title: Self-Motivated, Task-Independent Reinforcement Learning for Robots

abstract: This paper describes a method for designing robots to learn self-motivated behaviors rather than externally specified behaviors. Self-motivation is viewed as an emergent property arising from two competing pressures: the need to accurately predict the environment while simultaneously wanting to seek out novelty in the environment. The robot’s internal prediction error is used to generate a reinforcement signal that pushes the robot to focus on areas of high error or novelty. A set of experiments are performed on a simulated robot to demonstrate the feasibility of this approach. The simulated robot is based directly on an existing platform and uses pixelated blob vision as its primary sensor.


title: A dynamic programming approach to sequencing problems

abstract: This paper explores a dynamic programming approach to the solution of three sequencing problems: a scheduling problem involving arbitrary cost functions, the traveling-salesman problem, and an assembly line balancing problem. Each of the problems is shown to admit of numerical solution through the use of a simple recursion scheme; these recursion schemes also exhibit similarities and contrasts in the structures of the three problems. For large problems, direct solution by means of dynamic programming is not practical, but procedures are given for obtaining good approximate results by solving sequences of smaller derived problems. Experience with a computer program for the solution of traveling-salesman problems is presented.


Sentence Embedding for Neural Machine Translation Domain Adaptation


title: MoireGraphs: radial focus+context visualization and interaction for graphs with visual nodes

abstract: Graph and tree visualization techniques enable interactive exploration of complex relations while communicating topology. However, most existing techniques have not been designed for situations where visual information such as images is also present at each node and must be displayed. This paper presents MoireGraphs to address this need. MoireGraphs combine a new focus+context radial graph layout with a suite of interaction techniques (focus strength changing, radial rotation, level highlighting, secondary foci, animated transitions and node information) to assist in the exploration of graphs with visual nodes. The method is scalable to hundreds of displayed visual nodes.


title: Visual Path Prediction in Complex Scenes with Crowded Moving Objects

abstract: This paper proposes a novel path prediction algorithm for progressing one step further than the existing works focusing on single target path prediction. In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. To solve this problem, we first suggest a two-layered probabilistic model to find major movement patterns and their cooccurrence tendency. By utilizing the unsupervised learning results from the model, we present an algorithm to find the future location of any target object. Through extensive qualitative/quantitative experiments, we show that our algorithm can find a plausible future path in complex scenes with a large number of moving objects.


title: Detecting geographical references in the form of place names and associated spatial natural language

abstract: Recognizing spatial language in text documents, termed geoparsing, is useful for many applications, because together with mapping such language to lat/long values, also known as geocoding, it enables the connection of the unstructured textual realm with the structured realm of Geographic Information Systems (GIS) [11]. For example, news stories about events happening in a particular location can be explored on a map for a spatial understanding of these events, as implemented by applications like the European Media Monitor (EMM) [18] and NewsStand [13, 20]. Web pages, blogs, encyclopedia articles, news stories, tweets and travel reports can all benefit from such interlinking with maps, which requires the recognition of spatial language. Note that geoparsing can be considered as a more specific application of the task of Named Entity Recognition and Classification (NERC): NERC is concerned with automatically recognizing proper nouns of any kind, often meant to include monetary amounts, dates, and other types, while geoparsing is the NERC task applied to locations specifically. Geoparsing is also known by many names in the literature, including geotagging, georecognition, and toponym recognition, but for consistency, here we will refer only to geoparsing. In this paper, we provide an overview of the challenges related to geoparsing, several families of geoparsing methods, existing systems and data collections available for performing geoparsing, and open research questions related to geoparsing.


title: Obstacle Avoidance for Low-Speed Autonomous Vehicles With Barrier Function

abstract: This paper presents an obstacle avoidance algorithm for low speed autonomous vehicles (AV), with guaranteed safety. A supervisory control algorithm is constructed based on a barrier function method, which works in a plug-and-play fashion with any lower level navigation algorithm. When the risk of collision is low, the barrier function is not active; when the risk is high, based on the distance to an “avoidable set,” the barrier function controller will intervene, using a mixed integer program to ensure safety with minimal control effort. This method is applied to solve the navigation and pedestrian avoidance problem of a low speed AV. Its performance is compared with two benchmark algorithms: a potential field method and the Hamilton–Jacobi method.


title: CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures

abstract: Learning commonsense causal and temporal relation between events is one of the major steps towards deeper language understanding. This is even more crucial for understanding stories and script learning. A prerequisite for learning scripts is a semantic framework which enables capturing rich event structures. In this paper we introduce a novel semantic annotation framework, called Causal and Temporal Relation Scheme (CaTeRS), which is unique in simultaneously capturing a comprehensive set of temporal and causal relations between events. By annotating a total of 1,600 sentences in the context of 320 five-sentence short stories sampled from ROCStories corpus, we demonstrate that these stories are indeed full of causal and temporal relations. Furthermore, we show that the CaTeRS annotation scheme enables high inter-annotator agreement for broad-coverage event entity annotation and moderate agreement on semantic link annotation.


title: Knowledge management in the public sector: stakeholder partnerships in the public policy development

abstract: Purpose – How knowledge management theories and frameworks are applied in the public sector is not well understood due to little evidence being published in the literature. This paper aims to identify core issues and challenges that governments face in delivering effective public policy, particularly challenges presented by increasing community expectations, and to highlight the importance of developing public policy via knowledge‐based partnerships with its stakeholders.Design/methodology/approach – Arguments draw on relevant theory in knowledge management and related fields as well as from public sector experiences.Findings – The main discussion examines some issues, challenges and opportunities in public policy developments and proposes some practical models to assist governments develop and capitalize on more effective knowledge‐based stakeholder partnerships.Practical implications – This paper suggests that some existing process frameworks can provide a good starting‐point to capturing knowledge abou...


title: Online LCL filter compensation using embedded FRA

abstract: LCL filter control design is challenging because of presence of a resonant pole in the LCL filter transfer function. This paper presents a guideline for LCL filter design, which are often used in grid connected applications, with compensation based on complex poles and zeros. A Software Frequency Response Analyzer (SFRA) is integrated in the control software of the power converter. This enables periodical collection of frequency response data, which can be used to monitor movement of resonant poles due to aging, drifts and degradation of components. The information obtained from embedded SFRA can then be used to adjust the compensation. A feedback linearization method is illustrated to account for the grid voltage disturbances, which cannot be compensated by traditional PI controller. A solar micro inverter board is designed to prove the proposed scheme. Discussions about the embedded design challenges in integration of multiple power stage control, for a micro inverter using a single processor, are included and solutions presented.


title: Avoid a Void : The eradication of null dereferencing

abstract: All object-oriented programs, but also those in C or Pascal as soon as they use pointers, are subject to the risk of run-time crash due to “null pointer dereferencing”. Until recently this was the case even in statically typed languages. Tony Hoare has called this problem his “billion-dollar mistake”. In the type system of ISO-standard Eiffel, the risk no longer exists: void safety (the absence of null pointer dereferencing) has become a property guaranteed by the type system and enforced by the compiler. The mechanism is fully implemented and major libraries and applications have been made void-safe. This presentation describes the principles of Eiffel’s void safety, their implementation and the lessons gained.


title: Electrochemical synthesis of nano-cobalt hexacyanoferrate at a sol–gel-coated electrode templated with β-cyclodextrin

abstract: The paper describes the time-dependent evolution of the electrochemical deposition of cobalt hexacyanoferrate (CoHCFe) on graphite foil electrode modified with electrochemically formed sol–gel film doped with β-cyclodextrin to impart porosity. With short-time electrodeposition, cyclic voltammetry (CV) shows a single redox couple typical of nano-sized clusters of CoHCFe, while at longer deposition times the CV’s shape evolves to the classical form of a bulk compound in which there are present two redox couples. The electrode modified with β-cyclodextrin (CD) included in the sol–gel film has an active surface that corresponds to pores created by CD stacks normal to the surface. Hence, the electrochemical formation of CoHCFe starts in these conductive pores; only at long deposition times do the clusters overlap to form moieties with the voltammetric characteristics of bulk CoHCFe.


title: Design of 6 × 6 dual-band MIMO antenna array for 4.5G/5G smartphone applications

abstract: A dual-band 6 × 6 antenna array operating in the LTE band7 downlink (2620–2690 MHz) and LTE band46 (5150–5925 MHz) for 4.5G/5G communication multi-input multi-output (MIMO) operation in the smartphone is presented. The proposed dual-band antenna array elements are symmetrically placed along the long edges of the smartphone, and the antenna element is composed of three parts: a L-shaped feeding monopole, a grounded monopole and a cuboid-shaped carrier. Acceptable isolation (better than about 10 dB), good efficiency of more than 60% and good envelop correlation coefficient (ECC) of less than 0.05 for any two antennas in the proposed array are obtained.


title: A Case Study in Treebank Collaboration and Comparison: Accusativus cum Infinitivo and Subordination in Latin

abstract: We describe here a collaboration between two separate treebank projects annotating data for the same language (Latin). By working together to create a common standard for the annotation of Latin syntax and sharing our annotated data as it is created, we are each able to rely on the resources and expertise of the other while also ensuring that our data will be compatible in the future. is compatibility allows us to conduct diachronic studies involving both datasets, and we add our results to an ongoing discussion of one such issue, the gradual replacement of the Accusativus cum Infinitivo construction in Latin with subordinate clauses headed by conjunctions such as quod and quia.


