wi-Fi infrastructure to provide Internet connectivity to RF-powered devices. To show Wi-Fi Backscatter's feasibility, we build a hardware prototype and demonstrate the first communication link between an RF-powered device and commodity Wi-Fi devices. We use off-the-shelf Wi-Fi devices including Intel Wi-Fi cards, Linksys Routers, and our organization's Wi-Fi infrastructure, and achieve communication rates of up to 1 kbps and ranges of up to 2.1 meters. We believe that this new capability can pave the way for the rapid deployment and adoption of RF-powered devices and achieve ubiquitous connectivity via nearby mobile devices that are Wi-Fi enabled.


title: Author gender identification from text

abstract: Text is still the most prevalent Internet media type. Examples of this include popular social networking applications such as Twitter, Craigslist, Facebook, etc. Other web applications such as e-mail, blog, chat rooms, etc. are also mostly text based. A question we address in this paper that deals with text based Internet forensics is the following: given a short text document, can we identify if the author is a man or a woman? This question is motivated by recent events where people faked their gender on the Internet. Note that this is different from the authorship attribution problem. In this paper we investigate author gender identification for short length, multi-genre, content-free text, such as the ones found in many Internet applications. Fundamental questions we ask are: do men and women inherently use different classes of language styles? If this is true, what are good linguistic features that indicate gender? Based on research in human psychology, we propose 545 psycho-linguistic and gender-preferential cues along with stylometric features to build the feature space for this identification problem. Note that identifying the correct set of features that indicate gender is an open research problem. Three machine learning algorithms (support vector machine, Bayesian logistic regression and AdaBoost decision tree) are then designed for gender identification based on the proposed features. Extensive experiments on large text corpora (Reuters Corpus Volume 1 newsgroup data and Enron e-mail data) indicate an accuracy up to 85.1% in identifying the gender. Experiments also indicate that function words, word-based features and structural features are significant gender discriminators.


title: Unsupervised Discovery of Drug Side-Effects from Heterogeneous Data Sources

abstract: Drug side-effects become a worldwide public health concern, which are the fourth leading cause of death in the United States. Pharmaceutical industry has paid tremendous effort to identify drug side-effects during the drug development. However, it is impossible and impractical to identify all of them. Fortunately, drug side-effects can also be reported on heterogeneous platforms (i.e., data sources), such as FDA Adverse Event Reporting System and various online communities. However, existing supervised and semi-supervised approaches are not practical as annotating labels are expensive in the medical field. In this paper, we propose a novel and effective unsupervised model Sifter to automatically discover drug side-effects. Sifter enhances the estimation on drug side-effects by learning from various online platforms and measuring platform-level and user-level quality simultaneously. In this way, Sifter demonstrates better performance compared with existing approaches in terms of correctly identifying drug side-effects. Experimental results on five real-world datasets show that Sifter can significantly improve the performance of identifying side-effects compared with the state-of-the-art approaches.


title: Fine-grained classification via mixture of deep convolutional neural networks

abstract: We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification problem is characterised by large intra-class variations and small inter-class variations. To overcome these problems our proposed MixDCNN system partitions images into K subsets of similar images and learns an expert DCNN for each subset. The output from each of the K DCNNs is combined to form a single classification decision. In contrast to previous techniques, we provide a formulation to perform joint end-to-end training of the K DCNNs simultaneously. Extensive experiments, on three datasets using two network structures (AlexNet and GoogLeNet), show that the proposed MixDCNN system consistently outperforms other methods. It provides a relative improvement of 12.7% and achieves state-of-the-art results on two datasets.


title: Business Ethics and Internal Social Criticism

abstract: The purpose of this paper is to present an understanding of busi- ness ethics based on a theory of intemal social criticism. Internal social criticism focuses on how members of a business organization debate the meanings of their shared traditions for the purpose of locating and cor- recting hypocrisy. Organizations have thick moral cultures that allow them to be self-governing moral communities. By considering organizations as interpretive moral communities, I challenge the conventional notion that moral criticism is based primarily on exogenous moral principles delivered by outside critics. I describe an interpretive process of busi- ness ethics and develop a theoretical model of internal social criticism. I also propose that organizational identification serves as a mechanism for inducing ethical behavior. I conclude by calling for more research that understands the development and use of existing moral principles inside of organizations.


title: End-to-End Relation Extraction using Markov Logic Networks

abstract: The task of end-to-end relation extraction consists of two sub-tasks: i) identifying entity mentions along with their types and ii) recognizing semantic relations among the entity mention pairs. %Identifying entity mentions along with their types and recognizing semantic relations among the entity mentions, are two very important problems in Information Extraction. It has been shown that for better performance, it is necessary to address these two sub-tasks jointly. We propose an approach for simultaneous extraction of entity mentions and relations in a sentence, by using inference in Markov Logic Networks (MLN). We learn three different classifiers : i) local entity classifier, ii) local relation classifier and iii) "pipeline" relation classifier which uses predictions of the local entity classifier. Predictions of these classifiers may be inconsistent with each other. We represent these predictions along with some domain knowledge using weighted first-order logic rules in an MLN and perform joint inference over the MLN to obtain a global output with minimum inconsistencies. Experiments on the ACE (Automatic Content Extraction) 2004 dataset demonstrate that our approach of joint extraction using MLNs outperforms the baselines of individual classifiers. Our end-to-end relation extraction performance is better than 2 out of 3 previous results reported on the ACE 2004 dataset.


