title: Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model

abstract: Owing to the sporadic nature of demand for aircraft maintenance repair parts, airline operators perceive difficulties in forecasting and are still looking for superior forecasting methods. This paper deals with techniques applicable to predicting spare parts demand for airline fleets. The experimental results of 13 forecasting methods, including those used by aviation companies, are examined and clarified through statistical analysis. The general linear model approach is used to explain the variation attributable to different experimental factors and their interactions. Actual historical data for hard-time and condition-monitoring components from an airlines operator are used, in order to compare different forecasting methods when facing intermittent demand. The results confirm the continued superiority of the weighted moving average, Holt and Croston method for intermittent demand, whereas most commonly used methods by airlines are found to be questionable, consistently producing poor forecasting performance. We have, however, devised a new approach to forecasting evaluation, a predictive error-forecasting model which compares and evaluates forecasting methods based on their factor levels when faced with intermittent demand. A simple example is presented to illustrate the performance of the mathematical model. It is suggested that these findings may be applicable to other industrial sectors, which have similar demand patterns to those of airlines.


title: Pavlov's Cockroach: Classical Conditioning of Salivation in an Insect

abstract: Secretion of saliva to aid swallowing and digestion is an important physiological function found in many vertebrates and invertebrates. Pavlov reported classical conditioning of salivation in dogs a century ago. Conditioning of salivation, however, has been so far reported only in dogs and humans, and its underlying neural mechanisms remain elusive because of the complexity of the mammalian brain. We previously reported that, in cockroaches Periplaneta americana, salivary neurons that control salivation exhibited increased responses to an odor after conditioning trials in which the odor was paired with sucrose solution. However, no direct evidence of conditioning of salivation was obtained. In this study, we investigated the effects of conditioning trials on the level of salivation. Untrained cockroaches exhibited salivary responses to sucrose solution applied to the mouth but not to peppermint or vanilla odor applied to an antenna. After differential conditioning trials in which an odor was paired with sucrose solution and another odor was presented without pairing with sucrose solution, sucrose-associated odor induced an increase in the level of salivation, but the odor presented alone did not. The conditioning effect lasted for one day after conditioning trials. This study demonstrates, for the first time, classical conditioning of salivation in species other than dogs and humans, thereby providing the first evidence of sophisticated neural control of autonomic function in insects. The results provide a useful model system for studying cellular basis of conditioning of salivation in the simpler nervous system of insects.


title: Artificial Intelligence and its Application in Different Areas

abstract: In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is the intelligence exhibited by machines or software. It is the subfield of computer science. Artificial Intelligence is becoming a popular field in computer science as it has enhanced the human life in many areas. Artificial intelligence in the last two decades has greatly improved performance of the manufacturing and service systems. Study in the area of artificial intelligence has given rise to the rapidly growing technology known as expert system. Application areas of Artificial Intelligence is having a huge impact on various fields of life as expert system is widely used these days to solve the complex problems in various areas as science, engineering, business, medicine, weather forecasting. The areas employing the technology of Artificial Intelligence have seen an increase in the quality and efficiency. This paper gives an overview of this technology and the application areas of this technology. This paper will also explore the current use of Artificial Intelligence technologies in the PSS design to damp the power system oscillations caused by interruptions, in Network Intrusion for protecting computer and communication networks from intruders, in the medical area- medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it and in the computer games.


title: Word Embedding based Correlation Model for Question/Answer Matching

abstract: With the development of community based question answering (Q\&A) services, a large scale of Q\&A archives have been accumulated and are an important information and knowledge resource on the web. Question and answer matching has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, we try to improve the matching accuracy by overcoming the lexical gap between question and answer pairs. A Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding, given a random pair of words, WEC can score their co-occurrence probability in Q\&A pairs and it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method's promising potential.


title: Inverse procedural modeling of facade layouts

abstract: In this paper, we address the following research problem: How can we generate a meaningful split grammar that explains a given facade layout? To evaluate if a grammar is meaningful, we propose a cost function based on the description length and minimize this cost using an approximate dynamic programming framework. Our evaluation indicates that our framework extracts meaningful split grammars that are competitive with those of expert users, while some users and all competing automatic solutions are less successful.


title: Service Innovations in the Healthcare Service Ecosystem: A Case Study

abstract: In the service economy, scholars and practitioners are even more focused on the development and appliance of innovative services. The importance of service innovation is rising in many sectors and among different organizations. Several disciplines (e.g., marketing, management, operations research, etc.) deal with this innovation, a concept widely used, but with different definitions. In this paper, service innovation has been analyzed according to the Service Dominant Logic (S-D Logic) and a service ecosystem perspective. The literature still calls for a greater understanding of how a new or renewed combination of resources affects the shaping of service ecosystems. To contribute to filling this gap, this study explores the practices that different actors, enact to co-create value in novel ways; i.e., service innovation. The paper is structured as follows. In the first section, the main academic contributions on service research have been reviewed, focusing on healthcare service innovation. This is followed by the research method and discussion of the research findings. Finally, the theoretical and managerial implications have been detailed and an agenda for future research suggested. The paper offers interesting insights to develop new or renewed practices that foster the reshaping and maintaining of a healthcare service ecosystem. Some recommendations are included to support managers in the development of service innovation strategies.


title: Ossifying renal tumor of infancy: findings at ultrasound, CT and MRI

abstract: A 4-month-old boy presented with persistent gross hematuria. At ultrasonography, a 3.5-cm echogenic mass with posterior shadowing and tumor vascularity was detected within the right renal pelvis. Precontrast CT showed a slightly hyperattenuating mass in the renal pelvis. At MRI the mass was heterogeneously hypointense on T2-weighted images and isointense on T1-weighted images. Contrast-enhanced CT and MRI both revealed peripheral enhancement of the mass. A histological diagnosis of ossifying renal tumor of infancy was made after open pyelostomy and tumor enucleation. We suggest that ossifying renal tumor of infancy should be considered when a mass with posterior acoustic shadowing and tumor vascularity on US, hyperattenuation on precontrast CT and hypointensity on T2-weighted MRI is seen within the renal pelvis of an infant with hematuria.


title: The Role of Depression in Verbal Memory Following Traumatic Brain Injury

abstract: The purpose of this study was to characterize the relationship between verbal memory and depression scores on the Personality Assessment Inventory following traumatic brain injury. Depression was associated with diminished delayed recall and recognition on the California Verbal Learning Test-II (CVLT-II), even after controlling for a neuropsychological composite score and/or a measure of motivation (i.e., the TOMM). There was no relationship between depression and recall on Verbal Paired Associates or Logical Memory when controlling for the same covariates. The findings were most consistent with depressed subjects failing to utilize the semantic organization of the CVLT-II list to enhance their learning.


title: Gratitude and Life Satisfaction in the United States and Japan

abstract: Abstract Being grateful has been associated with many positive outcomes, including greater happiness, positive affect, optimism, and self-esteem. There is limited research, however, on the associations between gratitude and different domains of life satisfaction across cultures. The current study examined the associations between gratitude and three domains of life satisfaction, including satisfaction in relationships, work, and health, and overall life satisfaction, in the United States and Japan. A total of 945 participants were drawn from two samples of middle aged and older adults, the Midlife Development in the United States and the Midlife Development in Japan. There were significant positive bivariate associations between gratitude and all four measures of life satisfaction. In addition, after adjusting for demographics, neuroticism, extraversion, and the other measures of satisfaction, gratitude was uniquely and positively associated with satisfaction with relationships and life overall but not with satisfaction with work or health. Furthermore, results indicated that women and individuals who were more extraverted and lived in the United States were more grateful and individuals with less than a high school degree were less grateful. The findings from this study suggest that gratitude is uniquely associated with specific domains of life satisfaction. Results are discussed with respect to future research and the design and implementation of gratitude interventions, particularly when including individuals from different cultures.


title: Andro-AutoPsy: Anti-malware system based on similarity matching of malware and malware creator-centric information

abstract: Abstract Mobile security threats have recently emerged because of the fast growth in mobile technologies and the essential role that mobile devices play in our daily lives. For that, and to particularly address threats associated with malware, various techniques are developed in the literature, including ones that utilize static, dynamic, on-device, off-device, and hybrid approaches for identifying, classifying, and defend against mobile threats. Those techniques fail at times, and succeed at other times, while creating a trade-off of performance and operation. In this paper, we contribute to the mobile security defense posture by introducing Andro-AutoPsy, an anti-malware system based on similarity matching of malware-centric and malware creator-centric information. Using Andro-AutoPsy, we detect and classify malware samples into similar subgroups by exploiting the profiles extracted from integrated footprints, which are implicitly equivalent to distinct characteristics. The experimental results demonstrate that Andro-AutoPsy is scalable, performs precisely in detecting and classifying malware with low false positives and false negatives, and is capable of identifying zero-day mobile malware.


