title: Determinants of Corporate Social Responsibility Disclosure: An Empirical Study of Polish Listed Companies

abstract: In this paper we explore whether a number of elements influence the levels of corporate social responsibility (CSR) disclosure in the annual reports of Polish companies. These elements include the following: company size, profitability, financial leverage, industry environmental sensitivity, board size, women on the board, internationalization, and reputation. We use content analysis to determine the quality of CSR disclosures. We test our hypotheses using a Tobit regression analysis on a sample of 60 reports from Polish companies listed on the Warsaw Stock Exchange. We find industry environmental sensitivity to have significant influence on CSR disclosures. Our research findings also reveal a relationship between company turnover, duration of the stock exchange listing, inclusion in the Respect Index portfolio and foreign capital share, and the level of CSR disclosures. This study extends the scope of previous studies by including non-commonly used independent variables: the company’s internationalization and reputation. To the authors’ knowledge, it is the primary step to investigating CSR reporting practices coupled with the corporate characteristics in a Central and Eastern European country such as Poland. The paper contributes to the understanding of determinants of CSR disclosure and offers findings which are potentially useful for both theory and practice.


title: Continuous mobile authentication using a novel Graphic Touch Gesture Feature

abstract: Behavioral biometric on mobile devices has begun to gain attention in recent years and the feasibility of touch gestures as a novel biometric modality has been investigated lately. In this paper, we propose a novel Graphic Touch Gesture Feature (GTGF) to extract the identity traits from the touch traces. The traces' movement and pressure dynamics are represented by intensity values and shapes of the GTGF. To evaluate its usability on the authentication problem, touch gesture datasets have been collected which includes six commonly used touch gestures. A Equal Error Rate of 2.62% has been achieved combining six gestures together, which demonstrated the effectiveness of the proposed methods.


title: The Severity of Depressive Symptoms vs. Serum Mg and Zn Levels in Postmenopausal Women

abstract: The aim of this study was to assess the severity of depressive symptoms in postmenopausal women, depending on serum Mg and Zn levels. The study involved 171 postmenopausal women from Poland, who were not using menopausal hormone therapy (MHT). The intensity of depressive symptoms was evaluated using a standard research technique, the Beck Depression Inventory (BDI). The plasma Mg and Zn concentrations were measured. Depressive symptoms of different severity levels were diagnosed in 36.8 % of the women. The mean serum Mg level was 1.53 ± 0.28 mg/dL, and Zn level was 72 ±14 μg/dL. The women with higher serum Mg and Zn levels had less depressive symptoms, and this observation is a precious information which can be used when planning depressive disorder prevention programmes.


title: Texture segmentation for seam carving

abstract: This paper analyzes the relationships between texture characteristics of an image and seam-carving (SC) algorithm for image retargeting (resizing). SC is a smart image content aware algorithm. The algorithm, changes the aspect ratio of an image while keeping the "important" objects untouched. Based on a subjective test, we combine between an image's characteristics and a saliency map which provides best results. Furthermore, in order to determine the significance of these results, we use principal component analysis (PCA) statistical method, which identifies those relationships in the subjective test's results. Finally, we present a novel saliency map for SC algorithm. This saliency map is based on a new classification model, which uses statistical connections between neighboring pixels and significantly reduces the level of distortion along the process, providing optimal preservation of important objects within the image.


title: IoT based Smart Home design using power and security management

abstract: The paper presents the design and implementation of an Ethernet-based Smart Home intelligent system for monitoring the electrical energy consumption based upon the real time tracking of the devices at home an INTEL GALILEO 2ND generation development board, which can be used in homes and societies. The proposed system works on real time monitoring and voice control, so that the electrical devices and switches can be remotely controlled and monitored with or without an android based app. It uses various sensors to not only monitor the real time device tracking but also maintaining the security of your house. It is monitored and controlled remotely from an android app using the Internet or the Intranet connectivity. The proposed outcome of the project aims as multiple benefits of saving on electricity bills of the home as well as keep the users updated about their home security with an option of controlling the switching of the devices by using their voice or simple toggle touch on their smartphone, and last but most importantly, monitor the usage in order to conserve the precious natural resources by reducing electrical energy consumption.


title: A viability theory for digital businesses: Exploring the evolutionary changes of revenue mechanisms to support managerial decisions

abstract: The ongoing integration of Information Technology (IT) into various areas of our lives has led to a plethora of digital products and services. To survive competition in the long run, these offerings not only have to keep up with constant technological developments, but also have to adapt from a business point of view. Managers of these digital businesses have to especially focus on the design and evolution of their business’ revenue mechanisms to ensure the viability of their offerings. The related decisions are not trivial, as managers have to be aware of the relevant contextual factors and have to react quickly to changes in the environment. This paper proposes a viability theory for digital businesses described by 17 propositions that may guide managers in the design of revenue mechanisms and thereby support the evolution as well as the viability of a digital business.


Player profiling and modelling in computer and video games


title: Mining User Behavioral Rules from Smartphone Data through Association Analysis

abstract: The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contextual information and mobile phone usage records through the device logs. This paper formulates the problem of mining behavioral association rules of individual mobile phone users utilizing their smartphone data. Association rule learning is the most popular technique to discover rules utilizing large datasets. However, it is well-known that a large proportion of association rules generated are redundant. This redundant production makes not only the rule-set unnecessarily large but also makes the decision making process more complex and ineffective. In this paper, we propose an approach that effectively identifies the redundancy in associations and extracts a concise set of behavioral association rules that are non-redundant. The effectiveness of the proposed approach is examined by considering the real mobile phone datasets of individual users.


title: Learning Color Constancy

abstract: We decided to test a surprisingly simple hypothesis; namely, that the relationship between an image of a scene and the chromaticity of scene illumination could be learned by a neural network. The thought was that if this relationship could be extracted by a neural network, then the trained network would be able to determine a scene's Illuminant from its image, which would then allow correction of the image colors to those relative to a standard illuminance, thereby providing color constancy. Using a database of surface reflectances and illuminants, along with the spectral sensitivity functions of our camera, we generated thousands of images of randomly selected illuminants lighting ‘scenes’ of 1 to 60 randomly selected reflectances. During the learning phase the network is provided the image data along with the chromaticity of its illuminant. After training, the network outputs (very quickly) the chro-maticity of the illumination given only the image data. We obtained surprisingly good estimates of the ambient illumination lighting from the network even when applied to scenes in our lab that were completely unrelated to the training data.


title: A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events

abstract: We present a sequential model for temporal relation classification between intra-sentence events. The key observation is that the overall syntactic structure and compositional meanings of the multi-word context between events are important for distinguishing among fine-grained temporal relations. Specifically, our approach first extracts a sequence of context words that indicates the temporal relation between two events, which well align with the dependency path between two event mentions. The context word sequence, together with a parts-of-speech tag sequence and a dependency relation sequence that are generated corresponding to the word sequence, are then provided as input to bidirectional recurrent neural network (LSTM) models. The neural nets learn compositional syntactic and semantic representations of contexts surrounding the two events and predict the temporal relation between them. Evaluation of the proposed approach on TimeBank corpus shows that sequential modeling is capable of accurately recognizing temporal relations between events, which outperforms a neural net model using various discrete features as input that imitates previous feature based models.


title: Point pattern Matching Using Irreducible Matrix and Relative Invariant

abstract: Point pattern matchingisanimportantproblem inthefieldsofcomputervision and patternrecognition.In this paper,new algorithms based onirreducible matrix andrelativeinvariantfor matchingtwosets ofpoints withthe same cardinality are proposed.Theirfundamentalideaistransformingthetwo dimensionalpointsets with n points intothe vectorsin n dimensional space. Considering these vectors as one dimensional point patterns,these new algorithms aim atreducingthe point matching problem to thatofsorting vectorsin n dimensionalspace aslong asthe sensornoise does notalterthe order ofthe elementsinthe vectors.Theoreticalanalysis and simulationresults show thatthe new algorithms are effective .


title: Bangladeshi Number Plate Detection: Cascade Learning vs. Deep Learning

abstract: This work investigated two different machine learning techniques: Cascade Learning and Deep Learning, to find out which algorithm performs better to detect the number plate of vehicles registered in Bangladesh. To do this, we created a dataset of about 1000 images collected from a security camera of Independent University, Bangladesh. Each image in the dataset were then labelled manually by selecting the Region of Interest (ROI). In the Cascade Learning approach, a sliding window technique was used to detect objects. Then a cascade classifier was employed to determine if the window contained object of interest or not. In the Deep Learning approach, CIFAR-10 dataset was used to pre-train a 15-layer Convolutional Neural Network (CNN). Using this pretrained CNN, a Regions with CNN (R-CNN) was then trained using our dataset. We found that the Deep Learning approach (maximum accuracy 99.60% using 566 training images) outperforms the detector constructed using Cascade classifiers (maximum accuracy 59.52% using 566 positive and 1022 negative training images) for 252 test images.