title: Shot Classification in Broadcast Soccer Video

abstract: In this paper, we present an effective hierarchical shot classification scheme for broadcast soccer video. We first partition a video into replay and non-replay shots with replay logo detection. Then, non-replay shots are further classified into Long, Medium, Close-up or Out-field types with color and texture features based on a decision tree. We tested the method on real broadcast FIFA soccer videos, and the experimental results demonstrate its effectiveness..


title: The Basel III net stable funding ratio adjustment speed and systemic risk

abstract: The theory on the timing of liquidity trades highlights two contrasting rational expectations equilibria for the liquidity adjustment speed effect, namely an immediate-trading equilibrium (trade at the onset of the liquidity shock) and a delayed-trading equilibrium (trade at the last resort). Using a partial adjustment model and an annual data sample of US bank holding companies from 1991 to 2012, we investigate the effect of Net Stable Funding Ratio (NSFR) adjustment speeds on systemic risk. We find that banks with the immediate-trading equilibrium tend to adjust the NSFR quickly in response to the Basel III liquidity requirement, thereby, reducing systemic risk. With the same level of the NSFR, our findings suggest that only the adjustment speed exerts a negative impact on systemic risk. Our evidence shows that small banks strengthen the effects of the negative impact of the NSFR adjustment speed on systemic risk. Our study sheds light on a real-time indicator of the NSFR for Basel III revisions before its implementation in 2018.


Bone marrow transplant for breast cancer: some insurers pay, some insurers don't.


title: On a sum involving Fourier coefficients of cusp forms

abstract: We improve the existing upper bound for the quantity |∑n⩽xa(n2)|, where a(n2) is the n2th Hecke eigenvalue of a normalized holomorphic cusp form (Hecke eigenform) of the full modular group SL(2, ℤ), whenever the weight of the original holomorphic cusp form (Hecke eigenform) lies in a certain range.


title: Future Relations between Humans and Artificial Intelligence: A Stakeholder Opinion Survey in Japan

abstract: Concern about the potential benefits and risks of artificial intelligence (AI) has given impetus to the establishment of AI research. AI requires responsible research and innovation (RRI) with the collaboration of various stakeholders, including the public, from the early stages [1]. However, stakeholders may have different opinions about AI and human-machine relations. We aim to discuss these viewpoints by conducting a questionnaire survey, and to suggest perspectives on future collaborations among stakeholders.


title: Wireless power transfer with 6.78-MHz operation frequency on biological media (pork muscle)

abstract: This paper presents the design and analysis of wireless power transfer (WPT) system on biological media. The WPT system operates at 6.78-MHz industrial, scientific, and medical (ISM) radio band. In addition, three building blocks: transmitter (TX), receiver (RX), and resonant tank are used to construct the WPT system. To gain more insight into the WPT on biological media, pork muscle is utilized in this work. Operating at 10-mm distance with pork muscle between TX and RX, the RX can produce 626-mV DC output voltage. It is able to derive the next stage due to the output voltage greater than 0.5-V to 0.6-V threshold voltage of the active devices. Moreover, the simplified equivalent circuit model with pork muscle is developed. Furthermore, the signal degradation because of the pork muscle is predicted.


title: Co-Clustering with Side Information for Text mining

abstract: Many of the text mining applications contain a huge amount of information from document in the form of text. This text can be very helpful for Text Clustering. This text also includes various kind of other information known as Side Information or Metadata. Examples of this side information include links to other web pages, title of the document, author name or date of Publication which are present in the text document. Such metadata may possess a lot of information for the clustering purposes. But this Side information may be sometimes noisy. Using such Side Information for producing clusters without filtering it, can result to bad quality of Clusters. So we use an efficient Feature Selection method to perform the mining process to select that Side Information which is useful for Clustering so as to maximize the advantages from using it. The proposed technique, CCSI (Co-Clustering with Side Information) system makes use of the process of Co-Clustering or Two-mode clustering which is a data mining technique that allows concurrently clustering of the rows and columns of a matrix.


title: On stepwise pattern recovery of the fused Lasso

abstract: We study the property of the Fused Lasso Signal Approximator (FLSA) for estimating a blocky signal sequence with additive noise. We transform the FLSA to an ordinary Lasso problem, and find that in general the resulting design matrix does not satisfy the irrepresentable condition that is known as an almost necessary and sufficient condition for exact pattern recovery. We give necessary and sufficient conditions on the expected signal pattern such that the irrepresentable condition holds in the transformed Lasso problem. However, these conditions turn out to be very restrictive. We apply the newly developed preconditioning method - Puffer Transformation (Jia and Rohe, 2015) to the transformed Lasso and call the new procedure the preconditioned fused Lasso. We give non-asymptotic results for this method, showing that as long as the signal-to-noise ratio is not too small, our preconditioned fused Lasso estimator always recovers the correct pattern with high probability. Theoretical results give insight into what controls the ability of recovering the pattern - it is the noise level instead of the length of the signal sequence. Simulations further confirm our theorems and visualize the significant improvement of the preconditioned fused Lasso estimator over the vanilla FLSA in exact pattern recovery. We provided necessary and sufficient conditions such that fused Lasso consistently recovers the piecewise constant pattern.We found that in general the fused Lasso is not consistent.We proposed a preconditioned fused Lasso to overcome the non-consistent issue.Simulation studies support our findings.