title: HomeCloud: An edge cloud framework and testbed for new application delivery

abstract: Conventional centralized cloud computing is a success for benefits such as on-demand, elasticity, and high colocation of data and computation. However, the paradigm shift towards “Internet of things” (IoT) will pose some unavoidable challenges: (1) massive data volume impossible for centralized datacenters to handle; (2) high latency between edge “things” and centralized datacenters; (3) monopoly, inhibition of innovations, and non-portable applications due to the proprietary application delivery in centralized cloud. The emergence of edge cloud gives hope to address these challenges. In this paper, we propose a new framework called “HomeCloud” focusing on an open and efficient new application delivery in edge cloud integrating two complementary technologies: Network Function Virtualization (NFV) and Software-Defined Networking (SDN). We also present a preliminary proof-of-concept testbed demonstrating the whole process of delivering a simple multi-party chatting application in the edge cloud. In the future, the HomeCloud framework can be further extended to support other use cases that demand portability, cost-efficiency, scalability, flexibility, and manageability. To the best of our knowledge, this framework is the first effort aiming at facilitating new application delivery in such a new edge cloud context.


title: Data preparation step for automated diagnosis based on HRV analysis and machine learning

abstract: This paper describes the data preparation step of a proposed method for automated diagnosis of various diseases based on heart rate variability (HRV) analysis and machine learning. HRV analysis - consisting of time-domain analysis, frequency-domain analysis, and nonlinear analysis - is employed because its resulting parameters are unique for each disease and can be used as the statistical symptoms for each disease, while machine learning techniques are employed to automate the diagnosis process. The input data consist of electrocardiogram (ECG) recordings. The proposed method is divided into three main steps, namely dataset preparation step, machine learning step, and disease classification step. The dataset preparation step aims to prepare the training data for machine learning step from raw ECG signals, and to prepare the test data for disease classification step from raw RRI signals. The machine learning step aims to obtain the classifier model and its performance metric from the prepared dataset. The disease classification step aims to perform disease diagnosis from the prepared dataset and the classifier model. The implementation of data preparation step is subsequently described with satisfactory result.


The effect of corticosteroid hormones on experimental herpes simplex keratitis. A clinical, histopathologic, and histochemical study.


title: Pagetoid reticulosis Woringer-Kolopp type, a particular variant of mycosis fungoides: a case report.

abstract: According to the definition of WHO, pagetoid reticulosis (PR) is characterized by the presence of unique skin lesion in the shape of patches or plaques caused by the proliferation of atypical T-cells in epidermis. We report the case of a 50-year-old female, presenting a 50×65 mm erythematous scaly patch, well-defined, slightly infiltrated, discrete polycyclic, on the right buttock, which appeared and developed in about 6-8 months. The clinical and dermatoscopic aspects suggested a Bowen's disease. A skin biopsy was performed. The histological examination of the pieces confirmed the presence of monoclonal lymphoid infiltrate with obvious phenomena of epidermotropism. Immuno-phenotypically the tumoral cells express intensively CD3 and CD8 and focally CD4. Surgical excision was performed without radiotherapy and chemotherapy. The patient was followed-up for 42 months without local relapses, distant lymph nodes or visceral metastases. Microscopic aspect of the lesion corresponds to stage I-II of mycosis fungoides (MF) (patches or plaques); the diagnosis was PR Woringer-Kolopp type. The most recent classification of skin lymphomas (WHO, 2008) confirms that the immunohistochemical profile of T-cells can be CD4+ and CD8- or CD4- and CD8+, and frequently CD30+, in contrast to the classical versions of MF. Pagetoid reticulosis type Woringer-Kolopp, a rare particular variant of mycosis fungoides, is suspected in cases with unique, distinctive lesion and confirmed only by histological immunohistochemistry. Treatment consists of surgical excision of the lesion with long follow-up.


title: Natural Language Processing for the Semantic Web

abstract: This book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications. Natural language processing and Semantic Web technologies have different, but complementary roles in data management. Combining these two technologies enables structured and unstructured data to merge seamlessly. Semantic Web technologies aim to convert unstructured data to meaningful representations, which benefit enormously from the use of NLP technologies, thereby enabling applications such as connecting text to Linked Open Data, connecting texts to each other, semantic searching, information visualization, and modeling of user behavior in online networks. The first half of this book describes the basic NLP processing tools: tokenization, part-of-speech tagging, and morphological analysis, in addition to the main tools required for an information extraction system (named entity recognition and relation extraction) which build on these components. The second half of the book explains how Semantic Web and NLP technologies can enhance each other, for example via semantic annotation, ontology linking, and population. These chapters also discuss sentiment analysis, a key component in making sense of textual data, and the difficulties of performing NLP on social media, as well as some proposed solutions. The book finishes by investigating some applications of these tools, focusing on semantic search and visualization, modeling user behavior, and an outlook on the future.


title: Urban channel models for smart city IoT-networks based on empirical measurements of LoRa-links at 433 and 868 MHz

abstract: The vision of Smart City is to enable new use cases being overall connected. Considering a large number of Internet of Things sensors and challenging channel characteristics, new technologies evolve facing these new challenges. A promising solution to meet such requirements are Low Power Wide Area Networks. In this paper, a LoRa availability analysis is carried out to analyze performance of a Low Power Wide Area Networks representative in the 433 MHz and 868 MHz ISM bands considering a Smart City Internet of Things scenario, resulting in signal ranges up to 5.8 km. Additional reliability measurements show that indoor and outdoor LoRa nodes are able to achieve a Packet Delivery Rate of over 99% within a range of 4.8 km, depending on their installation side. The results of these measurements are compared with established empirical channel models. Due to the insufficient prediction accuracy of the established models in the examined Smart City area of Dortmund, Germany, two new path loss models for urban areas are presented for both the 868 MHz and 433 MHz frequency bands.


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title: Segmentation of X-ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures

abstract: [1] Nondestructive imaging methods such as X-ray computed tomography (CT) yield high-resolution, three-dimensional representations of pore space and fluid distribution within porous materials. Steadily increasing computational capabilities and easier access to X-ray CT facilities have contributed to a recent surge in microporous media research with objectives ranging from theoretical aspects of fluid and interfacial dynamics at the pore scale to practical applications such as dense nonaqueous phase liquid transport and dissolution. In recent years, significant efforts and resources have been devoted to improve CT technology, microscale analysis, and fluid dynamics simulations. However, the development of adequate image segmentation methods for conversion of gray scale CT volumes into a discrete form that permits quantitative characterization of pore space features and subsequent modeling of liquid distribution and flow processes seems to lag. In this paper we investigated the applicability of various thresholding and locally adaptive segmentation techniques for industrial and synchrotron X-ray CT images of natural and artificial porous media. A comparison between directly measured and image-derived porosities clearly demonstrates that the application of different segmentation methods as well as associated operator biases yield vastly differing results. This illustrates the importance of the segmentation step for quantitative pore space analysis and fluid dynamics modeling. Only a few of the tested methods showed promise for both industrial and synchrotron tomography. Utilization of local image information such as spatial correlation as well as the application of locally adaptive techniques yielded significantly better results.


title: Qualitative navigation for mobile robots

abstract: We have developed a multi-level theory of spatial representation of the environment based upon the observation and re-acquisition of distinctive visual events, i.e., landmarks. The representation provides the theoretical foundations for a visual memory database that includes coordinate-free, topological representation of relative spatial location, yet smoothly integrates available metric knowledge of relative or absolute angles and distances


title: Face identification from low resolution near-infrared images

abstract: Face identification from low quality and low resolution Near-Infrared (NIR) face images is a challenging problem. Since surveillance cameras typically acquire images at a large standoff distance, the effective resolution of the face is not large enough to identify the individuals. Moreover for a 24-hour surveillance footage, images in low light and at nighttime are acquired in NIR mode which makes the identification problem even more challenging. We propose an effective method using both hand-crafted and learned features for face identification of low resolution NIR images. We show that learned features contribute considerably to the performance of identification algorithm, and that using both feature level and score level fusion in a hierarchal approach gives good performance. The results demonstrate the effectiveness of the proposed approach on images which are of low quality, low resolution and acquired under challenging illumination conditions in near-infrared mode by surveillance cameras.


title: Revisiting the Quantitative-Qualitative Debate: Implications for Mixed-Methods Research.

abstract: Health care research includes many studies that combine quantitative and qualitative methods. In this paper, we revisit the quantitative-qualitative debate and review the arguments for and against using mixed-methods. In addition, we discuss the implications stemming from our view, that the paradigms upon which the methods are based have a different view of reality and therefore a different view of the phenomenon under study. Because the two paradigms do not study the same phenomena, quantitative and qualitative methods cannot be combined for cross-validation or triangulation purposes. However, they can be combined for complementary purposes. Future standards for mixed-methods research should clearly reflect this recommendation.


title: Nurses' practice environment and satisfaction with schedule flexibility is related to intention to leave due to dissatisfaction: A multi-country, multilevel study.