title: Topic Modeling Based Classification of Clinical Reports

abstract: Electronic health records (EHRs) contain important clinical information about patients. Some of these data are in the form of free text and require preprocessing to be able to used in automated systems. Efficient and effective use of this data could be vital to the speed and quality of health care. As a case study, we analyzed classification of CT imaging reports into binary categories. In addition to regular text classification, we utilized topic modeling of the entire dataset in various ways. Topic modeling of the corpora provides interpretable themes that exist in these reports. Representing reports according to their topic distributions is more compact than bag-of-words representation and can be processed faster than raw text in subsequent automated processes. A binary topic model was also built as an unsupervised classification approach with the assumption that each topic corresponds to a class. And, finally an aggregate topic classifier was built where reports are classified based on a single discriminative topic that is determined from the training dataset. Our proposed topic based classifier system is shown to be competitive with existing text classification techniques and provides a more efficient and interpretable representation.


title: Reconciling mobile app privacy and usability on smartphones: could user privacy profiles help?

abstract: As they compete for developers, mobile app ecosystems have been exposing a growing number of APIs through their software development kits. Many of these APIs involve accessing sensitive functionality and/or user data and require approval by users. Android for instance allows developers to select from over 130 possible permissions. Expecting users to review and possibly adjust settings related to these permissions has proven unrealistic. In this paper, we report on the results of a study analyzing people's privacy preferences when it comes to granting permissions to different mobile apps. Our results suggest that, while people's mobile app privacy preferences are diverse, a relatively small number of profiles can be identified that offer the promise of significantly simplifying the decisions mobile users have to make. Specifically, our results are based on the analysis of settings of 4.8 million smartphone users of a mobile security and privacy platform. The platform relies on a rooted version of Android where users are allowed to choose between "granting", "denying" or "requesting to be dynamically prompted" when it comes to granting 12 different Android permissions to mobile apps they have downloaded.


title: RSSI-based Algorithm for Indoor Localization

abstract: Wireless node localization is one of the key technologies for wireless sensor networks. Outdoor localization can use GPS, AGPS (Assisted Global Positioning System) [6], but in buildings like supermarkets and underground parking, the accuracy of GPS and even AGPS will be greatly reduced. Since Indoor localization requests higher accuracy, using GPS or AGPS for indoor localization is not feasible in the current view. RSSI-based trilateral localization algorithm, due to its low cost, no additional hardware support, and easy-understanding, it becomes the mainstream localization algorithm in wireless sensor networks. With the development of wireless sensor networks and smart devices, the number of WIFI access point in these buildings is increasing, as long as a mobile smart device can detect three or three more known WIFI hotspots’ positions, it would be relatively easy to realize self-localization (Usually WIFI access points locations are fixed). The key problem is that the RSSI value is relatively vulnerable to the influence of the physical environment, causing large calculation error in RSSI-based localization algorithm. The paper proposes an improved RSSI-based algorithm, the experimental results show that compared with original RSSI-based localization algorithms the algorithm improves the localization accuracy and reduces the deviation.


title: Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

abstract: Convolutional networks (ConvNets) have achieved great successes in various challenging vision tasks. However, the performance of ConvNets would degrade when encountering the domain shift. The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions. Given that annotating the medical data is especially expensive, the supervised transfer learning approaches are not quite optimal. In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations. Specifically, our model is based on a dilated fully convolutional network for pixel-wise prediction. Moreover, we build a plug-and-play domain adaptation module (DAM) to map the target input to features which are aligned with source domain feature space. A domain critic module (DCM) is set up for discriminating the feature space of both domains. We optimize the DAM and DCM via an adversarial loss without using any target domain label. Our proposed method is validated by adapting a ConvNet trained with MRI images to unpaired CT data for cardiac structures segmentations, and achieved very promising results.


title: Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping

abstract: The manipulator workspace mapping is an important problem in robotics and has attracted significant attention in the community. However, most of the pre-existing algorithms have expensive time complexity due to the reliance on sophisticated kinematic equations. To solve this problem, this paper introduces subspace learning (SL), a variant of subspace embedding, where a set of robot and scope parameters is mapped to the corresponding workspace by a deep neural network (DNN). Trained on a large dataset of around $\mathbf{6\times 10^4}$ samples obtained from a MATLAB$^\circledR$ implementation of a classical method and sampling of designed uniform distributions, the experiments demonstrate that the embedding significantly reduces run-time from $\mathbf{5.23 \times 10^3}$ s of traditional discretization method to $\mathbf{0.224}$ s, with high accuracies (average F-measure is $\mathbf{0.9665}$ with batch gradient descent and resilient backpropagation).


title: Text-Independent Speaker Verification Using Long Short-Term Memory Networks

abstract: In this paper, an architecture based on Long Short-Term Memory Networks has been proposed for the text-independent scenario which is aimed to capture the temporal speaker-related information by operating over traditional speech features. For speaker verification, at first, a background model must be created for speaker representation. Then, in enrollment stage, the speaker models will be created based on the enrollment utterances. For this work, the model will be trained in an end-to-end fashion to combine the first two stages. The main goal of end-to-end training is the model being optimized to be consistent with the speaker verification protocol. The end- to-end training jointly learns the background and speaker models by creating the representation space. The LSTM architecture is trained to create a discrimination space for validating the match and non-match pairs for speaker verification. The proposed architecture demonstrate its superiority in the text-independent compared to other traditional methods.


title: WhoTracks.Me: Monitoring the online tracking landscape at scale.

abstract: We present the largest and longest measurement of online tracking to date based on real users. The data, which is made publicly available, is generated from more than 780 million page loads over the course of the last 10 months. Previous attempts to measure the tracking ecosystem, are done via measurement platforms that do not interact with websites the same way a user does. We instrument a crowd-sourced measurement of third-parties across the web via users who consent to data collection via a browser extension. The collection is done with privacy-by-design in mind, and introduces no privacy side effects. This approach overcomes limitations of previous work by collecting real web usage across multiple countries, ISP and browser configurations, and on difficult to crawl pages, such as those behind logins, giving a more accurate portrayal of the online-tracking ecosystem. ::: The data, which we plan to continue contributing to and maintain in the future, and WhoTracks.Me website - the living representation of the data, are available for researchers, regulators, journalists, web developers and users to detect tracking behaviours, analyse the tracking landscape, develop efficient tools, devise policies and raise awareness of the negative externalities tracking introduces. We believe this work provides the transparency needed to shine a light on a very opaque industry.


title: Inkjet-printed optoelectronics

abstract: Inkjet printing is a powerful and cost-effective technique for deposition of liquid inks with high accuracy, which is not only of great significance for graphic applications but also has enormous potential for the direct printing of optoelectronic devices. This review highlights a comprehensive overview of the progress that has been made in optoelectronics fabrication by the inkjet printing technique. The first part briefly covers the droplet-generation process in the nozzles of printheads and the physical properties affecting droplet formation and the profiles of the printed patterns. The second section outlines the recent activities related to applications of inkjet printing in optoelectronics fabrication including solar cells, light-emitting diodes, photodetectors and transparent electrodes. In each application field, the challenges with the inkjet printing process and the possible solutions are discussed before a few remarks. In the last section, a brief summary on the progress of inkjet printing fabrication of optoelectronics and an outlook for future research effort are presented.


title: Identification of Move Method Refactoring Opportunities

abstract: Placement of attributes/methods within classes in an object-oriented system is usually guided by conceptual criteria and aided by appropriate metrics. Moving state and behavior between classes can help reduce coupling and increase cohesion, but it is nontrivial to identify where such refactorings should be applied. In this paper, we propose a methodology for the identification of Move Method refactoring opportunities that constitute a way for solving many common feature envy bad smells. An algorithm that employs the notion of distance between system entities (attributes/methods) and classes extracts a list of behavior-preserving refactorings based on the examination of a set of preconditions. In practice, a software system may exhibit such problems in many different places. Therefore, our approach measures the effect of all refactoring suggestions based on a novel entity placement metric that quantifies how well entities have been placed in system classes. The proposed methodology can be regarded as a semi-automatic approach since the designer will eventually decide whether a suggested refactoring should be applied or not based on conceptual or other design quality criteria. The evaluation of the proposed approach has been performed considering qualitative, metric, conceptual, and efficiency aspects of the suggested refactorings in a number of open-source projects.


title: Merging of octree based 3D occupancy grid maps

abstract: A technique for merging 3D octree based occupancy grid maps is proposed and implemented. Octrees are a memory efficient way to represent a 3D environment by recursively subdividing space at multiple depths in a tree structure. The use of of an octree representation of a 3D environment allows large environments to be mapped while limiting the amount of memory used in comparison to other techniques. When multiple robots are used to map an environment a more accurate map of a larger space can be produced in less time. In this paper, the problem of merging octree based occupancy grid maps from independent robots into one global map of their environment is explored. Techniques are introduced to address information from sources coming from multiple depths in the map as well as relative transformations between maps that are not axis aligned. These techniques allow the octree representation of an environment to be extended to multiple robots. The application of these techniques is demonstrated by merging maps built by robots in a simulated environment. The contribution of this work lies in the introduction of a feasible method of merging memory efficient maps of a 3D environment. The results obtained in this paper demonstrate that the proposed strategies for octree based map mergers are valid.