title: A robust method for speech signal time-delay estimation in reverberant rooms

abstract: Conventional time-delay estimators exhibit dramatic performance degradations in the presence of multipath signals. This limits their application in reverberant enclosures, particularly when the signal of interest is speech and it may not possible to estimate and compensate for channel effects prior to time-delay estimation. This paper details an alternative approach which reformulates the problem as a linear regression of phase data and then estimates the time-delay through minimization of a robust statistical error measure. The technique is shown to be less susceptible to room reverberation effects. Simulations are performed across a range of source placements and room conditions to illustrate the utility of the proposed time-delay estimation method relative to conventional methods.


title: MR imaging of the placenta: what a radiologist should know

abstract: Imaging of the placenta can have a profound impact on patient management, owing to the morbidity and mortality associated with various placental conditions. Placental conditions affecting the mother and fetus include molar pregnancies, placental hematoma, abruption, previa, accreta, vasa previa, chorioangioma, and retained products of conception. Although uncommon, abnormalities of the placenta are important to recognize owing to the potential for maternal and fetal morbidity and mortality. Sonography remains the first imaging modality for evaluation of the placenta. Magnetic resonance (MR) imaging has many unique properties that make it well-suited for imaging of the placenta: the multi-planar capabilities, the improved tissue contrast that can be obtained using a variety of pulse sequences and parameters and the lack of ionizing radiation; MR imaging can be of added diagnostic value when further characterization is required. In this article, we review the appearances and the role of MRI in diagnosis and management of these conditions. We present our clinical perspective on diagnosing these challenging problems with MRI and review the imaging findings that can lead to a correct diagnosis.


title: A Foundation for the Study of Personal Cloud Computing in Organizations

abstract: The purpose of this paper is to provide a theoretical foundation for the study of Personal Cloud Computing in organizations; a foundation that would assist researchers and practitioners to develop a deeper understanding of the impacts of this type of information technology use on organizations. Currently, very little theorizing has been done about the management of Personal Cloud in organizations. This paper aims to address this gap by building a theoretical foundation using IT consumerization theory, self-determination theory (SDT), and information systems success. We argue that developing such a foundation will highlight where research attention needs to be directed and practice needs to be focused. We aim to provide a better understanding of the factors that influence organizational adoption of Personal Cloud and drive performance of individuals and organizations. The paper develops a comprehensive research model and provides testable propositions along with recommendations for testing in future research.


title: A fast method based on multiple clustering for name disambiguation in bibliographic citations

abstract: Name ambiguity in the context of bibliographic citation affects the quality of services in digital libraries. Previous methods are not widely applied in practice because of their high computational complexity and their strong dependency on excessive attributes, such as institutional affiliation, research area, address, etc., which are difficult to obtain in practice. To solve this problem, we propose a novel coarse-to-fine framework for name disambiguation which sequentially employs 3 common and easily accessible attributes i.e., coauthor name, article title, and publication venue. Our proposed framework is based on multiple clustering and consists of 3 steps: a clustering articles by coauthorship and obtaining rough clusters, that is fragments; b clustering fragments obtained in step 1 by title information and getting bigger fragments; c and clustering fragments obtained in step 2 by the latent relations among venues. Experimental results on a Digital Bibliography and Library Project DBLP data set show that our method outperforms the existing state-of-the-art methods by 2.4% to 22.7% on the average pairwise F1 score and is 10 to 100 times faster in terms of execution time.


title: Semantic Expansion of Hashtags for Enhanced Event Detection in Twitter

abstract: this work, we present an event detection method in Twitter based on clustering of hashtags and introduce an enhancement technique by using the semantic similarities between the hashtags. To this aim, we devised two methods for tweet vector generation and evaluated their effect on clustering and event detection performance in comparison to word-based vector generation methods. By analyzing the contexts of hashtags and their co-occurrence statistics with other words, we identify their paradigmatic relationships and similarities. We make use of this information while applying a lexico-semantic expansion on tweet contents before clustering the tweets based on their similarities. Our aim is to tolerate spelling errors and capture statements which actually refer to the same concepts. We evaluate our enhancement solution on a three-day dataset of tweets with Turkish content. In our evaluations, we observe clearer clusters, improvements in accuracy, and earlier event detection times.


title: Concept-to-text Generation via Discriminative Reranking

abstract: This paper proposes a data-driven method for concept-to-text generation, the task of automatically producing textual output from non-linguistic input. A key insight in our approach is to reduce the tasks of content selection ("what to say") and surface realization ("how to say") into a common parsing problem. We define a probabilistic context-free grammar that describes the structure of the input (a corpus of database records and text describing some of them) and represent it compactly as a weighted hypergraph. The hypergraph structure encodes exponentially many derivations, which we rerank discriminatively using local and global features. We propose a novel decoding algorithm for finding the best scoring derivation and generating in this setting. Experimental evaluation on the Atis domain shows that our model outperforms a competitive discriminative system both using BLEU and in a judgment elicitation study.


title: HfO2-Based OxRAM Devices as Synapses for Convolutional Neural Networks

abstract: In this paper, the use of HfO2-based oxide-based resistive memory (OxRAM) devices operated in binary mode to implement synapses in a convolutional neural network (CNN) is studied. We employed an artificial synapse composed of multiple OxRAM cells connected in parallel, thereby providing synaptic efficacies. Electrical characterization results show that the proposed HfO2-based OxRAM technology offers good electrical properties in terms of endurance ( $> 10^{8}$ cycles), speed ( 98%) is demonstrated for a complex visual pattern recognition application. We demonstrated that the resistance variability and the reduced memory window of the OxRAM cells when operated at extremely low programming conditions (<10 pJ per switching event) have a small impact on the performances of proposed OxRAM-based CNN (recognition rate 94%).


title: Effects of Continuous Auditory Feedback on Drawing Trajectory-Based Finger Gestures

abstract: The well-known “fat finger” issue limits the interaction performance of trajectory-based finger gestures. To alleviate this issue, this work focuses on the possibility of using additional continuous auditory feedback to assist trajectory-based finger gestures. First, the experiment validated that, with the visual feedback only, the bare fingertip led to more errors in drawing of intersectional points, endpoints of closed gestures, and gestural length and shape variability compared to when the finger-attached pen was used. Then, we designed different types of auditory feedback (discrete beep, static, gradual) to provide additional information on the spatial relationship between finger-contact point and the endpoints or intersections of predefined gestures. An experiment that evaluates the effects of individual or combination of designed auditory feedback on trajectory-based finger gestures was conducted. These results show a few differences between them. However, a combination of gradual (amplitude and frequency) continuous sound and beep reached the highest drawing accuracy for trajectory-based finger gestures, which is similar to that of a finger-attached pen. This research offers insights and implications for the future design of continuous auditory feedback on small touchscreens.


title: An Improved Control Strategy of Limiting the DC-Link Voltage Fluctuation for a Doubly Fed Induction Wind Generator

abstract: The paper presents to develop a new control strategy of limiting the dc-link voltage fluctuation for a back-to-back pulsewidth modulation converter in a doubly fed induction generator (DFIG) for wind turbine systems. The reasons of dc-link voltage fluctuation are analyzed. An improved control strategy with the instantaneous rotor power feedback is proposed to limit the fluctuation range of the dc-link voltage. An experimental rig is set up to valid the proposed strategy, and the dynamic performances of the DFIG are compared with the traditional control method under a constant grid voltage. Furthermore, the capabilities of keeping the dc-link voltage stable are also compared in the ride-through control of DFIG during a three-phase grid fault, by using a developed 2 MW DFIG wind power system model. Both the experimental and simulation results have shown that the proposed control strategy is more effective, and the fluctuation of the dc-link voltage may be successfully limited in a small range under a constant grid voltage and a non-serious grid voltage dip.


Learning Grounded Relational Symbols from Continuous Data for Abstract Reasoning


title: Authenticated Dictionaries: Real-World Costs and Trade-Offs

abstract: Authenticated dictionaries are a widely discussed paradigm to enable verifiable integrity for data storage on untrusted servers, such as today’s widely used “cloud computing” resources, allowing a server to provide a “proof,” typically in the form of a slice through a cryptographic data structure, that the results of any given query are the correct answer, including that the absence of a query result is correct. Persistent authenticated dictionaries (PADs) further allow queries against older versions of the structure. This research presents implementations of a variety of different PAD algorithms, some based on Merkle tree-style data structures and others based on individually signed “tuple” statements (with and without RSA accumulators). We present system throughput benchmarks, indicating costs in terms of time, storage, and bandwidth as well as considering how much money would be required given standard cloud computing costs. We conclude that Merkle tree PADs are preferable in cases with frequent updates, while tuple-based PADs are preferable with higher query rates. For Merkle tree PADs, red-black trees outperform treaps and skiplists. Applying Sarnak-Tarjan’s versioned node strategy, with a cache of old hashes at every node, to red-black trees yields the fastest Merkle tree PAD implementation, notably using half the memory of the more commonly used mutation-free path copying strategy. For tuple PADs, although we designed and implemented an algorithm using RSA accumulators that offers constant update size, constant storage per update, constant proof size, and sublinear computation per update, we found that RSA accumulators are so expensive that they are never worthwhile. We find that other optimizations in the literature for tuple PADs are more cost-effective.


