title: A TCAS-II Resolution Advisory Detection Algorithm

abstract: The Traffic Alert and Collision Avoidance System (TCAS) is a family of airborne systems designed to reduce the risk of mid-air collisions between aircraft. TCAS II, the current generation of TCAS devices, provides resolution advisories that direct pilots to maintain or increase vertical separation when aircraft distance and time parameters are beyond designed system thresholds. This paper presents a mathematical model of the TCAS II Resolution Advisory (RA) logic that assumes accurate aircraft state information. Based on this model, an algorithm for RA detection is also presented. This algorithm is analogous to a conflict detection algorithm, but instead of predicting loss of separation, it predicts resolution advisories. It has been formally verified that for a kinematic model of aircraft trajectories, this algorithm completely and correctly characterizes all encounter geometries between two aircraft that lead to a resolution advisory within a given lookahead time interval. The RA detection algorithm proposed in this paper is a fundamental component of a National Aeronautics and Space Administration (NASA) sense and avoid concept for the integration of Unmanned Aircraft Systems in civil airspace.


title: Instagram Unfiltered: Exploring Associations of Body Image Satisfaction, Instagram #Selfie Posting, and Negative Romantic Relationship Outcomes

abstract: Abstract The purpose of this study was to examine the predictors and consequences associated with Instagram selfie posting. Thus, this study explored whether body image satisfaction predicts Instagram selfie posting and whether Instagram selfie posting is then associated with Instagram-related conflict and negative romantic relationship outcomes. A total of 420 Instagram users aged 18 to 62 years (M = 29.3, SD = 8.12) completed an online survey questionnaire. Analysis of a serial multiple mediator model using bootstrapping methods indicated that body image satisfaction was sequentially associated with increased Instagram selfie posting and Instagram-related conflict, which related to increased negative romantic relationship outcomes. These findings suggest that when Instagram users promote their body image satisfaction in the form of Instagram selfie posts, risk of Instagram-related conflict and negative romantic relationship outcomes might ensue. Findings from the current study provide a baseline underst...


title: Optimal Shift-Reduce Constituent Parsing with Structured Perceptron

abstract: We present a constituent shift-reduce parser with a structured perceptron that finds the optimal parse in a practical runtime. The key ideas are new feature templates that facilitate state merging of dynamic programming and A* search. Our system achieves 91.1 F1 on a standard English experiment, a level which cannot be reached by other beam-based systems even with large beam sizes.1


title: Collaborative Joint Training With Multitask Recurrent Model for Speech and Speaker Recognition

abstract: Automatic speech and speaker recognition are traditionally treated as two independent tasks and are studied separately. The human brain in contrast deciphers the linguistic content, and the speaker traits from the speech in a collaborative manner. This key observation motivates the work presented in this paper. A collaborative joint training approach based on multitask recurrent neural network models is proposed, where the output of one task is backpropagated to the other tasks. This is a general framework for learning collaborative tasks and fits well with the goal of joint learning of automatic speech and speaker recognition. Through a comprehensive study, it is shown that the multitask recurrent neural net models deliver improved performance on both automatic speech and speaker recognition tasks as compared to single-task systems. The strength of such multitask collaborative learning is analyzed, and the impact of various training configurations is investigated.


title: Automated identification and characterization of parcels (AICP) with OpenStreetMap and Points of Interest

abstract: Against the paucity of information on urban parcels in China, we propose a method to automatically identify and characterize parcels using OpenStreetMap (OSM) and points of interest (POI) data. Parcels are the basic spatial units for fine-scale urban modeling, urban studies, and spatial planning. Conventional methods for identification and characterization of parcels rely on remote sensing and field surveys, which are labor intensive and resource consuming. Poorly developed digital infrastructure, limited resources, and institutional barriers have all hampered the gathering and application of parcel data in China. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based cellular automata model is adopted to select urban parcels. The method is applied to the entire state of China and identifies 82 645 urban parcels in 297 cities. Notwithstanding all the caveats of open and/or crowd-sourced data, our approach can produce a reasonably good approximation of parcels identified using conventional methods, thus it has the potential to become a useful tool.


title: Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition

abstract: Text in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR2013 and MSRA-TD500. The main motivation of Total-Text is to fill this gap and facilitate a new research direction for the scene text community. On top of the conventional horizontal and multi-oriented texts, it features curved-oriented text. Total-Text is highly diversified in orientations, more than half of its images have a combination of more than two orientations. Recently, a new breed of solutions that casted text detection as a segmentation problem has demonstrated their effectiveness against multi-oriented text. In order to evaluate its robustness against curved text, we fine-tuned DeconvNet and benchmark it on Total-Text. Total-Text with its annotation is available at this https URL


title: Performance Tuning of Hadoop MapReduce: A Noisy Gradient Approach

abstract: Hadoop MapReduce is a popular framework for distributed storage and processing of large datasets and is used for big data analytics. It has various configuration parameters which play an important role in deciding the performance i.e., the execution time of a given big data processing job. Default values of these parameters do not result in good performance and therefore it is important to tune them. However, there is inherent difficulty in tuning the parameters due to two important reasons - first, the parameter search space is large and second, there are cross-parameter interactions. Hence, there is a need for a dimensionality-free method which can automatically tune the configuration parameters by taking into account the cross-parameter dependencies. In this paper, we propose a novel Hadoop parameter tuning methodology, based on a noisy gradient algorithm known as the simultaneous perturbation stochastic approximation (SPSA). The SPSA algorithm tunes the selected parameters by directly observing the performance of the Hadoop MapReduce system. The approach followed is independent of parameter dimensions and requires only 2 observations per iteration while tuning. We demonstrate the effectiveness of our methodology in achieving good performance on popular Hadoop benchmarks namely Grep, Bigram, Inverted Index, Word Co-occurrence and Terasort. Our method, when tested on a 25 node Hadoop cluster shows 45-66% decrease in execution time of Hadoop jobs on an average, when compared to prior methods. Further, our experiments also indicate that the parameters tuned by our method are resilient to changes in number of cluster nodes, which makes our method suitable to optimize Hadoop when it is provided as a service on the cloud.


title: Affective interaction: How emotional agents affect users

abstract: Embodied agents have received large amounts of interest in recent years. They are often equipped with the ability to express emotion, but without understanding the impact this can have on the user. Given the amount of research studies that are utilising agent technology with affective capabilities, now is an important time to review the influence of synthetic agent emotion on user attitudes, perceptions and behaviour. We therefore present a structured overview of the research into emotional simulation in agents, providing a summary of the main studies, re-formulating appropriate results in terms of the emotional effects demonstrated, and an in-depth analysis illustrating the similarities and inconsistencies between different experiments across a variety of different domains. We highlight important lessons, future areas for research, and provide a set of guidelines for conducting further research.


title: The Apriori Algorithm – a Tutorial

abstract: Association rules are ”if-then rules” with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms. The first and arguably most influential algorithm for efficient association rule discovery is Apriori. In the following we will review basic concepts of association rule discovery including support, confidence, the apriori property, constraints and parallel algorithms. The core consists of a review of the most important algorithms for association rule discovery. Some familiarity with concepts like predicates, probability, expectation and random variables is assumed.