title: Facial expression recognition based on region specific appearance and geometric features

abstract: In this paper we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on two public databases: CK+ and MUG facial expression dataset.


title: Whole Blood Viscosity, Plasma Viscosity and Erythrocyte Aggregation in Nine Mammalian Species: Reference Values and Comparison of Data

abstract: In this study species-specific values for whole blood viscosity (WBV), plasma viscosity (PV) and erythrocyte aggregation (EA) were determined in a total of 360 animals. We used 40 individual adult animals of nine mammalian species: horse, pig, dog, cat, rat, cattle, sheep, rabbit and mouse. WBV measurements were carried out using a LS30 viscometer, PV was measured using OCR-D and EA was measured using a Myrenne aggregometer and the LS30 (aggregation index at low shear rate). At low shear rates (0.7 s−1 and 2.4 s−1) haematocrit (Hct)-standardized (40% Hct) samples showed a higher value of WBV and EA in horse, pig, dog and cat. In cattle, sheep, rabbit and mouse, EA and WBV were markedly decreased and EA was almost undetectable, although the plasma fibrinogen concentration was higher in these animals. Rats showed the highest WBV at low shear rate in native blood and WBV was not different from horse in Hct-standardized blood; however, EA was very low in the rat, a result that might be explained by mechanical or geometrical properties of the red blood cell. EA correlated with the plasma protein concentration in each species except dog and mouse. In horse, cattle and pig, EA correlated with the plasma fibrinogen concentration. At high shear rate (94 s−1), WBV was higher in cattle than cat and rat, and dog had higher values than horse, suggesting specific interspecies differences depending on low shear and high shear values of WBV, as a result of mechanisms that influence RBC flexibility. PV was highest in cattle and lowest in rabbit and mouse and did not correlate with WBV. Haemorheological parameters differed between the species. Each species has its own rheological fingerprint. The physiological significance of these variations among mammalian species has not yet been established. Viscosity contributes to endothelial cell shear stress. While haemorheological parameters differ across the species it may be postulated that factors influencing flow-mediated endothelial cell signal transduction are different among the species.


title: Examining the technology acceptance model using physician acceptance of telemedicine technology

abstract: The rapid growth of investment in information technology (IT) by organizations worldwide has made user acceptance an increasingly critical technology implementation and management issue. While such acceptance has received fairly extensive attention from previous research, additional efforts are needed to examine or validate existing research results, particularly those involving different technologies, user populations, and/or organizational contexts. In response, this paper reports a research work that examined the applicability of the Technology Acceptance Model (TAM) in explaining physicians' decisions to accept telemedicine technology in the health-care context. The technology, the user group, and the organizational context are all new to IT acceptance/adoption research. The study also addressed a pragmatic technology management need resulting from millions of dollars invested by health-care organizations in developing and implementing telemedicine programs in recent years. The model's overall fit, explanatory power, and the individual causal links that it postulates were evaluated by examining the acceptance of telemedicine technology among physicians practicing at public tertiary hospitals in Hong Kong. Our results suggested that TAM was able to provide a reasonable depiction of physicians' intention to use telemedicine technology. Perceived usefulness was found to be a significant determinant of attitude and intention but perceived ease of use was not. The relatively low R-square of the model suggests both the limitations of the parsimonious model and the need for incorporating additional factors or integrating with other IT acceptance models in order to improve its specificity and explanatory utility in a health-care context. Based on the study findings, implications for user technology acceptance research and telemedicine management are discussed.


title: Concerns about cultural neurosciences: A critical analysis

abstract: Abstract Ten years ago, neuroscientists began to study cultural phenomena by using functional MRI. Since then the number of publications in this field, termed cultural neuroscience (CN), has tremendously increased. In these studies, particular concepts of culture are implied, but rarely explicitly discussed. We argue that it is necessary to make these concepts a topic of debate in order to unravel the foundations of CN. From 40 fMRI studies we extracted two strands of reasoning: models investigating universal mechanisms for the formation of cultural groups and habits and, models assessing differences in characteristics among cultural groups. Both strands simplify culture as an inflexible set of traits and specificities. We question this rigid understanding of culture and highlight its hidden evaluative nature.


title: Measurement of Individual Differences Lessons From Memory Assessment in Research and Clinical Practice

abstract: An examination of test manuals and published research indicates that widely used memory tests (e.g., Verbal Paired Associates and Word List tests of the Wechsler Memory Scale, Rey Auditory Verbal Learning Test, and California Verbal Learning Test) are afflicted by severe ceiling effects. In the present study, the true extent of memory ability in healthy young adults was tested by giving 208 college undergraduates verbal paired-associate and verbal learning tests of various lengths; the findings demonstrate that healthy adults can remember much more than is suggested by the normative data for the memory tests just mentioned. The findings highlight the adverse effects of low ceilings in memory assessment and underscore the severe consequences of ceiling effects on score distributions, means, standard deviations, and all variability-dependent indices, such as reliability, validity, and correlations with other tests. The article discusses the optimal test lengths for verbal paired-associate and verbal list-le...


title: Breast Cancer Molecular Subtypes Respond Differently to Preoperative Chemotherapy

abstract: PURPOSE ::: Molecular classification of breast cancer has been proposed based on gene expression profiles of human tumors. Luminal, basal-like, normal-like, and erbB2+ subgroups were identified and were shown to have different prognoses. The goal of this research was to determine if these different molecular subtypes of breast cancer also respond differently to preoperative chemotherapy. ::: ::: ::: EXPERIMENTAL DESIGN ::: Fine needle aspirations of 82 breast cancers were obtained before starting preoperative paclitaxel followed by 5-fluorouracil, doxorubicin, and cyclophosphamide chemotherapy. Gene expression profiling was done with Affymetrix U133A microarrays and the previously reported "breast intrinsic" gene set was used for hierarchical clustering and multidimensional scaling to assign molecular class. ::: ::: ::: RESULTS ::: The basal-like and erbB2+ subgroups were associated with the highest rates of pathologic complete response (CR), 45% [95% confidence interval (95% CI), 24-68] and 45% (95% CI, 23-68), respectively, whereas the luminal tumors had a pathologic CR rate of 6% (95% CI, 1-21). No pathologic CR was observed among the normal-like cancers (95% CI, 0-31). Molecular class was not independent of conventional cliniocopathologic predictors of response such as estrogen receptor status and nuclear grade. None of the 61 genes associated with pathologic CR in the basal-like group were associated with pathologic CR in the erbB2+ group, suggesting that the molecular mechanisms of chemotherapy sensitivity may vary between these two estrogen receptor-negative subtypes. ::: ::: ::: CONCLUSIONS ::: The basal-like and erbB2+ subtypes of breast cancer are more sensitive to paclitaxel- and doxorubicin-containing preoperative chemotherapy than the luminal and normal-like cancers.


title: Avoiding plagiarism, self-plagiarism, and other questionable writing practices: A guide to ethical writing

abstract: In recognizing the importance of educating aspiring scientists in the responsible conduct of research (RCR), the Office of Research Integrity (ORI), began sponsoring in 2002 the creation of instructional resources to address this pressing need. The present guide on avoiding plagiarism and other inappropriate writing practices was created, in part, to meet this need. Its purpose is to help students, as well as professionals, identify and prevent such practices and to develop an awareness of ethical writing. This guide is one of the many products stemming from ORI‟s educational initiatives in the RCR.


title: DSConv: Efficient Convolution Operator

abstract: Quantization is a popular way of increasing the speed and lowering the memory usage of Convolution Neural Networks (CNNs). When labelled training data is available, network weights and activations have successfully been quantized down to 1-bit. The same cannot be said about the scenario when labelled training data is not available, e.g. when quantizing a pre-trained model, where current approaches show, at best, no loss of accuracy at 8-bit quantizations. We introduce DSConv, a flexible quantized convolution operator that replaces single-precision operations with their far less expensive integer counterparts, while maintaining the probability distributions over both the kernel weights and the outputs. We test our model as a plug-and-play replacement for standard convolution on most popular neural network architectures, ResNet, DenseNet, GoogLeNet, AlexNet and VGG-Net and demonstrate state-of-the-art results, with less than 1% loss of accuracy, without retraining, using only 4-bit quantization. We also show how a distillation-based adaptation stage with unlabelled data can improve results even further.


title: A Multi-Resolution Pyramid for Outdoor Robot Terrain Perception

abstract: This paper addresses the problem of outdoor terrain modeling for the purposes of mobile robot navigation. We propose an approach in which a robot acquires a set of terrain models at differing resolutions. Our approach addresses one of the major shortcomings of Bayesian reasoning when applied to terrain modeling, namely artifacts that arise from the limited spatial resolution of robot perception. Limited spatial resolution causes small obstacles to be detectable only at close range. Hence, a Bayes filter estimating the state of terrain segments must consider the ranges at which that terrain is observed. We develop a multi-resolution approach that maintains multiple navigation maps, and derive rational arguments for the number of layers and their resolutions. We show that our approach yields significantly better results in a practical robot system, capable of acquiring detailed 3-D maps in large-scale outdoor environments.