Detecting User Activities using the Accelerometer on Android Smartphones


title: Gene Editing With CRISPR/Cas9 RNA-Directed Nuclease

abstract: Genetic engineering of model organisms and cultured cells has for decades provided important insights into the mechanisms underlying cardiovascular development and disease. In the past few years the development of several nuclease systems has broadened the range of model/cell systems that can be engineered. Of these, the CRISPR (clustered regularly interspersed short palindromic repeats)/Cas9 (CRISPR-associated protein 9) system has become the favorite for its ease of application. Here we will review this RNA-guided nuclease system for gene editing with respect to its usefulness for cardiovascular studies and with an eye toward potential therapy. Studies on its off-target activity, along with approaches to minimize this activity will be given. The advantages of gene editing versus gene targeting in embryonic stem cells, including the breadth of species and cell types to which it is applicable, will be discussed. We will also cover its use in iPSC for research and possible therapeutic purposes; and we will review its use in muscular dystrophy studies where considerable progress has been made toward dystrophin correction in mice. The CRISPR/Ca9s system is also being used for high-throughput screening of genes, gene regulatory regions, and long noncoding RNAs. In addition, the CRISPR system is being used for nongene-editing purposes such as activation and inhibition of gene expression, as well as for fluorescence tagging of chromosomal regions and individual mRNAs to track their cellular location. Finally, an approach to circumvent the inability of post-mitotic cells to support homologous recombination-based gene editing will be presented. In conclusion, applications of the CRISPR/Cas system are expanding at a breath-taking pace and are revolutionizing approaches to gain a better understanding of human diseases.


title: Mining and Visualizing Social Data to Inform Marketing Decisions

abstract: Most of today's commercial companies heavily rely on social media and community management tools to interact with their clients and analyze their online behaviour. Nonetheless, these tools still lack evolved data mining and visualization features to tailor the analysis in order to support useful marketing decisions. We present an original methodology that aims at formalizing the marketing need of the company and develop a tool that can support it. The methodology is derived from the Cross-Industry Standard Process for Data Mining (CRISP-DM) and includes additional steps dedicated to the design and development of visualizations of mined data. We followed the methodology in two use cases with Swiss companies. First, we developed a prototype that aims at understanding the needs of tourists based on Flickr and Instagram data. In that use case, we extend the existing literature by enriching hashtags analysis methods with a semantic network based on Linked Data. Second, we analyzed internal customer data of an online discount retailer to help them define guerilla marketing measures. We report on the challenges of integrating Facebook data in the process. Informal feedback from domain experts confirms the strong potential of such advanced analytic features based on social data to inform marketing decisions.


title: Oral language and code-related precursors to reading: evidence from a longitudinal structural model.

abstract: This study examined code-related and oral language precursors to reading in a longitudinal study of 626 children from preschool through 4th grade. Code-related precursors, including print concepts and phonological awareness, and oral language were assessed in preschool and kindergarten. Reading accuracy and reading comprehension skills were examined in 1st through 4th grades. Results demonstrated that (a) the relationship between code-related precursors and oral language is strong during preschool; (b) there is a high degree of continuity over time of both code-related and oral language abilities; (c) during early elementary school, reading ability is predominantly determined by the level of print knowledge and phonological awareness a child brings from kindergarten; and (d) in later elementary school, reading accuracy and reading comprehension appear to be 2 separate abilities that are influenced by different sets of skills.


title: Deep Stacked Networks with Residual Polishing for Image Inpainting

abstract: Deep neural networks have shown promising results in image inpainting even if the missing area is relatively large. However, most of the existing inpainting networks introduce undesired artifacts and noise to the repaired regions. To solve this problem, we present a novel framework which consists of two stacked convolutional neural networks that inpaint the image and remove the artifacts, respectively. The first network considers the global structure of the damaged image and coarsely fills the blank area. Then the second network modifies the repaired image to cancel the noise introduced by the first network. The proposed framework splits the problem into two distinct partitions that can be optimized separately, therefore it can be applied to any inpainting algorithm by changing the first network. Second stage in our framework which aims at polishing the inpainted images can be treated as a denoising problem where a wide range of algorithms can be employed. Our results demonstrate that the proposed framework achieves significant improvement on both visual and quantitative evaluations.


title: Testing vulnerabilities in bluetooth low energy

abstract: Bluetooth Low Energy (BTLE) is pervasive in technology throughout all areas of our lives. In this research effort, experiments are performed to discover vulnerabilities in the Bluetooth protocol and given the right technology determine exploitation. Using a Bluetooth keyboard, practical examples of the Bluetooth Low Energy protocol were able to be provided. Because of the results garnered, it is recommended that Bluetooth Low Energy not be used for any connections that may transmit sensitive data, or with devices that may have access to sensitive networks.


title: Role of dietary phenols in mitigating microglia-mediated neuroinflammation

abstract: Chronic neuroinflammation is a pathological feature of a number of central nervous system (CNS) diseases and is mediated by sustained activation of microglial cells, the innate immune cells of the CNS. Studies have mainly focused on identifying the molecular and epigenetic mechanisms of microglial activation. This is crucial in designing therapeutic strategies for neuropathologies in which prolonged microglial activation is known to exacerbate disease condition. In recent years, increasing evidence show that naturally occurring compounds present in regular diet could function as “nutraceuticals,” arresting microglial activation, and thus conferring neuroprotection. This review summarizes our understanding of the role of dietary phenolic nutraceuticals in mitigating microglia-mediated neuroinflammation. Studies show that these natural phenols inhibit key signaling pathways in activated microglia such as the NFκB, MAPK and JAK-STAT that trigger microglia-mediated inflammation in various neuropathological conditions such as injury, infection, stroke, autism and neurodegenerative diseases, i.e., Alzheimer’s disease and Parkinson’s disease. The anti-inflammatory and antioxidant effect exerted by these natural phenols have shown considerable success in improving disease condition in animal models of neuropathologies, and thus seem to be suitable candidates for developing therapeutic strategies.


title: Finding Similar People to Guide Life Choices: Challenge, Design, and Evaluation

abstract: People often seek examples of similar individuals to guide their own life choices. For example, students making academic plans refer to friends; patients refer to acquaintances with similar conditions, physicians mention past cases seen in their practice. How would they want to search for similar people in databases? We discuss the challenge of finding similar people to guide life choices and report on a need analysis based on 13 interviews. Our PeerFinder prototype enables users to find records that are similar to a seed record, using both record attributes and temporal events found in the records. A user study with 18 participants and four experts shows that users are more engaged and more confident about the value of the results to provide useful evidence to guide life choices when provided with more control over the search process and more context for the results, even at the cost of added complexity.


title: Spatial latent dirichlet allocation

abstract: In recent years, the language model Latent Dirichlet Allocation (LDA), which clusters co-occurring words into topics, has been widely applied in the computer vision field. However, many of these applications have difficulty with modeling the spatial and temporal structure among visual words, since LDA assumes that a document is a "bag-of-words". It is also critical to properly design "words" and "documents" when using a language model to solve vision problems. In this paper, we propose a topic model Spatial Latent Dirichlet Allocation (SLDA), which better encodes spatial structures among visual words that are essential for solving many vision problems. The spatial information is not encoded in the values of visual words but in the design of documents. Instead of knowing the partition of words into documents a priori, the word-document assignment becomes a random hidden variable in SLDA. There is a generative procedure, where knowledge of spatial structure can be flexibly added as a prior, grouping visual words which are close in space into the same document. We use SLDA to discover objects from a collection of images, and show it achieves better performance than LDA.


title: A Novel CMOS High-Power Terahertz VCO Based on Coupled Oscillators: Theory and Implementation

abstract: We introduce a novel frequency tuning method for high-power terahertz sources in CMOS. In this technique, multiple core oscillators are coupled to generate, combine, and deliver their harmonic power to the output node without using varactors. By exploiting the theory of nonlinear dynamics, we control the coupling between the cores to set their phase shift and frequency. Using this method, two high-power terahertz VCOs are fabricated in a 65 nm LP bulk CMOS process. The first one has a measured output power of 0.76 mW at 290 GHz with 4.5% tuning range and the output power of the second VCO is 0.46 mW at 320 GHz with 2.6% tuning range. The output power of these signal sources is 4 orders of magnitude higher than previous CMOS VCOs and is even higher than VCOs implemented in compound semiconductors with much higher cut-off frequencies.