Effective and Efficient Multilabel Classification in Domains with Large Number of Labels


title: Current and Evolving Clinical Options for HIV-Infected Patients with Chronic Diarrhoea

abstract: Diarrhoeal diseases continue to play a major role in the lives of HIV-positive people, impacting negatively on quality of life and causing significant morbidity and mortality. Within the global HIV pandemic, there are distinct geographical variations in the aetiology of chronic diarrhoea and the strategies towards its diagnosis and management. This article aims to highlight the contemporary approach to chronic diarrhoea in HIV-positive people, expand upon the myriad agents responsible for this presentation, and explore present and future therapeutic and management options.


title: Improved wide-angle, fisheye and omnidirectional camera calibration

abstract: Abstract In this paper an improved method for calibrating wide-angle, fisheye and omnidirectional imaging systems is presented. We extend the calibration procedure proposed by Scaramuzza et al. by replacing the residual function and joint refinement of all parameters. In doing so, we achieve a more stable, robust and accurate calibration (up to factor 7) and can reduce the number of necessary calibration steps from five to three. After introducing the camera model and highlighting the differences from the current calibration procedure, we perform a comprehensive performance evaluation using several data sets and show the impact of the proposed calibration procedure on the calibration results.


title: Ataxia, Intellectual Disability, and Ocular Apraxia with Cerebellar Cysts: A New Disease?

abstract: Cerebellar cysts are rare findings in pediatric neuroimaging and rather characteristic for dystroglycanopathies and GPR56-related encephalopathy. We aim to report on seven children with cerebellar cysts showing absence of weakness and ruling out mutations within eight dystroglycanopathy genes and GPR56. Data about neurological and ophthalmological features, outcome, and creatine kinase values were collected from clinical histories and follow-up examinations. All MR images were qualitatively evaluated for infra- and supratentorial abnormalities. A SNP 6.0-Array was performed in three children. The POMT1, POMT2, POMGnT1, FKRP, FKTN, LARGE, ISPD, B3GALNT2, and GPR56 genes were screened in all patients by Sanger sequencing. Seven children from five families were studied. Ataxia, intellectual disability, and language impairment were found in all patients, ocular motor apraxia in five, and severe myopia in three. None of the patients had weakness, only three a minimally increased creatine kinase value. Qualitative neuroimaging evaluation showed cerebellar cysts and dysplasia in the cerebellar hemispheres and vermis in all children. Additional findings were an enlarged fourth ventricle in all children, vermian hypoplasia and brain stem morphological abnormalities in five. The SNP array showed no pathogenetic imbalances in all children evaluated. In all patients, no mutations were found in POMT1, POMT2, POMGnT1, FKRP, FKTN, LARGE, ISPD, B3GALNT2, and GPR56. The peculiar combination of the same clinical and neuroimaging findings in our patients highly suggests that this phenotype may represent a novel entity, possibly falling within the spectrum of dystroglycanopathies.


title: Standardization of a neutralizing anti-vaccinia antibodies titration method: an essential step for titration of vaccinia immunoglobulins and smallpox vaccines evaluation.

abstract: The possibility of mass population vaccination with smallpox vaccine implies the development of anti-vaccinia immunoglobulins for the treatment of severe side effects following vaccination. We have chosen to develop and validate the "gold standard method" (plaque reduction neutralization assay) to titrate neutralizing anti-vaccinia antibodies in two different French laboratories belonging to the Department of Defense (CRSSA) and to the French Health Products Safety Agency (Afssaps). The results of precision, linearity and accuracy of the method led to consider the method as validated. In parallel, we have prepared and lyophilized a pool of anti-vaccinia plasma samples issued from a unique donor and qualified this preparation versus the first British standard to use it as an in-house standard with a titer of 25 international units (IU). This work will allow to titrate, in IU, sera from vaccinated persons in order (i) to titrate purified anti-vaccinia immunoglobulin preparations for vaccine severe side effect treatments; (ii) to investigate the level of neutralizing antibodies in the general population; and (iii) to investigate clinical trials of new generation smallpox vaccines. In the future, this will allow comparability of studies on either smallpox vaccines or on the serological status of the population.


title: Optimizing image processing on multi-core CPUs with Intel parallel programming technologies

abstract: The rapid advance of computer hardware and popularity of multimedia applications enable multi-core processors with sub-word parallelism instructions to become a dominant market trend in desk-top PCs as well as high end mobile devices. This paper presents an efficient parallel implementation of 2D convolution algorithm demanding high performance computing power in multi-core desktop PCs. It is a representative computation intensive algorithm, in image and signal processing applications, accompanied by heavy memory access; on the other hand, their computational complexities are relatively low. The purpose of this study is to explore the effectiveness of exploiting the streaming SIMD (Single Instruction Multiple Data) extension (SSE) technology and TBB (Threading Building Block) run-time library in Intel multi-core processors. By doing so, we can take advantage of all the hardware features of multi-core processor concurrently for data- and task-level parallelism. For the performance evaluation, we implemented a 3?×?3 kernel based convolution algorithm using SSE2 and TBB with different combinations and compared their processing speeds. The experimental results show that both technologies have a significant effect on the performance and the processing speed can be greatly improved when using two technologies at the same time; for example, 6.2, 6.1, and 1.4 times speedup compared with the implementation of either of them are suggested for 256?×?256, 512?×?512, and 1024?×?1024 data sets, respectively.


title: Peer-to-Peer Botnets: Overview and Case Study

abstract: Botnets have recently been identified as one of the most important threats to the security of the Internet. Traditionally, botnets organize themselves in an hierarchical manner with a central command and control location. This location can be statically defined in the bot, or it can be dynamically defined based on a directory server. Presently, the centralized characteristic of botnets is useful to security professionals because it offers a central point of failure for the botnet. In the near future, we believe attackers will move to more resilient architectures. In particular, one class of botnet structure that has entered initial stages of development is peer-to-peer based architectures. In this paper, we present an overview of peer-to-peer botnets. We also present a case study of a Kademlia-based Trojan.Peacomm bot.


title: Torque Ripple Minimization of PMSM Based on Robust ILC Via Adaptive Sliding Mode Control

abstract: Torque ripples due to cogging torque, current measurement errors, and flux harmonics restrict the application of the permanent magnet synchronous motor (PMSM) that has a high-precision requirement. The torque pulsation varies periodically along with the rotor position, and it results in speed ripples, which further degrade the performance of the PMSM servo system. Iterative learning control (ILC), in parallel with the classical proportional integral (PI) controller (i.e., PI-ILC), is a conventional method to suppress the torque ripples. However, it is sensitive to the system uncertainties and external disturbances, i.e., it is paralyzed to nonperiodic disturbances. Therefore, this paper proposes a robust ILC scheme achieved by an adaptive sliding mode control (SMC) technique to further reduce the torque ripples and improve the antidisturbance ability of the servo system. ILC is employed to reduce the periodic torque ripples and the SMC is used to guarantee fast response and strong robustness. An adaptive algorithm is utilized to estimate the system lumped disturbances, including parameter variations and external disturbances. The estimated value is utilized to compensate the robust ILC speed controller in order to eliminate the effects of the disturbance, and it can suppress the sliding mode chattering phenomenon simultaneously. Experiments were carried out on a digital signal processor-field programmable gate array based platform. The obtained experimental results demonstrate that the robust ILC scheme has an improved performance with minimized torque ripples and it exhibits a satisfactory antidisturbance performance compared to the PI-ILC method.


title: A skeleton-based approach to grasp known objects with a humanoid robot

abstract: This paper is about grasping known objects of arbitrary shape with a humanoid robot. We extend our previous work, where we presented a grasp planning method using an object representation based on the medial axis transform (MAT). The MAT describes an object's topological skeleton and contains information about local symmetry properties and thickness valuable for grasp planning. So far, our previous work was only conducted in simulation. The contribution of this paper is the transfer of our grasp planning method to the real world. We present grasping experiments with challenging arbitrarily shaped objects where we execute the grasps generated by our grasp planner on a real humanoid robot with a five-finger hand.