title: Direct AC–AC Resonant Boost Converter for Efficient Domestic Induction Heating Applications

abstract: Domestic induction heating (IH) is currently the technology of choice in modern domestic applications due to its advantages regarding fast heating time, efficiency, and improved control. New design trends pursue the implementation of new cost-effective topologies with higher efficiency levels. In order to achieve this aim, a direct ac-ac boost resonant converter is proposed in this paper. The main features of this proposal are the improved efficiency, reduced component count, and proper output power control. A detailed analytical model leading to closed-form expressions of the main magnitudes is presented, and a converter design procedure is proposed. In addition, an experimental prototype has been designed and built to prove the expected converter performance and the accurateness of the analytical model. The experimental results are in good agreement with the analytical ones and prove the feasibility of the proposed converter for the IH application.


title: A framework for collaborative cloud-based fault detection and diagnosis in smart buildings

abstract: The potential for saving on energy related cost with timely and accurate Fault Detection and Diagnosis (FDD) in Building Management Systems (BMS) has been estimated to ca. 15–30% of the overall building energy-related cost. Due to the expansion of well-equipped smart buildings that feature multitudes of sensors and meters that enable collection of large amounts of data, FDD data-based methods have become very popular. Sensor and meter data, however, have been found as insufficient for FDD purposes, and it needs to be complemented with event/fault data that is more difficult to obtain. To account for the unavailability of event/fault data, BMS can benefit from sharing each other's data and utilize it for collaborative FDD. To support collaborative FDD and sharing of data, we rely on cloud computing. In this paper we present a framework for collaborative FDD of smart buildings that utilizes cloud computing and enables BMS to share and benefit from each other's data.


DATA MINING FOR PREDICTING CUSTOMER SATISFACTION IN FAST-FOOD


title: Privacy Risks with Facebook's PII-Based Targeting: Auditing a Data Broker's Advertising Interface

abstract: Sites like Facebook and Google now serve as de facto data brokers, aggregating data on users for the purpose of implementing powerful advertising platforms. Historically, these services allowed advertisers to select which users see their ads via targeting attributes. Recently, most advertising platforms have begun allowing advertisers to target users directly by uploading the personal information of the users who they wish to advertise to (e.g., their names, email addresses, phone numbers, etc.); these services are often known as custom audiences. Custom audiences effectively represent powerful linking mechanisms, allowing advertisers to leverage any PII (e.g., from customer data, public records, etc.) to target users. In this paper, we focus on Facebook's custom audience implementation and demonstrate attacks that allow an adversary to exploit the interface to infer users' PII as well as to infer their activity. Specifically, we show how the adversary can infer users' full phone numbers knowing just their email address, determine whether a particular user visited a website, and de-anonymize all the visitors to a website by inferring their phone numbers en masse. These attacks can be conducted without any interaction with the victim(s), cannot be detected by the victim(s), and do not require the adversary to spend money or actually place an ad. We propose a simple and effective fix to the attacks based on reworking the way Facebook de-duplicates uploaded information. Facebook's security team acknowledged the vulnerability and has put into place a fix that is a variant of the fix we propose. Overall, our results indicate that advertising platforms need to carefully consider the privacy implications of their interfaces.


title: Internet Flattening: Monitoring and Analysis of Inter-Domain Routing

abstract: The decrease of AS-level route length, called Internet flattening, has a significance impact on the the design of next generation global routing system. However, there is little quantitative assessment of its factors. We report our findings through monitoring and analysis of inter-domain routing. Using BGP routing table and update messages from RouteViews and RIPE RIS, we explore Internet flattening from a view of global inter-domain routing system. Our study shows that Internet flattening is from two dominating sources: (1) ASes close to Tier 1 contribute 36% of the total decrease in route length; and (2) Routes bypassing Tier 1 is responsible for 53% of the total decrease in route length. Our measurement results also indicate that multi-homing is not an important reason of Internet flattening. Besides, leading Content Providers contribute to Internet flattening ten times than that by Internet Service Providers. Base on our result, we propose mechanisms for the performance improvement for Content Providers.


title: A Customized Bolus Produced Using a 3-Dimensional Printer for Radiotherapy

abstract: Objective ::: Boluses are used in high-energy radiotherapy in order to overcome the skin sparing effect. In practice though, commonly used flat boluses fail to make a perfect contact with the irregular surface of the patient’s skin, resulting in air gaps. Hence, we fabricated a customized bolus using a 3-dimensional (3D) printer and evaluated its feasibility for radiotherapy. ::: ::: ::: Methods ::: We designed two kinds of bolus for production on a 3D printer, one of which was the 3D printed flat bolus for the Blue water phantom and the other was a 3D printed customized bolus for the RANDO phantom. The 3D printed flat bolus was fabricated to verify its physical quality. The resulting 3D printed flat bolus was evaluated by assessing dosimetric parameters such as D1.5 cm, D5 cm, and D10 cm. The 3D printed customized bolus was then fabricated, and its quality and clinical feasibility were evaluated by visual inspection and by assessing dosimetric parameters such as Dmax, Dmin, Dmean, D90%, and V90%. ::: ::: ::: Results ::: The dosimetric parameters of the resulting 3D printed flat bolus showed that it was a useful dose escalating material, equivalent to a commercially available flat bolus. Analysis of the dosimetric parameters of the 3D printed customized bolus demonstrated that it is provided good dose escalation and good contact with the irregular surface of the RANDO phantom. ::: ::: ::: Conclusions ::: A customized bolus produced using a 3D printer could potentially replace commercially available flat boluses.


title: Challenges and enabling technologies for energy aware mobile radio networks

abstract: Mobile communications are increasingly contributing to global energy consumption. In this article, a holistic approach for energy efficient mobile radio networks is presented. The matter of having appropriate metrics and evaluation methods that allow assessing the energy efficiency of the entire system is discussed. The mutual supplementary saving concepts comprise component, link and network levels. At the component level the power amplifier complemented by a transceiver and a digital platform supporting advanced power management are key to efficient radio implementations. Discontinuous transmission by base stations, where hardware components are switched off, facilitate energy efficient operation at the link level. At the network level, the potential for reducing energy consumption is in the layout of networks and their management, that take into account slowly changing daily load patterns, as well as highly dynamic traffic fluctuations. Moreover, research has to analyze new disruptive architectural approaches, including multi-hop transmission, ad-hoc meshed networks, terminal-to-terminal communications, and cooperative multipoint architectures.


title: International standards for HCI and usability

abstract: Over the last 15 years, a comprehensive range of international standards has been developed to define the general principles of user-centred design and good practice in user interface design. Most of the standards specify general principles rather than the precise details of the interface. The paper briefly describes how standards are created and reviews the definitions of usability. HCI and usability standards are described in the categories: usability definitions, use in context, software interface and interaction, hardware interface, documentation, the development process and capability of the organization. The applicability of the standards is discussed.


title: Anonysense: privacy-aware people-centric sensing

abstract: Personal mobile devices are increasingly equipped with the capability to sense the physical world (through cameras, microphones, and accelerometers, for example) and the, network world (with Wi-Fi and Bluetooth interfaces). Such devices offer many new opportunities for cooperative sensing applications. For example, users' mobile phones may contribute data to community-oriented information services, from city-wide pollution monitoring to enterprise-wide detection of unauthorized Wi-Fi access points. This people-centric mobile-sensing model introduces a new security challenge in the design of mobile systems: protecting the privacy of participants while allowing their devices to reliably contribute high-quality data to these large-scale applications. We describe AnonySense, a privacy-aware architecture for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our trust model, and the security properties that drove the design of the AnonySense system. We evaluate our prototype implementation through experiments that indicate the feasibility of this approach, and through two applications: a Wi-Fi rogue access point detector and a lost-object finder.


title: ISSN: 2229-4333(Print) | ISSN: 0976-8491(Online) Ant Colony Optimization: A Technique used for Image Processing 1

abstract: Ant colony optimization (ACO) is a technique which can be used for various applications. Ant colony Optimization is an optimization technique that is based on the foraging behaviour of real ant colonies. Ant colony optimization is applied for the image processing which are on the basis continuous optimization. This paper proposes an ant colony optimization (ACO) based algorithm for continuous optimization problems on images like image edge detection, image compression, image segmentation, structural damage monitoring etc in image processing .This paper represents that how ACO is applied for various applications in image processing. The algorithm can find the optimal solution for problem. The results show feasibility of the algorithm in terms of accuracy and continuous optimization. Ant Colony Optimization: The ant colony optimization algorithm (ACO) is a probabilistic technique for solving many problems which can be reduced to finding good paths through graphs. Although real ants are blind, they are capable of finding shortest path from food source to their nest by exploiting a liquid substance, called pheromone, which they release on the transit route (1). This algorithm is a member of ant colony algorithms family, in swarm intelligence methods, and it constitutes some met heuristic optimizations. Ant Colony Optimization (ACO) is a population-based, general search technique for the solution of complex continuous problems which is inspired by the pheromone track laying behaviour of real ant colonies. The behaviour of ant is intimidated in artificial ant colonies for the search of estimated solutions to discrete optimization problems, to continuous optimization problems, and to important problems in telecommunications, such as routing and load balancing. Initially proposed by Marco Dorigo in 1992 in his PhD thesis, the first algorithm was aiming to search for an optimal path in a graph, based on the behaviour of ants looking for a path between their colony and a source of food. The ant colony optimization (ACO) meta- heuristic a colony of artificial ants assists in finding good solutions to difficult discrete optimization problems (2). The choice is to allocate the computational resources to a set of relatively simple agents (artificial ants) that communicate indirectly by stigmergy. Good solutions are an emergent property of the agents' cooperative interaction. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behaviour of ants. The main underlying idea, loosely inspired by the behaviour of real ants, is that of a parallel search over several constructive computational threads based on local problem data and on a dynamic memory structure containing information on the quality of previously obtained result. The collective behaviour emerging from the interaction of the different search threads has proved effective in solving combinatorial optimization (CO) problems. The developed AS strategy attempts to simulate behaviour of real ants with the addition of several artificial characteristics: visibility, memory, and discrete time to resolve many complex problems successfully such as the travelling salesman problem (TSP) ,vehicle routing problem (VRP), and best path planning, Even though many changes have been applied to the ACO algorithms during the past years, their fundamental ant behavioural mechanism that is positive feedback process demonstrated by a colony of ants is still the same. Ant's algorithm has also plenty of networking applications such as in communication networks and electrical distribution networks. II. Ant Colony System Algorithm Different steps of a simple ant colony system algorithm are as follows.