Centering Theory in Spanish: Coding Manual *


title: Lysosomal triacylglycerol lipase activity in L6 myoblasts and its changes on differentiation

abstract: L6 myoblasts, before fusion, accumulate large stores of neutral lipid when cultured in medium supplemented with fatty acid. Upon fusion to terminally differentiated myotubes, a noticeable decrease in these neutral-lipid stores was observed. Triacylglycerol lipase activity was examined in L6 myoblasts at various stages of cell differentiation to assess a possible role for this enzyme in the above phenomenon. In this first study to demonstrate lipolytic activity in cultured muscle cells, the activity was found to be totally dependent on the presence of a detergent, either Cutscum or Triton X-100, during homogenization. The inhibition by many thiol-specific reagents [N-ethylmaleimide, p-chloromercuribenzoate, iodoacetate, 5,5'-dithiobis-(2-nitrobenzoic acid)] suggest that a thiol group is at or near the active site. The observed acidic pH optimum (5.5-6.0), the acute inhibition by chlorpromazine (a lysosomal lipase inhibitor) and the distribution of lipolytic activity upon cell fractionation (which co-sediments with acid phosphatase, a lysosomal marker enzyme) suggest that the lipase may be of lysosomal origin. Under the optimal conditions described, the triacylglycerol lipase activity of L6 myoblasts was determined to be 2.9 +/- 0.4 nmol of oleic acid released/min per mg of DNA. This activity increased 3-fold, to 9.0 +/- 1.6 nmol/min per mg, in the myotube phase. This increase in lipolytic activity may be responsible for the observed decrease in neutral-lipid stores of differentiating myoblasts.


title: Multi-view Human Action Recognition: A Survey

abstract: While single-view human action recognition has attracted considerable research study in the last three decades, multi-view action recognition is, still, a less exploited field. This paper provides a comprehensive survey of multi-view human action recognition approaches. The approaches are reviewed following an application-based categorization: methods are categorized based on their ability to operate using a fixed or an arbitrary number of cameras. Finally, benchmark databases frequently used for evaluation of multi-view approaches are briefly described.


title: Prediction of suicidal behavior in clinical research by lifetime suicidal ideation and behavior ascertained by the electronic Columbia-Suicide Severity Rating Scale

abstract: OBJECTIVE: To evaluate whether lifetime suicidal ideation with intention to act and/or suicidal behaviors reported at baseline predict risk of prospectively reporting suicidal behavior during subsequent study participation. METHOD: Data from studies using the electronic Columbia-Suicide Severity Rating Scale (eC-SSRS) to prospectively monitor suicidal ideation and behaviors between September 2009 and May 2011 were analyzed. Studies included patients with major depressive disorder, insomnia, posttraumatic stress disorder, epilepsy, and fibromyalgia. Records for 35,224 eC-SSRS assessments were extracted. Incomplete assessments and eC-SSRS records from patients missing a baseline assessment or with no prospective follow-up assessments were excluded. Baseline lifetime eC-SSRS reports were categorized as negative (no lifetime ideation with intent to act or prior suicidal behavior) or positive (lifetime ideation with intent to act but no prior behavior, no ideation with intent to act but prior behavior, or both lifetime ideation with intent and prior behavior). RESULTS: 3,776 patients completed a baseline and 1 or more follow-up assessments. The mean follow-up period was 64 days. Of patients with negative lifetime reports, 2.4% subsequently reported suicidal behavior during study participation, compared to 12.0% of patients with lifetime ideation with intent only (OR = 5.55; 95% CI, 2.65-11.59), 9.6% of patients with lifetime behavior only (OR = 4.33; 95% CI, 2.94-6.39), and 18.3% of patients with both (OR = 9.13; 95% CI, 6.47-12.88). Sensitivity and specificity of positive reports for identifying suicidal behaviors were 0.67 and 0.76, respectively. CONCLUSIONS: Patients reporting lifetime suicidal ideation with intent to act and/or prior suicidal behavior at baseline are 4 to 9 times more likely to prospectively report suicidal behavior during study participation. Language: en


title: Using Topic Segmentation Models for the Automatic Organisation of MOOCs resources

abstract: As online courses such as MOOCs become increasingly popular, there has been a dramatic increase for the demand for methods to facilitate this type of organisation. While resources for new courses are often freely available, they are generally not suitably organised into easily manageable units. In this paper, we investigate how state-of-the-art topic segmentation models can be utilised to automatically transform unstructured text into coherent sections, which are suitable for MOOCs content browsing. The suitability of this method with regards to course organisation is confirmed through experiments with a lecture corpus, configured explicitly according to MOOCs settings. Experimental results demonstrate the reliability and scalability of this approach over various academic disciplines. The findings also show that the topic segmentation model which used discourse cues displayed the best results overall.


title: Deep learning based personality recognition from Facebook status updates

abstract: Many approaches have been proposed to automatically infer users personality from their social networks activities. However, the performance of these approaches depends heavily on the data representation. In this work, we apply deep learning methods to automatically learn suitable data representation for the personality recognition task. In our experiments, we used the Facebook status updates data. We investigated several neural network architectures such as fully-connected (FC) networks, convolutional networks (CNN) and recurrent networks (RNN) on the myPersonality shared task and compared them with some shallow learning algorithms. Our experiments showed that CNN with average pooling is better than both the RNN and FC. Convolutional architecturewith average pooling achieved the best results 60.0±6.5%.


title: A line-structure-preserving approach to image resizing

abstract: This paper proposes a content-aware image resizing method which simultaneously preserves both salient image features and important line structure properties: parallelism, collinearity and orientation. When there are prominent line structures in the image, image resizing methods without explicitly taking these properties into account could produce line structure distortions in their results. Since the human visual system is very sensitive to line structures, such distortions often become noticeable and disturbing. Our method couples mesh deformations for image resizing with similarity transforms for line features. Mesh deformations are used to control content preservation while similarity transforms are analyzed in the Hough space to maintain line structure properties. Our method strikes a good balance between preserving content and maintaining line structure properties. Experiments show the proposed method often outperforms methods without taking line structures into account, especially for scenes with prominent line structures.


title: Mouse Control using a Web Camera based on Colour Detection

abstract: In this paper we present an approach for Human computer Interaction (HCI), where we have tried to control the mouse cursor movement and click events of the mouse using hand gestures. Hand gestures were acquired using a camera based on colour detection technique. This method mainly focuses on the use of a Web Camera to develop a virtual human computer interaction device in a cost effective manner.


title: Context Traits Dynamic Behaviour Adaptation Through Run-Time Trait Recomposition

abstract: Context-oriented programming emerged as a new paradigm to support fine-grained dynamic adaptation of software behaviour according to the context of execution. Though existing context-oriented approaches permit the adaptation of individual methods, in practice behavioural adaptations to specific contexts often require the modification of groups of interrelated methods. Furthermore, existing approaches impose a composition semantics that cannot be adjusted on a domain-specific basis. The mechanism of traits seems to provide a more appropriate level of granularity for defining adaptations, and brings along a flexible composition mechanism that can be exploited in a dynamic setting. This paper explores how to achieve context-oriented programming by using traits as units of adaptation, and trait composition as a mechanism to introduce behavioural adaptations at run time. First-class contexts reify relevant aspects of the environment in which the application executes, and they directly influence the trait composition of the objects that make up the application. To resolve conflicts arising from dynamic composition of behavioural adaptations, programmers can explicitly encode composition policies. With all this, the notion of context traits offers a promising approach to implementing dynamically adaptable systems. To validate the context traits model we implemented a JavaScript library and conducted case studies on context-driven adaptability.


title: Adapting deep RankNet for personalized search

abstract: RankNet is one of the widely adopted ranking models for web search tasks. However, adapting a generic RankNet for personalized search is little studied. In this paper, we first continue-trained a variety of RankNets with different number of hidden layers and network structures over a previously trained global RankNet model, and observed that a deep neural network with five hidden layers gives the best performance. To further improve the performance of adaptation, we propose a set of novel methods categorized into two groups. In the first group, three methods are proposed to properly assess the usefulness of each adaptation instance and only leverage the most informative instances to adapt a user-specific RankNet model. These assessments are based on KL-divergence, click entropy or a heuristic to ignore top clicks in adaptation queries. In the second group, two methods are proposed to regularize the training of the neural network in RankNet: one of these methods regularize the error back-propagation via a truncated gradient approach, while the other method limits the depth of the back propagation when adapting the neural network. We empirically evaluate our approaches using a large-scale real-world data set. Experimental results exhibit that our methods all give significant improvements over a strong baseline ranking system, and the truncated gradient approach gives the best performance, significantly better than all others.


title: Compact Deep Convolutional Neural Networks With Coarse Pruning

abstract: The learning capability of a neural network improves with increasing depth at higher computational costs. Wider layers with dense kernel connectivity patterns furhter increase this cost and may hinder real-time inference. We propose feature map and kernel level pruning for reducing the computational complexity of a deep convolutional neural network. Pruning feature maps reduces the width of a layer and hence does not need any sparse representation. Further, kernel pruning converts the dense connectivity pattern into a sparse one. Due to coarse nature, these pruning granularities can be exploited by GPUs and VLSI based implementations. We propose a simple and generic strategy to choose the least adversarial pruning masks for both granularities. The pruned networks are retrained which compensates the loss in accuracy. We obtain the best pruning ratios when we prune a network with both granularities. Experiments with the CIFAR-10 dataset show that more than 85% sparsity can be induced in the convolution layers with less than 1% increase in the missclassification rate of the baseline network.


title: CMOS-MEMS resonant demodulator for near-zero-power RF wake-up receiver

abstract: We demonstrate a CMOS-MEMS resonant demodulator for use in a nanoWatt-power RF wake-up sensor. "Near-zero" power operation is enabled through voltage step-up and frequency keying in passive MEMS elements. The integrated MEMS demodulator minimizes parasitic capacitance to maximize gain and performs high-Q filtering to prevent false triggering due to interference signal feedthrough. High-frequency testing of a demodulator with a minimum transduction gap size of 400 nm agrees with device models.


title: Gender differences in mate selection preferences: a test of the parental investment model.