title: Eta Squared, Partial Eta Squared, and Misreporting of Effect Size in Communication Research

abstract: Communication researchers, along with social scientists from a variety of disciplines, are increasingly recognizing the importance of reporting effect sizes to augment significance tests. Serious errors in the reporting of effect sizes, however, have appeared in recently published articles. This article calls for accurate reporting of estimates of effect size. Eta squared (η 2 ) is the most commonly reported estimate of effect sized for the ANOVA. The classical formulation of eta squared (Pearson, 1911; Fisher, 1928) is distinguished from the lesser known partial eta squared (Cohen, 1973), and a mislabeling problem in the statistical software SPSS (1998) is identified. What SPSS reports as eta squared is really partial eta squared. Hence, researchers obtaining estimates of eta squared from SPSS are at risk of reporting incorrect values. Several simulations are reported to demonstrate critical issues. The strengths and limitations of several estimates of effect size used in ANOVA are discussed, as are the implications of the reporting errors. A list of suggestions for researchers is then offered.


title: Future therapeutic options in food allergy.

abstract: Up to 5% of young children and 2% of adults suffer from food allergy. Among them many have immunoglobulin E (IgE)-mediated food allergy, a condition with potentially fatal allergic reactions. Several studies have addressed possible definite treatment options for food allergy. Immunotherapy, by the oral route or by systemic injections shows promising preliminary results, but current interpretation of these therapeutic options are mostly handicapped by studies with insufficient scientific support, or by severe side-effects. Currently, no studies can support pharmacotherapy. Finally, most promising results were recently published with anti-IgE antibodies in a human trial, or various approaches in a mouse model of food allergy (chinese herbal medicine, specific modulation of the T cell response). Rapidly evolving findings might provide hope for a cure of food allergy in the near future.


title: Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms

abstract: Clustering non-Euclidean data is difficult, and one of the most used algorithms besides hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also simply referred to as k-medoids.


title: Can Active Memory Replace Attention?

abstract: Several mechanisms to focus attention of a neural network on selected parts of its input or memory have been used successfully in deep learning models in recent years. Attention has improved image classification, image captioning, speech recognition, generative models, and learning algorithmic tasks, but it had probably the largest impact on neural machine translation. Recently, similar improvements have been obtained using alternative mechanisms that do not focus on a single part of a memory but operate on all of it in parallel, in a uniform way. Such mechanism, which we call active memory, improved over attention in algorithmic tasks, image processing, and in generative modelling. So far, however, active memory has not improved over attention for most natural language processing tasks, in particular for machine translation. We analyze this shortcoming in this paper and propose an extended model of active memory that matches existing attention models on neural machine translation and generalizes better to longer sentences. We investigate this model and explain why previous active memory models did not succeed. Finally, we discuss when active memory brings most benefits and where attention can be a better choice.


title: ALOHA-KV: high performance read-only and write-only distributed transactions

abstract: There is a trend in recent database research to pursue coordination avoidance and weaker transaction isolation under a long-standing assumption: concurrent serializable transactions under read-write or write-write conflicts require costly synchronization, and thus may incur a steep price in terms of performance. In particular, distributed transactions, which access multiple data items atomically, are considered inherently costly. They require concurrency control for transaction isolation since both read-write and write-write conflicts are possible, and they rely on distributed commitment protocols to ensure atomicity in the presence of failures. This paper presents serializable read-only and write-only distributed transactions as a counterexample to show that concurrent transactions can be processed in parallel with low-overhead despite conflicts. Inspired by the slotted ALOHA network protocol, we propose a simpler and leaner protocol for serializable read-only write-only transactions, which uses only one round trip to commit a transaction in the absence of failures irrespective of contention. Our design is centered around an epoch-based concurrency control (ECC) mechanism that minimizes synchronization conflicts and uses a small number of additional messages whose cost is amortized across many transactions. We integrate this protocol into ALOHA-KV, a scalable distributed key-value store for read-only write-only transactions, and demonstrate that the system can process close to 15 million read/write operations per second per server when each transaction batches together thousands of such operations.


title: Rule-based machine translation from English to Telugu with emphasis on prepositions

abstract: This paper deals with adaptive rule based machine translation from English to Telugu. This is a proposed approach and it is a rule-based methodology. Set of production rules, training set for English and Telugu sentences and English to telugu dictionary are developed for this purpose. In the process of machine translation, handling prepositions is the main issue. There are many different kinds of prepositions that are being used in English, which can be translated as postpositions in Telugu. Time, Gender, place, context and many other features play an important role in selecting the appropriate postposition in Telugu. Some prepositions will have different meanings and the appropriate one should be selected based on the context. Through the proposed algorithm frequently used prepositions can be handled and translated perfectly.


title: Fake News Detection on Social Media using Geometric Deep Learning

abstract: Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access and rapid dissemination. This however comes at the cost of dubious trustworthiness and significant risk of exposure to 'fake news', intentionally written to mislead the readers. Automatically detecting fake news poses challenges that defy existing content-based analysis approaches. One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing. Recent studies have shown that fake and real news spread differently on social media, forming propagation patterns that could be harnessed for the automatic fake news detection. Propagation-based approaches have multiple advantages compared to their content-based counterparts, among which is language independence and better resilience to adversarial attacks. In this paper we show a novel automatic fake news detection model based on geometric deep learning. The underlying core algorithms are a generalization of classical CNNs to graphs, allowing the fusion of heterogeneous data such as content, user profile and activity, social graph, and news propagation. Our model was trained and tested on news stories, verified by professional fact-checking organizations, that were spread on Twitter. Our experiments indicate that social network structure and propagation are important features allowing highly accurate (92.7% ROC AUC) fake news detection. Second, we observe that fake news can be reliably detected at an early stage, after just a few hours of propagation. Third, we test the aging of our model on training and testing data separated in time. Our results point to the promise of propagation-based approaches for fake news detection as an alternative or complementary strategy to content-based approaches.


title: Design of Triple Band Differential Rectenna for RF Energy Harvesting

abstract: A triple band differential rectenna for RF energy harvesting applications is proposed in this paper. The rectenna is designed to operate in frequency bands of universal mobile telecommunication service (2.1 GHz), lower WLAN/Wi-Fi (2.4–2.48 GHz), and WiMAX (3.3–3.8 GHz). For designing the proposed rectenna, first a differentially fed multiband slot antenna that works as the front-end receiving unit is designed, fabricated, and tested to check its performance. It is observed that a peak antenna gain of 7, 5.5, and 9.2 dBi is achieved at 2, 2.5, and 3.5 GHz, respectively. In the next step, a triple band differential rectifier is designed using the Villard voltage doubler where interdigital capacitors (IDCs) in lieu of lumped components are used. The full rectifier circuit comprising of the rectifying unit and impedance matching circuit is fabricated and tested to check its performance in the desired bands. The peak RF-dc conversion efficiency of 68% is obtained using the three-tone measurement. In the final stage, both antenna and the rectifier circuit are integrated through SMA connecter in order to implement the proposed rectenna. Measurement of the proposed rectenna shows an approximate maximum efficiency of 53% at 2 GHz, 31% at 2.5 GHz, and 15.56% at 3.5 GHz.