title: Review on Heart Disease Prediction System using Data Mining Techniques

abstract: Data mining is the computer based process of analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict future trends, allowing business to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally taken much time consuming to resolve. The huge amounts of data generated for prediction of heart disease are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. Result from using neural networks is nearly 100% in one paper (10) and in (6). So that the prediction by using data mining algorithm given efficient results. Applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease. Cholesterol: - abnormal levels of lipids (fats) in the blood are risk factor of heart diseases. Cholesterol is a soft, waxy substance found among the lipids in the bloodstream and in all the body's cells. High level of triglyceride (most common type of fat in body) combined with high levels of LDL (low density lipoprotein) cholesterol speed up atherosclerosis increasing the risk of heart diseases. High blood pressure: - High blood pressure also known as HBP or hypertension is a widely misunderstood medical condition. High blood pressure increase the risk of the walls of our blood vessels walls becoming overstretched and injured. Also increase the risk of having heart attack or stroke and of developing heart failure, kidney failure and peripheral vascular disease.


title: KYPO Cyber Range: Design and Use Cases

abstract: The physical and cyber worlds are increasingly intertwined and ::: exposed to cyber attacks. The KYPO cyber range provides complex ::: cyber systems and networks in a virtualized, fully controlled ::: and monitored environment. Time-efficient and cost-effective ::: deployment is feasible using cloud resources instead of a ::: dedicated hardware infrastructure. This paper describes the ::: design decisions made during it’s development. We prepared a ::: set of use cases to evaluate the proposed design decisions and ::: to demonstrate the key features of the KYPO cyber range. It was ::: especially cyber training sessions and exercises with hundreds ::: of participants which provided invaluable feedback for KYPO ::: platform development.


title: Lens based beamforming principles for 77 GHz automotive radar sensors

abstract: Highly integrated 77 GHz SiGe-radar sensors with dielectric lenses as focusing element are perfectly suited for the demand of a high performance but yet cost effective radar sensor and may be a key feature for the availability of such systems in all vehicle classes. In this contribution amplitude and phase distributions of the dielectric lenses are analyzed with respect to their effects on angular measurements and a beamforming concept with switchable transmit (TX) beams is presented.


title: A new time-of-flight mass spectrometer for electron-ion and ion-ion coincidence experiments (PEPICO, PIPICO and PEPIPICO)

abstract: A time-of-flight mass spectrometer (TOFMS), has been designed and built at the Federal University of Rio de Janeiro (Brazil), with the aim of studying the ionic fragmentation of gas-phase compounds, using synchrotron radiation. Our main research interest consists on the observation of single, double and multiple ionization as a function of the incident photon energy, with particular emphasis on core-excited molecules. These processes are usually associated with the formation of very fast ions. The spectrometer employs, accordingly, a collimating lens which allows for the collection of ions having kinetic energies up to 40 eV with almost 100% efficiency. By changing the lens voltage, the spectrometer can also be operated in a highly discriminating condition, which provides an easy and accurate method for the determination of the kinetic energy release. In order to be tested, the spectrometer was adapted to a brazilian beamline installed at CAMD (Center for Advanced Microstructures and Devices). Mass and co...


title: Mining dependency in distributed systems through unstructured logs analysis

abstract: Dependencies among system components are crucial to locating root errors in a distributed system. In this paper, we propose an approach to mine intercomponent dependencies from unstructured logs. The technique requires neither additional system instrumentation nor any application specific knowledge. In the approach, we first parse each log message into its log key and parameters. Then, we find dependent log key pairs belong to different components by leveraging co-occurrence analysis and parameter correspondence. After that, we use Bayesian decision theory to estimate the dependency direction of each dependent log key pair. We further apply time delay consistency to remove false positive detections. Case studies on Hadoop show that the technique successfully identifies the dependencies among the distributed system components.


title: Dual-side and three-dimensional microelectrode arrays fabricated from ultra-thin silicon substrates

abstract: A method for fabricating planar implantable microelectrode arrays was demonstrated using a process that relied on ultra-thin silicon substrates, which ranged in thickness from 25 to 50 µm. The challenge of handling these fragile materials was met via a temporary substrate support mechanism. In order to compensate for putative electrical shielding of extracellular neuronal fields, separately addressable electrode arrays were defined on each side of the silicon device. Deep reactive ion etching was employed to create sharp implantable shafts with lengths of up to 5 mm. The devices were flip-chip bonded onto printed circuit boards (PCBs) by means of an anisotropic conductive adhesive film. This scalable assembly technique enabled three-dimensional (3D) integration through formation of stacks of multiple silicon and PCB layers. Simulations and measurements of microelectrode noise appear to suggest that low impedance surfaces, which could be formed by electrodeposition of gold or other materials, are required to ensure an optimal signal-to-noise ratio as well a low level of interchannel crosstalk.


title: A robust model for paper reviewer assignment

abstract: Automatic expert assignment is a common problem encountered in both industry and academia. For example, for conference program chairs and journal editors, in order to collect "good" judgments for a paper, it is necessary for them to assign the paper to the most appropriate reviewers. Choosing appropriate reviewers of course includes a number of considerations such as expertise and authority, but also diversity and avoiding conflicts. In this paper, we explore the expert retrieval problem and implement an automatic paper-reviewer recommendation system that considers aspects of expertise, authority, and diversity. In particular, a graph is first constructed on the possible reviewers and the query paper, incorporating expertise and authority information. Then a Random Walk with Restart (RWR) [1] model is employed on the graph with a sparsity constraint, incorporating diversity information. Extensive experiments on two reviewer recommendation benchmark datasets show that the proposed method obtains performance gains over state-of-the-art reviewer recommendation systems in terms of expertise, authority, diversity, and, most importantly, relevance as judged by human experts.


title: A Multiscale Fusion Convolutional Neural Network for Plant Leaf Recognition

abstract: Plant leaf recognition is a computer vision task used to automatically recognize plant species. It is very challenging since rich plant leaf morphological variations, such as sizes, textures, shapes, venation, and so on. Most existing plant leaf methods typically normalize all plant leaf images to the same size and recognize them at one scale, resulting in unsatisfactory performances. In this letter, a multiscale fusion convolutional neural network (MSF-CNN) is proposed for plant leaf recognition at multiple scales. First, an input image is down-sampled into multiples low resolution images with a list of bilinear interpolation operations. Then, these input images with different scales are step-by-step fed into the MSF-CNN architecture to learn discriminative features at different depths. At this stage, the feature fusion between two different scales is realized by a concatenation operation, which concatenates feature maps learned on different scale images from a channel view. Along with the depth of the MSF-CNN, multiscale images are progressively handled and the corresponding features are fused. Third, the last layer of the MSF-CNN aggregates all discriminative information to obtain the final feature for predicting the plant species of the input image. Experiments show the proposed MSF-CNN method is superior to multiple state-of-the art plant leaf recognition methods on the MalayaKew Leaf dataset and the LeafSnap Plant Leaf dataset.


title: TwiNER: named entity recognition in targeted twitter stream

abstract: Many private and/or public organizations have been reported to create and monitor targeted Twitter streams to collect and understand users' opinions about the organizations. Targeted Twitter stream is usually constructed by filtering tweets with user-defined selection criteria e.g. tweets published by users from a selected region, or tweets that match one or more predefined keywords. Targeted Twitter stream is then monitored to collect and understand users' opinions about the organizations. There is an emerging need for early crisis detection and response with such target stream. Such applications require a good named entity recognition (NER) system for Twitter, which is able to automatically discover emerging named entities that is potentially linked to the crisis. In this paper, we present a novel 2-step unsupervised NER system for targeted Twitter stream, called TwiNER. In the first step, it leverages on the global context obtained from Wikipedia and Web N-Gram corpus to partition tweets into valid segments (phrases) using a dynamic programming algorithm. Each such tweet segment is a candidate named entity. It is observed that the named entities in the targeted stream usually exhibit a gregarious property, due to the way the targeted stream is constructed. In the second step, TwiNER constructs a random walk model to exploit the gregarious property in the local context derived from the Twitter stream. The highly-ranked segments have a higher chance of being true named entities. We evaluated TwiNER on two sets of real-life tweets simulating two targeted streams. Evaluated using labeled ground truth, TwiNER achieves comparable performance as with conventional approaches in both streams. Various settings of TwiNER have also been examined to verify our global context + local context combo idea.