title: Analysis of brute force attack using TG — Dataset

abstract: In this paper we compare and evaluate the effectiveness of the brute force methodology using dataset of known password. It is a known fact that user chosen passwords are easily recognizable and crackable, by using several password recovery techniques; Brute force attack is one of them. For rescuing such attacks several organizations proposed the password creation rules which stated that password must include number and special characters for strengthening it and protecting against various password cracking attacks such as Dictionary attack, brute force attack etc. The result of this paper and proposed methodology helps in evaluating the system and account security for measuring the degree of authentication by estimating the password strength. The experiment is conducted on our proposed dataset (TG-DATASET) that contain an iterative procedure for creating the alphanumeric password string like a∗, b∗, c∗ and so on. The proposed dataset is prepared due to non-availability of iterative password in any existing password data sets.


title: BAG: A designer-oriented integrated framework for the development of AMS circuit generators

abstract: We introduce BAG, the Berkeley Analog Generator, an integrated framework for the development of generators of Analog and Mixed Signal (AMS) circuits. Such generators are parameterized design procedures that produce sized schematics and correct layouts optimized to meet a set of input specifications. BAG extends previous work by implementing interfaces to integrate all steps of the design flow into a single environment and by providing helper classes -- both at the schematic and layout level -- to aid the designer in developing truly parameterized and technology-independent circuit generators. This simplifies the codification of common tasks including technology characterization, schematic and testbench translation, simulator interfacing, physical verification and extraction, and parameterized layout creation for common styles of layout. We believe that this approach will foster design reuse, ease technology migration, and shorten time-to-market, while remaining close to the classical design flow to ease adoption. We have used BAG to design generators for several circuits, including a Voltage Controlled Oscillator (VCO) and a Switched-Capacitor (SC) voltage regulator in a CMOS 65nm process. We also present results from automatic migration of our designs to a 40nm process.


title: Machine-component group analysis versus the similarity coefficient method in cellular manufacturing application

abstract: Abstract Among approaches to the machine cell formation problem, the similarity coefficient method and machine-component group analysis have been widely used to form machine cells for cellular manufacturing applications. While algorithms based on machine-component group analysis work effectively in cases of disjoint machine-component groups, they do not provide satisfactory solutions in the presence of bottleneck machines. The similarity coefficient method overcomes this problem. This paper compares these two approaches when applied to the machine cell formation problem.


title: Learning the parts of objects by non-negative matrix factorization

abstract: Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.


title: Wideband, Wide-Scan Planar Array of Connected Slots Loaded With Artificial Dielectric Superstrates

abstract: Microwave broadband wide-scan antenna arrays are typically implemented resorting to vertical arrangements of printed circuit boards (PCBs). Here, we propose a planar solution realized with a single multi-layer PCB, with consequent reduction in cost and complexity of the array. It consists of an array of connected slots backed by a metallic reflector and loaded with superstrates. Artificial dielectric layers (ADLs) are used in place of real dielectrics to realize the superstrates, as they are characterized by very low surface-wave losses. For the unit-cell design, we developed an analysis tool based on closed-form expressions and thus requiring minimal computational resources. Finite-array simulations are also performed by generalizing the analysis method to account for the truncation effects. The presence of the ADL superstrate allows reducing the distance between the array plane and the backing reflector while maintaining good matching performance. A realistic feed structure is also proposed, which consists of a microstrip line connected to a coaxial feed. Such a solution does not require balanced-to-unbalanced transitions, which often limit the achievable bandwidth. The proposed structure achieves in simulations more than an octave bandwidth (6.5–14.5 GHz), within a scanning range of $\pm 50$ ° in all azimuth planes.


Resources-Events-Agents Design Theory: A Revolutionary Approach to Accounting and Enterprise System Design


title: Industrial electricity tariffs

abstract: The author looks at how electricity tariffs in the UK are arrived at in practice. Costs of electricity production are used to arrive at bulk supply tariffs and area board tariffs. Marginal-cost messages in electricity tariffs for consumers are looked at as a means of improving cost effectiveness of energy consumption. >


title: A retrospective study of a hybrid document-context based retrieval model

abstract: This paper describes our novel retrieval model that is based on contexts of query terms in documents (i.e., document contexts). Our model is novel because it explicitly takes into account of the document contexts instead of implicitly using the document contexts to find query expansion terms. Our model is based on simulating a user making relevance decisions, and it is a hybrid of various existing effective models and techniques. It estimates the relevance decision preference of a document context as the log-odds and uses smoothing techniques as found in language models to solve the problem of zero probabilities. It combines these estimated preferences of document contexts using different types of aggregation operators that comply with different relevance decision principles (e.g., aggregate relevance principle). Our model is evaluated using retrospective experiments (i.e., with full relevance information), because such experiments can (a) reveal the potential of our model, (b) isolate the problems of the model from those of the parameter estimation, (c) provide information about the major factors affecting the retrieval effectiveness of the model, and (d) show that whether the model obeys the probability ranking principle. Our model is promising as its mean average precision is 60-80% in our experiments using different TREC ad hoc English collections and the NTCIR-5 ad hoc Chinese collection. Our experiments showed that (a) the operators that are consistent with aggregate relevance principle were effective in combining the estimated preferences, and (b) that estimating probabilities using the contexts in the relevant documents can produce better retrieval effectiveness than using the entire relevant documents.


title: A Natural Language Database Interface Based on a Probabilistic Context Free Grammar

abstract: This paper presents a natural language interface to relational database. It introduces some classical NLDBI products and their applications and proposes the architecture of a new NLDBI system including its probabilistic context free grammar, the inside and outside probabilities which can be used to construct the parse tree, an algorithm to calculate the probabilities, and the usage of dependency structures and verb subcategorization in analyzing the parse tree. Some experiment results are given to conclude the paper.