abstract: BACKGROUND ::: Nursing turnover is a major issue for health care managers, notably during the global nursing workforce shortage. Despite the often hierarchical structure of the data used in nursing studies, few studies have investigated the impact of the work environment on intention to leave using multilevel techniques. Also, differences between intentions to leave the current workplace or to leave the profession entirely have rarely been studied. ::: ::: ::: OBJECTIVE ::: The aim of the current study was to investigate how aspects of the nurse practice environment and satisfaction with work schedule flexibility measured at different organisational levels influenced the intention to leave the profession or the workplace due to dissatisfaction. ::: ::: ::: DESIGN ::: Multilevel models were fitted using survey data from the RN4CAST project, which has a multi-country, multilevel, cross-sectional design. The data analysed here are based on a sample of 23,076 registered nurses from 2020 units in 384 hospitals in 10 European countries (overall response rate: 59.4%). Four levels were available for analyses: country, hospital, unit, and individual registered nurse. Practice environment and satisfaction with schedule flexibility were aggregated and studied at the unit level. Gender, experience as registered nurse, full vs. part-time work, as well as individual deviance from unit mean in practice environment and satisfaction with work schedule flexibility, were included at the individual level. Both intention to leave the profession and the hospital due to dissatisfaction were studied. ::: ::: ::: RESULTS ::: Regarding intention to leave current workplace, there is variability at both country (6.9%) and unit (6.9%) level. However, for intention to leave the profession we found less variability at the country (4.6%) and unit level (3.9%). Intention to leave the workplace was strongly related to unit level variables. Additionally, individual characteristics and deviance from unit mean regarding practice environment and satisfaction with schedule flexibility were related to both outcomes. Major limitations of the study are its cross-sectional design and the fact that only turnover intention due to dissatisfaction was studied. ::: ::: ::: CONCLUSIONS ::: We conclude that measures aiming to improve the practice environment and schedule flexibility would be a promising approach towards increased retention of registered nurses in both their current workplaces and the nursing profession as a whole and thus a way to counteract the nursing shortage across European countries.


title: Radio transformer networks: Attention models for learning to synchronize in wireless systems

abstract: We introduce learned attention models into the radio machine learning domain for the task of modulation recognition by leveraging spatial transformer networks and introducing new radio domain appropriate transformations. This attention model allows the network to learn a localization network capable of synchronizing and normalizing a radio signal blindly with zero knowledge of the signals structure based on optimization of the network for classification accuracy, sparse representation, and regularization. Using this architecture we are able to outperform our prior results in accuracy vs signal to noise ratio against an identical system without attention, however we believe such an attention model has implication far beyond the task of modulation recognition.


title: A security metric for structured security analysis of cyber-physical systems supporting SAE J3061

abstract: Complexity in modern vehicles has increased dramatically during the last years due to new features and applications. Modern vehicles are connected to the Internet as well as to other vehicles in close proximity and to the environment for different novel comfort services and safety-related applications. Enabling such services and applications requires wireless interfaces to the vehicle and therefore leads to open interfaces to the outside world. Attackers can use those interfaces to impair the privacy of the vehicle owner or to take control (of parts of) the vehicle, which strongly endangers the safety of the passengers as well as other road users. To avoid such attacks and to ensure the safety of modern vehicles, sophisticated structured processes and methods are needed. In this paper we propose a security metric to analyse cyberphysical systems (CPS) in a structured way. Its application leads to a secure system configuration with comparable as well as reusable results. Additionally, the security metric can be used to support the conceptual phase for the development of CPS specified in the new SAE security standard SAE J3061. A case study has been carried out to illustrate the application of the security metric.


title: Big data anonymization with spark

abstract: Privacy is an important issue for big data including sensitive attributes. In the case of directly sharing or publishing these data, privacy breach occurs. In order to overcome this problem, previous studies were focused on developing big data anonymization techniques on Hadoop environment. When compared to Hadoop, Spark facilitates to develop faster applications with the help of keeping data in memory instead of hard disk. Despite a number of projects were developed on Hadoop, now this trend is shifting to Spark. In addition, the problem of anonymizing big data streams for real-time applications can be solved with Spark technology. Hence to sum up, Spark is the main technology facilitates developing both faster anonymization applications and big data stream anonymization solutions. In this study, anonymization techniques, big data technologies and privacy preserving big data publishing was reviewed and a big data anonymization model based on Spark was proposed for the first time. It is expected that the proposed model might help to researchers to solve big data privacy issues and also provide solutions for new generation privacy violations problems.


title: Interphase Mutual Inductance in Polyphase Inductive Power Transfer Systems

abstract: Roadway powered electric vehicles with minimal or no onboard energy storage have been proposed for many years, but the concept has only recently become feasible via three-phase inductive power transfer (IPT) systems. A wide zone can be created over which power transfer is relatively constant. This gives good tolerance to the alignment of the pickup relative to the track allowing simple low-cost pickup structures to be used. While three-phase IPT tracks make the vehicle pickup and power transfer simpler, they are difficult for the power supply to drive due to the presence of mutual coupling between the track phases resulting from the physical layout of the track. These mutual inductances induce voltages within each track phase that, because of the inductor-capacitor-inductor network, cause large currents within the power supply inverter and imbalances within the system. This paper presents an analytical assessment of the problems caused by the interphase mutual inductance, and three possible solutions. Two of the methods involve modifications to the track layout to alter or remove the mutual inductances, while the third and preferred technique requires additional ferrite cores between the various phases to compensate this adverse mutual inductance without affecting the power transfer to the pickup loads.


title: Neural style transfer: a paradigm shift for image-based artistic rendering?

abstract: In this meta paper we discuss image-based artistic rendering (IB-AR) based on neural style transfer (NST) and argue, while NST may represent a paradigm shift for IB-AR, that it also has to evolve as an interactive tool that considers the design aspects and mechanisms of artwork production. IB-AR received significant attention in the past decades for visual communication, covering a plethora of techniques to mimic the appeal of artistic media. Example-based rendering represents one the most promising paradigms in IB-AR to (semi-)automatically simulate artistic media with high fidelity, but so far has been limited because it relies on pre-defined image pairs for training or informs only low-level image features for texture transfers. Advancements in deep learning showed to alleviate these limitations by matching content and style statistics via activations of neural network layers, thus making a generalized style transfer practicable. We categorize style transfers within the taxonomy of IB-AR, then propose a semiotic structure to derive a technical research agenda for NSTs with respect to the grand challenges of NPAR. We finally discuss the potentials of NSTs, thereby identifying applications such as casual creativity and art production.


title: What is Shannon information?

abstract: Despite of its formal precision and its great many applications, Shannon’s theory still offers an active terrain of debate when the interpretation of its main concepts is the task at issue. In this article we try to analyze certain points that still remain obscure or matter of discussion, and whose elucidation contribute to the assessment of the different interpretative proposals about the concept of information. In particular, we argue for a pluralist position, according to which the different views about information are no longer rival, but different interpretations of a single formal concept.


title: Unsupervised Image-to-Image Translation with Self-Attention Networks

abstract: Unsupervised image translation aims to learn the transformation from a source domain to another target domain given unpaired training data. Several state-of-the-art works have yielded impressive results in the GANs-based unsupervised image-to-image translation. It fails to capture strong geometric or structural changes between domains, or it produces unsatisfactory result for complex scenes, compared to local texture mapping tasks such as style transfer. Recently, SAGAN (Han Zhang, 2018) showed that the self-attention network produces better results than the convolution-based GAN. However, the effectiveness of the self-attention network in unsupervised image-to-image translation tasks have not been verified. In this paper, we propose an unsupervised image-to-image translation with self-attention networks, in which long range dependency helps to not only capture strong geometric change but also generate details using cues from all feature locations. In experiments, we qualitatively and quantitatively show superiority of the proposed method compared to existing state-of-the-art unsupervised image-to-image translation task.


title: Real-time Heart Attack Mobile Detection Service (RHAMDS): An IoT use case for Software Defined Networks

abstract: Continuous growing interest in IoT applications particularly for a smart city setting has attracted many researchers. E-health applications in IoT networks are the newest area of interest in this research field. On the other hand, networking and communications fields are witnessing a revolution through the new concepts of Mobile Edge Computing (MEC) characterised by latency sensitivity and geographical awareness. Moreover, the Software Defined Network (SDN) is an innovative network paradigm that allows programming of the network through the separation of the data plane and the control plane and provides global intelligence for the network. We marry this technologies to propose a novel IoT e-health service; Real-time Heart Attack Mobile Detection Service (RHAMDS) through voice control and gesture control using smart watches. RHAMDS aims to improve response time of emergency aid for heart attack patients, in vehicular networks in particular, and to prevent the possible resulting vehicle collisions. In this paper, we present the proposed RHAMDS's network architecture, workflow, and model variations.


title: Neuropsychological assessment in illiterates: Visuospatial and memory abilities

abstract: Abstract A basic neuropsychological battery of visuospatial and memory abilities was administered to extreme educational groups (illiterates and professionals). Subjects were matched according to sex and age. The following visuospatial tasks were included: figure copy (cube, house, and Rey-Osterrieth complex figure), telling time, recognition of superimposed figures, recognition of a map, and drawing of the plan of the room. The following memory tasks were used: basic information, digit retention (forward and backward), memory curve, delayed verbal recall, sentence repetition, logical memory, delayed logical memory, immediate recall of the Rey-Osterrieth complex figure, immediate reproduction of a cube, visuospatial memory, and sequential memory. In visuospatial tasks all differences between the two groups were statistically significant. Five of the seven visuospatial tasks (all but telling time and recognition of superimposed figures) showed differences between age groups with a better performance found in the younger groups and four of the tasks (cube, house, Rey-Osterrith complex figure copying, and telling time) were significant between sexes with a better performance in men. In memory tasks, with the exception of the immediate memory of sentences, all tasks showed statistically significant differences between educational groups. Eight of the 13 memory tasks (digits forward and backward, delayed memory of words, immediate and delayed logical memory, Rey-Osterrieth immediate memory, cube immediate memory, and sequential memory) showed significant differences for age while 4 of the tasks (digits backward, memory curve, Rey-Osterrieth immediate memory, and cube immediate memory) were significant for sex. Results are analyzed with regard to current theories in cognitive psychology and anthropology. Emphasis is placed on the finding that cognitive skills usually examined by neuropsychological tests represent learned and highly trained abilities.


