title: Development of quadruped walking robot with spherical shell-mechanical design for rotational locomotion

abstract: We have proposed and developed a new quadruped walking robot with a spherical shell, called “QRoSS”. QRoSS is a transformable robot that can store its legs in the spherical shell. The shell not only absorbs external forces from all directions, but also improves mobile performance because of its round shape. In rescue operations at a disaster site, carrying robots into a site is dangerous for operators because doing so may result in a second accident. If QRoSS is used, instead of carrying robots in, they are thrown in, making the operation safe and easy. We developed QRoSS-I and conducted basic experiments to verify performance which includes landing, rising and walking through a series of movements. This paper reports details of a mechanical design to perform a rotational locomotion using spherical shell and development of the prototype model of QRoSS-II. We discuss the rising motion and the rotational locomotion from the results of Basic experiments.


title: Data-free parameter pruning for Deep Neural Networks

abstract: Deep Neural nets (NNs) with millions of parameters are at the heart of many state-of-the-art computer vision systems today. However, recent works have shown that much smaller models can achieve similar levels of performance. In this work, we address the problem of pruning parameters in a trained NN model. Instead of removing individual weights one at a time as done in previous works, we remove one neuron at a time. We show how similar neurons are redundant, and propose a systematic way to remove them. Our experiments in pruning the densely connected layers show that we can remove upto 85\% of the total parameters in an MNIST-trained network, and about 35\% for AlexNet without significantly affecting performance. Our method can be applied on top of most networks with a fully connected layer to give a smaller network.


Chukwa: A large-scale monitoring system


title: Scenario-Based Digital Forensics Challenges in Cloud Computing

abstract: The aim of digital forensics is to extract information to answer the 5Ws (Why, When, Where, What, and Who) from the data extracted from the evidence. In order to achieve this, most digital forensic processes assume absolute control of digital evidence. However, in a cloud environment forensic investigation, this is not always possible. Additionally, the unique characteristics of cloud computing create new technical, legal and architectural challenges when conducting a forensic investigation. We propose a hypothetical scenario to uncover and explain the challenges forensic practitioners face during cloud investigations. Additionally, we also provide solutions to address the challenges. Our hypothetical case scenario has shown that, in the long run, better live forensic tools, development of new methods tailored for cloud investigations and new procedures and standards are indeed needed. Furthermore, we have come to the conclusion that forensic investigations biggest challenge is not technical but legal.


title: RNA Binding Properties of Bunyamwera Virus Nucleocapsid Protein and Selective Binding to an Element in the 5' Terminus of the Negative-Sense S Segment

abstract: Bunyamwera virus (BUN) is the prototype of the genus Bunyavirus and the family Bunyaviridae and possesses a single-stranded negative-sense tripartite RNA genome. The three RNA segments, termed L (large), M (medium), and S (small), encode six proteins. The L segment codes for the L protein, the viral RNA-dependent RNA polymerase, which is responsible for both transcribing and replicating the genome RNAs. The M segment encodes the two virion glycoproteins, G1 and G2, and a nonstructural protein, NSm, as a polyprotein which is probably cotranslationally cleaved by host proteases. The S segment encodes the nucleocapsid (N) protein and, in an overlapping reading frame, a second nonstructural protein called NSs (reviewed in reference 8). ::: ::: In common with other negative-sense RNA viruses, the bunyavirus genome RNA segments are replicated via full-length cRNAs termed antigenomes. Both the negative-sense genome and positive-sense antigenome RNAs are encapsidated by the N protein and are associated with the viral polymerase in ribonucleoprotein complexes called nucleocapsids. It is only within the nucleocapsid that the RNA is transcriptionally active. Bunyavirus genome and antigenome RNAs contain highly conserved, complementary terminal sequences that may form panhandle structures in vivo and are probably responsible for the circular appearance of isolated nucleocapsids (19, 20, 22, 26, 28). ::: ::: Full-length genome and antigenome segments are usually the only RNAs that are encapsidated in the infected cell. Viral mRNAs, which are not encapsidated, are truncated at the 3′ end and contain a nontemplated capped primer on the 5′ terminus (2, 4, 9, 14, 21). It is therefore likely that the terminal sequences of the genome and antigenome RNAs are involved in the encapsidation process. This theory is supported by the observation that an antisense chloramphenicol acetyltransferase gene flanked by BUN terminal sequences was successfully encapsidated by N and transcribed by L in an in vivo system (7). Raju and Kolakofsky (25) reported that in infected cells a minority of encapsidated bunyavirus transcripts either had the 3′-terminal truncation similar to mRNA but not the 5′ primer sequence or were full-length transcripts containing a capped primer on the 5′ terminus (26). Taken together, these data suggest that the encapsidation signal is probably within the 5′ terminal sequence of genome and antigenome RNAs. ::: ::: Investigations concerning the N proteins of viruses in the Hantavirus (11, 29) and Tospovirus (27) genera of the Bunyaviridae have shown that binding of the N proteins to RNA in in vitro assays is essentially sequence nonspecific. However, Severson et al. (29) reported hantavirus N protein to have a preference for full-length hantavirus S segment RNA over RNA comprising only internal sequences, and Gott et al. (11) reported hantavirus N to have a preference for double-stranded RNA. ::: ::: The BUN N protein is a 26.7-kDa basic protein of 233 amino acids that does not show any sequence similarity to the hantavirus (ca. 50-kDa) or tospovirus (ca. 29-kDa) N proteins. Like the analogous nucleocapsid proteins of all members of the Bunyaviridae family, it contains no RNA binding consensus sequence (8). In this paper we describe investigations into the binding of bacterially expressed BUN N protein to negative-sense BUN S segment RNA sequences in vitro. We also provide an analysis of the relative selectivity of the binding of N to such sequences with the intent to construct a model for the selective encapsidation of full-length viral genome and antigenome RNA.


title: Pebbles: fine-grained data management abstractions for modern operating systems

abstract: Support for fine-grained data management has all but disappeared from modern operating systems such as Android and iOS. Instead, we must rely on each individual application to manage our data properly - e.g., to delete our emails, documents, and photos in full upon request; to not collect more data than required for its function; and to back up our data to reliable backends. Yet, research studies and media articles constantly remind us of the poor data management practices applied by our applications. We have developed Pebbles, a fine-grained data management system that enables management at a powerful new level of abstraction: application-level data objects, such as emails, documents, notes, notebooks, bank accounts, etc. The key contribution is Pebbles's ability to discover such high-level objects in arbitrary applications without requiring any input from or modifications to these applications. Intuitively, it seems impossible for an OS-level service to understand object structures in unmodified applications, however we observe that the high-level storage abstractions embedded in modern OSes - relational databases and object-relational mappers - bear significant structural information that makes object recognition possible and accurate.


title: Image guided depth enhancement via deep fusion and local linear regularizaron

abstract: Depth maps captured by RGB-D cameras are often noisy and incomplete at edge regions. Most existing methods assume that there is a co-occurrence of edges in depth map and its corresponding color image, and improve the quality of depth map guided by the color image. However, when the color image is noisy or richly detailed, the high frequency artifacts will be introduced into depth map. In this paper, we propose a deep residual network based on deep fusion and local linear regularization for guided depth enhancement. The presented scheme can effectively extract the correlation between depth map and color image in the deep feature space. To reduce the difficulty of training, a specific layer of network which introduces a local linear regularization constraint on the output depth is designed. Experiments on various applications, including depth denoising, super-resolution and inpainting, demonstrate the effectiveness and reliability of our proposed approach.


title: To create or to recall? Neural mechanisms underlying the generation of creative new ideas

abstract: This fMRI study investigated brain activation during creative idea generation using a novel approach allowing spontaneous self-paced generation and expression of ideas. Specifically, we addressed the fundamental question of what brain processes are relevant for the generation of genuinely new creative ideas, in contrast to the mere recollection of old ideas from memory. In general, creative idea generation (i.e., divergent thinking) was associated with extended activations in the left prefrontal cortex and the right medial temporal lobe, and with deactivation of the right temporoparietal junction. The generation of new ideas, as opposed to the retrieval of old ideas, was associated with stronger activation in the left inferior parietal cortex which is known to be involved in mental simulation, imagining, and future thought. Moreover, brain activation in the orbital part of the inferior frontal gyrus was found to increase as a function of the creativity (i.e., originality and appropriateness) of ideas pointing to the role of executive processes for overcoming dominant but uncreative responses. We conclude that the process of idea generation can be generally understood as a state of focused internally-directed attention involving controlled semantic retrieval. Moreover, left inferior parietal cortex and left prefrontal regions may subserve the flexible integration of previous knowledge for the construction of new and creative ideas.