abstract: Evolutionary-related hypotheses about gender differences in mate selection preferences were derived from Trivers parental investment model which contends that women are more likely than men to seek a mate who possesses nonphysical characteristics that maximize the survival or reproductive prospects of their offspring and were examined in a meta-analysis of mate selection research (questionnaire studies analyses of personal advertisements). As predicted women accorded more weight than men to socioeconomic status ambitiousness character and intelligence and largest gender differences were observed for cues to resource acquisition (status ambitiousness). Also as predicted gender differences were not found in preferences for characteristics unrelated to progeny survival (sense of humor "personality"). Where valid comparison could be made the findings were generally invariant across generations cultures and research paradigms. (authors)


title: Diagnosis of Stroke-Associated Pneumonia: Recommendations From the Pneumonia in Stroke Consensus Group.

abstract: BACKGROUND AND PURPOSE ::: Lower respiratory tract infections frequently complicate stroke and adversely affect outcome. There is currently no agreed terminology or gold-standard diagnostic criteria for the spectrum of lower respiratory tract infections complicating stroke, which has implications for clinical practice and research. The aim of this consensus was to propose standardized terminology and operational diagnostic criteria for lower respiratory tract infections complicating acute stroke. ::: ::: ::: METHODS ::: Systematic literature searches of multiple electronic databases were undertaken. An evidence review and 2 rounds of consensus consultation were completed before a final consensus meeting in September 2014, held in Manchester, United Kingdom. Consensus was defined a priori as ≥75% agreement between the consensus group members. ::: ::: ::: RESULTS ::: Consensus was reached for the following: (1) stroke-associated pneumonia (SAP) is the recommended terminology for the spectrum of lower respiratory tract infections within the first 7 days after stroke onset; (2) modified Centers for Disease Control and Prevention (CDC) criteria are proposed for SAP as follows-probable SAP: CDC criteria met, but typical chest x-ray changes absent even after repeat or serial chest x-ray; definite SAP: CDC criteria met, including typical chest x-ray changes; (3) there is limited evidence for a diagnostic role of white blood cell count or C-reactive protein in SAP; and (4) there is insufficient evidence for the use of other biomarkers (eg, procalcitonin). ::: ::: ::: CONCLUSIONS ::: Consensus operational criteria for the terminology and diagnosis of SAP are proposed based on the CDC criteria. These require prospective evaluation in patients with stroke to determine their reliability, validity, impact on clinician behaviors (including antibiotic prescribing), and clinical outcomes.


title: A new generation Power-Data Hybrid Transmission System based-on G.SHDSL and power filter

abstract: Long-distance Power-Data Hybrid Transmission System provides long-term easy, safe, and reliable power supply and data transmission method for the observation and sampling of deep sea. A hybrid transmission system based on G.SHDSL has been designed and the hybrid transmission of electric energy, subsea video and the data of surface and underwater unit on one coaxial armored cable has been realized in our past researches, and it can be applied to supply the power for the control system that including television grab bucket, deep sea drilling, integrated towed vehicle, visual multi-barrel sampler. For increasingly vaster underwater investigation, ground fault detection and water leakage detection module based on original system have been designed in this paper to provide safer and more comprehensive power supply for underwater application.


title: Exploring Personalized Command Recommendations based on Information Found in Web Documentation

abstract: Prior work on command recommendations for feature-rich software has relied on data supplied by a large community of users to generate personalized recommendations. In this work, we explore the feasibility of using an alternative data source: web documentation. Specifically, our approach uses QF-Graphs, a previously proposed technique that maps higher-level tasks (i.e., search queries) to commands referenced in online documentation. Our approach uses these command-to-task mappings as an automatically generated plan library, enabling our prototype system to make personalized recommendations for task-relevant commands. Through both offline and online evaluations, we explore potential benefits and drawbacks of this approach.


title: Low-friction tendon-driven robot hand with carpal tunnel mechanism in the palm by optimal 3D allocation of pulleys

abstract: Underactuated hands usually have high adaptability in power grasping but they are limited in pinching task with fingertip. In this paper, we propose the design of a tendon-driven underactuated hand that is capable of fingertip pinching by utilizing our proposed coupling mechanism. To reduce the friction resulting from tendon routing, we introduce the carpal tunnel mechanism that replace all sliding-contact tendon routing with the pulley system allocated in 3-dimensional space. The prototype of 11-DOF anthropomorphic robot hand is fabricated using rapid prototyping. Experiments are done to prove the effectiveness of our proposed coupling mechanism and low-friction tendon-driven system for underactuated robot hand.


title: The Human BNST: Functional Role in Anxiety and Addiction

abstract: The consequences of chronic stress on brain structure and function are far reaching. Whereas stress can produce short-term adaptive changes in the brain, chronic stress leads to long-term maladaptive changes that increase vulnerability to psychiatric disorders, such as anxiety and addiction. These two disorders are the most prevalent psychiatric disorders in the United States, and are typically chronic, disabling, and highly comorbid. Emerging evidence implicates a tiny brain region—the bed nucleus of the stria terminalis (BNST)—in the body’s stress response and in anxiety and addiction. Rodent studies provide compelling evidence that the BNST plays a central role in sustained threat monitoring, a form of adaptive anxiety, and in the withdrawal and relapse stages of addiction; however, little is known about the role of BNST in humans. Here, we review current evidence for BNST function in humans, including evidence for a role in the production of both adaptive and maladaptive anxiety. We also review preliminary evidence of the role of BNST in addiction in humans. Together, these studies provide a foundation of knowledge about the role of BNST in adaptive anxiety and stress-related disorders. Although the field is in its infancy, future investigations of human BNST function have tremendous potential to illuminate mechanisms underlying stress-related disorders and identify novel neural targets for treatment.


title: Social Learning Theory of Aggression

abstract: “The massive threats to human welfare are generally brought about by deliberate acts…, It is the principled resort to aggression that is of greatest social concern but most ignored in psychological theorizing and research.”


title: Optimal Tuning of PID Parameters Using Iterative Learning Approach

abstract: PID is the most predominant industrial controller that constitutes more than 90% feedback loops. Time domain performance of PID, including overshoot, settling time and rise time, is directly relevant to the tuning of PID parameters. In this work we propose an optimal tuning method for PID by means of iterative learning. PID parameters will be updated whenever the same control task is repeated. A novel property of the new tuning method is that the time domain performance can be incorporated directly into the objective function to be minimized. Another novel property is that the optimal tuning does not require as much the process model knowledge as other PID tuning methods. The new tuning method is essentially applicable to any processes that are stabilizable by the PID controller.


Incorporating genetics into the identification and treatment of Idiopathic Pulmonary Fibrosis


title: Maintaining views incrementally

abstract: We present incremental evaluation algorithms to compute changes to materialized views in relational and deductive database systems, in response to changes (insertions, deletions, and updates) to the relations. The view definitions can be in SQL or Datalog, and may use UNION, negation, aggregation ( e.g. SUM, MIN), linear recursion, and general recursion. We first present a counting algorithm that tracks the number of alternative derivations (counts) for each derived tuple in a view. The algorithm works with both set and duplicate semantics. We present the algorithm for nonrecursive views (with negation and aggregation), and show that the count for a tuple can be computed at little or no cost above the cost of deriving the tuple. The algorithm is optimal in that it computes exactly those view tuples that are inserted or deleted. Note that we store only the number of derivations, not the derivations themselves. We then present the Delete and Rederive algorithm, DRed, for incremental maintenance of recursive views (negation and aggregation are permitted). The algorithm works by first deleting a superset of the tuples that need to be deleted, and then rederiving some of them. The algorithm can also be used when the view definition is itself altered.


Non-Invasive Glucose Monitoring: A Review of Challenges and Recent Advances


title: Multiple Forecasting Algorithms for Demand Forecasting in the Fashion Industry

abstract: Due to the global economic crises, demand forecasting is becoming a critical issue to assure high level customers' service and strategies effectiveness. This is even more important for the fashion industry were forecasts are used both for yearly demand estimations and for real-time forecasts. Yearly estimations are usually important because it is difficult in terms of production rate to face demand deviation in short time and, to this end, they allow increasing the responsiveness of the system. Also real time estimations are important for implementing better transshipment policies between stores belonging to the same supply chain echelon. In this paper the problem of demand forecasting in the fashion industry is approached from a different point of view. In particular, a forecasting model based on the use of multiple autoregressive algorithms and disaggregation policies is proposed. A case study showcases the validity of the proposed algorithms by using a Modeling a Simulation based approach.


title: A balanced-fed 45° linearly polarized slot array antenna using SIW technology

abstract: A 45° linearly polarized slot array antenna with balanced feeding network is proposed in this paper for automotive radar applications. The radiation element consists of one 45° inclined slot and two reflection cancellation vias. The balanced meandering feeding network proposed in this paper is able to achieve a relatively wide impedance bandwidth resulting from the traveling-wave propagation. In addition, it can obtain a wide gain bandwidth and a stable broadside radiation as the standing-wave array antennas do. A prototype array antenna with 4 radiation elements is designed and fabricated at 24 GHz to validate the proposed design. The experimental results agree well with simulation predictions. The prototype array antenna can properly work from 23.9 GHz to 25.0 GHz (4.5 %) with its reflection coefficient under −10 dB. Besides, the gain bandwidth of the prototype antenna is 23.9 GHz- 25 GHz (4.5 %) with the gain dropped by 1 dB. The maximum gain of the prototype array is around 10.81 dBi at 24.7 GHz.


title: Testing an intervention for preventing osteoporosis in postmenopausal breast cancer survivors.