title: High throughput and programmable online trafficclassifier on FPGA

abstract: Machine learning (ML) algorithms have been shown to be effective in classifying the dynamic internet traffic today. Using additional features and sophisticated ML techniques can improve accuracy and can classify a broad range of application classes. Realizing such classifiers to meet high data rates is challenging. In this paper, we propose two architectures to realize complete online traffic classifier using flow-level features. First, we develop a traffic classifier based on C4.5 decision tree algorithm and Entropy-MDL discretization algorithm. It achieves an accuracy of 97.92% when classifying a traffic trace consisting of eight application classes. Next, we accelerate our classifier using two architectures on FPGA. One architecture stores the classifier in on-chip distributed RAM. It is designed to sustain a high throughput. The other architecture stores the classifier in block RAM. It is designed to operate with small hardware footprint and thus built at low hardware cost. Experimental results show that our high throughput architecture can sustain a throughput of $550$ Gbps assuming 40 Byte packet size. Our low cost architecture demonstrates a 22% better resource efficiency than the high throughput design. It can be easily replicated to achieve $449$ Gbps while supporting 160 input traffic streams concurrently. Both architectures are parameterizable and programmable to support any binary-tree-based traffic classifier. We develop a tool which allows users to easily map a binary-tree-based classifier to hardware. The tool takes a classifier as input and automatically generates the Verilog code for the corresponding hardware architecture.


title: Feature Extraction Using Wavelet Transform for Radar Emitter Signals

abstract: In this paper, an approach for intra-pulse feature extraction of radar emitter signals is proposed based on wavelet transform. On the basis of the multi-resolution characteristics of wavelet transform, two energy entropies, as a two-dimensional feature vector to reveal the difference of radar modulation signals, are respectively extracted from approximation coefficient and multi-scale detail coefficients preserved through inter-scale correlations denoise in wavelet transform domain. In order to demonstrate the effectiveness and feasibility of the proposed approach, computer simulations indicates that the entropy features of ten typical radar emitter signals extracted by wavelet transform have good performance of clustering and suppressing noise when SNR is 0dB. In addition, proposed approach can separate the same modulation signals with different parameters.


title: Efficient Measurement of the User Experience. A Portuguese Version

abstract: Abstract For the international use of software products it is important to know the culture, language and behavior of the citizens. Means: internationalization and location. To be able to evaluate these products it is significant to know how people from a country behave and express their feelings. This article presents a questionnaire which was initially developed in German, English and Spanish and is now available after a complex transformation in Portuguese.


title: Nostalgia as a repository of social connectedness: The role of attachment-related avoidance.

abstract: Individuals who are low (compared with high) in attachment-related avoidance rely on social bonds to regulate distress, and the authors hypothesized that nostalgia can be a repository of such social connectedness. Studies 1–3 showed a positive association between loneliness and nostalgia when attachment-related avoidance was low, but not when it was high. Study 4 revealed that low-avoidance individuals derived more social connectedness from nostalgia than did high-avoidance individuals. Study 5 extended these findings and demonstrated that, in addition to being a source of social connectedness, nostalgia increased participants’ perceived capacity to provide emotional support to others. As in the case of social connectedness, this beneficial effect of nostalgia was significantly stronger when attachment-related avoidance was low (compared with high).


abstract: An online approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups has been developed to help to improve current diagnostic standards.


title: Motor thalamus integration of cortical, cerebellar and basal ganglia information: implications for normal and parkinsonian conditions

abstract: Motor thalamus (Mthal) is implicated in the control of movement because it is strategically located between motor areas of the cerebral cortex and motor-related subcortical structures, such as the cerebellum and basal ganglia (BG). The role of BG and cerebellum in motor control has been extensively studied but how Mthal processes inputs from these two networks is unclear. Specifically, there is considerable debate about the role of BG inputs on Mthal activity. This review summarises anatomical and physiological knowledge of the Mthal and its afferents and reviews current theories of Mthal function by discussing the impact of cortical, BG and cerebellar inputs on Mthal activity. One view is that Mthal activity in BG and cerebellar-receiving territories is primarily “driven” by glutamatergic inputs from the cortex or cerebellum, respectively, whereas BG inputs are modulatory and do not strongly determine Mthal activity. This theory is steeped in the assumption that the Mthal processes information in the same way as sensory thalamus, through interactions of modulatory inputs with a single driver input. Another view, from BG models, is that BG exert primary control on the BG-receiving Mthal so it effectively relays information from BG to cortex. We propose a new “super-integrator” theory where each Mthal territory processes multiple driver or driver-like inputs (cortex and BG, cortex and cerebellum), which are the result of considerable integrative processing. Thus, BG and cerebellar Mthal territories assimilate motivational and proprioceptive motor information previously integrated in cortico-BG and cortico-cerebellar networks, respectively, to develop sophisticated motor signals that are transmitted in parallel pathways to cortical areas for optimal generation of motor programmes. Finally, we briefly review the pathophysiological changes that occur in the BG in parkinsonism and generate testable hypotheses about how these may affect processing of inputs in the Mthal.


title: A Proof Of The Block Model Threshold Conjecture

abstract: We study a random graph model called the “stochastic block model” in statistics and the “planted partition model” in theoretical computer science. In its simplest form, this is a random graph with two equal-sized classes of vertices, with a within-class edge probability of q and a between-class edge probability of q′.


title: Performance Analysis of Real Time Intrusion Detection and Prevention System using Snort

abstract: Abstract: Nowadays Intrusion Detection and Prevention System represents an essential line of defense against variety of web attacks that can compromise with the security and proper functioning of the entire security system. With the evolution of internet, possibilities and opportunities are limitless, unfortunately, so too are the risks and chances of malicious intrusions. Network is interconnection or links, for example network of road, network of computer. Security is the freedom from danger or anxiety so Network Security is about securing and protecting the network (externally and internally) from Distributed Denial of Service attacks, rapidly propagating viruses, self-replicating worms and other attacks. Network security begins with authorization and authentication. In this paper capturing of network traffic, performance and reports analysis generated by snort and corresponding alert ratio of signatures for the particular attack are to be evaluated. This intrusion detection system is one of the security defense tools for computer networks. In recent years this research has lacked in direction and focus today SNORT stands out as the most widely deployed IDS, We survey the existing techniques, types and architectures of Intrusion Detection Systems in the literature. Performance analysis of real time Intrusion Detection and prevention system and traffic analysis by Snort from the network are to carried out in this paper.


DETECTING SIGNALS IN NOISY DATA-CAN ENSEMBLE CLASSIFIERS HELP IDENTIFY ADVERSE DRUG REACTION IN TWEETS ?


title: A Methodology for Scenario-Based Requirements Capture

abstract: Scenarios are an intuitive way for software project stakeholders to capture requirements, describe test cases and evaluate software architectures. Typically, however, coming up with a complete scenario specification is difficult. Basic (or nominal) scenarios are easy to write down, but more complex or less common (non-nominal) scenarios are harder to elicit. In particular, there is currently no good way of knowing when enough scenarios have been developed and scenario writing should stop. What is needed is a methodology for developing a scenario specification from an initial set of nominal scenarios, accompanied by metrics for deciding when the specification is complete. We sketch out a preliminary form of such a methodology in this paper.


title: Optimization of convolutional neural network using microcanonical annealing algorithm

abstract: Convolutional neural network (CNN) is one of the most prominent architectures and algorithm in Deep Learning. It shows a remarkable improvement in the recognition and classification of objects. This method has also been proven to be very effective in a variety of computer vision and machine learning. As in other deep learning, however, training this approach is interesting yet challenging. Recently, some metaheuristic algorithms have been used to optimize CNN using Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Harmony Search. In this paper, another type of metaheuristic algorithms with different strategy has been proposed, i.e. Microcanonical Annealing to optimize Convolutional Neural Network. The performance of the proposed method is tested using the MNIST and CIFAR-10 datasets. Although experiment results of MNIST dataset indicate the increase in computation time (1.02x–1.38x), nevertheless this proposed method can considerably enhance the performance of the original CNN (up to 4.60%). On the CIFAR10 dataset, currently, state of the art is 96.53% using fractional pooling, while this proposed method achieves 99.14%.