title: The Role of Implicit Argumentation in Nominal SRL

abstract: Nominals frequently surface without overtly expressed arguments. In order to measure the potential benefit of nominal SRL for downstream processes, such nominals must be accounted for. In this paper, we show that a state-of-the-art nominal SRL system with an overall argument F1 of 0.76 suffers a performance loss of more than 9% when nominals with implicit arguments are included in the evaluation. We then develop a system that takes implicit argumentation into account, improving overall performance by nearly 5%. Our results indicate that the degree of implicit argumentation varies widely across nominals, making automated detection of implicit argumentation an important step for nominal SRL.


title: Leveraging SDN layering to systematically troubleshoot networks

abstract: Today's networks are maintained by "masters of complexity": network admins who have accumulated the wisdom to troubleshoot complex problems, despite a limiting toolset. This position paper advocates a more structured troubleshooting approach that leverages architectural layering in Software-Defined Networks (SDNs). In all networks, high-level intent (policy) must correctly map to low-level forwarding behavior (hardware configuration). In SDNs, intent is explicitly expressed, forwarding semantics are explicitly defined, and each architectural layer fully specifies the behavior of the network. Building on these observations, we show how recently-developed troubleshooting tools fit into a coherent workflow that detects mistranslations between layers to precisely localize sources of errant control logic. Our goals are to explain the overall picture, show how the pieces fit together to enable a systematic workflow, and highlight the questions that remain. Once this workflow is realized, network admins can formally verify that their network is operating correctly, automatically troubleshoot bugs, and systematically track down their root cause -- freeing admins to fix problems, rather than diagnose their symptoms.


title: Multi-Body Non-Rigid Structure-from-Motion

abstract: Conventional structure-from-motion (SFM) research is primarily concerned with the 3D reconstruction of a single, rigidly moving object seen by a static camera, or a static and rigid scene observed by a moving camera --in both cases there are only one relative rigid motion involved. Recent progress have extended SFM to the areas of {multi-body SFM} (where there are {multiple rigid} relative motions in the scene), as well as {non-rigid SFM} (where there is a single non-rigid, deformable object or scene). Along this line of thinking, there is apparently a missing gap of "multi-body non-rigid SFM", in which the task would be to jointly reconstruct and segment multiple 3D structures of the multiple, non-rigid objects or deformable scenes from images. Such a multi-body non-rigid scenario is common in reality (e.g. two persons shaking hands, multi-person social event), and how to solve it represents a natural {next-step} in SFM research. By leveraging recent results of subspace clustering, this paper proposes, for the first time, an effective framework for multi-body NRSFM, which simultaneously reconstructs and segments each 3D trajectory into their respective low-dimensional subspace. Under our formulation, 3D trajectories for each non-rigid structure can be well approximated with a sparse affine combination of other 3D trajectories from the same structure (self-expressiveness). We solve the resultant optimization with the alternating direction method of multipliers (ADMM). We demonstrate the efficacy of the proposed framework through extensive experiments on both synthetic and real data sequences. Our method clearly outperforms other alternative methods, such as first clustering the 2D feature tracks to groups and then doing non-rigid reconstruction in each group or first conducting 3D reconstruction by using single subspace assumption and then clustering the 3D trajectories into groups.


title: Neural Darwinism and consciousness

abstract: Abstract Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the ‘dynamic core’). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well.


title: Modeling bug report quality

abstract: Software developers spend a significant portion of their resources handling user-submitted bug reports. For software that is widely deployed, the number of bug reports typically outstrips the resources available to triage them. As a result, some reports may be dealt with too slowly or not at all. We present a descriptive model of bug report quality based on a statistical analysis of surface features of over 27,000 publicly available bug reports for the Mozilla Firefox project. The model predicts whether a bug report is triaged within a given amount of time. Our analysis of this model has implications for bug reporting systems and suggests features that should be emphasized when composing bug reports. We evaluate our model empirically based on its hypothetical performance as an automatic filter of incoming bug reports. Our results show that our model performs significantly better than chance in terms of precision and recall. In addition, we show that our modelcan reduce the overall cost of software maintenance in a setting where the average cost of addressing a bug report is more than 2% of the cost of ignoring an important bug report.


title: Survey on web spam detection: principles and algorithms

abstract: Search engines became a de facto place to start information acquisition on the Web. Though due to web spam phenomenon, search results are not always as good as desired. Moreover, spam evolves that makes the problem of providing high quality search even more challenging. Over the last decade research on adversarial information retrieval has gained a lot of interest both from academia and industry. In this paper we present a systematic review of web spam detection techniques with the focus on algorithms and underlying principles. We categorize all existing algorithms into three categories based on the type of information they use: content-based methods, link-based methods, and methods based on non-traditional data such as user behaviour, clicks, HTTP sessions. In turn, we perform a subcategorization of link-based category into five groups based on ideas and principles used: labels propagation, link pruning and reweighting, labels refinement, graph regularization, and featurebased. We also define the concept of web spam numerically and provide a brief survey on various spam forms. Finally, we summarize the observations and underlying principles applied for web spam detection.


title: Increased Solubility, Dissolution and Physicochemical Studies of Curcumin- Polyvinylpyrrolidone K-30 Solid Dispersions

abstract: Solid dispersions (SD) of curcumin- polyvinylpyrrolidone in the ratio of 1:2, 1:4, 1:5, 1:6, and 1:8 were prepared in an attempt to increase the solubility and dissolution. Solubility, dissolution, powder X-ray diffraction (XRD), differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR) of solid dispersions, physical mixtures (PM) and curcumin were evaluated. Both solubility and dissolution of curcumin solid dispersions were significantly greater than those observed for physical mixtures and intact curcumin. The powder X-ray diffractograms indicated that the amorphous curcumin was obtained from all solid dispersions. It was found that the optimum weight ratio for curcumin:PVP K-30 is 1:6. The 1:6 solid dispersion still in the amorphous from after storage at ambient temperature for 2 years and the dissolution profile did not significantly different from freshly prepared.


title: SEM: A Softmax-based Ensemble Model for CTR estimation in Real-Time Bidding advertising

abstract: In Real-Time Bidding (RTB) advertising, evaluating the Click-Through Rate (CTR) of a bid request and an ad is important for bidding strategy optimization on Demand-Side Platforms (DSPs). The regression-based approaches are popular for CTR estimation in RTB since this kind of approach is highly efficient and scalable. The information of the bid request and the ad contains categorical attributes (such URL) and numerical attributes (such ad size). To vectorize the information for the input of regression-based approaches, the categorical attributes will be expanded to several binary features in general. However, some categorical attributes have infinite possible values (such as URL). Thus, for these attributes, only observed values in training will be transformed into binary features. If there is a new attribute or value in online environment, this information will be lost after vectorization. In this paper, we first exploit the feature hashing trick to transform the categorical and numerical attributes into the large fixed size vector. Since the vector is large and sparse, we propose a Softmax-based Ensemble Model, SEM, which adopts only a few key features after feature hashing for CTR estimation. The experimental results demonstrate that our proposed approach is able to adapt to the harsh environments in RTB, and outperforms the state-of-the-art approaches effectively when only less than 50 features are adopted in two real datasets.


title: Anorectal malformations

abstract: Anorectal malformations comprise a wide spectrum of diseases, which can affect boys and girls, and involve the distal anus and rectum as well as the urinary and genital tracts. They occur in approximately 1 in 5000 live births. Defects range from the very minor and easily treated with an excellent functional prognosis, to those that are complex, difficult to manage, are often associated with other anomalies, and have a poor functional prognosis. The surgical approach to repairing these defects changed dramatically in 1980 with the introduction of the posterior sagittal approach, which allowed surgeons to view the anatomy of these defects clearly, to repair them under direct vision, and to learn about the complex anatomic arrangement of the junction of rectum and genitourinary tract. Better imaging techniques, and a better knowledge of the anatomy and physiology of the pelvic structures at birth have refined diagnosis and initial management, and the analysis of large series of patients allows better prediction of associated anomalies and functional prognosis. The main concerns for the surgeon in correcting these anomalies are bowel control, urinary control, and sexual function. With early diagnosis, management of associated anomalies and efficient meticulous surgical repair, patients have the best chance for a good functional outcome. Fecal and urinary incontinence can occur even with an excellent anatomic repair, due mainly to associated problems such as a poorly developed sacrum, deficient nerve supply, and spinal cord anomalies. For these patients, an effective bowel management program, including enema and dietary restrictions has been devised to improve their quality of life.