abstract: PURPOSE ::: To test a 12-month multicomponent intervention for preventing or treating osteoporosis in 21 postmenopausal women who had completed treatment (except Tamoxifen) for breast cancer, and for whom hormone replacement therapy (HRT) was contraindicated. ::: ::: ::: DESIGN ::: Pilot intervention study. ::: ::: ::: METHODS ::: The intervention consisted of home-based strength and weight training exercises, 5 or 10 mg alendronate per day, 1500 mg calcium per day, 400 IU vitamin D per day, education on osteoporosis, and facilitative strategies to promote adherence to the intervention. Outcome measures were: adherence to the intervention, dynamic balance, muscle strength, and bone mineral density (BMD) of the hip, spine, and forearm. ::: ::: ::: FINDINGS AND CONCLUSIONS ::: Adherence to calcium, vitamin D, and alendronate therapy was above 95%, and adherence to strength training exercises was above 85%. Over the 12 months, the 21 participants had significant improvements in dynamic balance, muscle strength for hip flexion, hip extension, and knee flexion, and BMD of the spine and hip. Participants had a significant decrease in BMD of the forearm. Three of the 21 women who had measurable bone loss at baseline had normal BMD after 12 months of the intervention.


title: Poaching Detection Technologies—A Survey

abstract: Between 1960 and 1990, 95% of the black rhino population in the world was killed. In South Africa, a rhino was killed every 8 h for its horn throughout 2016. Wild animals, rhinos and elephants, in particular, are facing an ever increasing poaching crisis. In this paper, we review poaching detection technologies that aim to save endangered species from extinction. We present requirements for effective poacher detection and identify research challenges through the survey. We describe poaching detection technologies in four domains: perimeter based, ground based, aerial based, and animal tagging based technologies. Moreover, we discuss the different types of sensor technologies that are used in intruder detection systems such as: radar, magnetic, acoustic, optic, infrared and thermal, radio frequency, motion, seismic, chemical, and animal sentinels. The ultimate long-term solution for the poaching crisis is to remove the drivers of demand by educating people in demanding countries and raising awareness of the poaching crisis. Until prevention of poaching takes effect, there will be a continuous urgent need for new (combined) approaches that take up the research challenges and provide better protection against poaching in wildlife areas.


title: NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles

abstract: It has been shown that most machine learning algorithms are susceptible to adversarial perturbations. Slightly perturbing an image in a carefully chosen direction in the image space may cause a trained neural network model to misclassify it. Recently, it was shown that physical adversarial examples exist: printing perturbed images then taking pictures of them would still result in misclassification. This raises security and safety concerns. ::: However, these experiments ignore a crucial property of physical objects: the camera can view objects from different distances and at different angles. In this paper, we show experiments that suggest that current constructions of physical adversarial examples do not disrupt object detection from a moving platform. Instead, a trained neural network classifies most of the pictures taken from different distances and angles of a perturbed image correctly. We believe this is because the adversarial property of the perturbation is sensitive to the scale at which the perturbed picture is viewed, so (for example) an autonomous car will misclassify a stop sign only from a small range of distances. ::: Our work raises an important question: can one construct examples that are adversarial for many or most viewing conditions? If so, the construction should offer very significant insights into the internal representation of patterns by deep networks. If not, there is a good prospect that adversarial examples can be reduced to a curiosity with little practical impact.


title: Online Sentence Novelty Scoring for Topical Document Streams

abstract: The enormous amount of information on the Internet has raised the challenge of highlighting new information in the context of already viewed content. This type of intelligent interface can save users time and prevent frustration. Our goal is to scale up novelty detection to large web properties like Google News and Yahoo News. We present a set of lightweight features for online novelty scoring and fast nonlinear feature transformation methods. Our experimental results on the TREC 2004 shared task datasets show that the proposed method is not only efficient but also very powerful, significantly surpassing the best system at TREC 2004.


title: Graph-based multimodal clustering for social multimedia

abstract: Real world datasets often consist of data expressed through multiple modalities. Clustering such datasets is in most cases a challenging task as the involved modalities are often heterogeneous. In this paper we propose a graph-based multimodal clustering approach. The proposed approach utilizes an example relevant clustering in order to learn a model of the "same cluster" relationship between a pair of items. This model is subsequently used in order to organize the items of the collection to be clustered in a graph, where the nodes represent the items and a link between a pair of nodes exists if the model predicted that the corresponding pair of items belong to the same cluster. Eventually, a graph clustering algorithm is applied on the graph in order to produce the final clustering. The proposed approach is applied on two problems that are typically treated using clustering techniques; in particular, it is applied on the problem of detecting social events and to the problem of discovering different landmark views in collections of social multimedia.


title: IMC-C225, an anti-epidermal growth factor receptor monoclonal antibody, for treatment of head and neck cancer

abstract: Squamous cell carcinoma (SCC) of the head and neck (HN increased expression is associated with enhanced tumour invasion, resistance to chemotherapy and decreased patient survival. Several approaches have been developed to achieve EGFR blockade as an anticancer treatment strategy, including an anti-EGFR monoclonal antibody (mAb), IMC-C225, which competetively binds to the extracellular receptor site to prevent binding by natural EGFR ligands (EGF and TGF-α). Preclinical studies evaluating this chimeric mAb in human cancer cell lines in vitro and human tumour xenografts in vivo have demonstrated its potent antitumour activity. The clinical efficacy of IMC-C225 appears to involve multiple anticancer mechanisms, including inhibition of cell cycle progression, ...


title: Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views

abstract: Object viewpoint estimation from 2D images is an essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, and a lack of powerful features. Inspired by the growing availability of 3D models, we propose a framework to address both issues by combining render-based image synthesis and CNNs (Convolutional Neural Networks). We believe that 3D models have the potential in generating a large number of images of high variation, which can be well exploited by deep CNN with a high learning capacity. Towards this goal, we propose a scalable and overfit-resistant image synthesis pipeline, together with a novel CNN specifically tailored for the viewpoint estimation task. Experimentally, we show that the viewpoint estimation from our pipeline can significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark.


title: A Deep Semi-NMF Model for Learning Hidden Representations

abstract: Semi-NMF is a matrix factorization technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original features contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies can not interpret. In this work we propose a novel model, Deep Semi-NMF, that is able to learn such hidden representations that allow themselves to an interpretation of clustering according to different, unknown attributes of a given dataset. We show that by doing so, our model is able to learn low-dimensional representations that are better suited for clustering, outperforming Semi-NMF, but also other NMF variants.


title: User Satisfaction: An Overarching Measure of Enterprise System Success

abstract: User Satisfaction is one of the most extensively used dimensions for Information Systems (IS) success evaluation with a large body of literature and standardized instruments of User Satisfaction. Despite the extensive literature on User Satisfaction, there exist much controversy over the measures of User Satisfaction and the adequacy of User Satisfaction measures to gauge the level of success in complex, contemporary IS. Recent studies in IS have suggested treating User Satisfaction as an overarching construct of success, rather than a measure of success. Further perplexity is introduced over the alleged overlaps between User Satisfaction measures and the measures of IS success (e.g. system quality, information quality) suggested in the literature. The following study attempts to clarify the aforementioned confusions by gathering data from 310 Enterprise System users and analyzing 16 User Satisfaction instruments. The statistical analysis of the 310 responses and the content analysis of the 16 instruments suggest the appropriateness of treating User Satisfaction as an overarching measure of success rather a dimension of success. ::: Keywords: User Satisfaction, Enterprise Systems, Information System Success


title: Incremental Graph Computations: Doable and Undoable

abstract: The incremental problem for a class Q of graph queries aims to compute, given a query Q in 'Q, graph G, output Q(G) and updates Δ G to G as input, changes Δ O to Q(G) such that Q(G ⊕ Δ G) = Q(G) ⊕ Δ O. It is called bounded if its cost can be expressed as a polynomial function in the sizes of Q, Δ G and Δ O. It is to reduce computations on possibly big G to small Δ G and Δ O. No matter how desirable, however, our first results are negative: for common graph queries such as graph traversal, connectivity, keyword search and pattern matching, their incremental problems are unbounded. In light of the negative results, we propose two characterizations for the effectiveness of incremental computation: (a) localizable, if its cost is decided by small neighbors of nodes in Δ G instead of the entire G; and (b) bounded relative to a batch algorithm T, if the cost is determined by the sizes of Δ G and changes to the affected area that is necessarily checked by T. We show that the incremental computations above are either localizable or relatively bounded, by providing corresponding incremental algorithms. That is, we can either reduce the incremental computations on big graphs to small data, or incrementalize batch algorithms by minimizing unnecessary recomputation. Using real-life graphs, we experimentally verify the effectiveness of our algorithms.


title: Value-aware Quantization for Training and Inference of Neural Networks

abstract: We propose a novel value-aware quantization which applies aggressively reduced precision to the majority of data while separately handling a small amount of large data in high precision, which reduces total quantization errors under very low precision. We present new techniques to apply the proposed quantization to training and inference. The experiments show that our method with 3-bit activations (with 2% of large ones) can give the same training accuracy as full-precision one while offering significant (41.6% and 53.7%) reductions in the memory cost of activations in ResNet-152 and Inception-v3 compared with the state-of-the-art method. Our experiments also show that deep networks such as Inception-v3, ResNet-101 and DenseNet-121 can be quantized for inference with 4-bit weights and activations (with 1% 16-bit data) within 1% top-1 accuracy drop.