title: 2.1 28Gb/s 560mW multi-standard SerDes with single-stage analog front-end and 14-tap decision-feedback equalizer in 28nm CMOS

abstract: A high-speed SerDes must meet multiple challenges including high-speed operation, intensive equalization technique, low power consumption, small area and robustness. In order to meet new standards, such a OIF CEI-25G-LR, CEI-28G-MR/SR/VSR, IEEE802.3bj and 32G-FC, data-rates are increased to 25 to 28Gb/s, which is more than 75% higher than the previous generation of SerDes. For SerDes applications with several hundreds of lanes integrated in single chip, power consumption is very important factor while maintaining high performance. There are several previous works at 28Gb/s or higher data-rate [1-2]. They use an unrolled DFE to meet the critical timing margin, but the unrolled DFE structure increases the number of DFE slicers, increasing the overall power and die area. In order to tackle these challenges, we introduce several circuits and architectural techniques. The analog front-end (AFE) uses a single-stage architecture and a compact on-chip passive inductor in the transimpedance amplifier (TIA), providing 15dB boost. The boost is adaptive and its adaptation loop is decoupled from the decision-feedback equalizer (DFE) adaptation loop by the use of a group-delay adaptation (GDA) algorithm. DFE has a half-rate 1-tap unrolled structure with 2 total error latches for power and area reduction. A two-stage sense-amplifier-based slicer achieves a sensitivity of 15mV and DFE timing closure. We also develop a high-speed clock buffer that uses a new active-inductor circuit. This active-inductor circuit has the capability to control output-common-mode voltage to optimize circuit operating points.


title: RuleGrowth: mining sequential rules common to several sequences by pattern-growth

abstract: Mining sequential rules from large databases is an important topic in data mining fields with wide applications. Most of the relevant studies focused on finding sequential rules appearing in a single sequence of events and the mining task dealing with multiple sequences were far less explored. In this paper, we present RuleGrowth, a novel algorithm for mining sequential rules common to several sequences. Unlike other algorithms, RuleGrowth uses a pattern-growth approach for discovering sequential rules such that it can be much more efficient and scalable. We present a comparison of RuleGrowth's performance with current algorithms for three public datasets. The experimental results show that RuleGrowth clearly outperforms current algorithms for all three datasets under low support and confidence threshold and has a much better scalability.


title: Detection of chewing from piezoelectric film sensor signals using ensemble classifiers

abstract: Selection and use of pattern recognition algorithms is application dependent. In this work, we explored the use of several ensembles of weak classifiers to classify signals captured from a wearable sensor system to detect food intake based on chewing. Three sensor signals (Piezoelectric sensor, accelerometer, and hand to mouth gesture) were collected from 12 subjects in free-living conditions for 24 hrs. Sensor signals were divided into 10 seconds epochs and for each epoch combination of time and frequency domain features were computed. In this work, we present a comparison of three different ensemble techniques: boosting (AdaBoost), bootstrap aggregation (bagging) and stacking, each trained with 3 different weak classifiers (Decision Trees, Linear Discriminant Analysis (LDA) and Logistic Regression). Type of feature normalization used can also impact the classification results. For each ensemble method, three feature normalization techniques: (no-normalization, z-score normalization, and minmax normalization) were tested. A 12 fold cross-validation scheme was used to evaluate the performance of each model where the performance was evaluated in terms of precision, recall, and accuracy. Best results achieved here show an improvement of about 4% over our previous algorithms.


title: Speculative Buffer Overflows: Attacks and Defenses

abstract: Practical attacks that exploit speculative execution can leak confidential information via microarchitectural side channels. The recently-demonstrated Spectre attacks leverage speculative loads which circumvent access checks to read memory-resident secrets, transmitting them to an attacker using cache timing or other covert communication channels. We introduce Spectre1.1, a new Spectre-v1 variant that leverages speculative stores to create speculative buffer overflows. Much like classic buffer overflows, speculative out-of-bounds stores can modify data and code pointers. Data-value attacks can bypass some Spectre-v1 mitigations, either directly or by redirecting control flow. Control-flow attacks enable arbitrary speculative code execution, which can bypass fence instructions and all other software mitigations for previous speculative-execution attacks. It is easy to construct return-oriented-programming (ROP) gadgets that can be used to build alternative attack payloads. We also present Spectre1.2: on CPUs that do not enforce read/write protections, speculative stores can overwrite read-only data and code pointers to breach sandboxes. We highlight new risks posed by these vulnerabilities, discuss possible software mitigations, and sketch microarchitectural mechanisms that could serve as hardware defenses. We have not yet evaluated the performance impact of our proposed software and hardware mitigations. We describe the salient vulnerability features and additional hypothetical attack scenarios only to the detail necessary to guide hardware and software vendors in threat analysis and mitigations. We advise users to refer to more user-friendly vendor recommendations for mitigations against speculative buffer overflows or available patches.


title: Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis.

abstract: We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN). Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network. Furthermore, such methods have only been shown to be reliable for relatively simple human motions, such as walking or running. In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion. The acRNN is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training. Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r.t. different styles.


title: A Deployed People-to-People Recommender System in Online Dating

abstract: Online dating is a prime application area for recommender systems, as users face an abundance of choice, must act on limited information, and are participating in a competitive matching market. This article reports on the successful deployment of a people-to-people recommender system on a large commercial online dating site. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. Results taken a few months after deployment show that the recommender system delivered its projected benefits.


title: Telecom strategies for service discovery in microservice environments

abstract: In order to communicate with another service, a service needs to know the address of other services. This knowledge can be acquired by a process called service discovery (SD). In a microservice architecture (MSA), unlike SOA, a service discovery framework has not been provided out of the box. In a telecom environment additional requirements may arise, besides the basic one of discoverability. In this paper we shortly present the state of the art in service discovery with reflections on the specific needs of MSA and in particular in the context of telecom applications. In this context open source frameworks are investigated and compared.


title: Comparing Frequency- and Style-Based Features for Twitter Author Identification.

abstract: Author identification is a subfield of Natural Language Processing (NLP) that uses machine learning techniques to identify the author of a text. Most previous research focused on long texts with the assumption that a minimum text length threshold exists under which author identification would no longer be effective. This paper examines author identification in short texts far below this threshold, focusing on messages retrieved from Twitter (maximum length: 140 characters) to determine the most effective feature set for author identification. Both Bag-of-Words (BOW) and Style Marker feature sets were extracted and evaluated through a series of 15 experiments involving up to 12 authors with large and small dataset sizes. Support Vector Machines (SVM) were used for all experiments. Our results achieve classification accuracies approaching that of longer texts, even for small dataset sizes of 60 training instances per author. Style Marker feature sets were found to be significantly more useful than BOW feature sets as well as orders of magnitude faster, and are therefore suggested for potential applications in future research.


title: Empirical evaluation of scoring functions for Bayesian network model selection

abstract: In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike's information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure; the BDeu score is sensitive to the parameter settings; and the fNML performs pretty well on small datasets. We also tested a greedy hill climbing algorithm and observed similar results as the optimal algorithm.


title: Graded relevance ranking for synonym discovery

abstract: Interest in domain-specific search is steadfastly increasing, yielding a growing need for domain-specific synonym discovery. Existing synonym discovery methods perform poorly when faced with the realistic task of identifying a target term's synonyms from among many candidates. We approach domain-specific synonym discovery as a graded relevance ranking problem in which a target term's synonym candidates are ranked by their quality. In this scenario a human editor uses each ranked list of synonym candidates to build a domain-specific thesaurus. We evaluate our method for graded relevance ranking of synonym candidates and find that it outperforms existing methods.