title: Secure and Distributed Data Discovery and Dissemination in Wireless Sensor Networks

abstract: A data discovery and dissemination protocol for wireless sensor networks (WSNs) is responsible for updating configuration parameters of, and distributing management commands to, the sensor nodes. All existing data discovery and dissemination protocols suffer from two drawbacks. First, they are based on the centralized approach; only the base station can distribute data items. Such an approach is not suitable for emergent multi-owner-multi-user WSNs. Second, those protocols were not designed with security in mind and hence adversaries can easily launch attacks to harm the network. This paper proposes the first secure and distributed data discovery and dissemination protocol named DiDrip . It allows the network owners to authorize multiple network users with different privileges to simultaneously and directly disseminate data items to the sensor nodes. Moreover, as demonstrated by our theoretical analysis, it addresses a number of possible security vulnerabilities that we have identified. Extensive security analysis show DiDrip is provably secure. We also implement DiDrip in an experimental network of resource-limited sensor nodes to show its high efficiency in practice.


title: Micro magnetic silicone elastomer membrane actuator

abstract: We present results of the design, fabrication, and testing of a microfabricated, membrane-type magnetic actuator. Magnetic pieces made of electroplated Permalloy (Ni80Fe20) are embedded in a thin flexible membrane made of silicone elastomer. When an external magnetic field is applied, a torque generated on the magnetic pieces produces membrane displacement. Permalloy pieces that are 100-mm-wide, 870mm-long, and 22-mm-thick are strategically positioned in a 2-mm-square, 40-mm-thick polydimethylsiloxane (PDMS) membrane (Sylgard 184). This design, produced through numerical simulations, is optimized to realize large membrane displacements. Tests performed on this membrane actuator showed displacements >80 mm in the presence of a 2:85 10 5 A/m external magnetic field. Larger displacements are possible with greater magnetization fields. This type of membrane actuator can be applied to the fabrication of tetherless micropumps for use in microfluidic systems. # 2001 Elsevier Science B.V. All rights reserved.


title: Influence of the Manufacturing Process of a Claw-Pole Alternator on Its Stator Shape and Acoustic Noise

abstract: This paper shows the influence of the manufacturing process of a claw-pole alternator on its acoustic noise. First, the stator welds and the assembly of the stator in the brackets are linked to deformations of the inner diameter of the stator. Then, the influences of these deformations on the magnetic forces and the subsequent acoustic noise are investigated. Results show that the deformations caused by the manufacturing process significantly increase the sound power level of particular orders.


title: Generating Sentences from a Continuous Space

abstract: The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work, we introduce and study an RNN-based variational autoencoder generative model that incorporates distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features. Samples from the prior over these sentence representations remarkably produce diverse and well-formed sentences through simple deterministic decoding. By examining paths through this latent space, we are able to generate coherent novel sentences that interpolate between known sentences. We present techniques for solving the difficult learning problem presented by this model, demonstrate its effectiveness in imputing missing words, explore many interesting properties of the model's latent sentence space, and present negative results on the use of the model in language modeling.


title: Improving Acoustic Models in TORGO Dysarthric Speech Database

abstract: Assistive speech-based technologies can improve the quality of life for people affected with dysarthria, a motor speech disorder. In this paper, we explore multiple ways to improve Gaussian mixture model and deep neural network (DNN) based hidden Markov model (HMM) automatic speech recognition systems for TORGO dysarthric speech database. This work shows significant improvements over the previous attempts in building such systems in TORGO. We trained speaker-specific acoustic models by tuning various acoustic model parameters, using speaker normalized cepstral features and building complex DNN-HMM models with dropout and sequence-discrimination strategies. The DNN-HMM models for severe and severe-moderate dysarthric speakers were further improved by leveraging specific information from dysarthric speech to DNN models trained on audio files from both dysarthric and normal speech, using generalized distillation framework. To the best of our knowledge, this paper presents the best recognition accuracies for TORGO database till date.


title: ZnO Nanostructures for Tissue Engineering Applications

abstract: This review focuses on the most recent applications of zinc oxide (ZnO) nanostructures for tissue engineering. ZnO is one of the most investigated metal oxides, thanks to its multifunctional properties coupled with the ease of preparing various morphologies, such as nanowires, nanorods, and nanoparticles. Most ZnO applications are based on its semiconducting, catalytic and piezoelectric properties. However, several works have highlighted that ZnO nanostructures may successfully promote the growth, proliferation and differentiation of several cell lines, in combination with the rise of promising antibacterial activities. In particular, osteogenesis and angiogenesis have been effectively demonstrated in numerous cases. Such peculiarities have been observed both for pure nanostructured ZnO scaffolds as well as for three-dimensional ZnO-based hybrid composite scaffolds, fabricated by additive manufacturing technologies. Therefore, all these findings suggest that ZnO nanostructures represent a powerful tool in promoting the acceleration of diverse biological processes, finally leading to the formation of new living tissue useful for organ repair.


Expanding the Theory of Traumatic Bonding as it Relates to Forgiveness, Romantic Attachment, and Intention to Return


title: Equiangular Spiral Antenna Backed by a Shallow Cavity With Absorbing Strips

abstract: When a conducting shallow cavity is placed behind an equiangular spiral to obtain a unidirectional beam, the inherent wideband characteristics of the spiral deteriorate. To restore the wideband characteristics, a ring-shaped absorbing strip (R-ABS) is placed under the spiral arms. Analysis of the equiangular spiral with the R-ABS is performed using the finite-difference time-domain method. It is found that the R-ABS successfully restores the wideband characteristics. Subsequently, the R-ABS is divided into two absorbing strips, each specified by an arc-angle . Analysis reveals that the radiation characteristics obtained using the R-ABS are reproduced when the arc-angle is greater than . Throughout this paper an extremely small cavity depth is selected for the analysis: 0.07 wavelength at the lower operating design frequency of 3 GHz.


title: CLUSTERING TO MINIMIZE THE MAXIMUM INTERCLUSTER DISTANCE*

abstract: The problem of clustering a set of points so as to minimize the maximum intercluster distance is studied. An O(kn) approximation algorithm, where n is the number of points and k is the number of clusters, that guarantees solutions with an objective function value within two times the optimal solution value is presented. This approximation algorithm succeeds as long as the set of points satisfies the triangular inequality. We also show that our approximation algorithm is best possible, with respect to the approximation bound, if PZ NP.


title: Using Factor Analysis to Generate Clusters of Agile Practices (A Guide for Agile Process Improvement)

abstract: In this paper, factor analysis is applied on a set of data that was collected to study the effectiveness of 58 different agile practices. The analysis extracted 15 factors, each was associated with a list of practices. These factors with the associated practices can be used as a guide for agile process improvement. Correlations between the extracted factors were calculated, and the significant correlation findings suggested that people who applied iterative and incremental development and quality assurance practices had a high success rate, that communication with the customer was not very popular as it had negative correlations with governance and iterative and incremental development. Also, people who applied governance practices also applied quality assurance practices. Interestingly success rate related negatively with traditional analysis methods such as Gantt chart and detailed requirements specification.


title: 2RW dual-port SRAM design challenges in advanced technology nodes

abstract: We examine appropriate bitcell layouts for two read/write (2RW) 8T dual-port (DP) SRAM in advanced planar/FinFET technologies. 256-kbit 2RW DP SRAM macros with highly symmetrical 8T DP bitcell were designed and fabricated using 16 nm FinFET technology. The read/write assist with wordline overdrive reduces Vmln by 120 mV, achieving successful operation at below 0.5 V.


title: Cultivating youth resilience to prevent bullying and cyberbullying victimization

abstract: Abstract In an effort to better prevent and respond to bullying and cyberbullying, schools are recognizing a need to focus on positive youth development. One often-neglected developmental construct in this rubric is resilience , which can help students successfully respond to the variety of challenges they face. Enhancing this internal competency can complement the ever-present efforts of schools as they work to create a safe and supportive learning environment by shaping the external environment around the child. Based on a national sample of 1204 American youth between the ages of 12 and 17, we explore the relationship between resilience and experience with bullying and cyberbullying. We also examine whether resilient youth who were bullied (at school and online) were less likely to be significantly impacted at school. Results show resilience is a potent protective factor, both in preventing experience with bullying and mitigating its effect. Implications for school and community-based interventions are offered.


title: When A Small Leak Sinks A Great Ship: Deanonymizing Tor Hidden Service Users Through Bitcoin Transactions Analysis

abstract: With the rapid increase of threats on the Internet, people are continuously seeking privacy and anonymity. Services such as Bitcoin and Tor were introduced to provide anonymity for online transactions and Web browsing. Due to its pseudonymity model, Bitcoin lacks retroactive operational security, which means historical pieces of information could be used to identify a certain user. We investigate the feasibility of deanonymizing users of Tor hidden services who rely on Bitcoin as a payment method by exploiting public information leaked from online social networks, the Blockchain, and onion websites. This, for example, allows an adversary to link a user with @alice Twitter address to a Tor hidden service with private.onion address by finding at least one past transaction in the Blockchain that involves their publicly declared Bitcoin addresses. ::: To demonstrate the feasibility of this deanonymization attack, we carried out a real-world experiment simulating a passive, limited adversary. We crawled 1.5K hidden services and collected 88 unique Bitcoin addresses. We then crawled 5B tweets and 1M BitcoinTalk forum pages and collected 4.2K and 41K unique Bitcoin addresses, respectively. Each user address was associated with an online identity along with its public profile information. By analyzing the transactions in the Blockchain, we were able to link 125 unique users to 20 Tor hidden services, including sensitive ones, such as The Pirate Bay and Silk Road. We also analyzed two case studies in detail to demonstrate the implications of the resulting information leakage on user anonymity. In particular, we confirm that Bitcoin addresses should always be considered exploitable, as they can be used to deanonymize users retroactively. This is especially important for Tor hidden service users who actively seek and expect privacy and anonymity.