Frozen blood and lessons learned from 9/11


title: Computer vision in automated parking systems: Design, implementation and challenges

abstract: Abstract Automated driving is an active area of research in both industry and academia. Automated parking, which is automated driving in a restricted scenario of parking with low speed manoeuvring, is a key enabling product for fully autonomous driving systems. It is also an important milestone from the perspective of a higher end system built from the previous generation driver assistance systems comprising of collision warning, pedestrian detection, etc. In this paper, we discuss the design and implementation of an automated parking system from the perspective of computer vision algorithms. Designing a low-cost system with functional safety is challenging and leads to a large gap between the prototype and the end product, in order to handle all the corner cases. We demonstrate how camera systems are crucial for addressing a range of automated parking use cases and also, to add robustness to systems based on active distance measuring sensors, such as ultrasonics and radar. The key vision modules which realize the parking use cases are 3D reconstruction, parking slot marking recognition, freespace and vehicle/pedestrian detection. We detail the important parking use cases and demonstrate how to combine the vision modules to form a robust parking system. To the best of the authors' knowledge, this is the first detailed discussion of a systemic view of a commercial automated parking system.


title: Applying Social Network Analysis Techniques to Community-Driven Libre Software Projects

abstract: Source code management repositories of large, long-lived libre (free, open source) software projects can be a source of valuable data about the organizational structure, evolution, and knowledge exchange in the corresponding development communities. Unfortunately, the sheer volume of the available information renders it almost unusable without applying methodologies which highlight the relevant information for a given aspect of the project. Such methodology is proposed in this article, based on well known concepts from the social networks analysis field, which can be used to study the relationships among developers and how they collaborate in different parts of a project. It is also applied to data mined from some well known projects (Apache, GNOME, and KDE), focusing on the characterization of their collaboration network architecture. These cases help to understand the potentials of the methodology and how it is applied, but also shows some relevant results which open new paths in the understanding of the informal organization of libre software development communities.


title: Big Data Analytics with Datalog Queries on Spark

abstract: There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics.


title: The string B-tree: a new data structure for string search in external memory and its applications

abstract: We introduce a new text-indexing data structure, the String B-Tree , that can be seen as a link between some traditional external-memory and string-matching data structures. In a short phrase, it is a combination of B-trees and Patricia tries for internal-node indices that is made more effective by adding extra pointers to speed up search and update operations. Consequently, the String B-Tree overcomes the theoretical limitations of inverted files, B-trees, prefix B-trees, suffix arrays, compacted tries and suffix trees. String B-trees have the same worst-case performance as B-trees but they manage unbounded-length strings and perform much more powerful search operations such as the ones supported by suffix trees. String B-trees are also effective in main memory (RAM model) because they improve the online suffix tree search on a dynamic set of strings. They also can be successfully applied to database indexing and software duplication.


title: A Machine Learning Model for Stock Market Prediction

abstract: Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange.


title: KinectFaceDB: A Kinect Database for Face Recognition

abstract: The recent success of emerging RGB-D cameras such as the Kinect sensor depicts a broad prospect of 3-D data-based computer applications. However, due to the lack of a standard testing database, it is difficult to evaluate how the face recognition technology can benefit from this up-to-date imaging sensor. In order to establish the connection between the Kinect and face recognition research, in this paper, we present the first publicly available face database (i.e., KinectFaceDB 1 ) based on the Kinect sensor. The database consists of different data modalities (well-aligned and processed 2-D, 2.5-D, 3-D, and video-based face data) and multiple facial variations. We conducted benchmark evaluations on the proposed database using standard face recognition methods, and demonstrated the gain in performance when integrating the depth data with the RGB data via score-level fusion. We also compared the 3-D images of Kinect (from the KinectFaceDB) with the traditional high-quality 3-D scans (from the FRGC database) in the context of face biometrics, which reveals the imperative needs of the proposed database for face recognition research. 1 Online at http://rgb-d.eurecom.fr


title: Grounded Analytic Research: Building Theory from a Body of Research

abstract: In this paper, we present Grounded Analytic Research (GAR) as a method to build theory by synthesizing empirical findings from multiple studies that has been conducted by a single researcher. GAR incorporates concepts from grounded theory, analytic research and systematic literature review. The method was applied in a doctoral dissertation work to build the theoretical concept of Demand Sustainability of public Internet access in the context of bridging the digital divide. We describe GAR and compare it with existing theory building methods that are similar to it in epistemology and ontology.


Automatic scraper of celebrity images from heterogeneous websites based on face recognition and sorting for profiling


title: Learning visual similarity for product design with convolutional neural networks

abstract: Popular sites like Houzz, Pinterest, and LikeThatDecor, have communities of users helping each other answer questions about products in images. In this paper we learn an embedding for visual search in interior design. Our embedding contains two different domains of product images: products cropped from internet scenes, and products in their iconic form. With such a multi-domain embedding, we demonstrate several applications of visual search including identifying products in scenes and finding stylistically similar products. To obtain the embedding, we train a convolutional neural network on pairs of images. We explore several training architectures including re-purposing object classifiers, using siamese networks, and using multitask learning. We evaluate our search quantitatively and qualitatively and demonstrate high quality results for search across multiple visual domains, enabling new applications in interior design.


title: Can source code auditing software identify common vulnerabilities and be used to evaluate software security?

abstract: Software vulnerabilities are a growing problem (c.f. MITRE's CVE, http://cve.mitre.org). Moreover, many of the mistakes leading to vulnerabilities are repeated often. Source code auditing tools could be a great help in identifying common mistakes, or in evaluating the security of software. We investigated the effectiveness of the auditing tools we could access, using the following criteria: number of false positives, false negatives by comparison to known vulnerabilities, and time required to validate the warnings related to vulnerabilities. Some of the known vulnerabilities could not be found by any code auditor, because they were fairly unusual or involved knowledge not contained or codified in the source code. The coding problems that could be identified consisted of string format vulnerabilities, buffer overflows, race conditions, memory leaks, and symlink attacks. However, we found it extremely time-consuming to validate warnings related to the latter four types, because the number of false positives was very high, and because it was not easily apparent if they were real vulnerabilities. These required that the code be audited locally, by people familiar with the code, and carefully inspected to see if the values could be manipulated in such a way as to produce malicious effects. However, the string format vulnerabilities were much easier to recognize. In small and medium scale projects, the open source program Pscan was useful in finding a mix of coding style issues that could potentially enable string format vulnerabilities, as well as actual vulnerabilities. The limitations of Pscan were more obvious in large scale projects like OpenBSD, as more false positives occurred. Clearly, auditing source code for all vulnerabilities remains a time-consuming process, even with the help of the current tools, and more research is needed in identifying and avoiding other common mistakes.


title: Notes on Hierarchical Splines, DCLNs and i-theory

abstract: We define an extension of classical additive splines for multivariate function approximation that we call hierarchical splines. We show that the case of hierarchical, additive, piece-wise linear splines includes present-day Deep Convolutional Learning Networks (DCLNs) with linear rectifers and pooling (sum or max). We discuss how these observations together with i-theory may provide a framework for a general theory of deep networks.


title: Design of an RFID antenna gate with multiple loop antennas

abstract: This paper presents the research on the realization of an antenna gate structure for passive radio frequency identification (RFID) systems operating at 13.56 MHz. An optimized configuration of antennas allowing the detection of tags in various directions is described. The reliability of the system is increased by steering the antennas in an appropriate manner. The antennas are not only multiplexed in time but also the phase difference between the antennas is used to shape the magnetic field to detect any transponder within the operating field. Measurements on the proposed antenna gate have shown some promising results. This construction will be used in an identification application of surgical instrument baskets within the central sterilization department of a hospital.


title: REVIEW Quantitative mass spectrometry in proteomics: a critical review

abstract: The quantification of differences between two or more physiological states of a biological system is among the most important but also most challenging technical tasks in proteomics. In addition to the classical methods of differential protein gel or blot staining by dyes and fluorophores, mass-spectrometry-based quantification methods have gained increasing popularity over the past five years. Most of these methods employ differential stable isotope labeling to create a specific mass tag that can be recognized by a mass spectrometer and at the same time provide the basis for quantification. These mass tags can be introduced into proteins or peptides (i) metabolically, (ii) by chemical means, (iii) enzymatically, or (iv) provided by spiked synthetic peptide standards. In contrast, label-free quantification approaches aim to correlate the mass spectrometric signal of intact proteolytic peptides or the number of peptide sequencing events with the relative or absolute protein quantity directly. In this review, we critically examine the more commonly used quantitative mass spectrometry methods for their individual merits and discuss challenges in arriving at meaningful interpretations of quantitative proteomic data.


title: The microbiota-gut-brain axis in obesity.

abstract: Changes in microbial diversity and composition are increasingly associated with several disease states including obesity and behavioural disorders. Obesity-associated microbiota alter host energy harvesting, insulin resistance, inflammation, and fat deposition. Additionally, intestinal microbiota can regulate metabolism, adiposity, homoeostasis, and energy balance as well as central appetite and food reward signalling, which together have crucial roles in obesity. Moreover, some strains of bacteria and their metabolites might target the brain directly via vagal stimulation or indirectly through immune-neuroendocrine mechanisms. Therefore, the gut microbiota is becoming a target for new anti-obesity therapies. Further investigations are needed to elucidate the intricate gut-microbiota-host relationship and the potential of gut-microbiota-targeted strategies, such as dietary interventions and faecal microbiota transplantation, as promising metabolic therapies that help patients to maintain a healthy weight throughout life.