title: Walking, Cycling, and Obesity Rates in Europe, North America, and Australia

abstract: PURPOSE ::: This study was designed to examine the relationship between active transportation (defined as the percentage of trips taken by walking, bicycling, and public transit) and obesity rates (BMI > or = 30 kg . m-2) in different countries. ::: ::: ::: METHODS ::: National surveys of travel behavior and health indicators in Europe, North America, and Australia were used in this study; the surveys were conducted in 1994 to 2006. In some cases raw data were obtained from national or federal agencies and then analyzed, and in other cases summary data were obtained from published reports. ::: ::: ::: RESULTS ::: Countries with the highest levels of active transportation generally had the lowest obesity rates. Europeans walked more than United States residents (382 versus 140 km per person per year) and bicycled more (188 versus 40 km per person per year) in 2000. ::: ::: ::: DISCUSSION ::: Walking and bicycling are far more common in European countries than in the United States, Australia, and Canada. Active transportation is inversely related to obesity in these countries. Although the results do not prove causality, they suggest that active transportation could be one of the factors that explain international differences in obesity rates.


title: Applications of Deep Learning and Reinforcement Learning to Biological Data

abstract: Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics , bioimaging , medical imaging , and (brain/body)–machine interfaces . These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.


title: Expectation and purpose: understanding users' mental models of mobile app privacy through crowdsourcing

abstract: Smartphone security research has produced many useful tools to analyze the privacy-related behaviors of mobile apps. However, these automated tools cannot assess people's perceptions of whether a given action is legitimate, or how that action makes them feel with respect to privacy. For example, automated tools might detect that a blackjack game and a map app both use one's location information, but people would likely view the map's use of that data as more legitimate than the game. Our work introduces a new model for privacy, namely privacy as expectations. We report on the results of using crowdsourcing to capture users' expectations of what sensitive resources mobile apps use. We also report on a new privacy summary interface that prioritizes and highlights places where mobile apps break people's expectations. We conclude with a discussion of implications for employing crowdsourcing as a privacy evaluation technique.


title: A reference architecture for real-time microservice API consumption

abstract: Modern web frameworks and backend-as-a-service providers make it possible for real-time updates to a NoSQL data model to be reflected in the user interfaces of multiple subscribing end-user applications. However, it remains difficult for users to dynamically discover and instantly make use of the data provided by the plethora of REST APIs in existence across various cloud providers today. This paper presents a reference architecture built on the idea of a scalable NoSQL database that allows multiple subscribers to receive instant notifications of database changes through the use of a "livequery". By keeping one WebSocket connection open between each client web browser and an Object Synchronization Server, this paper shows how data from multiple disparate REST APIs can be organized and transmitted to interested clients via the database. An example is given featuring a collaborative rich-text editor that makes use of a Named-Entity Recognition microservice.


title: Housekeeping by Leishmania

abstract: The observation that differentiation of parasite life cycle stages involves structural and functional reorganisation is very familiar. A recent paper by Besteiro and colleagues provides an intriguing insight into cellular remodelling during differentiation. They show that it is essential for Leishmania to tidy up and put the house in order before it can differentiate to mammal-infective stages.


title: An Energy-Efficient and Robust Voltage Level Converter for Nanoelectronics

abstract: Low-power design has recently become very important especially in nanoelectronic VLSI circuits and systems. Functioning of circuits at ultra-low voltages leads to lower power consumption per operation. An efficient method is to separate the logic blocks based on their performance requirement and applying a specific supply voltage for each block. In order to prevent an enormous static current in these multi-VDD circuits, voltage level converters are essential. This study presents an energy-efficient and robust single-supply level converter (SSLC) based on multi-threshold carbon nanotube FETs (CNTFETs). Unique characteristics of the CNTFET device and transistor stacking are utilized suitably to reduce the power and energy consumption of the proposed LC. The results of the extensive simulations, conducted using 32nm CNTFET technology of Stanford University indicate the superiority of the proposed design in terms energy-efficiency and robustness to process, voltage and temperature variations, as compared to the other conventional and state-of-the-art LC circuits, previously presented in the literature. The results demonstrate almost on average 35%, 55%, 90% and 68% improvements in terms of delay, total power, static power and energy consumption, respectively. Index Terms—Nanoelectronics, Carbon nanotube field effect transistor (CNTFET), Low-power design, Voltage level converter


title: Chaperone-mediated autophagy: a unique way to enter the lysosome world.

abstract: All cellular proteins undergo continuous synthesis and degradation. This permanent renewal is necessary to maintain a functional proteome and to allow rapid changes in levels of specific proteins with regulatory purposes. Although for a long time lysosomes were considered unable to contribute to the selective degradation of individual proteins, the discovery of chaperone-mediated autophagy (CMA) changed this notion. Here, we review the characteristics that set CMA apart from other types of lysosomal degradation and the subset of molecules that confer cells the capability to identify individual cytosolic proteins and direct them across the lysosomal membrane for degradation.


Ecological cue-validity of proximity and of other Gestalt factors.


A Secure Sketch-based Authentication Scheme for Telecare Medicine Information Systems.


title: A High-Linearity, 17 ps Precision Time-to-Digital Converter Based on a Single-Stage Vernier Delay Loop Fine Interpolation

abstract: This paper presents a time-to-digital converter (TDC) architecture capable of reaching high-precision and high-linearity with moderate area occupation per measurement channel. The architecture is based on a coarse counter and a couple of two-stage interpolators that exploit the cyclic sliding scale technique in order to improve the conversion linearity. The interpolators are based on a new coarse-fine synchronization circuit and a new single-stage Vernier delay loop fine interpolation. In a standard cost-effective 0.35 μm CMOS technology the TDC reaches a dynamic range of 160 ns, 17.2 ps precision and differential non-linearity better than 0.9% LSB rms. The TDC building block was designed in order to be easily assembled in a multi-channel monolithic TDC chip. Coupled with a SPAD photodetector it is aimed for TCSPC applications (like FLIM, FCS, FRET) and direct ToF 3-D ranging.


title: A Preliminary Taxonomy of Crowdsourcing

abstract: Many firms are now asking how they can benefit from the new form of outsourcing labelled “crowdsourcing”. Like many other forms of outsourcing, crowdsourcing is now being “talked up” by a somewhat credulous trade press. However, the term crowdsourcing has been used to describe several related, but different phenomena, and what might be successful with one form of crowdsourcing may not be with another. In this paper the notion of crowdsourcing is decomposed to create a taxonomy that expands our understanding of what is meant by the term. This taxonomy focuses on the different capability levels of crowdsourcing suppliers; different motivations; and different allocation of benefits. The management implications of these distinctions are then considered in light of what we know about other forms of outsourcing.


title: The Sleepy Model of Consensus

abstract: The literature on distributed computing (as well as the cryptography literature) typically considers two types of players—honest players and corrupted players. Resilience properties are then analyzed assuming a lower bound on the fraction of honest players. Honest players, however, are not only assumed to follow the prescribed the protocol, but also assumed to be online throughout the whole execution of the protocol. The advent of “large-scale” consensus protocols (e.g., the blockchain protocol) where we may have millions of players, makes this assumption unrealistic. In this work, we initiate a study of distributed protocols in a “sleepy” model of computation where players can be either online (awake) or offline (asleep), and their online status may change at any point during the protocol. The main question we address is: ::: Can we design consensus protocols that remain resilient under “sporadic participation”, where at any given point, only a subset of the players are actually online? ::: As far as we know, all standard consensus protocols break down under such sporadic participation, even if we assume that \(99\%\) of the online players are honest.


title: Some HCI Priorities for GDPR-Compliant Machine Learning

abstract: In this short paper, we consider the roles of HCI in enabling the better governance of consequential machine learning systems using the rights and obligations laid out in the recent 2016 EU General Data Protection Regulation (GDPR)---a law which involves heavy interaction with people and systems. Focussing on those areas that relate to algorithmic systems in society, we propose roles for HCI in legal contexts in relation to fairness, bias and discrimination; data protection by design; data protection impact assessments; transparency and explanations; the mitigation and understanding of automation bias; and the communication of envisaged consequences of processing.