Hong-Cheong Looi A Model of Factors Influencing Electronic Commerce Adoption among SMEs in Brunei Darussalam A Model of Factors Influencing Electronic Commerce Adoption among Small and Medium


title: EEL: machine-independent executable editing

abstract: EEL (Executable Editing Library) is a library for building tools to analyze and modify an executable (compiled) program. The systems and languages communities have built many tools for error detection, fault isolation, architecture translation, performance measurement, simulation, and optimization using this approach of modifying executables. Currently, however, tools of this sort are difficult and time-consuming to write and are usually closely tied to a particular machine and operating system. EEL supports a machine- and system-independent editing model that enables tool builders to modify an executable without being aware of the details of the underlying architecture or operating system or being concerned with the consequences of deleting instructions or adding foreign code.


Democratic Leadership : The Lessons of Exemplary Models for Democratic Governance


title: Video transcoding: an overview of various techniques and research issues

abstract: One of the fundamental challenges in deploying multimedia systems, such as telemedicine, education, space endeavors, marketing, crisis management, transportation, and military, is to deliver smooth and uninterruptible flow of audio-visual information, anytime and anywhere. A multimedia system may consist of various devices (PCs, laptops, PDAs, smart phones, etc.) interconnected via heterogeneous wireline and wireless networks. In such systems, multimedia content originally authored and compressed with a certain format may need bit rate adjustment and format conversion in order to allow access by receiving devices with diverse capabilities (display, memory, processing, decoder). Thus, a transcoding mechanism is required to make the content adaptive to the capabilities of diverse networks and client devices. A video transcoder can perform several additional functions. For example, if the bandwidth required for a particular video is fluctuating due to congestion or other causes, a transcoder can provide fine and dynamic adjustments in the bit rate of the video bitstream in the compressed domain without imposing additional functional requirements in the decoder. In addition, a video transcoder can change the coding parameters of the compressed video, adjust spatial and temporal resolution, and modify the video content and/or the coding standard used. This paper provides an overview of several video transcoding techniques and some of the related research issues. We introduce some of the basic concepts of video transcoding, and then review and contrast various approaches while highlighting critical research issues. We propose solutions to some of these research issues, and identify possible research directions.


title: Antenna Configurations for 4x4 MIMO in LTE - Field Measurements

abstract: The 3GPP LTE standard for mobile broadband includes multi-antenna transmission modes that improve performance, both in terms of coverage, spectral efficiency and peak throughput. The antenna system design, both at the eNB and at the UE is critical to a well performing system; it should be designed with the intended performance profile in mind. Field trials were performed in order to investigate the relative performance of several four and two transmit antenna setups in an LTE system. In general, multi-antenna technology gave substantial performance gains over single antenna transmission. A closely spaced co-polarized configuration gave the best performance for users with poor channel quality while dual-polarized and well-spaced antenna configurations gave better performance for users with good channel quality. The trial also shows that UE antenna polarization is an important parameter that must be kept in mind when designing the eNB antenna system.


title: Food animals and antimicrobials: impacts on human health.

abstract: Antimicrobials are valuable therapeutics whose efficacy is seriously compromised by the emergence and spread of antimicrobial resistance. The provision of antibiotics to food animals encompasses a wide variety of nontherapeutic purposes that include growth promotion. The concern over resistance emergence and spread to people by nontherapeutic use of antimicrobials has led to conflicted practices and opinions. Considerable evidence supported the removal of nontherapeutic antimicrobials (NTAs) in Europe, based on the "precautionary principle." Still, concrete scientific evidence of the favorable versus unfavorable consequences of NTAs is not clear to all stakeholders. Substantial data show elevated antibiotic resistance in bacteria associated with animals fed NTAs and their food products. This resistance spreads to other animals and humans-directly by contact and indirectly via the food chain, water, air, and manured and sludge-fertilized soils. Modern genetic techniques are making advances in deciphering the ecological impact of NTAs, but modeling efforts are thwarted by deficits in key knowledge of microbial and antibiotic loads at each stage of the transmission chain. Still, the substantial and expanding volume of evidence reporting animal-to-human spread of resistant bacteria, including that arising from use of NTAs, supports eliminating NTA use in order to reduce the growing environmental load of resistance genes.


title: Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning

abstract: Zero-shot methods in language, vision and other domains rely on a cross-space mapping function that projects vectors from the relevant feature space (e.g., visualfeature-based image representations) to a large semantic word space (induced in an unsupervised way from corpus data), where the entities of interest (e.g., objects images depict) are labeled with the words associated to the nearest neighbours of the mapped vectors. Zero-shot cross-space mapping methods hold great promise as a way to scale up annotation tasks well beyond the labels in the training data (e.g., recognizing objects that were never seen in training). However, the current performance of cross-space mapping functions is still quite low, so that the strategy is not yet usable in practical applications. In this paper, we explore some general properties, both theoretical and empirical, of the cross-space mapping function, and we build on them to propose better methods to estimate it. In this way, we attain large improvements over the state of the art, both in cross-linguistic (word translation) and cross-modal (image labeling) zero-shot experiments.


title: Power and Frequency Control of Induction Furnace Using Fuzzy Logic Controller

abstract: This paper introduces a new method to control resonance frequency and output power of induction heating coil. Induction heating coil can be controlled by single phase sinusoidal pulse width modulation (SPWM) inverter .All electrical requirements beside magnetic permeability and resistivity variation for modeling induction heating coil have been considered to make simulations practical .Control blocks using Fuzzy logic which control both active and reactive power have been designed .The system modeling and Fuzzy logic controllers are simulated in MATLAB/SIMULINK and FUZZY LOGIC toolbox .The results of the simulations show the effectiveness and superiority of this control system.