Applying Lewin's Change Model in the Development of a Learning Organization


title: Ant Colony Technique for Optimizing the Order of Cascaded SVM Classifier for Sunflower Seed Classification

abstract: In Seed Testing Laboratory (STL), test for other distinguishable variety (ODV) is carried out for the seed sample received from the seed producer after harvesting. This is done to obtain foundation/certified tag from the seed certification department before marketing the product. This is currently done manually by observing the morphological characteristics of the seeds through the naked eye. This is a time-consuming process for the STL and there is a chance for human error in identifying ODV. Hence, there is the need for the machine vision technique to automate the process of identifying ODV in the seed testing laboratory. Various techniques for varietal identification of sunflower seeds are explored in this paper. Experiments are performed on a dataset that contains ten varieties of sunflower seeds and the success rate achieved using various techniques is reported in this paper. In cascaded support vector machine (SVM), the order in which the classifier blocks are arranged plays an important role in improving the classification rate. The main contribution of this paper lies in the manipulation of ant colony optimization technique for obtaining the order of cascaded SVM by maximizing the total probability of correct decision. When the SVM is cascaded in optimum order, classification rate has been increased from $88.32\%$ (obtained using the actual order) to $98.82\%$ for kernel linear discriminant analysis based boundary descriptors. The closed form expression for computing the total probability of correct detection of the constructed cascaded SVM classifier is also reported in this paper.


title: Scraping the ACM Digital Library

abstract: As part of a larger project to automatically reference link the online scholarly literature, an attempt to analyze PDF documents was undertaken. The ACM Digital Library was used as the corpus for these experiments. With the current PDF and HTML analysis tools, roughly 80% accuracy was obtained in the automatic extraction of reference linking information.


title: Midface distraction following Le Fort III and monobloc osteotomies: problems and solutions.

abstract: Distraction osteogenesis has been used increasingly for midfacial advancement in patients with syndromic craniosynostosis and in severe developmental hypoplasia of the midface. In these patients, the degree of advancement required is often so great that restriction of the adjacent soft tissues may preclude stable advancement in one stage, Whereas distraction is an ideal solution by which to gradually lengthen both the bones and the soft tissues, potential problems remain in translating the distraction forces to the midface. In these patients, severe developmental hypoplasia may be associated with weak union between the zygoma and the maxilla, increasing the chance of zygomaticomaxillary dysjunction when using internal devices that translate distraction force to the maxilla through the zygoma. Eight cases are reported in which either internal or external distraction systems were used for mid-face advancement following Le Fort III (n = 7) or monobloc (n = 1) osteotomies. Cases of patients in whom hypoplasia at the zygomaticomaxillary junction altered or impaired plans for midface distraction were reported from three host institutions. Seven patients had midface hypoplasia associated with syndromic craniosynostosis, and one patient had severe developmental midface hypoplasia. The distraction protocol was modified to successfully complete midface advancement in light of weakness at the zygomaticomaxillary junction in seven patients. Modifications included change from an internal to an external distraction system in two patients, rigid fixation and bone graft stabilization of the midface in one patient, and plate stabilization of a fractured or unstable zygomaticomaxillary junction followed by resumption of internal distraction in four patients. Previous infection and bone loss involving both malar complexes precluded one patient from being a candidate for an internal distraction system. Using a problem-based approach, successful advancement of the midface ranging from 9 to 26 mm at the occlusal level as measured by preoperative and postoperative cephalograms was undergone by all patients. Advantages and disadvantages of the respective distraction systems are reviewed to better understand unique patient characteristics leading to the successful use of these devices for correction of severe midface hypoplasia.


title: The blockchain: a new framework for robotic swarm systems

abstract: Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. However, several of the heterogeneous characteristics that make them ideal for certain future applications --- robot autonomy, decentralized control, collective emergent behavior, etc. --- hinder the evolution of the technology from academic institutions to real-world problems. Blockchain, an emerging technology originated in the Bitcoin field, demonstrates that by combining peer-to-peer networks with cryptographic algorithms a group of agents can reach an agreement on a particular state of affairs and record that agreement without the need for a controlling authority. The combination of blockchain with other distributed systems, such as robotic swarm systems, can provide the necessary capabilities to make robotic swarm operations more secure, autonomous, flexible and even profitable. This work explains how blockchain technology can provide innovative solutions to four emergent issues in the swarm robotics research field. New security, decision making, behavior differentiation and business models for swarm robotic systems are described by providing case scenarios and examples. Finally, limitations and possible future problems that arise from the combination of these two technologies are described.


title: Mining Tables From Large Scale HTML Texts

abstract: Table is a very common presentation scheme, but few papers touch on table extraction in text data mining. This paper focuses on mining tables from large-scale HTML texts. Table filtering, recognition, interpretation, and presentation are discussed. Heuristic rules and cell similarities are employed to identify tables. The F-measure of table recognition is 86.50%. We also propose an algorithm to capture attribute-value relationships among table cells. Finally, more structured data is extracted and presented.


title: Design of cyclic and d-amino acids containing peptidomimetics for inhibition of protein-protein interactions of HER2-HER3.

abstract: HER2 receptors are surface proteins belonging to the epidermal growth factor family of receptors. Their numbers are elevated in breast, lung, and ovarian cancers. HER2-positive cancers are aggressive, have higher mortality rate, and have a poor prognosis. We have designed peptidomimetics that bind to HER2 and block the HER2-mediated dimerization of epidermal growth factor family of receptors. Among these, a symmetrical cyclic peptidomimetic (compound 18) exhibited antiproliferative activity in HER2-overexpressing lung cancer cell lines with IC50 values in the nanomolar concentration range. To improve the stability of the peptidomimetic, d-amino acids were introduced into the peptidomimetic, and several analogs of compound 18 were designed. Among the analogs of compound 18, compound 32, a cyclic, d-amino acid-containing peptidomimetic, was found to have an IC50 value in the nanomolar range in HER2-overexpressing cancer cell lines. The antiproliferative activity of compound 32 was also measured by using a 3D cell culture model that mimics the in vivo conditions. The binding of compound 32 to the HER2 protein was studied by surface plasmon resonance. In vitro stability studies indicated that compound 32 was stable in serum for 48 hours and intact peptide was detectable in vivo for 12 hours. Results from our studies indicated that 1 of the d-amino acid analogs of 18, compound 32, binds to the HER2 extracellular domain, inhibiting the phosphorylation of kinase of HER2.


title: TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation

abstract: Action segmentation as a milestone towards building automatic systems to understand untrimmed videos has received considerable attention in the recent years. It is typically being modeled as a sequence labeling problem but contains intrinsic and sufficient differences than text parsing or speech processing. In this paper, we introduce a novel hybrid temporal convolutional and recurrent network (TricorNet), which has an encoder-decoder architecture: the encoder consists of a hierarchy of temporal convolutional kernels that capture the local motion changes of different actions; the decoder is a hierarchy of recurrent neural networks that are able to learn and memorize long-term action dependencies after the encoding stage. Our model is simple but extremely effective in terms of video sequence labeling. The experimental results on three public action segmentation datasets have shown that the proposed model achieves superior performance over the state of the art.


title: Laplacian Score for Feature Selection

abstract: In supervised learning scenarios, feature selection has been studied widely in the literature. Selecting features in unsupervised learning scenarios is a much harder problem, due to the absence of class labels that would guide the search for relevant information. And, almost all of previous unsupervised feature selection methods are "wrapper" techniques that require a learning algorithm to evaluate the candidate feature subsets. In this paper, we propose a "filter" method for feature selection which is independent of any learning algorithm. Our method can be performed in either supervised or unsupervised fashion. The proposed method is based on the observation that, in many real world classification problems, data from the same class are often close to each other. The importance of a feature is evaluated by its power of locality preserving, or, Laplacian Score. We compare our method with data variance (unsupervised) and Fisher score (supervised) on two data sets. Experimental results demonstrate the effectiveness and efficiency of our algorithm.


title: Dying cancer patients talk about physician and patient roles in DNR decision making

abstract: Background Within medical and bioethical discourse, there are many models depicting the relationships between, and roles of, physician and patient in medical decision making. Contestation similarly exists over the roles of physician and patient with regard to the decision not to provide cardiopulmonary resuscitation (CPR) following cardiac arrest [the do-not-resuscitate or do-not-resuscitate (DNR) decision], but there is little analysis of patient perspectives. ::: ::: Objective Analyse what patients with cancer within weeks before dying say about the decision to forego CPR and the roles of patient and physician in this decision. ::: ::: Design and participants Discursive analysis of qualitative data gathered during semi-structured interviews with 28 adult cancer patients close to death and attending palliative or oncology clinics of an Australian teaching hospital. ::: ::: Results Participants’ descriptions of appropriate patient or physician roles in decisions about CPR appeared related to how they conceptualized the decision: as a personal or a medical issue, with patient and doctor respectively identified as appropriate decision makers; or alternatively, both medical and personal, with various roles assigned embodying different versions of a shared decision-making process. Participants’ endorsement of physicians as decision makers rested upon physicians’ enactment of the rational, knowledgeable and compassionate expert, which legitimized entrusting them to make the DNR decision. Where this was called into question, physicians were positioned as inappropriate decision makers. ::: ::: Conclusion When patients’ and physicians’ understandings of the best decision, or of the preferred role of either party, diverge, conflict may ensue. In order to elicit and negotiate with patient preferences, flexibility is required during clinical interactions about decision making.