title: Consumer attitudes toward blogger's sponsored recommendations and purchase intention: The effect of sponsorship type, product type, and brand awareness

abstract: Abstract Sponsored recommendation blog posts, a form of online consumer review, are blog articles written by bloggers who receive benefits from sponsoring marketers to review and promote products on their personal blog. Because national regulations require that marketer sponsorship must be revealed in the blog post, sponsored recommendation posts can no longer conceal their marketing intent. Consumer’s attitudes toward sponsored recommendation posts are thus a vital issue in assessing the effectiveness of the advertisement. This study uses a 2(sponsorship type) × 2(product type) × 2(brand awareness) experimental design and a total of 613 valid samples to examine consumer attitudes toward sponsored recommendation posts and purchase intention. The results show that when products recommended in blog posts are search goods or have high brand awareness, consumers have highly positive attitudes toward sponsored recommendation posts, which improves purchase intention. The directly-monetary/indirect-monetary benefits received by the bloggers have no significant effect on readership attitudes. Using these features in blog writings appears to improve online readers’ trust toward and the credibility of sponsored recommendation posts and thus can be a vital online marketing tool for marketers.


title: Large residual multiple view 3D CNN for false positive reduction in pulmonary nodule detection

abstract: Pulmonary nodules detection play a significant role in the early detection and treatment of lung cancer. False positive reduction is the one of the major parts of pulmonary nodules detection systems. In this study a novel method aimed at recognizing real pulmonary nodule among a large group of candidates was proposed. The method consists of three steps: appropriate receptive field selection, feature extraction and a strategy for high level feature fusion and classification. The dataset consists of 888 patient's chest volume low dose computer tomography (LDCT) scans, selected from publicly available LIDC-IDRI dataset. This dataset was marked by LUNA16 challenge organizers resulting in 1186 nodules. Trivial data augmentation and dropout were applied in order to avoid overfitting. Our method achieved high competition performance metric (CPM) of 0.735 and sensitivities of 78.8% and 83.9% at 1 and 4 false positives per scan, respectively. This study is also accompanied by detailed descriptions and results overview in comparison with the state of the art solutions.


title: A 550-$\mu\hbox{W}$ 10-b 40-MS/s SAR ADC With Multistep Addition-Only Digital Error Correction

abstract: A speed-enhanced 10-b asynchronous SAR ADC with multistep addition-only digital error correction (ADEC) is presented with a straightforward DAC switching algorithm. The capacitor DAC is virtually divided into three sub-DACs for ADEC with negligible hardware overhead. The redundant decision cycles between stages reconfigure the capacitor connection of the DAC. These redundancies guarantee 10-b linearity under 4-b-accurate DAC settling in the MSB decision and the optimally designed ADC enhances the conversion speed by 37%. A prototype ADC was implemented in a CMOS 0.13-μm technology. The chip consumes 550 μW and achieves a 50.6-dB SNDR at 40 MS/s under a 1.2-V supply. The figure-of-merit (FOM) is 50 fJ/conversion-step.


abstract: A programmable dual hysteretic window comparator is presented in this paper. The comparator uses a cascoded flipped voltage follower (CASFVF), a high speed V-I converter, and a current comparator to enhance the response time and accuracy. Moreover, the positive and negative hysteretic thresholds can be programmable, respectively. Simulation results in 0.25-mum CMOS technology demonstrate the validity of the designed approach.


title: Automatic syllable stress detection using prosodic features for pronunciation evaluation of language learners

abstract: A robust language learning system, designed to help students practice a foreign language along with a machine tutor, must provide meaningful feedback to users by isolating and localizing their pronunciation errors. This paper presents a new technique for automatic syllable stress detection that is tailored for language-learning purposes. Our method, which uses basic prosodic features and others related to the fundamental frequency slope and RMS energy range, is at least as accurate as an expert human listener, but requires no human supervision other than a pre-defined dictionary of expected lexical stress patterns for all words in the system's vocabulary. Optimal feature choices exhibited an 87-89% accuracy compared with human-tagged stress labels, exceeding the inter-human agreement commonly held to be about 80%.


title: Computational models of analogy

abstract: Analogical mapping is a core process in human cognition. A number of valuable computational models of analogy have been created, capturing aspects of how people compare representations, retrieve potential analogs from memory, and learn from the results. In the past 25 years, this area has progressed rapidly, fueled by strong collaboration between psychologists and Artificial Intelligence (AI) scientists, with contributions from linguists and philosophers as well. There is now considerable consensus regarding the constraints governing the mapping process.However,computational modelsstilldifferintheirfocus,withsomeaimed at capturing the range of analogical phenomena at the cognitive level and others aimed at modeling how analogical processes might be implemented in neural systems. Some recent work has focused on modeling interactions between analogy and other processes, and on modeling analogy as a part of larger cognitive systems.  2010 John Wiley & Sons, Ltd. WIREs Cogn Sci


title: Social media text mining and network analysis for decision support in natural crisis management

abstract: A core issue in crisis management is to extract from the mass of incoming information what is important for situational awareness during mass emergencies. Based on a case study we develop a prototypical application, TweetComp1, which is integrated into the decision-support component of a Tsunami early warning system and demonstrates the applicability of our approach. This paper describes four novel approaches using focused twitter crawling, trustworthiness analysis, geo-parsing, and multilingual tweet classification in the context of how they could be used for monitoring crises. The validity of our state-of-the art text mining and network analysis technologies will be verified in different experiments based on a human annotated gold standard corpus.


title: Analyzing Intensive Intrusion Alerts via Correlation

abstract: Traditional intrusion detection systems (IDSs) focus on low-level attacks or anomalies, and raise alerts independently, though there may be logical connections between them. In situations where there are intensive intrusions, not only will actual alerts be mixed with false alerts, but the amount of alerts will also become unmanageable. As a result, it is difficult for human users or intrusion response systems to understand the alerts and take appropriate actions. Several complementary alert correlation methods have been proposed to address this problem. As one of these methods, we have developed a framework to correlate intrusion alerts using prerequisites of intrusions. In this paper, we continue this work to study the feasibility of this method in analyzing real-world, intensive intrusions. In particular, we develop three utilities (called adjustable graph reduction, focused analysis, and graph decomposition) to facilitate the analysis of large sets of correlated alerts. We study the effectiveness of the alert correlation method and these utilities through a case study with the network traffic captured at the DEF CON 8 Capture the Flag (CTF) event. Our results show that these utilities can simplify the analysis of large amounts of alerts, and also reveals several attack strategies that were repeatedly used in the DEF CON 8 CTF event.


Three-Dimensional Container Loading: A Simulated Annealing Approach


title: Performance Improvement of Low-Latency V2I Uplink Using Superposed Cooperative V2V Transmission

abstract: In cooperative intelligent transport systems, local dynamic maps (LDMs) are used to achieve safe and efficient transport systems. To transmit LDM data, a reliable, low-latency wireless communication is required. However, the transmission quality between base stations and distant vehicles often degrades, resulting in delays due to retransmission requested by upper-layer protocols. To solve it, in this paper, we propose an uplink cooperative relay transmission scheme using two timeslots in the physical layer, where a vehicle close to a base station relays data from a more distant vehicle. The scheme uses non-orthogonal superposition relaying at the close vehicle and maximum ratio combining at the base station, and improves quality of the data received by the distant vehicle. In addition, high-latency retransmission can be avoided using this scheme. The performance of the proposed scheme is evaluated with link budget calculations and numerical simulations, and it is shown that the proposed scheme greatly enhances a throughput of the distant vehicle.