title: Low-Pressure-Assisted Large-Area (>800 mm2) Sintered-Silver Bonding for High-Power Electronic Packaging

abstract: Nanosilver paste, as a kind of new thermal interface material (TIM) with superior thermal performance, has the potential to be a replacement for the conventional TIMs, such as greases, gels, and phase-change materials. In this paper, by combining double-print method and rapid-dry process, we achieved robust large-area bonded profile (>800 mm2) based on low-pressure-assisted sintering of silver paste. The low-temperature sintering process and sintering mechanism were discussed. The silver paste consists of nanosilver particles, microsilver flakes, and organics including dispersants, binders, and thinners. According to the thermogravimetric analysis and corresponding scanning electron microscopy observation, most organics were removed at temperature range from 70 °C to 160 °C. The nanosilver particles were instantly sintered and formed sintering necks at temperature range from 200 °C to 220 °C. The sintering of microsilver flakes needs higher temperature and longer time than the sintering of nanosilver particles. At 275 °C, microsilver flakes grew into spherical microsilver big particles at the expense of smaller nanosilver sintering necks and small microsilver flakes. Finally, robust sintered-silver-bonded plates were achieved by an optimized low-pressure-assisted sintering profile. The large-area bonded plate showed low porosity and high shear strength. Effects of a couple of sintering factors, including dry temperature, pressure on sinter quality, and fracture mode, were studied. The large-area bonded plates showed cohesive failure in sintered-silver bond line.


title: Vegetables detection from the glossary shop for the blind.

abstract: Automatic recognitions of vegetables from images are often termed as vegetable vision. It is a technology to facilitate the checkout process by analysing real images of vegetables and match with the picture and inform the blind. It is always related to image processing, which can control the classification, qualification and segmentation of images and recognize the image. It is a recognition system for super market and grocery stores for the blind. From the captured images multiple recognition clues such as colour, shape, size, texture and size are extracted and analysed to classify and recognize the vegetables. If the system can identify uniquely, the blind human inform the vegetable name and information and can take the vegetable that she/he wants.


title: Internet addiction: Meta-synthesis of qualitative research for the decade 1996-2006

abstract: Internet addiction is ''an individual's inability to control their Internet use, which in turn leads to feelings of distress and functional impairment of daily activities'' [Shapira, N., Lessig, M., Goldsmith, T., Szabo, S., Lazoritz, M., Gold, M. et al. (2003). Problematic Internet use: Proposed classification and diagnostic criteria. Depression and Anxiety, 17(4), 207-216]. Previous research in this field has offered inconclusive data on whether Internet addiction can be classified as a disorder. This study provides an in-depth and comprehensive analysis of internet addiction through a meta-synthesis of qualitative studies on excessive Internet use published during the period of 1996-2006. Several constructs pertaining to the domain of Internet addiction have been identified and a theoretical model of Internet addiction has been proposed.


title: TRank: Ranking Entity Types Using the Web of Data

abstract: Much of Web search and browsing activity is today centered around entities. For this reason, Search Engine Result Pages (SERPs) increasingly contain information about the searched entities such as pictures, short summaries, related entities, and factual information. A key facet that is often displayed on the SERPs and that is instrumental for many applications is the entity type. However, an entity is usually not associated to a single generic type in the background knowledge bases but rather to a set of more specific types, which may be relevant or not given the document context. For example, one can find on the Linked Open Data cloud the fact that Tom Hanks is a person, an actor, and a person from Concord, California. All those types are correct but some may be too general to be interesting (e.g., person), while other may be interesting but already known to the user (e.g., actor), or may be irrelevant given the current browsing context (e.g., person from Concord, California). In this paper, we define the new task of ranking entity types given an entity and its context. We propose and evaluate new methods to find the most relevant entity type based on collection statistics and on the graph structure interconnecting entities and types. An extensive experimental evaluation over several document collections at different levels of granularity (e.g., sentences, paragraphs, etc.) and different type hierarchies (including DBPedia, Freebase, and schema.org) shows that hierarchy-based approaches provide more accurate results when picking entity types to be displayed to the end-user while still being highly scalable.


title: Effects of emoticons on the acceptance of negative feedback in computer-mediated communication

abstract: Delivering negative performance feedback is inevitable in the workplace. However, recipients may feel uncomfortable and behave defensively, and may be unwilling to accept negative feedback mainly because they fear losing face. Such unproductive responses are heightened when negative feedback is delivered through computer-mediated communication (CMC) channels in which many nonverbal cues in face-to-face communication cannot be used to alleviate the concerns of losing face. This study examines the effectiveness of emoticons, which are designed as surrogates for facial expressions in CMC environments, in conveying social and emotional signals of the feedback provider. Specifically, based on the feedback process model and the dissonance reduction theory, this study investigates the differing effects of two types of emoticons (i.e., liking and disliking ones) on the acceptance of negative feedback by considering feedback specificity as a contingent factor. Our results suggest that using liking emoticons increases perceived good intention of the feedback provider and decreases perceived feedback negativity when the feedback is specific; however, it has no significant effect for unspecific feedback. By contrast, our results suggest that using disliking emoticons decreases perceived good intention of the feedback provider and increases perceived feedback negativity when the feedback is unspecific, whereas such effects are not significant for specific feedback. In turn, both perceived good intention of the feedback provider and perceived feedback negativity affect acceptance of the negative feedback.


title: Real-Time GPU-Based Ultrasound Simulation Using Deformable Mesh Models

abstract: This paper presents a real-time capable graphics processing unit (GPU)-based ultrasound simulator suitable for medical education. The main focus of the simulator is to synthesize realistic looking ultrasound images in real-time including artifacts, which are essential for the interpretation of this data. The simulation is based on a convolution-enhanced ray-tracing approach and uses a deformable mesh model. Deformations of the mesh model are calculated using the PhysX engine. Our method advances the state of the art for real-time capable ultrasound simulators by following the path of the ultrasound pulse, which enables better simulation of ultrasound-specific artifacts. An evaluation of our proposed method in comparison with recent generative slicing-based strategies as well as real ultrasound images is performed. Hereby, a gelatin ultrasound phantom containing syringes filled with different media is scanned with a real transducer. The obtained images are then compared to images which are simulated using a slicing-based technique and our proposed method. The particular benefit of our method is the accurate simulation of ultrasound-specific artifacts, like range distortion, refraction and acoustic shadowing. Several test scenarios are evaluated regarding simulation time, to show the performance and the bottleneck of our method. While being computationally more intensive than slicing techniques, our simulator is able to produce high-quality images in real-time, tracing over 5000 rays through mesh models with more than 2 000 000 triangles of which up to 200 000 may be deformed each frame.


title: AVEC 2011–The first international audio/visual emotion challenge

abstract: The Audio/Visual Emotion Challenge and Workshop (AVEC 2011) is the first competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and audiovisual emotion analysis, with all participants competing under strictly the same conditions. This paper first describes the challenge participation conditions. Next follows the data used - the SEMAINE corpus - and its partitioning into train, development, and test partitions for the challenge with labelling in four dimensions, namely activity, expectation, power, and valence. Further, audio and video baseline features are introduced as well as baseline results that use these features for the three sub-challenges of audio, video, and audiovisual emotion recognition.


title: An extended IoT framework with semantics, big data, and analytics

abstract: Many experts claim that data will be the most valuable commodity in the 21st century. At the same time, two of the most influential components of this era, Big Data and IoT are moving very fast, on a collision course with the methodologies that are associated with conventional data processing and database systems. As a result, new approaches like NoSQL databases, distributed architectures, etc. started appearing on the stage. Meanwhile, another technology, ontology and semantic data processing can be a very convenient catalyzer that might assist in smoothly providing this transformation process. In this paper, we propose a combined framework that brings Big Data, IoT, and semantic web together to build an augmented framework for this new era. We not only list the components of such a system and define the necessary bindings that needs to be integrated together, but also provide a realistic use case that demonstrates how the model can implement the desired functionality and achieve the goals of such a model.


title: Assessment of machine learning algorithms in cloud computing frameworks

abstract: In the past decade, digitization of information has led to a data explosion in both volume and complexity. While traditional computing frameworks have failed to provide adequate computing power for the now common data-intensive computing tasks, cloud computing provides an effective alternative to enhance computing power. Machine learning algorithms are powerful analytical methods that allow machines to recognize patterns and facilitate human learning. However, the performance of individual machine learning algorithms within each cloud computing framework remains largely unknown. Furthermore, the lack of a robust selection methodology matching input data with effective machine learning algorithms limits the ability of practitioners to make effective use of cloud computing. This research compares various machine learning algorithms on the widely adopted Apache Mahout framework and the recently introduced GraphLab framework. Whereas previous work has examined the computational architectures of various cloud computing frameworks, this work focuses on a problem-based approach to architecture selection. The experimental results demonstrate that GraphLab generally outperforms Mahout with respect to runtime, scalability, and usability. However, Mahout outperforms GraphLab when the experiment focus shifts to error measurement.