title: Case Scenario: Management of Intraoperative Hypoxemia during One-lung Ventilation

abstract: H YPOXEMIA that may reasonably be defined by an arterial hemoglobin oxygen saturation of less than 90% occurs in 5–10% of patients during one-lung ventilation (OLV). The physiopathology of hypoxemia is complex, and the management of intraoperative hypoxemia during OLV remains a challenge for anesthesiologists. In life-threatening hypoxemia, correct oxygenation should be restored rapidly. This may require discontinuing surgery to eliminate reversible causes of hypoxemia. Several strategies can then be applied to prevent and correct hypoxemia during OLV.


title: Operability of Folded Microstrip Patch-Type Tag Antenna in the UHF RFID Bands Within 865-928 MHz

abstract: The emerging use of passive radio frequency identification (RFID) systems at ultra-high-frequency (UHF) spectrum requires application specific tag antenna designs for challenging applications. Many objects containing conductive material need novel tag antenna designs for reliable identification. The operability of folded microstrip patch-type tag antenna for objects containing conductive material is analyzed and tested within the UHF RFID bands used at the moment mainly in Europe and in North and South America. First the operability of the tag antenna design and the effects of conductive material are modeled with simulations based on finite element method (FEM). The performance of the tag antenna design affixed to a package containing metallic foil is verified with read range measurements. The results show that the antenna design is operable in both of the UHF RFID bands within 865-928 MHz


ERRONEOUS DATA IN GLAZIOU COLLECTIONS OF MELASTOMATACEAE


title: Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) are effective models for reducing spectral variations and modeling spectral correlations in acoustic features for automatic speech recognition (ASR). Hybrid speech recognition systems incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models (HMMs/GMMs) have achieved the state-of-the-art in various benchmarks. Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it feasible to train an end-to-end speech recognition system instead of hybrid settings. However, RNNs are computationally expensive and sometimes difficult to train. In this paper, inspired by the advantages of both CNNs and the CTC approach, we propose an end-to-end speech framework for sequence labeling, by combining hierarchical CNNs with CTC directly without recurrent connections. By evaluating the approach on the TIMIT phoneme recognition task, we show that the proposed model is not only computationally efficient, but also competitive with the existing baseline systems. Moreover, we argue that CNNs have the capability to model temporal correlations with appropriate context information.


title: An Anti-hebbian Learning Rule to Represent Drive Motivations for Reinforcement Learning

abstract: We present a motivational system for an agent undergoing reinforcement learning (RL), which enables it to balance multiple drives, each of which is satiated by different types of stimuli. Inspired by drive reduction theory, it uses Minor Component Analysis (MCA) to model the agent’s internal drive state, and modulates incoming stimuli on the basis of how strongly the stimulus satiates the currently active drive. The agent’s dynamic policy continually changes through least-squares temporal difference updates. It automatically seeks stimuli that first satiate the most active internal drives, then the next most active drives, etc. We prove that our algorithm is stable under certain conditions. Experimental results illustrate its behavior.


title: Matrix and Tensor Factorization Techniques for Recommender Systems

abstract: This book presents the algorithms used to provide recommendations by exploiting matrix factorization and tensor decomposition techniques. It highlights well-known decomposition methods for recommender systems, such as Singular Value Decomposition (SVD), UV-decomposition, Non-negative Matrix Factorization (NMF), etc. and describes in detail the pros and cons of each method for matrices and tensors. This book provides a detailed theoretical mathematical background of matrix/tensor factorization techniques and a step-by-step analysis of each method on the basis of an integrated toy example that runs throughout all its chapters and helps the reader to understand the key differences among methods. It also contains two chapters, where different matrix and tensor methods are compared experimentally on real data sets, such as Epinions, GeoSocialRec, Last.fm, BibSonomy, etc. and provides further insights into the advantages and disadvantages of each method. The book offers a rich blend of theory and practice, making it suitable for students, researchers and practitioners interested in both recommenders and factorization methods. Lecturers can also use it for classes on data mining, recommender systems and dimensionality reduction methods.


title: BotGuard: Lightweight real-time botnet detection in software defined networks

abstract: The distributed detection of botnets may induce heavy computation and communication costs to network devices. Each device in related scheme only has a regional view of Internet, so it is hard to detect botnet comprehensively. In this paper, we propose a lightweight real-time botnet detection framework called Bot-Guard, which uses the global landscape and flexible configurability of software defined network (SDN) to identify botnets promptly. SDN, as a new network framework, can make centralized control in botnet detection, but there are still some challenges in such detections. We give a convex lens imaging graph (CLI-graph) to depict the topology characteristics of botnet, which allows SDN controller to locate attacks separately and mitigate the burden of network devices. The theoretical and experimental results prove that our scheme is capable of timely botnet detecting in SDNs with the accuracy higher than 90% and the delay less than 56 ms.


title: Why did you take this photo: a study on user intentions in digital photo productions

abstract: Why do people take pictures? While substantial work has been done on the intentions of people producing photos in specific domains (e.g. Flickr users or mobile phone cameras) the general case - arbitrary users taking photos with arbitrary devices - has not yet been investigated in detail. In this short paper, we present an exploratory study on the intentions and goals of people taking digital photos. 40 different specific photo taking situations of 10 people where investigated. We classified the situations and reflected them into context of existing work to investigate the applicability and usefulness of classifications existing for specific domains. Our findings show that applicability of existing models for small domains is limited and they indicate the need of a generalized taxonomy. We further identify hypotheses and research questions for future work in this area.


title: A deep learning based approach with adversarial regularization for Doppler weather radar ECHO prediction

abstract: Precipitation nowcasting is an important component for accurate weather modeling and Doppler radar data acts as an important input for nowcasting models. In this work, we propose a deep learning based approach for radar echo states prediction. Our approach uses a hybrid structure of convolutions within Long Short Term Memory recurrent network structure and a discriminator network is added in the loss objective to refine the predictions acting as a regularizer. This models the spatio-temporal nature of the problem explicitly in the neural network. We show that this model can be applied for fine grained short term precipitation prediction with improvement in evaluation metrics as compared to strong baselines. The proposed model improves recall score by 11% compared to without adversarial regularization. Results are presented using usual train, test strategy for the task of echo state prediction and derived precipitation based skill scores on the data from Seattle, WA, USA.


title: Old concepts, new ideas: approaches to translation shifts

abstract: This paper traces the development of the translation shift concept from its origins in the linguistics-oriented era of translation studies to its current revival in computer-based approaches: after a presentation of the traditional approaches by John C. Catford, Jean-Paul Vinay and Jean Darbelnet, Eugene A. Nida, and Kitty van Leuven-Zwart, several recent studies that have integrated the concept of translation shifts are introduced and discussed. This comparison of old and new approaches reveals that the attitudes towards shifts have changed from mildly prescriptive to neutrally descriptive. The paper concludes with a general evaluation of the place of linguistic approaches in translation studies. ::: Dieser Artikel untersucht die Entwicklung des Shift-Konzepts von seinen Ursprungen in der linguistisch orientierten Phase der Ubersetzungswissenschaft bis hin zu seiner derzeitigen Neubelebung in computerbasierten Ansatzen. Es werden zunachst die traditionellen Ansatze von John C. Catford, Jean-Paul Vinay und Jean Darbelnet, Eugene A. Nida sowie Kitty van Leuven-Zwart dargestellt und dann einige neuere Untersuchungen, die sich mit dem Shift-Konzept befassen, vorgestellt und diskutiert. Durch diese Gegenuberstellung wird deutlich, dass die praskriptiven Standpunkte mittlerweile einer neutral deskriptiven Herangehensweise gewichen ist. Der Artikel endet mit einer generellen Einschatzung des Stellenwerts linguistischer Ansatze in der Ubersetzungswissenschaft.


title: An Improved DBSCAN Clustering Algorithm for Multi-density Datasets

abstract: In this paper, we proposed a DBSCAN-based clustering algorithm called NNDD-DBSCAN with the main focus of handling multi-density datasets and reducing parameter sensitivity. The NNDD-DBSCAN used a new distance measuring method called nearest neighbor density distance (NNDD) which makes the new algorithm can clustering properly in multi-density datasets. By analyzing the relationship between the threshold of nearest neighbor density distance and the threshold of nearest neighborcollection, we give a heuristic method to find the appropriate nearest neighbor density distance threshold and reducing parameter sensitivity. Experimental results show that the NNDD-DBSCAN has a good robustadaptation and can get the ideal clustering result both in single density datasets and multi-density datasets.


title: (k, g)-cages are 3-connected

abstract: Abstract A ( k, g )- cage is a graph that has the least number of vertices among all k -regular graphs with girth g . It has been conjectured that the connectivity of each ( k, g -cage is k , and a proof exists for k = 3. We prove here that all cages are 3-connected, a step towards a proof of the conjecture.


title: Boosting Variational Inference

abstract: Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization problem: to find the closest distribution to the exact posterior over some family of distributions. For practical reasons, the family of distributions in VI is usually constrained so that it does not include the exact posterior, even as a limit point. Thus, no matter how long VI is run, the resulting approximation will not approach the exact posterior. We propose to instead consider a more flexible approximating family consisting of all possible finite mixtures of a parametric base distribution (e.g., Gaussian). For efficient inference, we borrow ideas from gradient boosting to develop an algorithm we call boosting variational inference (BVI). BVI iteratively improves the current approximation by mixing it with a new component from the base distribution family and thereby yields progressively more accurate posterior approximations as more computing time is spent. Unlike a number of common VI variants including mean-field VI, BVI is able to capture multimodality, general posterior covariance, and nonstandard posterior shapes.