Learning to Detect and Match Keypoints with Deep Architectures


title: High-Speed Application Protocol Parsing and Extraction for Deep Flow Inspection

abstract: In this paper, we propose FlowSifter, a framework for automated online application protocol field extraction. FlowSifter is based on a new grammar model called Counting Regular Grammars (CRG) and a corresponding automata model called Counting Automata (CA). The CRG and CA models add counters with update functions and transition guards to regular grammars and finite state automata. These additions give CRGs and CAs the ability to parse and extract fields from context sensitive application protocols. These additions also facilitate fast and stackless approximate parsing of recursive structures. These new grammar models enable FlowSifter to generate optimized Layer 7 field extractors from simple extraction specifications. We compare FlowSifter against both BinPAC and UltraPAC, which represent the state-of-the-art field extractors. Our experiments show that when compared to BinPAC parsers, FlowSifter runs more than 21 times faster and uses 49 times less memory. When compared to UltraPAC parsers, FlowSifter extractors run 12 times faster and use 24 times less memory.


title: Robust Privacy-Preserving Image Sharing over Online Social Networks (OSNs)

abstract: Sharing images online has become extremely easy and popular due to the ever-increasing adoption of mobile devices and online social networks (OSNs). The privacy issues arising from image sharing over OSNs have received significant attention in recent years. In this article, we consider the problem of designing a secure, robust, high-fidelity, storage-efficient image-sharing scheme over Facebook, a representative OSN that is widely accessed. To accomplish this goal, we first conduct an in-depth investigation on the manipulations that Facebook performs to the uploaded images. Assisted by such knowledge, we propose a DCT-domain image encryption/decryption framework that is robust against these lossy operations. As verified theoretically and experimentally, superior performance in terms of data privacy, quality of the reconstructed images, and storage cost can be achieved.


title: Control of an Electrical Prosthesis With an SSVEP-Based BCI

abstract: Brain-computer interfaces (BCIs) are systems that establish a direct connection between the human brain and a computer, thus providing an additional communication channel. They are used in a broad field of applications nowadays. One important issue is the control of neuroprosthetic devices for the restoration of the grasp function in spinal-cord-injured people. In this communication, an asynchronous (self-paced) four-class BCI based on steady-state visual evoked potentials (SSVEPs) was used to control a two-axes electrical hand prosthesis. During training, four healthy participants reached an online classification accuracy between 44% and 88%. Controlling the prosthetic hand asynchronously, the participants reached a performance of 75.5 to 217.5 s to copy a series of movements, whereas the fastest possible duration determined by the setup was 64 s. The number of false negative (FN) decisions varied from 0 to 10 (the maximal possible decisions were 34). It can be stated that the SSVEP-based BCI, operating in an asynchronous mode, is feasible for the control of neuroprosthetic devices with the flickering lights mounted on its surface.


title: Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva

abstract: This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva’s software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. This article describes Minerva’s major software components, and provides a comparative analysis of the results obtained in the Smithsonian museum. During two weeks of highly successful operation, the robot interacted with thousands of people, both in the museum and through the Web, traversing more than 44km at speeds of up to 163 cm/sec in the unmodie d museum.


title: Some Weak Convergence Theorems for a Family of Asymptotically Nonexpansive Nonself Mappings

abstract: A one-step iteration with errors is considered for a family of asymptotically nonexpansive nonself mappings. Weak convergence of the purposed iteration is obtained in a Banach space.


title: I am a Smartwatch and I can Track my User's Arm

abstract: This paper aims to track the 3D posture of the entire arm - both wrist and elbow - using the motion and magnetic sensors on smartwatches. We do not intend to employ machine learning to train the system on a specific set of gestures. Instead, we aim to trace the geometric motion of the arm, which can then be used as a generic platform for gesture-based applications. The problem is challenging because the arm posture is a function of both elbow and shoulder motions, whereas the watch is only a single point of (noisy) measurement from the wrist. Moreover, while other tracking systems (like indoor/outdoor localization) often benefit from maps or landmarks to occasionally reset their estimates, such opportunities are almost absent here. While this appears to be an under-constrained problem, we find that the pointing direction of the forearm is strongly coupled to the arm's posture. If the gyroscope and compass on the watch can be made to estimate this direction, the 3D search space can become smaller; the IMU sensors can then be applied to mitigate the remaining uncertainty. We leverage this observation to design ArmTrak, a system that fuses the IMU sensors and the anatomy of arm joints into a modified hidden Markov model (HMM) to continuously estimate state variables. Using Kinect 2.0 as ground truth, we achieve around 9.2 cm of median error for free-form postures; the errors increase to 13.3 cm for a real time version. We believe this is a step forward in posture tracking, and with some additional work, could become a generic underlay to various practical applications.


title: Developing Learning System Continuance with Teachers and Students: Case Study of the Echo360 Lecture Capturing System.

abstract: Research on learning system continuance has been focused mostly on students’ conscious behaviour. While the institutional decision to adopt and sustain the deployment of a new technology provides strong support for organisational adoption, the key factor for achieving long-term continuance relies on individual behaviour from different stakeholders, e.g., students, support staff and teachers. Existing literature on Information Systems (IS) continuance, however, suffers from weaknesses. On the one hand, perceptions from other stakeholders, such as teachers and support staff, are often neglected in the literature. On the other hand, there is a theoretical gap in explaining continuance behaviour through traditional models because unconscious automatic behaviour (e.g., IS habit) plays a more critical role in influencing continuance behaviour than previously expected. This study takes a qualitative approach to identify perceptions from students and teachers on which features and usage patterns of a lecture capturing system (Echo360) would develop long-term continuance behaviour through both conscious and unconscious behaviours. Our results suggest that long-term continuance can be achieved by a combination of Information Technology (IT) artefact extension (e.g., providing better search functionality and multimedia tagging) and developed IS habits through curriculum design.


title: A systematic review of systematic review process research in software engineering

abstract: Context: Many researchers adopting systematic reviews (SRs) have also published papers discussing problems with the SR methodology and suggestions for improving it. Since guidelines for SRs in software engineering (SE) were last updated in 2007, we believe it is time to investigate whether the guidelines need to be amended in the light of recent research. Objective: To identify, evaluate and synthesize research published by software engineering researchers concerning their experiences of performing SRs and their proposals for improving the SR process. Method: We undertook a systematic review of papers reporting experiences of undertaking SRs and/or discussing techniques that could be used to improve the SR process. Studies were classified with respect to the stage in the SR process they addressed, whether they related to education or problems faced by novices and whether they proposed the use of textual analysis tools. Results: We identified 68 papers reporting 63 unique studies published in SE conferences and journals between 2005 and mid-2012. The most common criticisms of SRs were that they take a long time, that SE digital libraries are not appropriate for broad literature searches and that assessing the quality of empirical studies of different types is difficult. Conclusion: We recommend removing advice to use structured questions to construct search strings and including advice to use a quasi-gold standard based on a limited manual search to assist the construction of search stings and evaluation of the search process. Textual analysis tools are likely to be useful for inclusion/exclusion decisions and search string construction but require more stringent evaluation. SE researchers would benefit from tools to manage the SR process but existing tools need independent validation. Quality assessment of studies using a variety of empirical methods remains a major problem.