COMPARATIVE ANALYSIS OF RECORD LINKAGE DECISION RULES


title: Identifying Tampered Regions Using Singular Value Decomposition in Digital Image Forensics

abstract: Detecting tampered regions and proving the authenticity and integrity of a digital image becomes increasingly important in digital forensics and multimedia security. In this paper we propose a novel framework for identifying the location of copy-move image tampering by applying the singular value decomposition(SVD). In the proposed passive techniques, SVD served to produce algebraic and geometric invariant and feature vectors. Experimental results demonstrate the validity of the proposed approach to tampered images undergone some attacks like Gaussian blur filtering, Gaussian white noise contamination, lossy JPEG compression, etc.


title: Understanding Indoor Scene: Spatial Layout Estimation, Scene Classification, and Object Detection

abstract: In this paper, we seek to understand scene from different viewpoints such as estimating the spatial layout of indoor scenes, detecting objects in the scene and making scene classification. In the previous work, every step has been done in a separate process so in this work we combine the three steps in one algorithm. To understand the indoor scene this requires interpreting the estimation of multiple scene elements and studying the relation between them. We propose a method that takes a single indoor image and then the relation between scene elements can be automatically learned and applied to totally understand the indoor scenes. Our proposed method consists of two steps: the first step is estimating the spatial layout of indoor scenes and this is done by extracting line segments and estimating the three orthogonal vanishing points. The orientation of the surfaces in the scene is defined by the vanishing points and so constructs the layout of the walls, ceiling, and floor. The second step is studying the relation between scene elements and this means that studying the relation between scene classification, objects in the scene, and layout estimation. We conduct extensive experiments to evaluate our proposed method performance on a single indoor image. We achieve superior accuracy results on two public datasets and provide a robust estimation of scene type, estimation of spatial layout and 3D objects.


title: PixNet: interference-free wireless links using LCD-camera pairs

abstract: Given the abundance of cameras and LCDs in today's environment, there exists an untapped opportunity for using these devices for communication. Specifically, cameras can tune to nearby LCDs and use them for network access. The key feature of these LCD-camera links is that they are highly directional and hence enable a form of interference-free wireless communication. This makes them an attractive technology for dense, high contention scenarios. The main challenge however, to enable such LCD-camera links is to maximize coverage, that is to deliver multiple Mb/s over multi-meter distances, independent of the view angle. To do so, these links need to address unique types of channel distortions, such as perspective distortion and blur. This paper explores this novel communication medium and presents PixNet, a system for transmitting information over LCD-camera links. PixNet generalizes the popular OFDM transmission algorithms to address the unique characteristics of the LCD-camera link which include perspective distortion, blur, and sensitivity to ambient light. We have built a prototype of PixNet using off-the-shelf LCDs and cameras. An extensive evaluation shows that a single PixNet link delivers data rates of up to 12 Mb/s at a distance of 10 meters, and works with view angles as wide as 120 degree°.


title: Ensemble-based classifiers

abstract: The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics and AI considered the use of ensemble methodology. This paper, review existing ensemble techniques and can be served as a tutorial for practitioners who are interested in building ensemble based systems.


title: Busbar design for SiC-based H-bridge PEBB using 1.7 kV, 400 a SiC MOSFETs operating at 100 kHz

abstract: This paper presents a systematic study of the busbar design and optimization for SiC-based H-bridge power electronics building block (PEBB) used in high-frequency and high-power applications. Step-by-step guidelines are presented in which the design considerations and analysis are given. This paper presents a double-sided busbar concept to create a compact PEBB design with improved thermal and switching performance, which result from having double-side cooling and symmetric minimized current commutation loop inductances, respectively. The proposed concept is verified experimentally by evaluating the high-speed switching performance of the PEBB up to 400 A.


title: Investigating the Persuasion Potential of Recommender Systems from a Quality Perspective: An Empirical Study

abstract: Recommender Systems (RSs) help users search large amounts of digital contents and services by allowing them to identify the items that are likely to be more attractive or useful. RSs play an important persuasion role, as they can potentially augment the users’ trust towards in an application and orient their decisions or actions towards specific directions. This article explores the persuasiveness of RSs, presenting two vast empirical studies that address a number of research questions. First, we investigate if a design property of RSs, defined by the statistically measured quality of algorithms, is a reliable predictor of their potential for persuasion. This factor is measured in terms of perceived quality, defined by the overall satisfaction, as well as by how users judge the accuracy and novelty of recommendations. For our purposes, we designed an empirical study involving 210 subjects and implemented seven full-sized versions of a commercial RS, each one using the same interface and dataset (a subset of Netflix), but each with a different recommender algorithm. In each experimental configuration we computed the statistical quality (recall and F-measures) and collected data regarding the quality perceived by 30 users. The results show us that algorithmic attributes are less crucial than we might expect in determining the user’s perception of an RS’s quality, and suggest that the user’s judgment and attitude towards a recommender are likely to be more affected by factors related to the user experience. Second, we explore the persuasiveness of RSs in the context of large interactive TV services. We report a study aimed at assessing whether measurable persuasion effects (e.g., changes of shopping behavior) can be achieved through the introduction of a recommender. Our data, collected for more than one year, allow us to conclude that, (1) the adoption of an RS can affect both the lift factor and the conversion rate, determining an increased volume of sales and influencing the user’s decision to actually buy one of the recommended products, (2) the introduction of an RS tends to diversify purchases and orient users towards less obvious choices (the long tail), and (3) the perceived novelty of recommendations is likely to be more influential than their perceived accuracy. Overall, the results of these studies improve our understanding of the persuasion phenomena induced by RSs, and have implications that can be of interest to academic scholars, designers, and adopters of this class of systems.


title: The Human Papillomavirus E6 protein and its contribution to malignant progression

abstract: The Human Papillomavirus (HPV) E6 protein is one of three oncoproteins encoded by the virus. It has long been recognized as a potent oncogene and is intimately associated with the events that result in the malignant conversion of virally infected cells. In order to understand the mechanisms by which E6 contributes to the development of human malignancy many laboratories have focused their attention on identifying the cellular proteins with which E6 interacts. In this review we discuss these interactions in the light of their respective contributions to the malignant progression of HPV transformed cells.


title: SymDetector: detecting sound-related respiratory symptoms using smartphones

abstract: This paper proposes SymDetector, a smartphone based application to unobtrusively detect the sound-related respiratory symptoms occurred in a user's daily life, including sneeze, cough, sniffle and throat clearing. SymDetector uses the built-in microphone on the smartphone to continuously monitor a user's acoustic data and uses multi-level processes to detect and classify the respiratory symptoms. Several practical issues are considered in developing SymDetector, such as users' privacy concerns about their acoustic data, resource constraints of the smartphone and different contexts of the smartphone. We have implemented SymDetector on Galaxy S3 and evaluated its performance in real experiments involving 16 users and 204 days. The experimental results show that SymDetector can detect these four types of respiratory symptoms with high accuracy under various conditions.


title: Development and Navigation of a Mobile Robot for Floating Production Storage and Offloading Ship Hull Inspection

abstract: This paper describes the current development status of a mobile robot designed to inspect the outer surface of large oil ship hulls and floating production storage and offloading platforms. These vessels require a detailed inspection program, using several nondestructive testing techniques. A robotic crawler designed to perform such inspections is presented here. Locomotion over the hull is provided through magnetic tracks, and the system is controlled by two networked PCs and a set of custom hardware devices to drive motors, video cameras, ultrasound, inertial platform, and other devices. Navigation algorithm uses an extended-Kalman-filter (EKF) sensor-fusion formulation, integrating odometry and inertial sensors. It was shown that the inertial navigation errors can be decreased by selecting appropriate Q and R matrices in the EKF formulation.


title: Semi-supervised learning by disagreement

abstract: In many real-world tasks, there are abundant unlabeled examples but the number of labeled training examples is limited, because labeling the examples requires human efforts and expertise. So, semi-supervised learning which tries to exploit unlabeled examples to improve learning performance has become a hot topic. Disagreement-based semi-supervised learning is an interesting paradigm, where multiple learners are trained for the task and the disagreements among the learners are exploited during the semi-supervised learning process. This survey article provides an introduction to research advances in this paradigm.


Singing Voice Synthesis Based on Deep Neural Networks.


title: A Human Aware Mobile Robot Motion Planner

abstract: Robot navigation in the presence of humans raises new issues for motion planning and control when the humans must be taken explicitly into account. We claim that a human aware motion planner (HAMP) must not only provide safe robot paths, but also synthesize good, socially acceptable and legible paths. This paper focuses on a motion planner that takes explicitly into account its human partners by reasoning about their accessibility, their vision field and their preferences in terms of relative human-robot placement and motions in realistic environments. This planner is part of a human-aware motion and manipulation planning and control system that we aim to develop in order to achieve motion and manipulation tasks in the presence or in synergy with humans.


title: Simulation of computer architectures: simulators, benchmarks, methodologies, and recommendations

abstract: Simulators have become an integral part of the computer architecture research and design process. Since they have the advantages of cost, time, and flexibility, architects use them to guide design space exploration and to quantify the efficacy of an enhancement. However, long simulation times and poor accuracy limit their effectiveness. To reduce the simulation time, architects have proposed several techniques that increase the simulation speed or throughput. To increase the accuracy, architects try to minimize the amount of error in their simulators and have proposed adding statistical rigor to their simulation methodology. Since a wide range of approaches exist and since many of them overlap, this paper describes, classifies, and compares them to aid the computer architect in selecting the most appropriate one.


title: A toxicological and dermatological assessment of aryl alkyl alcohols when used as fragrance ingredients.