title: Self-compassion moderates body comparison and appearance self-worth's inverse relationships with body appreciation

abstract: Abstract Although research on positive body image has increased, little research has explored which variables protect body appreciation during body-related threats. Self-compassion may be one such variable. Individuals high in self-compassion are mindful, kind, and nurturing toward themselves during situations that threaten their adequacy, while recognizing that being imperfect is part of “being human.” In this study, we investigated whether two body-related threats (i.e., body comparison and appearance contingent self-worth) were more weakly related to body appreciation when self-compassion was high among an online sample of 263 women ( M age = 35.26, SD = 12.42). Results indicated that self-compassion moderated the inverse relationships between body related threats and body appreciation. Specifically, when self-compassion was very high, body comparison and appearance contingent self-worth were unrelated to body appreciation. However, when self-compassion was low, these relationships were strong. Self-compassion, then, may help preserve women's body appreciation during body-related threats.


title: A load-adapative cloud resource scheduling model based on ant colony algorithm

abstract: Dynamic scheduling cloud resources according to the change of the load are key to improve cloud computing on-demand service capabilities. This paper proposes a load-adaptive cloud resource scheduling model based on ant colony algorithm. By real-time monitoring virtual machine of performance parameters, once judging overload, it schedules fast cloud resources using ant colony algorithm to bear some load on the load-free node. So that it can meet changing load requirements. By analyzing an example result, the model can meet the goals and requirements of self-adaptive cloud resources scheduling and improve the efficiency of the resource utilization.


title: Example-centric programming: integrating web search into the development environment

abstract: The ready availability of online source-code examples has fundamentally changed programming practices. However, current search tools are not designed to assist with programming tasks and are wholly separate from editing tools. This paper proposes that embedding a task-specific search engine in the development environment can significantly reduce the cost of finding information and thus enable programmers to write better code more easily. This paper describes the design, implementation, and evaluation of Blueprint, a Web search interface integrated into the Adobe Flex Builder development environment that helps users locate example code. Blueprint automatically augments queries with code context, presents a code-centric view of search results, embeds the search experience into the editor, and retains a link between copied code and its source. A comparative laboratory study found that Blueprint enables participants to write significantly better code and find example code significantly faster than with a standard Web browser. Analysis of three months of usage logs with 2,024 users suggests that task-specific search interfaces can significantly change how and when people search the Web.


title: Why We Sleep: The Temporal Organization of Recovery

abstract: If sleep does not serve an absolutely vital function, then it is the biggest mistake the evolutionary process has ever made,” Allan Rechtschaffen said. Studies of sleep and sleep deprivation suggest that the functions of sleep include recovery at the cellular, network, and endocrine system levels, energy conservation and ecological adaptations, and a role in learning and synaptic plasticity.


title: ELABORATION OF PRODUCTS DERIVED FROM THE EO DATA AND GIS INFO-LAYERS FOR FLOODING RISK ANALYSIS

abstract: The risk of flooding due to runoff is a major concern in many areas around the globe and especially in Romania. The modern management of spatial data related of river flooding largely relies on the functional facilities supplied by the Geographic Information System (GIS) info-layers, combined with Earth Observation (EO) data-derived information, Digital Elevation Models (DEM) and hydrological modeling. The paper assumes this multidisciplinary approach, in view to establish a methodology, which should further allow the elaboration of products useful for flooding risk analysis, such as: more accurate updated maps of land cover/land use, comprehensive thematic maps at various spatial scales with the extent of the flooded areas and the affected zones, maps of the hazard prone areas, etc. The supplying of the products, extracted directly from EO data or obtained after regrouping digitizing and integrating them in a GIS environment, has highly contributed to the elaboration of precise thematic maps at different scales, with corresponding generalizing levels. There are stressed the facilities supplied by the geo-referenced information and the satellite images to manage floodings during their characteristic phases: before, during and after floodings; accent is laid on the pre and post-crisis phases. The application was developed for the Arges hydrographic basin in Romania, a critical area, keeping in mind that it withholds many localities, including the capital and also important economic centers.


Dimensions of Abusive Language on Twitter.


title: Some Perspectives on Deinstitutionalization

abstract: The authors discuss what can be learned from our experience with deinstitutionalization. The deinstitutionalization of mentally ill persons has three components: the release of these individuals from hospitals into the community, their diversion from hospital admission, and the development of alternative community services. The greatest problems have been in creating adequate and accessible community resources. Where community services have been available and comprehensive, most persons with severe mental illness have significantly benefited. On the other hand, there have been unintended consequences of deinstitutionalization—a new generation of uninstitutionalized persons who have severe mental illness, who are homeless, or who have been criminalized and who present significant challenges to service systems. Among the lessons learned from deinstitutionalization are that successful deinstitutionalization involves more than simply changing the locus of care; that service planning must be tailored to the ne...


title: Buddhists minority in Malay Muslims majority of Pasir Mas, Kelantan

abstract: Although Malaysia is a predominantly Muslim country, other religions such as Buddhism, Hinduism, Christianity, Sikhism, Taoism, and other traditional belief systems are also practiced. The objective of this study is to investigate the real pattern of relationship of Buddhists minority in Malay Muslims majority in one of the east coast states in Malaysia, Kelantan. A small town, Pasir Mas is chosen as it consists of two major religions, Islam and Buddhism, forming two religious communities living side by side in a traditional village namely Kg. Tendong. It is an ethno-religious study of Malay Muslims and Chinese as well as Siamese Buddhists in terms of their religious interaction in the said location. The study in Pasir Mas could be perceived as a subset study over the dominant set of ethnic groups in Malaysia. In order to measure the quality of interaction, some indicators have been prepared to be applied on a total of one hundred and forty (140) respondents that were drawn from Muslims and Buddhists. The sample size together with the above-mentioned indicators showed that the quality of interaction is encouraging in the sense that the universal values of the two religions, namely Islam and Buddhism that bind people together. On the other hand, the discouraging factor of interaction among the residents of Pasir Mas was that trivial issues of religious differences. Therefore, the significance of the study lies mainly in showing the level of interaction between Muslims and Buddhists in Pasir Mas. This result is essential to the policy makers to develop a better pattern of inter-racial interaction in a remote area of Peninsular Malaysia.


title: Increasing Reproducibility in IR: Findings from the Dagstuhl Seminar on "Reproducibility of Data-Oriented Experiments in e-Science"

abstract: The Dagstuhl Seminar on "Reproducibility of Data-Oriented Experiments in e-Science", held on 24-29 January 2016, focused on the core issues and approaches to reproducibility of experiments from a multidisciplinary point of view, sharing the experience coming from several fields of computer science. In this paper, we discuss, summarize, and adapt the main findings of the seminar to the context of IR evaluation -- both system-oriented and user-oriented -- in order to raise awareness in our community and stimulate the fields towards and increased reproducibility of our experiments.


title: CAPTURING THE VISUAL LANGUAGE OF SOCIAL MEDIA EXPLOITING WEB IMAGE SEARCH FOR USER INTEREST PROFILING

abstract: With the rapid growth in the usage of social networks worldwide, uploading and sharing of user-generated content, both text and visual, has become increasingly prevalent. An analysis of the content a user shares and engages with can provide valuable insights into an individual's preferences and lifestyle. In this paper, we present a system to automatically infer a user's interests by analysing the content of the photos they share online. We propose a way to leverage web image search engines for detecting high-level semantic concepts, such as interests, in images, without relying on a large set of labeled images. We demonstrate the effectiveness of our system through quantitative and qualitative results on data collected from Instagram.


title: Detection of SSH Brute Force Attacks Using Aggregated Netflow Data

abstract: The SSH Brute force attack is one of the most prevalent attacks in computer networks. These attacks aim to gain ineligible access to users' accounts by trying plenty of different password combinations. The detection of this type of attack at the network level can overcome the scalability issue of host-based detection methods. In this paper, we provide a machine learning approach for the detection of SSH brute force attacks at the network level. Since extracting discriminative features for any machine learning task is a fundamental step, we explain the process of extracting discriminative features for the detection of brute force attacks. We incorporate domain knowledge about SSH brute force attacks as well as the analysis of a representative collection of the data to define the features. We collected real SSH traffic from a campus network. We also generated some failed login data that a legitimate user who has forgotten his/her password can produce as normal traffic that can be similar to the SSH brute force attack traffic. Our inspection on the collected brute force Netflow data and the manually produced SSH failed login data showed that the Netflow features are not discriminative enough to discern brute force traffic from the failed login traffic produced by a legitimate user. We introduced an aggregation of Netflows to extract the proper features for building machine learning models. Our results show that the models built upon these features provide excellent performances for the detection of brute force attacks.