Situational Awareness from Environmental Sounds


title: Author Verification Using Common N-Gram Profiles of Text Documents

abstract: Authorship verification is the problem of answering the question whether or not a sample text document was written by a specific person, given a few other documents known to be authored by them. We propose a proximity based method for one-class classification that applies the Common N-Gram (CNG) dissimilarity measure. The CNG dissimilarity (Keet al., 2003) is based on the differences in the frequencies of n-grams of tokens (characters, words) that are most common in the considered documents. Our method utilizes the pairs of most dissimilar documents among documents of known authorship. We evaluate various variants of the method in the setting of a single classifier or an ensemble of classifiers, on a multilingual authorship verification corpus of the PAN 2013 Author Identification evaluation framework. Our method yields competitive results when compared to the results achieved by the participants of the PAN 2013 competition on the entire set, as well as separately on two subsets — English and Spanish ones — out of the three language subsets of the corpus.


title: Skin Reactions to Pine Processionary Caterpillar Thaumetopoea pityocampa Schiff

abstract: Pine caterpillar, Thaumetopoea pityocampa Schiff, is a phyto- and xylophagous lepidopteran, responsible for the delay in the growth or the death of various types of pines. Besides nature damage, pine caterpillar causes dermatological reactions in humans by contact with its irritating larvae hairs. Although the dermatitis occurs among outdoor professionals, it is primarily extraprofessional. Contamination generally occurs in pinewoods, rarely in cities. Means of contamination comprise direct contact with the nest or the processional caterpillar and indirect contact with air dispersed hairs. The dermatitis is generally observed in late spring and particularly from April to June, among campers and tourers. The eruption has its onset 1–12 hours after contact with the hairs and presents with intense and continuous itching. Morphologically, it is strophulus-like and consists of papulous, excoriated, and pinkish lesions on an oedematous base. Diagnosis is usually straightforward. The pathogenetic mechanism of the affection is mechanical, pharmacological, and allergic in nature. Besides skin, T. pityocampa Schiff can involve the eyes and rarely the airways. Despite the considerable damages to humans and nature, pine caterpillar infestation is an underestimated problem; medical literature lists few studies, and often relevant information is referred to local media and popular wisdom.


title: Presentation attack detection algorithm for face and iris biometrics

abstract: Biometric systems are vulnerable to the diverse attacks that emerged as a challenge to assure the reliability in adopting these systems in real-life scenario. In this work, we propose a novel solution to detect a presentation attack based on exploring both statistical and Cepstral features. The proposed Presentation Attack Detection (PAD) algorithm will extract the statistical features that can capture the micro-texture variation using Binarized Statistical Image Features (BSIF) and Cepstral features that can reflect the micro changes in frequency using 2D Cepstrum analysis. We then fuse these features to form a single feature vector before making a decision on whether a capture attempt is a normal presentation or an artefact presentation using linear Support Vector Machine (SVM). Extensive experiments carried out on a publicly available face and iris spoof database show the efficacy of the proposed PAD algorithm with an Average Classification Error Rate (ACER) = 10.21% on face and ACER = 0% on the iris biometrics.


title: Dependency Link Derivation Process and Theorems of Requirements Traceability Matrix

abstract: This work proposes traceability link derivation process and theorems to establish the trace links between system objectives, requirements, design models and system/subsystems/components. The traceability relation definitions and three kinds of traceability links are defined by adopting requirements traceability reference models. Based on the definitions and graph theory, the dependency link derivation theorems are provided to derive traceability relations of requirements traceability matrix in an automatic way. The proposed approach can be served as a basis for requirements management that can deal with the requirements traceability issues, such as degree of satisfaction, strength of dependency, dependency link direction and change impact analysis.


title: Development of a miniature self-stabilization jumping robot

abstract: We present the design and implementation of a new jumping robot for mobile sensor network. Unlike other jumping robots, the robot is based on a simple two-mass-spring model. After we throw it on ground, it can stabilize itself and then jump once. The detailed mechanism design including the load holding and self-stabilization are presented. Jumping heights and distances with different robot weights are measured and compared with calculated values from the two-mass-spring model.


title: Neutron flux spectra investigations in breeding blanket assembly containing lithium titanate and Lead irradiated with DT neutrons

abstract: Abstract A benchmark experiment was performed to obtain the neutron spectra in a breeding blanket assembly consisting of lithium titanate and Lead using multiple foil activation technique with unfolding code SAND-II-SNL. The purpose of the experiment was to validate the calculated neutron spectra and neutron induced reaction rates with MCNP-4A code and FENDL-2.1 & FENDL-3.0 transport libraries in the layered geometry of lithium titanate and Lead. Measured reaction rates in different activation foils were compared with MCNP-IRDFF-1.05 and FISPACT-EAF2007 calculated values. In general, FENDL-3.0 calculated reaction rates are closer to the measured reaction rates than FENDL-2.1 calculated rates. Calculated Lead multiplication is improved from FENDL-2.1 to FENDL-3.0 up to 2%. Unfolded neutron spectra in the multiplier zones are slightly underestimated by calculations in the secondary neutron emission range of Pb(n, x n) reactions indicating the need of improvement in the (n, 2n) cross-section of Lead isotopes. The observed underestimation in the Lead multiplication puts the tritium production calculations of breeding blankets using Lead as multiplier on conservative side. The inelastic scattering range of Lead isotopes is overestimated by calculations as large as 80% in one of the breeder zone. This observation requires improvement in the inelastic scattering cross-sections of Lead isotopes in FENDL evaluations.