abstract: The aryl alkyl alcohol (AAA) fragrance ingredients are a diverse group of chemical structures with similar metabolic and toxicity profiles. The AAA fragrances demonstrate low acute and subchronic dermal and oral toxicity. No carcinogenicity in rats or mice was observed in 2-year chronic testing of benzyl alcohol or α-methylbenzyl alcohol; the latter did induce species and gender-specific renal adenomas in male rats at the high dose. There was no to little genotoxicity, mutagenicity, or clastogenicity in the mutagenic in vitro bacterial assays, and in vitro mammalian cell assays. All in vivo micronucleus assays were negative. NOAELs for maternal and developmental toxicity are far in excess of current human exposure levels. At concentrations likely to be encountered by consumers, AAA fragrance ingredients are non-irritating to the skin. The potential for eye irritation is minimal. With the exception of benzyl alcohol and to a lesser extent phenethyl and 2-phenoxyethyl AAA alcohols, human sensitization studies, diagnostic patch tests and human induction studies, indicate that AAA fragrance ingredients generally have no or low sensitization potential. Available data indicate that the potential for photosensitization is low. It is concluded that these materials would not present a safety concern at current levels of use as fragrance ingredients.


title: Combating Against Anti-Forensics Aligned with E-mail Forensics

abstract: Knowledge on cyber forensics is increasing on par with the cyber crime incidents. Cyber criminals’ uses sophisticated technological knowledge and always they plan to escape from the clutches of law. This paper elaborates e-mail forensics and categories of anti-forensics that can be applicable to the email forensics. This paper elucidates the process of identifying such anti-forensics applied in e-mail forensics. This paper proposes a methodology for combating against anti-forensics in this regard. General Terms Information Security, Cyber Forensics.


title: Object recognition and full pose registration from a single image for robotic manipulation

abstract: Robust perception is a vital capability for robotic manipulation in unstructured scenes. In this context, full pose estimation of relevant objects in a scene is a critical step towards the introduction of robots into household environments. In this paper, we present an approach for building metric 3D models of objects using local descriptors from several images. Each model is optimized to fit a set of calibrated training images, thus obtaining the best possible alignment between the 3D model and the real object. Given a new test image, we match the local descriptors to our stored models online, using a novel combination of the RANSAC and Mean Shift algorithms to register multiple instances of each object. A robust initialization step allows for arbitrary rotation, translation and scaling of objects in the test images. The resulting system provides markerless 6-DOF pose estimation for complex objects in cluttered scenes. We provide experimental results demonstrating orientation and translation accuracy, as well a physical implementation of the pose output being used by an autonomous robot to perform grasping in highly cluttered scenes.


title: Reconfigurable Asynchronous Logic

abstract: Challenges in mapping asynchronous logic to a flexible substrate include developing a balance between circuit-level flexibility, mapping complexity, and logic overhead. We have developed a reconfigurable dataflow architecture that addresses these challenges, and have also created the necessary synthesis flow required to map designs to the architecture. The architecture exploits some of the unique features of asynchronous logic, and attains a performance that significantly exceeds previous asynchronous FPGAs.


title: A magnetic thin film microrobot with two operating modes

abstract: Magnetic principles have proved successful for untethered submillimeter microrobotics, although challenges still exist in areas of propulsion and control. This paper presents the design, analysis, and performance results for a bimorph thin film magnetic microrobot utilizing the magnetostrictive principle as a secondary oscillating operation mode. The microrobot is no larger than 580 µm in its planar dimension and its total thickness is less than 5 µm. As a robot with magnetic material, it can be operated in a pushing/pulling mode in orthogonal directions for movement in a plane, while it's powered with an external magnetic field as low as 1 mT. For the secondary oscillating operation mode utilizing the magnetostrictive principle, in-plane strain is induced, resulting in bending and blocking forces on the robot. These forces are theoretically calculated to prove enough drive force can be generated in this mode. The design is further abstracted and translated into a piezoelectric cantilever FEM model to confirm the theorectical results. Microrobot fabrication and test-bed development based on this analysis is shown, which enabled us to participate in the final competition in the 2010 NIST Mobile Microrobot Challenge, with good performance in the dash and freestyle events. Finally, we discuss the testing results in various dry and fluid environments along with recommendations for future investigation and improvements. Keywords: microrobot, magnetostrictive, bimorph


title: Automatic Outlier Detection: A Bayesian Approach

abstract: In order to achieve reliable autonomous control in advanced robotic systems like entertainment robots, assistive robots, humanoid robots and autonomous vehicles, sensory data needs to be absolutely reliable, or some measure of reliability must be available. Bayesian statistics can offer favorable ways of accomplishing such robust sensory data pre-processing. In this paper, we introduce a Bayesian way of dealing with outlier-infested sensory data and develop a "black box" approach to removing outliers in real-time and expressing confidence in the estimated data. We develop our approach in the framework of Bayesian linear regression with heteroscedastic noise. Essentially, every measured data point is assumed to have its individual variance, and the final estimate is achieved by a weighted regression over observed data. An expectation-maximization algorithm allows us to estimate the variance of each data point in an incremental algorithm. With the exception of a time horizon (window size) over which the estimation process is averaged, no open parameters need to be tuned, and no special assumption about the generative structure of the data is required. The algorithm works efficiently in realtime. We evaluate our method on synthetic data and on a pose estimation problem of a quadruped robot, demonstrating its ease of usability, competitive nature with well-tuned alternative algorithms and advantages in terms of robust outlier removal


title: Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources

abstract: Renewable energy resources such as wind and solar are an important component of a microgrid. However, the inherent intermittency and variability of such resources complicates microgrid operations. Meanwhile, more controllable loads (e.g., plug-in electric vehicles), distributed generators (e.g., micro gas turbines and diesel generators), and distributed energy storage devices (e.g., battery banks) are being integrated into the microgrid operation. To address the operational challenges associated with these technologies and energy resources, this paper formulates a stochastic problem for microgrid energy scheduling. The proposed problem formulation minimizes the expected operational cost of the microgrid and power losses while accommodating the intermittent nature of renewable energy resources. Case studies are performed on a modified IEEE 37-bus test feeder. The simulation results demonstrate the effectiveness and accuracy of the proposed stochastic microgrid energy scheduling model.


title: Low temperature episodes in development of blowflies: implications for postmortem interval estimation

abstract: . Traditionally the calculation of accumulated degree days or hours (ADD or ADH) involves the concept of a minimum threshold temperature below which development ceases. Hence in fluctuating conditions, where temperatures drop below this threshold, there may be periods of time when development is taken to be zero. This has important implications when the calculation of postmortem interval (PMI) is based on the ADD or ADH of larval dipterans. Normal development of larvae of the blowflies Calliphora vicina Robineau-Desvoidy and C. vomitoria L. (Diptera: Calliphoridae) at 20°C was interrupted by cold episodes. The expectation was that total development time would increase by the period at low (therefore no development) temperature but the total ADD or ADH should be the same as non-cold treated cohorts. The results, however, showed that total ADH for both species decreased linearly with increasing temperature with no evidence of any minimum threshold temperature effect. The increased ADH at low temperatures may be due to either continued but reduced development or a delay in development restarting after the cold episode. Use of ADH in PMI estimations has shortcomings particularly during the winter period where low temperatures are involved or where there are sudden summer cold spells during the development period. As blowfly development progresses from egg to pupa such errors will be compounded.


title: Hyaluronic acid click hydrogels emulate the extracellular matrix.

abstract: Hydrogels are used to create 3D microenvironments with properties that direct cell function. The current study demonstrates the versatility of hyaluronic acid (HA)-based hydrogels with independent control over hydrogel properties such as mechanics, architecture, and the spatial distribution of biological factors. Hydrogels were prepared by reacting furan-modified HA with bis-maleimide-poly(ethylene glycol) in a Diels-Alder click reaction. Biomolecules were photopatterned into the hydrogel by two-photon laser processing, resulting in spatially defined growth factor gradients. The Young's modulus was controlled by either changing the hydrogel concentration or the furan substitution on the HA backbone, thereby decoupling the hydrogel concentration from mechanical properties. Porosity was controlled by cryogelation, and the pore size distribution, by the thaw temperature. The addition of galactose further influenced the porosity, pore size, and Young's modulus of the cryogels. These HA-based hydrogels offer a tunable platform with a diversity of properties for directing cell function, with applications in tissue engineering and regenerative medicine.


title: Development of a robot balanced on a ball — Application of passive motion to transport —

abstract: This paper proposes a robot balanced on a ball. In contrast to an inverted pendulum with two wheels, such as the Segway Human Transporter, an inverted pendulum using a ball can traverse in any direction without changing its orientation, thereby enabling stable motion. Such robots can be used in place of the two-wheeled robots. The robot proposed in this paper is equipped with three omnidirectional wheels with stepping motors that drive the ball and two sets of rate gyroscopes and accelerometers as attitude sensors. The robot has a simple design; it is controlled with a 16-bit microcontroller and runs on Ni-MH batteries. It can not only stand still but also traverse on floor and pivot around its vertical axis. Inverted pendulum control is applied in two axes for attitude control, and commanded motions are converted into velocity commands for the three wheels. The mechanism, control method, and experimental results including application to transport are described in this paper.