title: Joint Optimization of Multiple Performance Metrics in Online Video Advertising

abstract: The field of online advertising, in essence, deals with the problem of presenting ads to online users in the most appropriate contexts to achieve a multitude of advertiser goals. A vast amount of work in online advertising has been focused on optimizing banner display advertising campaigns where the main goal lies in direct response metrics, often as clicks or conversions. In this paper, we explore the newly popularized space of online video advertising, where brand recognition is the key focus. We propose a framework based on a feedback mechanism where we optimize multiple video specific performance indicators while making sure the delivery constraints (budget and user reach) of advertisers are satisfied. While our main focus is on improving metrics such as engagement (amount of view time), and viewability (whether a campaign is within eyesight of a user), we also discuss the possibilities of expanding to other metrics. We demonstrate the benefit of our framework via empirical results in multiple real-world advertising campaigns. To the best of our knowledge, this is the first paper that deals with the unique challenges arising from the nature of online video advertising.


title: Toward black-box detection of logic flaws in web applications

abstract: Web applications play a very important role in many critical areas, including online banking, health care, and personal communication. This, combined with the limited security training of many web developers, makes web applications one of the most common targets for attackers. In the past, researchers have proposed a large number of white- and black-box techniques to test web applications for the presence of several classes of vulnerabilities. However, traditional approaches focus mostly on the detection of input validation flaws, such as SQL injection and cross-site scripting. Unfortunately, logic vulnerabilities specific to particular applications remain outside the scope of most of the existing tools and still need to be discovered by manual inspection. In this paper we propose a novel black-box technique to detect logic vulnerabilities in web applications. Our approach is based on the automatic identification of a number of behavioral patterns starting from few network traces in which users interact with a certain application. Based on the extracted model, we then generate targeted test cases following a number of common attack scenarios.


title: Cognition and personality: an analysis of an emerging field.

abstract: It is now well established that individuals can differ consistently in their average levels of behaviour across different contexts. There have recently been calls to apply the same adaptive framework to interindividual differences in cognition. These calls have culminated in the suggestion that variation in personality and cognition should correlate. We suggest that both these appealing notions are conceptually and logistically problematic. We identify the first crucial step for establishing any cognition-personality relationship. This is to determine the degree to which cognitive abilities yield consistent task performance. We then suggest how to establish whether such consistency exists. Finally, we discuss why formulating predictions about how cognition might be related to personality is much more difficult than is currently realised.


title: Suspicious human activity recognition: a review

abstract: Suspicious human activity recognition from surveillance video is an active research area of image processing and computer vision. Through the visual surveillance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very difficult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Recent decade witnessed a good number of publications in the field of visual surveillance to recognize the abnormal activities. Furthermore, a few surveys can be seen in the literature for the different abnormal activities recognition; but none of them have addressed different abnormal activities in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of suspicious activity recognition from the surveillance videos in the last decade. We include a brief introduction of the suspicious human activity recognition with its issues and challenges. This paper consists of six abnormal activities such as abandoned object detection, theft detection, fall detection, accidents and illegal parking detection on road, violence activity detection, and fire detection. In general, we have discussed all the steps those have been followed to recognize the human activity from the surveillance videos in the literature; such as foreground object extraction, object detection based on tracking or non-tracking methods, feature extraction, classification; activity analysis and recognition. The objective of this paper is to provide the literature review of six different suspicious activity recognition systems with its general framework to the researchers of this field.


title: Extremely Low-Profile, Single-Arm, Wideband Spiral Antenna Radiating a Circularly Polarized Wave

abstract: The antenna characteristics are described of a single-arm spiral (SAS) antenna. A balun circuit, required for a conventional two-arm balanced-mode spiral (TAS), is not necessary for feeding this SAS. First, the radiation pattern of an SAS having a disc/ground-plane is investigated. When the radius of the disc is smaller than the radius of the first-mode active region on the spiral and the spacing between the disc and spiral plane is small, the SAS is found to radiate a circularly polarized (CP) bidirectional beam, whose radiation pattern is almost symmetric with respect to the antenna axis normal to the spiral plane. Second, from a practical standpoint, the CP bidirectional beam is transformed into a CP unidirectional beam by placing a conducting cavity behind an SAS with a disc. It is revealed that the SAS has a good VSWR (less than 2) and a good axial ratio (less than 3 dB) over a design frequency range of 3 GHz to 10 GHz, where a cavity/antenna height of 7 mm (0.07 wavelength at the lowest design frequency) is chosen. The frequency response of the gain for the SAS is found to be similar to that for a conventional TAS, and the radiation efficiency for the SAS is slightly larger than that for the TAS. It is concluded that the SAS with a small disc backed by a cavity realizes a circularly polarized, low-profile, wideband antenna with a simple feed system that does not require a balun circuit.


Attention-based LSTM for Aspect-level Sentiment Classification


title: Use of laser-induced ionization to detect soot inception in premixed flames

abstract: Experimental measurements of laser-induced ionization were performed for ethene–air premixed flames operated near the soot inception point. Soot was ionized with a pulsed laser operated at 532 nm. The ionization signal was collected with a tungsten electrode located in the postflame region. Ionization signals were collected by use of both single-electrode and dual-electrode configurations. Earlier laser-induced-ionization studies focused on the use of a single biased electrode to generate the electric field, with the burner head serving as the path to ground. In many practical combustion systems, a path to ground is not readily available. To apply the laser-induced-ionization diagnostic to these geometries, a dual-electrode geometry must be employed. The influence of electrode configuration, flame equivalence ratio, and flame height on ionization signal detection was determined. The efficacy of the laser-induced-ionization diagnostic in detecting soot inception in the postflame region of a premixed flame by use of a dual-electrode configuration was investigated. Of the dual-electrode configurations tested, the dual-electrode geometry oriented parallel to the laser beam was observed to be most sensitive for detecting the soot inception point in a premixed flame.


title: Mapping the brain's metaphor circuitry: metaphorical thought in everyday reason

abstract: An overview of the basics of metaphorical thought and language from the perspective of Neurocognition, the integrated interdisciplinary study of how conceptual thought and language work in the brain. The paper outlines a theory of metaphor circuitry and discusses how everyday reason makes use of embodied metaphor circuitry.


title: Calendar queues: a fast 0(1) priority queue implementation for the simulation event set problem

abstract: A new priority queue implementation for the future event set problem is described in this article. The new implementation is shown experimentally to be O(1) in queue size for the priority increment distributions recently considered by Jones in his review article. It displays hold times three times shorter than splay trees for a queue size of 10,000 events. The new implementation, called a calendar queue, is a very simple structure of the multiple list variety using a novel solution to the overflow problem.


title: Exact and Inexact Subsampled Newton Methods for Optimization

abstract: The paper studies the solution of stochastic optimization problems in which approximations to the gradient and Hessian are obtained through subsampling. We first consider Newton-like methods that employ these approximations and discuss how to coordinate the accuracy in the gradient and Hessian to yield a superlinear rate of convergence in expectation. The second part of the paper analyzes an inexact Newton method that solves linear systems approximately using the conjugate gradient (CG) method, and that samples the Hessian and not the gradient (the gradient is assumed to be exact). We provide a complexity analysis for this method based on the properties of the CG iteration and the quality of the Hessian approximation, and compare it with a method that employs a stochastic gradient iteration instead of the CG method. We report preliminary numerical results that illustrate the performance of inexact subsampled Newton methods on machine learning applications based on logistic regression.


title: HVAC control methods - a review

abstract: Making buildings more energy efficient while keeping thermal comfort has been an important topic in last decades, including improving efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems. One approach to this goal is to use better control methods. This paper presents a review of methods which have been researched in scientific papers in last couple of years. Most papers focus on one of the following trends: improving “classical” control methods; use of predictable control based on models; use of intelligent control methods.


title: Theory and design of uniform DFT, parallel, quadrature mirror filter banks

abstract: In this paper, the theory of uniform DFT, parallel, quadrature mirror filter (QMF) banks is developed. The QMF equations, i.e., equations that need to be satisfied for exact reconstruction of the input signal, are derived. The concept of decimated filters is introduced, and structures for both analysis and synthesis banks are derived using this concept. The QMF equations, as well as closed-form expressions for the synthesis filters needed for exact reconstruction of the input signal x(n) , are also derived using this concept. In general, the reconstructed. signal \hat{x}(n) suffers from three errors: aliasing, amplitude distortion, and phase distortion. Conditions for exact reconstruction (i.e., all three distortions are zero, and \hat{x}(n) is equal to a delayed version of x(n)) of the input signal are derived in terms of the decimated filters. Aliasing distortion can always be completely canceled. Once aliasing is canceled, it is possible to completely eliminate amplitude distortion (if suitable IIR filters are employed) and completely eliminate phase distortion (if suitable FIR filters are employed). However, complete elimination of all three errors is possible only with some simple, pathalogical stable filter transfer functions. In general, once aliasing is canceled, the other distortions can be minimized rather than completely eliminated. Algorithms for this are presented. The properties of FIR filter banks are then investigated. Several aspects of IIR filter banks are also studied using the same framework.