title: Analysis of new techniques to obtain quality training sets

abstract: This paper presents new algorithms to identify and eliminate mislabelled, noisy and atypical training samples for supervised learning and more specifically, for nearest neighbour classification. The main goal of these approaches is to enhance the classification accuracy by improving the quality of the training data. Several experiments with synthetic and real data sets are carried out in order to illustrate the behaviour of the schemes proposed here and compare their performance with that of other traditional techniques. It is also analysed the ability of these new algorithms to "reduce" the possible overlapping among regions of different classes.


title: Privacypreserving logistic regression

abstract: This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases. We focus on privacy-preserving logistic regression. First we apply an idea of Dwork et al. [6] to design a privacy-preserving logistic regression algorithm. This involves bounding the sensitivity of regularized logistic regression, and perturbing the learned classifier with noise proportional to the sensitivity. ::: ::: We then provide a privacy-preserving regularized logistic regression algorithm based on a new privacy-preserving technique: solving a perturbed optimization problem. We prove that our algorithm preserves privacy in the model due to [6]. We provide learning guarantees for both algorithms, which are tighter for our new algorithm, in cases in which one would typically apply logistic regression. Experiments demonstrate improved learning performance of our method, versus the sensitivity method. Our privacy-preserving technique does not depend on the sensitivity of the function, and extends easily to a class of convex loss functions. Our work also reveals an interesting connection between regularization and privacy.


title: Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks

abstract: The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In this paper, we therefore study a lifelong version of the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting. One agent has to be assigned to each delivery task. This agent has to first move to a given pickup location and then to a given delivery location while avoiding collisions with other agents. We present two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS). Theoretically, we show that they solve all well-formed MAPD instances, a realistic subclass of MAPD instances. Experimentally, we compare them against a centralized strawman MAPD algorithm without this guarantee in a simulated warehouse system. TP can easily be extended to a fully distributed MAPD algorithm and is the best choice when real-time computation is of primary concern since it remains efficient for MAPD instances with hundreds of agents and tasks. TPTS requires limited communication among agents and balances well between TP and the centralized MAPD algorithm.


title: Linear Diversity Combining Techniques

abstract: This paper provides analyses of three types of diversity combining systems in practical use. These are: selection diversity, maximal-ratio diversity, and equal-gain diversity systems. Quantitative measures of the relative performance (under realistic conditions) of the three systems are provided. The effects of various departures from ideal conditions, such as non-Rayleigh fading and partially coherent signal or noise voltages, are considered. Some discussion is also included of the relative merits of predetection and postdetection combining and of the problems in determining and using long-term distributions. The principal results are given in graphs and tables, useful in system design. It is seen that the simplest possible combiner, the equal-gain system, will generally yield performance essentially equivalent to the maximum obtainable from any quasi-linear system. The principal application of the results is to diversity communication systems and the discussion is set in that context, but many of the results are also applicable to certain radar and navigation systems.


title: Trust as a mediator of the relationship between organizational justice and work outcomes: test of a social exchange model

abstract: Data obtained from full-time employees of a public sector organization in India were used to test a social exchange model of employee work attitudes and behaviors. LISREL results revealed that whereas the three organizational justice dimensions (distributive, procedural and interactional) were related to trust in organization only interactional justice was related to trust in supervisor. The results further revealed that relative to the hypothesized fully mediated model a partially mediated model better fitted the data. Trust in organization partially mediated the relationship between distributive and procedural justice and the work attitudes of job satisfaction, turnover intentions, and organizational commitment but fully mediated the relationship between interactional justice and these work attitudes. In contrast, trust in supervisor fully mediated the relationship between interactional justice and the work behaviors of task performance and the individually- and organizationally-oriented dimensions of citizenship behavior.


title: Improvements to a complexity metric: CB measure

abstract: Controlling the complexity of software applications is an essential part of the software development process as it directly affects maintenance activities such as reusability, understandability, modifiability and testability. However, as stated by Tom DeMarco “You cannot control what you cannot measure”. Thus, over the years many complexity metrics have been proposed with the intention of controlling and minimizing the complexity associated with software. However, majority of these proposed complexity metrics are based on only one aspect of complexity. The CB measure introduced by Chhillar and Bhasin is one metric which relies on a number of complexity factors to decide on the complexity of a program. However, it also has some shortcomings and can be further improved. Thus, this paper attempts to propose some additional complexity factors that the CB measure has not considered, to further improve it. The paper also presents an extensive coverage about the software complexity metrics proposed in the literature.


title: Another look at measures of forecast accuracy

abstract: We discuss and compare measures of accuracy of univariate time series forecasts. The methods used in the M-competition and the M3-competition, and many of the measures recommended by previous authors on this topic, are found to be inadequate, and many of them are degenerate in commonly occurring situations. Instead, we propose that the mean absolute scaled error become the standard measure for comparing forecast accuracy across multiple time series.


title: Contrast-based image attention analysis by using fuzzy growing

abstract: Visual attention analysis provides an alternative methodology to semantic image understanding in many applications such as adaptive content delivery and region-based image retrieval. In this paper, we propose a feasible and fast approach to attention area detection in images based on contrast analysis. The main contributions are threefold: 1) a new saliency map generation method based on local contrast analysis is proposed; 2) by simulating human perception, a fuzzy growing method is used to extract attended areas or objects from the saliency map; and 3) a practicable framework for image attention analysis is presented, which provides three-level attention analysis, i.e., attended view, attended areas and attended points. This framework facilitates visual analysis tools or vision systems to automatically extract attentions from images in a manner like human perception. User study results indicate that the proposed approach is effective and practicable.