title: Supply chain management: theory, practice and future challenges

abstract: Purpose – The purpose of this paper is to critically assess current developments in the theory and practice of supply management and through such an assessment to identify barriers, possibilities and key trends.Design/methodology/approach – The paper is based on a three‐year detailed study of six supply chains which encompassed 72 companies in Europe. The focal firms in each instance were sophisticated, blue‐chip corporations operating on an international scale. Managers across at least four echelons of the supply chain were interviewed and the supply chains were traced and observed.Findings – The paper reveals that supply management is, at best, still emergent in terms of both theory and practice. Few practitioners were able – or even seriously aspired – to extend their reach across the supply chain in the manner prescribed in much modern theory. The paper identifies the range of key barriers and enablers to supply management and it concludes with an assessment of the main trends.Research limitations/imp...


title: Anomaly Detection in Time Series Data Based on Holt-Winters Method

abstract: In today's world the amount of collected data increases every day, this is a trend which is likely to continue. At the same time the potential value of the data does also increase due to the consta ...


title: Visualizing the affective structure of a text document

abstract: This paper introduces an approach for graphically visualizing the affective structure of a text document. A document is first affectively analyzed using a unique textual affect sensing engine, which leverages commonsense knowledge to classify text more reliably and comprehensively than can be achieved with keyword spotting methods alone. Using this engine, sentences are annotated using six basic Ekman emotions. Colors used to represent each of these emotions are sequenced into a color bar, which represents the progression of affect through a text document. Smoothing techniques allow the user to vary the granularity of the affective structure being displayed on the color bar. The bar is hyperlinked in a way such that it can be used to easily navigate the document. A user evaluation demonstrates that the proposed method for visualizing and navigating a document's affective structure facilitates a user's within-document information foraging activity.


title: Convolutional neural network for vehicle detection in low resolution traffic videos

abstract: Recent works on Convolutional Neural Network (CNN) in object detection and identification show its superior performance over other systems. It is being used on several machine vision tasks such as in face detection, OCR and traffic monitoring. These systems, however, use high resolution images which contain significant pattern information as compared to the typical cameras, such as for traffic monitoring, which are low resolution, thus, suffer low SNR. This work investigates the performance of CNN in detection and classification of vehicles using low quality traffic cameras. Results show an average accuracy equal to 94.72% is achieved by the system. An average of 51.28 ms execution time for a 2GHz CPU and 22.59 ms execution time for NVIDIA Fermi GPU are achieved making the system applicable to be implemented in real-time using 4-input traffic video with 6 fps.


Extending Full Text Search for Legal Document Collections Using Word Embeddings.


title: Finger vein recognition with manifold learning

abstract: Finger vein is a promising biometric pattern for personal identification in terms of its security and convenience. However, so residual information, such as shade produced by various thicknesses of the finger muscles, bones, and tissue networks surrounding the vein, are also captured in the infrared images of finger vein. Meanwhile, the pose variation of the finger may also cause failure to recognition. In this paper, for the first time, we address this problem by unifying manifold learning and point manifold distance concept. The experiments based on the TED-FV database demonstrate that the proposed algorithmic framework is robust and effective.


title: Improving the speed of neural networks on CPUs

abstract: Recent advances in deep learning have made the use of large, deep neural networks with tens of millions of parameters suitable for a number of applications that require real-time processing. The sheer size of these networks can represent a challenging computational burden, even for modern CPUs. For this reason, GPUs are routinely used instead to train and run such networks. This paper is a tutorial for students and researchers on some of the techniques that can be used to reduce this computational cost considerably on modern x86 CPUs. We emphasize data layout, batching of the computation, the use of SSE2 instructions, and particularly leverage SSSE3 and SSE4 fixed-point instructions which provide a 3× improvement over an optimized floating-point baseline. We use speech recognition as an example task, and show that a real-time hybrid hidden Markov model / neural network (HMM/NN) large vocabulary system can be built with a 10× speedup over an unoptimized baseline and a 4× speedup over an aggressively optimized floating-point baseline at no cost in accuracy. The techniques described extend readily to neural network training and provide an effective alternative to the use of specialized hardware.


title: Cell migration, pattern formation and cell fate during head development in lungfishes and amphibians

abstract: SummaryIn the evolution of land-living vertebrates, the transition from spending the entire life cycle in the water to first a biphasic (adult on land, eggs and larvae in water) and later a terrestrial life-history mode was achieved by changes in developmental processes and regulatory mechanisms. Lungfishes, salamanders and frogs are studied as examples of species which span this transition. The migration and fate of the embryonic cells that form the head is studied, using experimental embryology (extirpation and transplantation of cells), molecular markers and novel microscopy techniques — such as confocal microscopy. Knowing the migratory routes and fates of the cells that form head structures is important for an elucidation of the changes that took place e.g. when gill arches transformed into head cartilages, and when the specialised larval mouth structures present in today’s frogs and toads arose as an evolutionary innovation. Results so far indicate that the early migration and pattern formation of neural crest cells in the head region is surprisingly conserved. Both the amphibians investigated and the Australian lungfish have the same number of migrating neural crest streams, and the identity of the streams is preserved. The major difference lies in the timing of migration, where there has been a heterochronic shift such that cell migration starts much later in the Australian lungfish than in the amphibians. The molecular mechanisms regulating the formation of streams of cranial neural crest cells seem, at least in part, to be differential expression of ephrins and ephrin receptors, which mediate cell sorting. Our understanding of the behaviour of migrating cells (primarily the more well characterised neural crest cells) could be enhanced by a modelling approach. I present preliminary ideas on how this could be achieved, inspired by recent work on Dictyostelium development and our own previous work on pigment cells and their pattern formation during salamander embryogenesis.


A Reinforcement Learning Approach for Dynamic Search


Temporal Effect of Repeated Spinal Manipulation on Mortality Ratio : A Case Report


title: Face Frontalization Using an Appearance-Flow-Based Convolutional Neural Network

abstract: Facial pose variation is one of the major factors making face recognition (FR) a challenging task. One popular solution is to convert non-frontal faces to frontal ones on which FR is performed. Rotating faces causes facial pixel value changes. Therefore, existing CNN-based methods learn to synthesize frontal faces in color space. However, this learning problem in a color space is highly non-linear, causing the synthetic frontal faces to lose fine facial textures. In this paper, we take the view that the nonfrontal-frontal pixel changes are essentially caused by geometric transformations (rotation, translation, and so on) in space. Therefore, we aim to learn the nonfrontal-frontal facial conversion in the spatial domain rather than the color domain to ease the learning task. To this end, we propose an appearance-flow-based face frontalization convolutional neural network (A3F-CNN). Specifically, A3F-CNN learns to establish the dense correspondence between the non-frontal and frontal faces. Once the correspondence is built, frontal faces are synthesized by explicitly “moving” pixels from the non-frontal one. In this way, the synthetic frontal faces can preserve fine facial textures. To improve the convergence of training, an appearance-flow-guided learning strategy is proposed. In addition, generative adversarial network loss is applied to achieve a more photorealistic face, and a face mirroring method is introduced to handle the self-occlusion problem. Extensive experiments are conducted on face synthesis and pose invariant FR. Results show that our method can synthesize more photorealistic faces than the existing methods in both the controlled and uncontrolled lighting environments. Moreover, we achieve a very competitive FR performance on the Multi-PIE, LFW and IJB-A databases.


title: Choice-specific sequences in parietal cortex during a virtual-navigation decision task

abstract: The neural circuit dynamics in the mouse posterior parietal cortex are found to involve sequences of neural activation rather than longer-lived sustained neural activity states.


title: BloomFlash: Bloom Filter on Flash-Based Storage

abstract: The bloom filter is a probabilistic data structure that provides a compact representation of a set of elements. To keep false positive probabilities low, the size of the bloom filter must be dimensioned a priori to be linear in the maximum number of keys inserted, with the linearity constant ranging typically from one to few bytes. A bloom filter is most commonly used as an in memory data structure, hence its size is limited by the availability of RAM space on the machine. As datasets have grown over time to Internet scale, so have the RAM space requirements of bloom filters. If sufficient RAM space is not available, we advocate that flash memory may serve as a suitable medium for storing bloom filters, since it is about one-tenth the cost of RAM per GB while still providing access times orders of magnitude faster than hard disk. We present BLOOMFLASH, a bloom filter designed for flash memory based storage, that provides a new dimension of trade off with bloom filter access times to reduce RAM space usage (and hence system cost). The simple design of a single flat bloom filter on flash suffers from many performance bottlenecks, including in-place bit updates that are inefficient on flash and multiple reads and random writes spread out across many flash pages for a single lookup or insert operation. To mitigate these performance bottlenecks, BLOOMFLASH leverages two key design innovations: (i) buffering bit updates in RAM and applying them in bulk to flash that helps to reduce random writes to flash, and (ii) a hierarchical bloom filter design consisting of component bloom filters, stored one per flash page, that helps to localize reads and writes on flash. We use two real-world data traces taken from representative bloom filter applications to drive and evaluate our design. BLOOMFLASH achieves bloom filter access times in the range of few tens of microseconds, thus allowing up to order of tens of thousands operations per sec.