title: FingerPing: Recognizing Fine-grained Hand Poses using Active Acoustic On-body Sensing

abstract: FingerPing is a novel sensing technique that can recognize various fine-grained hand poses by analyzing acoustic resonance features. A surface-transducer mounted on a thumb ring injects acoustic chirps (20Hz to 6,000Hz) to the body. Four receivers distributed on the wrist and thumb collect the chirps. Different hand poses of the hand create distinct paths for the acoustic chirps to travel, creating unique frequency responses at the four receivers. We demonstrate how FingerPing can differentiate up to 22 hand poses, including the thumb touching each of the 12 phalanges on the hand as well as 10 American sign language poses. A user study with 16 participants showed that our system can recognize these two sets of poses with an accuracy of 93.77% and 95.64%, respectively. We discuss the opportunities and remaining challenges for the widespread use of this input technique.


title: Strengthening concept learning by repeated testing

abstract: The aim of this study was to examine whether repeated testing with feedback benefits learning compared to rereading of introductory psychology key-concepts in an educational context. The testing ef ...


title: Ciphertext-policy attribute-based encryption: an expressive, efficient, and provably secure realization

abstract: We present a new methodology for realizing Ciphertext-Policy Attribute Encryption (CP-ABE) under concrete and noninteractive cryptographic assumptions in the standard model. Our solutions allow any encryptor to specify access control in terms of any access formula over the attributes in the system. In our most efficient system, ciphertext size, encryption, and decryption time scales linearly with the complexity of the access formula. The only previous work to achieve these parameters was limited to a proof in the generic group model. ::: ::: We present three constructions within our framework. Our first system is proven selectively secure under a assumption that we call the decisional Parallel Bilinear Diffie-Hellman Exponent (PBDHE) assumption which can be viewed as a generalization of the BDHE assumption. Our next two constructions provide performance tradeoffs to achieve provable security respectively under the (weaker) decisional Bilinear-Diffie-Hellman Exponent and decisional Bilinear Diffie-Hellman assumptions.


title: A Survey on Heterogeneous Face Recognition: Sketch, Infra-red, 3D and Low-resolution

abstract: Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research. Display Omitted Provide a comprehensive review of established techniques in HFRProvide a thorough review of recent developments in HFROffer a detailed account of datasets and benchmarks commonly used for evaluationAssess the state of the field and discuss promising directions for future research


title: Global Analysis of a Macromolecular Interaction Measured on BIAcore

abstract: Abstract We demonstrate that the interaction between myoglobin and an immobilized anti-myoglobin antibody measured on BIAcore 2000 can be described by a simple bimolecular reaction mechanism. We improved the quality of the sensor data by correcting for refractive index changes and nonspecific binding using a blank sensor surface. Applying nonlinear least squares analysis, we simultaneously fitted the association and dissociation phase data generated for a range of myoglobin concentrations injected across the antibody surface. The ability to globally fit these data to a simple binding model indicates that effects related to the sensor surface, like mass transport and the dextran matrix, did not complicate the observed binding responses. These results illustrate the potential of BIAcore to monitor macromolecular interactions in real-time and the utility of global analysis to resolve the reaction kinetics.


title: Neuropsychological Status of Heroin Users Undergoing Methadone Maintenance in Harm Reduction Program and Therapeutic Community

abstract: The neuropsychological consequences of opioid abuse are particularly evident in attention, memory and executive functioning, but it remains unclear whether these consequences persist in heroin users doing methadone in harm reduction programs and therapeutic community treatments. Thus, the current study aimed to assess these cognitive domains in distinct clinical groups of heroin users undergoing methadone maintenance. The sample consisted of 110 participants divided in four groups (low threshold methadone program, short-term community treatment, long-term community treatment, drug-free controls). These groups were compared regarding memory and attentional abilities. Multiple linear regressions were then conducted to obtain standardized effect sizes for significant comparisons. Results showed a better attentional and memory function in patients that were in opioid dependence treatment in community opposed to patients in harm reduction programs (p’s < 0.05). Standardized effect sizes suggest larger improvements in cognition in long-term heroin-abstinent individuals doing methadone maintenance. These results highlight the detrimental effect of heroin use in cognitive function, but also suggest that this decrement may be reversed during long-term opioid dependence treatment.


title: LR-CNN for fine-grained classification with varying resolution

abstract: In this work, we present an extended study of image representations for fine-grained classification with respect to image resolution. Understudied in literature, this parameter yet presents many practical and theoretical interests, e.g. in embedded systems where restricted computational resources prevent treating high-resolution images. It is thus interesting to figure out which representation provides the best results in this particular context. On this purpose, we evaluate Fisher Vectors and deep representations on two significant fine-grained oriented datasets: FGVC Aircraft and PPMI. We also introduce LR-CNN, a deep structure designed for classification of low-resolution images with strong semantic content. This net provides rich compact features and outperforms both pre-trained deep features and Fisher Vectors.


title: Rhytisma polaris: morphological and molecular characterization of a new species from Spitsbergen Island, Norway

abstract: Rhytisma polaris, which causes tar spot disease on Salix polaris on Spitsbergen Island, is described. The most characteristic morphological feature of this new species are ascospores distinctly broader than those of other Rhytisma species. rDNA ITS and LSU sequence analysis also indicated that R. polaris is sufficiently distinct from other Rhytisma species to justify the new species status.


A MultiView Approach for Formalizing UML State Machine Diagrams Using Z Notation


title: Super-Resolution Method for Face Recognition Using Nonlinear Mappings on Coherent Features

abstract: Low-resolution (LR) of face images significantly decreases the performance of face recognition. To address this problem, we present a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analysis (PCA) based features of high-resolution (HR) and LR face images. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the PCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the Facial Recognition Technology, University of Manchester Institute of Science and Technology, and Olivetti Research Laboratory databases show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.


title: COBRA framework to evaluate e-government services: A citizen-centric perspective

abstract: PIAP-GA-2008-230658) from the European Union Framework Program and another grant (NPRP 09-1023-5-158) from the Qatar National Research Fund (amember of Qatar Foundation)


title: Decision Making In The ERP Community

abstract: This paper, although bibliographical in nature, stresses the importance of researching ERP from the perspective of the ERP community, defined here as a triadic group composed of (1) an ERP vendor, (2) an ERP consultant and (3) an implementing organisation; and focuses on understanding the relationships and inter-dependencies that exist between these actors. In this novel perspective, the second contribution of this paper is to introduce the concept of category manipulation – that is to say non-decision making - to the area of IS in general and more specifically to ERP research. As far as the authors are aware, this paper is the first to examine the ERP decision making process under this light. The researchers' objective is to structure their ideas in an effort to lay the foundations for a model of ERP decision making that can inform both the practice and investigation of ERP implementation.


title: Detecting credit card fraud by ANN and logistic regression

abstract: With the developments in information technology and improvements in communication channels, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS terminals through Internet or mail orders. As a result, fraud detection is the essential tool and probably the best way to stop such fraud types. In this study, classification models based on Artificial Neural Networks (ANN) and Logistic Regression (LR) are developed and applied on credit card fraud detection problem. This study is one of the firsts to compare the performance of ANN and LR methods in credit card fraud detection with a real data set.


UvA-DARE ( Digital Academic Repository ) Coevolution of Lexical Meaning and Pragmatic Use


title: Heimdall: Mitigating the Internet of Insecure Things

abstract: The Internet of Things (IoT) is built of many small smart objects continuously connected to the Internet. This makes these devices an easy target for attacks exploiting vulnerabilities at the network, application, and mobile level. With that it comes as no surprise that distributed denial of service attacks leveraging these vulnerable devices have become a new standard for effective botnets. In this paper, we propose Heimdall, a whitelist-based intrusion detection technique tailored to IoT devices. Heimdall operates on routers acting as gateways for IoT as a homogeneous defense for all devices behind the router. Our experimental results show that our defense mechanism is effective and has minimal overhead.


title: Electric vehicles : How much range is required for a day ’ s driving ?

abstract: Abstract One full year of high-resolution driving data from 484 instrumented gasoline vehicles in the US is used to analyze daily driving patterns, and from those infer the range requirements of electric vehicles (EVs). We conservatively assume that EV drivers would not change their current gasoline-fueled driving patterns and that they would charge only once daily, typically at home overnight. Next, the market is segmented into those drivers for whom a limited-range vehicle would meet every day’s range need, and those who could meet their daily range need only if they make adaptations on some days. Adaptations, for example, could mean they have to either recharge during the day, borrow a liquid-fueled vehicle, or save some errands for the subsequent day. From this analysis, with the stated assumptions, we infer the potential market share for limited-range vehicles. For example, we find that 9% of the vehicles in the sample never exceeded 100 miles in one day, and 21% never exceeded 150 miles in one day. These drivers presumably could substitute a limited-range vehicle, like electric vehicles now on the market, for their current gasoline vehicle without any adaptation in their driving at all. For drivers who are willing to make adaptations on 2 days a year, the same 100 mile range EV would meet the needs of 17% of drivers, and if they are willing to adapt every other month (six times a year), it would work for 32% of drivers. Thus, it appears that even modest electric vehicles with today’s limited battery range, if marketed correctly to segments with appropriate driving behavior, comprise a large enough market for substantial vehicle sales. An additional analysis examines driving versus parking by time of day. On the average weekday at 5 pm, only 15% of the vehicles in the sample are on the road; at no time during the year are fewer than 75% of vehicles parked. Also, because the return trip home is widely spread in time, even if all cars plug in and begin charging immediately when they arrive home and park, the increased demand on the electric system is less problematic than prior analyses have suggested.