title: A New Method for Grayscale Image Segmentation Based on Affinity Propagation Clustering Algorithm

abstract: The space complexity of Affinity Propagation (AP) clustering algorithm is high, and it is difficult to be applied to image processing problem directly. In order to overcome this shortcoming, this paper proposes a gray image segmentation method based on gray level histogram and AP clustering algorithm, named GLHAP. In the proposed algorithm, in order to decrease the input data of algorithm AP, only several high frequency gray values as the input data of the image are first obtained from the gray level histogram, and after processing the results of AP, image segmentation results can be obtained. Finally, the experiment results show that the proposed algorithm GLHAP is more effective and efficient than the compared with algorithms FCM and K-means.


title: PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

abstract: High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that supports this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success.


title: Convolutional Neural Networks with Recurrent Neural Filters

abstract: We introduce a class of convolutional neural networks (CNNs) that utilize recurrent neural networks (RNNs) as convolution filters. A convolution filter is typically implemented as a linear affine transformation followed by a non-linear function, which fails to account for language compositionality. As a result, it limits the use of high-order filters that are often warranted for natural language processing tasks. In this work, we model convolution filters with RNNs that naturally capture compositionality and long-term dependencies in language. We show that simple CNN architectures equipped with recurrent neural filters (RNFs) achieve results that are on par with the best published ones on the Stanford Sentiment Treebank and two answer sentence selection datasets.


title: Understanding Back-Translation at Scale

abstract: An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number of methods to generate synthetic source sentences. We find that in all but resource poor settings back-translations obtained via sampling or noised beam outputs are most effective. Our analysis shows that sampling or noisy synthetic data gives a much stronger training signal than data generated by beam or greedy search. We also compare how synthetic data compares to genuine bitext and study various domain effects. Finally, we scale to hundreds of millions of monolingual sentences and achieve a new state of the art of 35 BLEU on the WMT'14 English-German test set.


title: COFGA: Classification Of Fine-Grained Features In Aerial Images

abstract: Classification between thousands of classes in high-resolution images is one of the heavily studied problems in deep learning over the last decade. However, the challenge of fine-grained multi-class classification of objects in aerial images, especially in low resource cases, is still challenging and an active area of research in the literature. Solving this problem can give rise to various applications in the field of scene understanding and classification and re-identification of specific objects from aerial images. In this paper, we provide a description of our dataset - COFGA of multi-class annotated objects in aerial images. We examine the results of existing state-of-the-art models and modified deep neural networks. Finally, we explain in detail the first published competition for solving this task.


XXII. Programming a Computer for Playing Chess 1


abstract: Learning Social Affordances and Using Them for Planning Kadir Firat Uyanik , Yigit Caliskan , Asil Kaan Bozcuoglu , Onur Yuruten , Sinan Kalkan , Erol Sahin {kadir, yigit, asil, oyuruten, skalkan, erol}@ceng.metu.edu.tr KOVAN Research Lab, Dept. of Computer Eng., METU, Ankara, Turkey Abstract This study extends the learning and use of affordances on robots on two fronts. First, we use the very same affordance learning framework that was used for learning the affordances of inanimate things to learn social affordances, that is affor- dances whose existence requires the presence of humans. Sec- ond, we use the learned affordances for making multi-step plans. Specifically, an iCub humanoid platform is equipped with a perceptual system to sense objects placed on a table, as well as the presence and state of humans in the environment, and a behavioral repertoire that consisted of simple object ma- nipulations as well as voice behaviors that are uttered simple verbs. After interacting with objects and humans, the robot learns a set of affordances with which it can make multi-step plans towards achieving a demonstrated goal. Introduction Motor competences of robots operating in our environments, is likely to remain inferior to ours on most fronts in the near future. In order to complete tasks that require competences beyond their abilities, the robots will need need to interact with humans in the environment towards compensating these deficiencies. The inspiration for our study comes from ba- bies and small children who can compensate the lack of their physical competences through the use of adults via social in- teraction . For instance, for a child, the reachability of a candy on a high shelf becomes possible only in the presence of an adult, as long as he can “manipulate” him properly using his social behaviors. In this paper, we extend an affordance framework proposed for robots towards learning interactions with inanimate ob- jects, to learning interactions with humans. The notion of af- fordances, proposed by Gibson (Gibson, 1986), emphasized the interaction between the agent and the environment, as op- posed to the agent or the environment only, and provided a unifying frameworks for the study. Contribution This study extends the learning and use of affordances on robots on two fronts. First, we use the very same affordance learning framework that was used for learning the affordances of inanimate things to learn social affordances 1 (viz. affor- dances whose existence requires the presence of humans). Second, we use learned affordances to make multi-step plans. In our earlier studies, we had proposed a framework that allowed the robot to learn affordances such as traversabil- ity of an environment (Ugur & S¸ahin, 2010) or graspability 1 We would like to note that the term, social affordances has been used in different contexts, e.g., for the possibilities emerging from social networks (Wellman et al., 2003), or the affordances of an en- vironment and properties of people that facilitate social interaction in a group of people (Kreijns & Kirschner, 2001). (Ugur, S¸ahin, & Oztop, 2009), liftability of objects (Dag, Atil, Kalkan, & Sahin, 2010) and showed how one can make multi- step plans using the learned affordances. In this paper, we argue that robots can use the very same framework to learn what a human may afford. Moreover, we enhance our prior study on multi-step planning (Ugur et al., 2009) via a new form of prototypes for effect representation. Specifically, we equipped the humanoid robot iCub with a perceptual system to sense tabletop objects, as well as the presence and state of humans in the environment, and a be- havioral repertoire that consisted of simple object manipu- lations and voice behaviors that uttered simple verbs. After interacting with objects and humans, we show that the robot is able to learn a set of affordances with which it can make multi-step plans towards achieving a demonstrated goal. Related Work The notion of affordances provides a perspective that puts the focus on the interaction (rather than the agent or the environ- ment) and was formalized as a relation a between an entity or environment e, a behavior b and the effect f of behavior b ¨ ¸ oluk, 2007; Monte- on e (S¸ahin, C¸akmak, Do˘gar, U˘gur, & Uc sano, Lopes, Bernardino, & Santos-Victor, 2008): a = (e, b, f ), For example, a behavior b li f t that produces an effect f li f ted on an object e cup forms an affordance relation (e cup , b li f t , f li f ted ). Note that an agent would require more of such relations on different objects and behaviors to learn more general affor- dance relations. The studies on learning and use of affordances have mostly been confined to inanimate things, such as objects (Fitzpatrick, Metta, Natale, Rao, & Sandini, 2003; Detry, Kraft, Buch, Kruger, & Piater, 2010; Atil, Dag, Kalkan, & S¸ahin, 2010; Dag et al., 2010) and tools (Sinapov & Stoytchev, 2008; Stoytchev, 2008) that the robot can interact with. In these studies, the robot interacts with the environ- ment through a set of actions, and learns to perceptually de- tect and actualize them. Moreover, with the exception of few studies (Ugur et al., 2009; Williams & Breazeal, 2012), the robots were only able to perceive the immediate affordances which can be actualized with a single-step action plan. Formalizations, such as 1, are proved to be practical with successful applications in navigation (Ugur & S¸ahin, 2010), and manipulation (Fitzpatrick et al., 2003; Detry et al., 2010; Ugur et al., 2009; Ugur, Oztop, & S¸ahin, 2011), conceptu- alization and language (Atil et al., 2010; Dag et al., 2010; Y¨ur¨uten et al., 2012), and vision (Dag et al., 2010). However,


title: The Support, Health, Activities, Resources, and Education program for early stage dementia: Results from a randomized controlled trial:

abstract: PurposeA randomized controlled trial was conducted to test the effectiveness of the Support, Health, Activities, Resources, and Education Program. This six-session psycho-educational program provides dyadic counseling for individuals in the early stages of dementia and their family caregivers. The goal is to prevent common problems in care that emerge during the course of dementia by (1) actively engaging the person with dementia in developing a balanced plan of future care with his/her caregiver, (2) increasing use of available services, (3) enhancing dyadic relationship functioning, (4) improving well-being, and (5) evoking satisfaction with components of the intervention.Design and methodsPersons with early stage dementia (n = 128) and their caregivers (n = 128) were randomly assigned either to Support, Health, Activities, Resources, and Education Program or a control condition. Intervention efficacy was evaluated for completion of a care plan, use of services, dyadic relationship functioning, particip...