title: Twenty-Five Years of Groupthink Theory and Research: Lessons from the Evaluation of a Theory

abstract: In this paper, we examine the historical development of the groupthink model and discuss recent responses to the body of empirical evidence amassed on the model. We conclude by articulating general lessons implied by the evolution of research on the groupthink model.


title: Improving Credit Scorecard Modeling Through Applying Text Analysis

abstract: In the credit card scoring and loans management, the prediction of the applicant's future behavior is an important decision support tool and a key factor in reducing the risk of Loan Default. A lot of data mining and classification approaches have been developed for the credit scoring purpose. For the best of our knowledge, building a credit scorecard by analyzing the textual data in the application form has not been explored so far. This paper proposes a comprehensive credit scorecard model technique that improves credit scorecard modeling though employing textual data analysis. This study uses a sample of loan application forms of a financial institution providing loan services in Yemen, which represents a real-world situation of the credit scoring and loan management. The sample contains a set of Arabic textual data attributes defining the applicants. The credit scoring model based on the text mining pre-processing and logistic regression techniques is proposed and evaluated through a comparison with a group of credit scorecard modeling techniques that use only the numeric attributes in the application form. The results show that adding the textual attributes analysis achieves higher classification effectiveness and outperforms the other traditional numerical data analysis techniques. Keywords—Credit Scoring; Textual Data Analysis; Logistic Regression; Loan Default. circumstances. Group of the existing credit scoring models will be discussed in section II. The data sources used in making a credit scoring decisions includes both the applicant form details and the information collected by a credit reference agency like the public registries, internal records in the bank from previous experiences, trans- actions, and any other activities initiated by the applicant in the bank (13). Beside all the work that has been done in this area, there is a drawback point related to the structure of the data used to build the statistical model. This limitation was a result of using only that data that could be represented as numeric values and neglecting the textual data regardless of its importance in purpose of simplifying the analysis calculations and the shape of the output of the model. This limitation results in decreasing the degree of efficiency of those models by neglecting some of the available valuable data that could be used to extract some features to increase the percentage of accuracy and correctness of the classification model. In this study, we aim to improve the existing credit scorecard modeling by employing the textual data analysis. The textual analysis results in extracting a group of textual features inserted to the credit scorecard to increase its accu- racy depending on statistical aspects. The resulting scorecard from the proposed methodology is compared with a group of scorecards generated from using only the numerical data analysis. Logistic Regression, Decision Tree, SVM, and Neural Network techniques are used in the comparison using a sample of 180 loan application forms collected from a financial institution providing loan services in Yemen. The results of the comparison using Error Rate, Recall, Precision, and F1- score show the improvement in the credit scorecard accuracy when employing the textual data analysis.


title: High-Performance Electric Drives for Aerospace More Electric Architectures Part I -- Electric Machines

abstract: This paper discusses the problem of obtaining electric machines (EM) for high performance electric drives (HPED) used in more electric architecture (MEA) applicable to aircraft, spacecraft, and military ground vehicles. The HPED are analyzed by those aspects of six sigma theory that relate to critical-to-quality (CTQ) subjects. Using this approach, weight, volume, reliability, efficiency, and cost CTQ are addressed to develop a balance among them, resulting in an optimized system. The influence of machine controllers and system considerations is discussed. As a part of the machine evaluation process, speeds, bearings, complexity, rotor mechanical and thermal limitations, torque pulsation, current, and power densities, are considered. A methodology for electric machine selection is demonstrated. An example of high-speed, high-performance machine application is shown. A system approach is used for overall electric machine selection and optimization. The presented material is a synopsis of the extensive engineering experience accumulated in Honeywell International.


title: Copper stimulation of LHRH release from median eminence explants. I. A divalent metal specific process that does not require extracellular calcium.

abstract: We have previously shown that chelated copper stimulates the release of LHRH from explants of the median eminence area (MEA) incubated under in vitro conditions, and that this stimulation involves a ligand specific interaction. To further elucidate the mechanism of action of copper, we addressed two questions: (a) What is the divalent metal [metal(II)] specificity for this release process? (b) Is the stimulation of LHRH release by CuHis dependent on influx of extracellular calcium? MEA obtained from adult male rats were incubated for 15 min with one of the following divalent metals Cu, Ni, Fe, Zn, Cd or Mn (each complexed to histidine at an equimolar ratio; 100 microM) and then in the absence of metal for an additional period of 30 min. We found that CuHis and to a lesser extent NiHis stimulated LHRH release, and that FeHis, ZnHis, CdHis or MnHis did not do so. In addition, MEA were incubated for 15 min with CuHis in the presence or absence of CaCl2. Under these two conditions, the temporal pattern and magnitude of CuHis-stimulated LHRH release were identical, indicating that extracellular calcium is not required for copper action. Since, in this series of metals, Cu2+ and Ni2+ are the most potent oxidizing agents, our finding strongly supports our previous proposition that an oxidation reaction is involved in the process of copper stimulation of LHRH release. It has yet to be elucidated whether copper action is totally independent of an increase in intracellular calcium or whether copper leads to LHRH release via mobilization of calcium from intracellular stores.