title: Modeling User Consumption Sequences

abstract: We study sequences of consumption in which the same item may be consumed multiple times. We identify two macroscopic behavior patterns of repeated consumptions. First, in a given user's lifetime, very few items live for a long time. Second, the last consumptions of an item exhibit growing inter-arrival gaps consistent with the notion of increasing boredom leading up to eventual abandonment. ::: ::: We then present what is to our knowledge the first holistic model of sequential repeated consumption, covering all observed aspects of this behavior. Our simple and purely combinatorial model includes no planted notion of lifetime distributions or user boredom; nonetheless, the model correctly predicts both of these phenomena. Further, we provide theoretical analysis of the behavior of the model confirming these phenomena. Additionally, the model quantitatively matches a number of microscopic phenomena across a broad range of datasets. ::: ::: Intriguingly, these findings suggest that the observation in a variety of domains of increasing user boredom leading to abandonment may be explained simply by probabilistic conditioning on an extinction event in a simple model, without resort to explanations based on complex human dynamics.


title: Probabilistic attainability maps: Efficiently predicting driver-specific electric vehicle range

abstract: This paper concerns the efficient computation of a confidence level with which a particular driver will be able to reach a particular destination given the current state of charge of the battery of an electric vehicle. This probability of attainability is simultaneously computed for all destinations in a realistically sized map while taking into account the driver, the environment, on-board auxiliary systems and the vehicle battery system as potential sources of estimation noise. The model uses a feature-based linear regression framework which allows for a computationally efficient implementation capable of providing real-time updates of the resulting probabilistic attainability map. It was deployed on an all-electric Nissan Leaf and evaluated using data from over 140 miles of driving. The system proposed produces results of a quality commensurate with state-of-the-art approaches in terms of prediction accuracy.


title: Capsules for Object Segmentation

abstract: Convolutional neural networks (CNNs) have shown remarkable results over the last several years for a wide range of computer vision tasks. A new architecture recently introduced by Sabour et al., referred to as a capsule networks with dynamic routing, has shown great initial results for digit recognition and small image classification. The success of capsule networks lies in their ability to preserve more information about the input by replacing max-pooling layers with convolutional strides and dynamic routing, allowing for preservation of part-whole relationships in the data. This preservation of the input is demonstrated by reconstructing the input from the output capsule vectors. Our work expands the use of capsule networks to the task of object segmentation for the first time in the literature. We extend the idea of convolutional capsules with locally-connected routing and propose the concept of deconvolutional capsules. Further, we extend the masked reconstruction to reconstruct the positive input class. The proposed convolutional-deconvolutional capsule network, called SegCaps, shows strong results for the task of object segmentation with substantial decrease in parameter space. As an example application, we applied the proposed SegCaps to segment pathological lungs from low dose CT scans and compared its accuracy and efficiency with other U-Net-based architectures. SegCaps is able to handle large image sizes (512 x 512) as opposed to baseline capsules (typically less than 32 x 32). The proposed SegCaps reduced the number of parameters of U-Net architecture by 95.4% while still providing a better segmentation accuracy.


title: Ear acupuncture and fMRI: a pilot study for assessing the specificity of auricular points

abstract: In recent years research explored different acupuncture stimulation techniques but interest has focused primarily on somatic acupuncture and on a limited number of acupoints. As regards ear Acupuncture (EA) there is still some criticism about the clinical specificity of auricular points/areas representing organs or structures of the body. The aim of this study was to verify through (Functional magnetic resonance imaging) fMRI the hypothesis of EA point specificity using two auricular points having different topographical locations and clinical significance. Six healthy volunteers underwent two experimental fMRI sessions: the first was dedicated to the stimulation of Thumb Auricular Acupoint (TAA) and the second to the stimulation of Brain Stem Auricular Acupoint (BSAA). The stimulation of the needle placed in the TAA of the left ear produced an increase in activation bilaterally in the parietal operculum, region of the secondary somatosensory area SII. Stimulation of the needle placed in the BSAA of the left ear showed a pattern that largely overlapped regions belonging to the pain matrix, as shown to be involved in previous somatic acupuncture studies but with local differences in the left amygdala, anterior cingulate cortex, and cerebellum. The differences in activation patterns between TAA and BSAA stimulation support the specificity of the two acupoints. Moreover, the peculiarity of the regions involved in BSAA stimulation compared to those involved in the pain matrix, is in accordance with the therapeutic indications of this acupoint that include head pain, dizziness and vertigo. Our results provide preliminary evidence on the specificity of two auricular acupoints; further research is warranted by means of fMRI both in healthy volunteers and in patients carrying neurological/psychiatric syndromes.


title: ObamaNet: Photo-realistic lip-sync from text

abstract: We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text. Contrary to other published lip-sync approaches, ours is only composed of fully trainable neural modules and does not rely on any traditional computer graphics methods. More precisely, we use three main modules: a text-to-speech network based on Char2Wav, a time-delayed LSTM to generate mouth-keypoints synced to the audio, and a network based on Pix2Pix to generate the video frames conditioned on the keypoints.


title: Regime Change Detection Using Directional Change Indicators in the Foreign Exchange Market to Chart Brexit

abstract: A regime change is a significant change in the collective trading behaviour in a financial market. Being able to detect the occurrence of regime change could lead to a better understanding and monitoring of financial markets. In this paper, a novel method is proposed to detect regime change, which makes use of a data-driven approach, that of directional change (DC). Compared to the conventional approach of using time series analysis, DC is an alternative approach to sample price movement. As variables observed under time series do not apply to DC, our first contributions is the identification of a new relevant indicator for regime change detection. Our second contribution is the comparison of both the DC approach and time series analysis, their ability to achieve regime change detection. The ability of both approaches in regime change detection is examined over a period of market uncertainty, that of Brexit. The results demonstrate that the DC approach is as effective as the time-series approach in detecting regime changes. Moreover, the DC approach is encouraging because some market regime changes are detected under DC, but are not found under time series. That means they support each other in the detection of regime change, and can also provide extra information to complement each other. Together, regime changes detected under both DC and time series provide a better insight into the market, which market participants and regulators could benefit from.


title: Boosting access parallelism to PCM-based main memory

abstract: Despite its promise as a DRAM main memory replacement, Phase Change Memory (PCM) has high write latencies which can be a serious detriment to its widespread adoption. Apart from slowing down a write request, the consequent high latency can also keep other chips of the same rank, that are not involved in this write, idle for long times. There are several practical considerations that make it difficult to allow subsequent reads and/or writes to be served concurrently from the same chips during the long latency write. This paper proposes and evaluates several novel mechanisms -- re-constructing data from error correction bits instead of waiting for chips currently busy to serve a read, rotating word mappings across chips of a PCM rank, and rotating the mapping of error detection/correction bits across these chips -- to overlap several reads with an ongoing write (RoW) and even a write with an ongoing write (WoW). The paper also presents the necessary micro-architectural enhancements nee-ded to implement these mechanisms, without significantly changing the current interfaces. The resulting PCM access parallelism (PCMap) system incorporating these enhancements, boosts the intra-rank-level parallelism during such writes from a very low baseline value of 2.4 to an average and maximum values of 4.5 and 7.4, respectively (out of a maximum of 8.0), across a wide spectrum of both multiprogrammed and multithreaded workloads. This boost in parallelism results in an average IPC improvement of 15.6% and 16.7% for the multi-progra-mmed and multi-threaded workloads, respectively.


title: The motivating function of thinking about the future: Expectations versus fantasies.

abstract: Two forms of thinking about the future are distinguished: expectations versus fantasies. Positive expectations (judging a desired future as likely) predicted high effort and successful performance, but the reverse was true for positive fantasies (experiencing one's thoughts and mental images about a desired future positively). Participants were graduates looking for a job (Study 1), students with a crush on a peer of the opposite sex (Study 2), undergraduates anticipating an exam (Study 3), and patients undergoing hip-replacement surgery (Study 4). Effort and performance were measured weeks or months (up to 2 years) after expectations and fantasies had been assessed. Implications for the self-regulation of effort and performance are discussed.


title: The next-to-shortest path problem on directed graphs with positive edge weights

abstract: Given an edge-weighted graph G and two distinct vertices s and t of G, the next-to-shortest path problem asks for a path from s to t of minimum length among all paths from s to t except the shortest ones. In this article, we consider the version where G is directed and all edge weights are positive. Some properties of the requested path are derived when G is an arbitrary digraph. In addition, if G is planar, an On3-time algorithm is proposed, where n is the number of vertices of G. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 653, 205-211 2015


title: A new approach to unsupervised text summarization

abstract: The paper presents a novel approach to unsupervised text summarization. The novelty lies in exploiting the diversity of concepts in text for summarization, which has not received much attention in the summarization literature. A diversity-based approach here is a principled generalization of Maximal Marginal Relevance criterion by Carbonell and Goldstein \cite{carbonell-goldstein98}. We propose, in addition, an information-centric approach to evaluation, where the quality of summaries is judged not in terms of how well they match human-created summaries but in terms of how well they represent their source documents in IR tasks such document retrieval and text categorization. To find the effectiveness of our approach under the proposed evaluation scheme, we set out to examine how a system with the diversity functionality performs against one without, using the BMIR-J2 corpus, a test data developed by a Japanese research consortium. The results demonstrate a clear superiority of a diversity based approach to a non-diversity based approach.