title: PMLAB: An Scripting Environment for Process Mining

abstract: In a decade of process mining research, several algorithms have been proposed to solve particular process mining tasks. At the same pace, tools have appeared both in the academic and the commercial domains. These tools have enabled the use of process mining practices to a rather limited extent. In this paper we advocate for a change in the mentality: process mining may be an exploratory discipline, and PMLAB - a Python-based scripting environment supporting this - is proposed. This demo presents the main features of the PMLAB environment.


title: Homogenization of metamaterials by field averaging (invited paper)

abstract: Over the past several years, metamaterials have been introduced and rapidly been adopted as a means of achieving unique electromagnetic material response. In metamaterials, artificially structured—often periodically positioned—inclusions replace the atoms and molecules of conventional materials. The scale of these inclusions is smaller than that of the electromagnetic wavelength of interest, so that a homogenized description applies. We present a homogenization technique in which macroscopic fields are determined via averaging the local fields obtained from a full-wave electromagnetic simulation or analytical calculation. The field-averaging method can be applied to homogenize any periodic structure with unit cells having inclusions of arbitrary geometry and material. By analyzing the dispersion diagrams and retrieved parameters found by field averaging, we review the properties of several basic metamaterial structures. © 2006 Optical Society of America OCIS codes: 160.0160, 160.1190, 260.2110, 350.5500.


title: Superpixel-based image recognition for food images

abstract: Food is an inseparable part of people's lives. Food image recognition has been attracting increasing attention due to the advances of Internet, imaging techniques and social media. Approaches for food image recognition are mainly focused on two main directions: low-level approaches and mid-level approaches. Low-level approaches extract low-level local features, such as SIFT or SURF, following feature encoding techniques. Mid-level approaches extract higher-level image parts and have shown promising results in many recognition problems. Compared with other image recognition problems, food images are highly deformable with large intra-class variance and small between-class variance. In this paper, considering that mid-level approaches' superior performance and superpixels segmentation methods' ability to successfully segment food parts, we propose a superpixel-based food image recognition framework to mine mid-level superpixel food parts-to-class similarity. We evaluate the proposed framework on UEC Food dataset, and show promising results when compared with existing state-of-the-art methods.


title: End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies

abstract: Steering a car through traffic is a complex task that is difficult to cast into algorithms. Therefore, researchers turn to training artificial neural networks from front-facing camera data stream along with the associated steering angles. Nevertheless, most existing solutions consider only the visual camera frames as input, thus ignoring the temporal relationship between frames. In this work, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (C-LSTM), that is end-to-end trainable, to learn both visual and dynamic temporal dependencies of driving. Additionally, We introduce posing the steering angle regression problem as classification while imposing a spatial relationship between the output layer neurons. Such method is based on learning a sinusoidal function that encodes steering angles. To train and validate our proposed methods, we used the publicly available Comma.ai dataset. Our solution improved steering root mean square error by 35% over recent methods, and led to a more stable steering by 87%.


title: Using Data Analytics to Detect Anomalous States in Vehicles

abstract: Vehicles are becoming more and more connected, this opens up a larger attack surface which not only affects the passengers inside vehicles, but also people around them. These vulnerabilities exist because modern systems are built on the comparatively less secure and old CAN bus framework which lacks even basic authentication. Since a new protocol can only help future vehicles and not older vehicles, our approach tries to solve the issue as a data analytics problem and use machine learning techniques to secure cars. We develop a Hidden Markov Model to detect anomalous states from real data collected from vehicles. Using this model, while a vehicle is in operation, we are able to detect and issue alerts. Our model could be integrated as a plug-n-play device in all new and old cars.


title: Automatic Evaluation of Neural Personality-based Chatbots

abstract: Stylistic variation is critical to render the utterances generated by conversational agents natural and engaging. In this paper, we focus on sequence-to-sequence models for open-domain dialogue response generation and propose a new method to evaluate the extent to which such models are able to generate responses that reflect different personality traits.


title: Components, Middleware and Web Services

abstract: Web services are a logical evolution of software components and middleware. Based on a comparison of Web services with middleware and components we come up with the statement that Web services are components and middleware in one. However, for the Web services to live up to their potential as an integrating technology for mission critical Webbased applications, it is necessary to provide viable coordination and transaction capabilities. We introduce the notion of hyperware for the framework that will play the same role for Web services the middleware plays for components.


title: Predicting Latency of Blockchain-Based Systems Using Architectural Modelling and Simulation

abstract: Blockchain is an emerging technology for sharing transactional data and computation without using a central trusted third party. It is an architectural choice to use a blockchain instead of traditional databases or protocols, and this creates trade-offs between non-functional requirements such as performance, cost, and security. However, little is known about predicting the behaviour of blockchain-based systems. This paper shows the feasibility of using architectural performance modelling and simulation tools to predict the latency of blockchain-based systems. We use established tools and techniques, but explore new blockchain-specific issues such as the configuration of the number of confirmation blocks and inter-block times. We report on a lab-based experimental study using an incident management system, showing predictions of median system level response time with a relative error mostly under 10%. We discuss how the approach can be used to support architectural decision-making, during the design of blockchain-based systems.


title: Impacts of Microplastics on the Soil Biophysical Environment

abstract: Soils are essential components of terrestrial ecosystems that experience strong pollution pressure. Microplastic contamination of soils is being increasingly documented, with potential consequences for soil biodiversity and function. Notwithstanding, data on effects of such contaminants on fundamental properties potentially impacting soil biota are lacking. The present study explores the potential of microplastics to disturb vital relationships between soil and water, as well as its consequences for soil structure and microbial function. During a 5-weeks garden experiment we exposed a loamy sand soil to environmentally relevant nominal concentrations (up to 2%) of four common microplastic types (polyacrylic fibers, polyamide beads, polyester fibers, and polyethylene fragments). Then, we measured bulk density, water holding capacity, hydraulic conductivity, soil aggregation, and microbial activity. Microplastics affected the bulk density, water holding capacity, and the functional relationship between the microbial activity and water stable aggregates. The effects are underestimated if idiosyncrasies of particle type and concentrations are neglected, suggesting that purely qualitative environmental microplastic data might be of limited value for the assessment of effects in soil. If extended to other soils and plastic types, the processes unravelled here suggest that microplastics are relevant long-term anthropogenic stressors and drivers of global change in terrestrial ecosystems.


title: Robust Motion Segmentation with Unknown Correspondences

abstract: Motion segmentation can be addressed as a subspace clustering problem, assuming that the trajectories of interest points are known. However, establishing point correspondences is in itself a challenging task. Most existing approaches tackle the correspondence estimation and motion segmentation problems separately. In this paper, we introduce an approach to performing motion segmentation without any prior knowledge of point correspondences. We formulate this problem in terms of Partial Permutation Matrices (PPMs) and aim to match feature descriptors while simultaneously encouraging point trajectories to satisfy subspace constraints. This lets us handle outliers in both point locations and feature appearance. The resulting optimization problem can be solved via the Alternating Direction Method of Multipliers (ADMM), where each subproblem has an efficient solution. Our experimental evaluation on synthetic and real sequences clearly evidences the benefits of our formulation over the traditional sequential approach that first estimates correspondences and then performs motion segmentation.


title: Can affective factors contribute to explain continuance intention of web-based services?

abstract: More than a decade has been passed since the birth of web-based services as novel network-based services. To explain why and how consumers are motivated to accept the web-based services, lots of technology acceptance theories such as technology acceptance model (TAM) and expectation-confirmation model (ECM) have been successfully proposed. However, the models do not fully explain why and how the consumers continue to use a specific web-based service. Meanwhile, affective factors such as intimacy have been regarded as essential factors for strengthening human relationships in consumer behavior. If a sort of relationships between consumers and web-based services has been built up due to repetitive usage, then we could assume that affective, as well as cognitive, factors may contribute to explain the consumers' continuous use. In light of this assumption, legacy ECM and human relationship-related theoretical models which include affective factors may be integrated for better explanation. Hence, the purpose of this paper is to propose an extended ECM which contains affective factors, as well as cognitive factors, related to maintaining relationship between consumers and service providers to explain why the consumers continue to use a specific information system. In this study, we mainly focus on two new constructs, familiarity and intimacy, and seek to examine how cognitive and affective factors are inter-related toward continuance intention. Moderating effects of emotion to alternatives, familiarity with alternative and intimacy with alternative, to IS continuance intention are also considered.


title: A spelling device for the paralysed

abstract: When Jean-Dominique Bauby suffered from a cortico-subcortical stroke that led to complete paralysis with totally intact sensory and cognitive functions, he described his experience in The Diving-Bell and the Butterfly1 as “something like a giant invisible diving-bell holds my whole body prisoner”. This horrifying condition also occurs as a consequence of a progressive neurological disease, amyotrophic lateral sclerosis, which involves progressive degeneration of all the motor neurons of the somatic motor system. These ‘locked-in’ patients ultimately become unable to express themselves and to communicate even their most basic wishes or desires, as they can no longer control their muscles to activate communication devices. We have developed a new means of communication for the completely paralysed that uses slow cortical potentials (SCPs) of the electro-encephalogram to drive an electronic spelling device.