title: Emotion Analysis and Classification: Understanding the Performers' Emotions Using the LMA Entities

abstract: The increasing availability of large motion databases, in addition to advancements in motion synthesis, has made motion indexing and classification essential for better motion composition. However, in order to achieve good connectivity in motion graphs, it is important to understand human behaviour; human movement though is complex and difficult to completely describe. In this paper, we investigate the similarities between various emotional states with regards to the arousal and valence of the Russell's circumplex model. We use a variety of features that encode, in addition to the raw geometry, stylistic characteristics of motion based on Laban Movement Analysis LMA. Motion capture data from acted dance performances were used for training and classification purposes. The experimental results show that the proposed features can partially extract the LMA components, providing a representative space for indexing and classification of dance movements with regards to the emotion. This work contributes to the understanding of human behaviour and actions, providing insights on how people express emotional states using their body, while the proposed features can be used as complement to the standard motion similarity, synthesis and classification methods.


title: The Impact of Intensive Mindfulness Training on Attentional Control, Cognitive Style, and Affect

abstract: To evaluate the impact of an intensive period of mindfulness meditation training on cognitive and affective function, a non-clinical group of 20 novice meditators were tested before and after participation in a 10-day intensive mindfulness meditation retreat. They were evaluated with self-report scales measuring mindfulness, rumination and affect, as well as performance tasks assessing working memory, sustained attention, and attention switching. Results indicated that those completing the mindfulness training demonstrated significant improvements in self-reported mindfulness, depressive symptoms, rumination, and performance measures of working memory and sustained attention, relative to a comparison group who did not undergo any meditation training. This study suggests future directions for the elucidation of the critical processes that underlie the therapeutic benefits of mindfulness-based interventions.


title: Noise Injection: Theoretical Prospects

abstract: Noise injection consists of adding noise to the inputs during neural network training. Experimental results suggest that it might improve the generalization ability of the resulting neural network. A justification of this improvement remains elusive: describing analytically the average perturbed cost function is difficult, and controlling the fluctuations of the random perturbed cost function is hard. Hence, recent papers suggest replacing the random perturbed cost by a (deterministic) Taylor approximation of the average perturbed cost function. This article takes a different stance: when the injected noise is gaussian, noise injection is naturally connected to the action of the heat kernel. This provides indications on the relevance domain of traditional Taylor expansions and shows the dependence of the quality of Taylor approximations on global smoothness properties of neural networks under consideration. The connection between noise injection and heat kernel also enables controlling the fluctuations of...


title: Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation

abstract: A general-purpose intelligent robot must be able to learn autonomously and be able to accomplish multiple tasks in order to be deployed in the real world. However, standard reinforcement learning approaches learn separate task-specific policies and assume the reward function for each task is known a priori. We propose a framework that learns event cues from off-policy data, and can flexibly combine these event cues at test time to accomplish different tasks. These event cue labels are not assumed to be known a priori, but are instead labeled using learned models, such as computer vision detectors, and then `backed up' in time using an action-conditioned predictive model. We show that a simulated robotic car and a real-world RC car can gather data and train fully autonomously without any human-provided labels beyond those needed to train the detectors, and then at test-time be able to accomplish a variety of different tasks. Videos of the experiments and code can be found at this https URL


title: MMSE reception and successive interference cancellation for MIMO systems with high spectral efficiency

abstract: In this paper, we investigate the performance in terms of symbol error probability (SEP) of multiple-input-multiple-output (MIMO) systems with high spectral efficiency. In particular, we consider the coherent detection of M-PSK signals in a flat Rayleigh-fading environment. We focus on spectrally efficient MIMO systems where, after serial-to-parallel conversion, several substreams of symbols are simultaneously transmitted by using an antenna array, thereby increasing the spectral efficiency. The reception is based on linear minimum mean-square-error (MMSE) combining, eventually followed by successive interference cancellation. Exact and approximate expressions are derived for an arbitrary number of transmitting and receiving antenna elements. Simulation results confirm the validity of our analytical methodology.


title: A fingerprint identification algorithm: Combining image enhancement and chaos synchronisation classifier

abstract: Fingerprint identification is very popular among the identification in biometric security systems. The identification process comprises of image enhancement, feature extraction and pattern classification. The adjustment of grey scale values improves the intensity values of the image. A region mask is generated which provides a stable sampling window to extract features. Multiple CS based classifier along with maximum likelihood matching is used for pattern identification. Using the samples taken from FVC2002 database the performance of the proposed method is tested.


title: Cluster Ensembles A Knowledge Reuse Framework for Combining Partitionings

abstract: It is widely recognized that combining multiple classification or regression models typically provides superior results compared to using a single, well-tuned model. However, there are no well known approaches to combining multiple non-hierarchical clusterings. The idea of combining cluster labelings without accessing the original features leads us to a general knowledge reuse framework that we call cluster ensembles. Our contribution in this paper is to formally define the cluster ensemble problem as an optimization problem and to propose three effective and efficient combiners for solving it based on a hypergraph model. Results on synthetic as well as real data sets are given to show that cluster ensembles can (i) improve quality and robustness, and (ii) enable distributed clustering.


title: Choice as an Aspect of Quality of Life for People With Intellectual Disabilities

abstract: Choice, a concept included in the quality of life approach, is frequently referred to in quality of life and related literature, but its components have not been described clearly. Drawing on conceptual considerations and research reports, the authors review and extend what is known about choice, and set out a conceptualization of its two main components: available opportunities and choice-making. The most important characteristics of opportunities are breadth and familiarity, and the most important characteristics of choice making are freedom, initiative, and skill. The authors consider the application of choice to supports and services by discussing numerous practical issues and providing suggestions for application. These are summarized as an overall four-step strategy for moving forward that sets the scene for more specific strategies to be developed and evaluated.