title: Evaluating Pedestrian Connectivity for Suburban Sustainability

abstract: A crucial ingredient for achieving urban sustainability is reducing society's dependence on the automobile. Residents of suburban developments are often dependent on their cars for trips to destinations within the neighborhood because of circuitous street layouts, lack of sidewalks, and long travel distances. The term ''pedestrian connectivity'' is introduced as a measure of both the direct- ness of route and the route distance for the pedestrian for each home-destination trip. The developed methodology for retrofitting pedestrian enhancements to an existing suburban neighborhood is coded as an ArcView GIS extension. Improve- ments include the addition of sidewalks and access pathways to isolated cul-de- sacs to make for shorter and more direct routes. Reduced energy consumption, and therefore greater sustainability, may be achieved by having suburban neighborhoods retrofitted in such a way as to allow people to walk for some of their needs and to be well connected to a regional transit system. Modeled results from a neighbor- hood in Hamilton, Ont., Canada, show how the retrofitted improvements could lead to measurably improved conditions for pedestrians.


title: The Science of Meditation and the State of Hypnosis

abstract: Abstract Two aspects of Buddhist meditation—concentration and mindfulness—are discussed in relationship to hypnosis. Mindfulness training facilitates the investigation of subjective responses to hypnosis. Concentration practice leads to altered states similar to those in hypnosis, both phenomenologically and neurologically. The similarities and differences between hypnosis and meditation are used to shed light on perennial questions: (1) Does hypnosis involve an altered state of consciousness? (2) Does a hypnotic induction increase suggestibility? I conclude that a model for hypnosis should include altered states as well as capacity for imaginative involvement and expectations.


title: A Compact Broadband Cross-Shaped Circularly Polarized Planar Monopole Antenna With a Ground Plane Extension

abstract: This letter presents a new broadband circularly polarized (CP) planar monopole antenna with a simple structure and compact size. Broadband CP operation is achieved by vertically extending one side of the ground plane, while a cross-shaped monopole structure and the presence of a slot in the ground plane assure a broad impedance bandwidth. The antenna is successfully simulated, and a prototype is fabricated. Measurement results indicate that the 3 dB axial-ratio bandwidth is about 42.6% (2.6 GHz, 4.8–7.4 GHz), fully covered by a –10 dB impedance bandwidth of 55.5% (3.35 GHz, 4.36–7.71 GHz). Furthermore, the proposed antenna can be used together with a metallic reflector to obtain directive radiation patterns with enhanced broadside gain.


title: Circle of Emotions in Life: Emotion Mapping in 2Dimensions

abstract: Understanding human emotions is a complex problem. Various tools and techniques have been employed by researchers across various domains to understand and infer human emotions. With the advent of smart machines, the problem took a different angle to try and machines themselves make to understand the emotions of its user (s). Human emotions had to be labeled and then were organized with inverse emotions being placed opposite to each other. Various continuous as well as discrete emotion-mapping models were proposed to identify and quantify human emotions. This paper is the result of studying around fifteen such models and comparing them with each other to come to a more concrete viewpoint towards understand human emotions.


title: Quantifying the Language of Schizophrenia in Social Media

abstract: Analyzing symptoms of schizophrenia has traditionally been challenging given the low prevalence of the condition, affecting around 1% of the U.S. population. We explore potential linguistic markers of schizophrenia using the tweets 1 of self-identified schizophrenia sufferers, and describe several natural language processing (NLP) methods to analyze the language of schizophrenia. We examine how these signals compare with the widelyused LIWC categories for understanding mental health (Pennebaker et al., 2007), and provide preliminary evidence of additional linguistic signals that may aid in identifying and getting help to people suffering from schizophrenia.


title: Recurrent Residual Learning for Action Recognition

abstract: Action recognition is a fundamental problem in computer vision with a lot of potential applications such as video surveillance, human computer interaction, and robot learning. Given pre-segmented videos, the task is to recognize actions happening within videos. Historically, hand crafted video features were used to address the task of action recognition. With the success of Deep ConvNets as an image analysis method, a lot of extensions of standard ConvNets were purposed to process variable length video data. In this work, we propose a novel recurrent ConvNet architecture called recurrent residual networks to address the task of action recognition. The approach extends ResNet, a state of the art model for image classification. While the original formulation of ResNet aims at learning spatial residuals in its layers, we extend the approach by introducing recurrent connections that allow to learn a spatio-temporal residual. In contrast to fully recurrent networks, our temporal connections only allow a limited range of preceding frames to contribute to the output for the current frame, enabling efficient training and inference as well as limiting the temporal context to a reasonable local range around each frame. On a large-scale action recognition dataset, we show that our model improves over both, the standard ResNet architecture and a ResNet extended by a fully recurrent layer.


title: Soft Tactile Skin Using an Embedded Ionic Liquid and Tomographic Imaging

abstract: Whole-body-contact sensing will be crucial in the quest to make robots capable of safe interaction with humans. This paper describes a novel design and a fabrication method of artificial tactile sensing skin for robots. The manufacturing method described in this paper allows easy filling of a complex microchannel network with a liquid conductor (e.g., room temperature ionic liquid (RTIL)). The proposed sensing skin can detect the magnitude and location of surface contacts using electrical impedance tomography (EIT), an imaging technique mostly used in the medical field and examined recently in conjunction with sensors based on a piezoresistive polymer sheet for robotic applications. Unlike piezoresistive polymers, our IL-filled artificial skin changes its impedance in a more predictable manner, since the measured value is determined by a simple function of the microchannel geometry only, rather than complex physical phenomena. As a proof of concept, we demonstrate that our EIT artificial skin can detect surface contacts and graphically show their magnitudes and locations.


title: Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer.

abstract: The increasing availability and maturity of DNA microarray technology has led to an explosion of cancer profiling studies. To extract maximum value from the accumulating mass of publicly available cancer gene expression data, methods are needed to evaluate, integrate, and intervalidate multiple datasets. Here we demonstrate a statistical model for performing meta-analysis of independent microarray datasets. Implementation of this model revealed that four prostate cancer gene expression datasets shared significantly similar results, independent of the method and technology used (i.e., spotted cDNA versus oligonucleotide). This interstudy cross-validation approach generated a cohort of genes that were consistently and significantly dysregulated in prostate cancer. Bioinformatic investigation of these genes revealed a synchronous network of transcriptional regulation in the polyamine and purine biosynthesis pathways. Beyond the specific implications for prostate cancer, this work establishes a much-needed model for the evaluation, cross-validation, and comparison of multiple cancer profiling studies.


title: The Waterfall Model in Large-Scale Development

abstract: Waterfall development is still a widely used way of working in software development companies. Many problems have been reported related to the model. Commonly accepted problems are for example to cope with change and that defects all too often are detected too late in the software development process. However, many of the problems mentioned in literature are based on beliefs and experiences, and not on empirical evidence. To address this research gap, we compare the problems in literature with the results of a case study at Ericsson AB in Sweden, investigating issues in the waterfall model. The case study aims at validating or contradicting the beliefs of what the problems are in waterfall development through empirical research.


title: MobiGUITAR - A Tool for Automated Model-Based Testing of Mobile Apps

abstract: As mobile devices become increasingly smarter and more powerful, so too must the engineering of their software. User-interface driven system testing of these devices is gaining popularity, with each vendor releasing some automation tool(s). We feel that these tools are inappropriate for amateur program- mers, an increasing fraction of the app developer population. We present MobiGUITAR for automated GUI-driven testing of Android apps. MobiGUITAR is based on observation, extraction, and abstraction of the run-time state of GUI widgets. The abstraction is a scalable state-machine model that, together with test coverage criteria, provides a way to automatically generate test cases. We apply MobiGUITAR to 4 open-source Android apps, automatically generate and execute 7,711 test cases, and report 10 new bugs. A number of bugs are "Android-specific," stemming from the event- and activity-driven nature of Android.


title: The Use of Convection-Enhanced Delivery with Liposomal Toxins in Neurooncology

abstract: Liposomes have long been effective delivery vehicles for transport of toxins to peripheral cancers. The combination of convection-enhanced delivery (CED) with liposomal toxins was originally proposed to circumvent the limited delivery of intravascular liposomes to the central nervous system (CNS) due to the blood-brain-barrier (BBB). CED offers markedly improved distribution of infused therapeutics within the CNS compared to direct injection or via drug eluting polymers, both of which depend on diffusion for parenchymal distribution. This review examines the basis for improved delivery of liposomal toxins via CED within the CNS, and discusses preclinical and clinical experience with these therapeutic techniques. How CED and liposomal technologies may influence future neurooncologic treatments are also considered.


title: SMART: An Application Framework for Real Time Big Data Analysis on Heterogeneous Cloud Environments

abstract: The amount of data that human activities generate poses a challenge to current computer systems. Big data processing techniques are evolving to address this challenge, with analysis increasingly being performed using cloud-based systems. Emerging services, however, require additional enhancements in order to ensure their applicability to highly dynamic and heterogeneous environments and facilitate their use by Small a Medium-sized Enterprises (SMEs). Observing this landscape in emerging computing system development, this work presents Small a Medium-sized Enterprise Data Analytic in Real Time (SMART) for addressing some of the issues in providing compute service solutions for SMEs. SMART offers a framework for efficient development of Big Data analysis services suitable to small and medium-sized organizations, considering very heterogeneous data sources, from wireless sensor networks to data warehouses, focusing on service composability for a number of domains. This paper presents the basis of this proposal and preliminary results on exploring application deployment on hybrid infrastructure.