title: A close examination of performance and power characteristics of 4G LTE networks

abstract: With the recent advent of 4G LTE networks, there has been increasing interest to better understand the performance and power characteristics, compared with 3G/WiFi networks. In this paper, we take one of the first steps in this direction. Using a publicly deployed tool we designed for Android called 4GTest attracting more than 3000 users within 2 months and extensive local experiments, we study the network performance of LTE networks and compare with other types of mobile networks. We observe LTE generally has significantly higher downlink and uplink throughput than 3G and even WiFi, with a median value of 13Mbps and 6Mbps, respectively. We develop the first empirically derived comprehensive power model of a commercial LTE network with less than 6% error rate and state transitions matching the specifications. Using a comprehensive data set consisting of 5-month traces of 20 smartphone users, we carefully investigate the energy usage in 3G, LTE, and WiFi networks and evaluate the impact of configuring LTE-related parameters. Despite several new power saving improvements, we find that LTE is as much as 23 times less power efficient compared with WiFi, and even less power efficient than 3G, based on the user traces and the long high power tail is found to be a key contributor. In addition, we perform case studies of several popular applications on Android in LTE and identify that the performance bottleneck for web-based applications lies less in the network, compared to our previous study in 3G [24]. Instead, the device's processing power, despite the significant improvement compared to our analysis two years ago, becomes more of a bottleneck.


title: Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning

abstract: This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state.


title: Deep neural networks for learning graph representations

abstract: In this paper, we propose a novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information. Different from other previous research efforts, we adopt a random surfing model to capture graph structural information directly, instead of using the sampling-based method for generating linear sequences proposed by Perozzi et al. (2014). The advantages of our approach will be illustrated from both theorical and empirical perspectives. We also give a new perspective for the matrix factorization method proposed by Levy and Goldberg (2014), in which the pointwise mutual information (PMI) matrix is considered as an analytical solution to the objective function of the skip-gram model with negative sampling proposed by Mikolov et al. (2013). Unlike their approach which involves the use of the SVD for finding the low-dimensitonal projections from the PMI matrix, however, the stacked denoising autoencoder is introduced in our model to extract complex features and model non-linearities. To demonstrate the effectiveness of our model, we conduct experiments on clustering and visualization tasks, employing the learned vertex representations as features. Empirical results on datasets of varying sizes show that our model outperforms other stat-of-the-art models in such tasks.


title: Locality-Aware Reduce Task Scheduling for MapReduce

abstract: MapReduce offers a promising programming model for big data processing. Inspired by functional languages, MapReduce allows programmers to write functional-style code which gets automatically divided into multiple map and/or reduce tasks and scheduled over distributed data across multiple machines. Hadoop, an open source implementation of MapReduce, schedules map tasks in the vicinity of their inputs in order to diminish network traffic and improve performance. However, Hadoop schedules reduce tasks at requesting nodes without considering data locality leading to performance degradation. This paper describes Locality-Aware Reduce Task Scheduler (LARTS), a practical strategy for improving MapReduce performance. LARTS attempts to collocate reduce tasks with the maximum required data computed after recognizing input data network locations and sizes. LARTS adopts a cooperative paradigm seeking a good data locality while circumventing scheduling delay, scheduling skew, poor system utilization, and low degree of parallelism. We implemented LARTS in Hadoop-0.20.2. Evaluation results show that LARTS outperforms the native Hadoop reduce task scheduler by an average of 7%, and up to 11.6%.


title: Unsupervised Learning of Visual Structure using Predictive Generative Networks

abstract: The ability to predict future states of the environment is a central pillar of intelligence. At its core, effective prediction requires an internal model of the world and an understanding of the rules by which the world changes. Here, we explore the internal models developed by deep neural networks trained using a loss based on predicting future frames in synthetic video sequences, using a CNN-LSTM-deCNN framework. We first show that this architecture can achieve excellent performance in visual sequence prediction tasks, including state-of-the-art performance in a standard 'bouncing balls' dataset (Sutskever et al., 2009). Using a weighted mean-squared error and adversarial loss (Goodfellow et al., 2014), the same architecture successfully extrapolates out-of-the-plane rotations of computer-generated faces. Furthermore, despite being trained end-to-end to predict only pixel-level information, our Predictive Generative Networks learn a representation of the latent structure of the underlying three-dimensional objects themselves. Importantly, we find that this representation is naturally tolerant to object transformations, and generalizes well to new tasks, such as classification of static images. Similar models trained solely with a reconstruction loss fail to generalize as effectively. We argue that prediction can serve as a powerful unsupervised loss for learning rich internal representations of high-level object features.


Cross-Cultural Research: a Decision Factor in Standardized Versus Non-Standardized Global Advertising


title: Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing

abstract: In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua's data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well.


title: Low dietary diversity is a predictor of child stunting in rural Bangladesh

abstract: BACKGROUND/OBJECTIVES ::: Dietary diversity is associated with overall quality and nutrient adequacy of the diet in low-income countries. We determined the association between dietary diversity and stunting among children aged 6-59 months in rural Bangladesh. ::: ::: ::: SUBJECTS/METHODS ::: In total, 165 111 under-fives who participated in the National Surveillance Project in 2003-2005 were included in the analysis. Dietary diversity score (DDS) was constructed through the summation of the number of days each of the nine food groups was consumed in the previous week. The association between stunting and DDS was determined adjusting for confounders using logistic regression models. All analyses were performed separately for children aged 6-11, 12-23 and 24-59 months. ::: ::: ::: RESULTS ::: One-half of the children were stunted. In multivariate analyses, compared with low DDS, high dietary diversity was associated with a 15, 26 and 31% reduced odds of being stunted among children aged 6-11, 12-23 and 24-59 months, respectively, after adjusting for all potential confounders (odds ratio (OR)=0.85, 95% confidence interval (CI): 0.76-0.94; OR=0.74, 95% CI: 0.69-0.79; OR=0.69, 95% CI: 0.66-0.73). In all groups, children who were still breastfed were more likely to have limited diversity (OR=1.88, 95% CI: 1.32-2.67; OR=1.71, 95% CI: 1.52-1.92; OR=1.15, 95% CI: 1.11-1.19). Those having diarrhea in the past week and coming from families with low socioeconomic status were more likely to have decreased diversity (P<0.05). ::: ::: ::: CONCLUSIONS ::: Reduced dietary diversity is a strong predictor of stunting in rural Bangladesh. The inclusion of a variety of food groups into complementary foods may be essential to improve child nutritional status.


Literature Review of Teamwork Models


title: A high gain multi-port bidirectional non-isolated DC-DC converter for renewable integration

abstract: A topology for bidirectional non-isolated multiport DC-DC converter is proposed in this paper. The energy generation from the renewable sources such as wind and solar aie intermittent in nature. A battery port is mandatory for this purpose. Hence, a high gain multiport bidirectional converter for DC micro-grid application with high conversion efficiency, reduced size, and low cost is proposed. The bidirectional power flow capability through a feedback back path to charge a battery from the load capacitor is also provided. Therefore, the battery is not only charged from the input source but also from the load capacitor. The number of outputs are independent from each other and the battery is charged from the second output. Steady state operation and theoretical analysis of the converter proposed is elaborated. To validate the performance and theoretical concepts, it is simulated in MATLAB/Simulink software. The results of simulation verify the appropriate performance of proposed topology.


title: Exploration of adaptive gait patterns with a reconfigurable linkage mechanism

abstract: Legged robots are able to move across irregular terrains and some can be energy efficient, but are often constrained by a limited range of gaits which can limit their locomotion capabilities considerably. This paper reports a reconfigurable design approach to robotic legged locomotion that produces a wide variety of gait cycles, opening new possibilities for innovative applications. In this paper, we present a distance-based formulation and its application to solve the position analysis problem of a standard Theo Jansen mechanism. By changing the configuration of a linkage, our objective in this study is to identify novel gait patterns of interest for a walking platform. The exemplary gait variations presented in this work demonstrate the feasibility of our approach, and considerably extend the capabilities of the original design to not only produce novel cum useful gait patterns but also to realize behaviors beyond locomotion.


title: Directed diffusion: a scalable and robust communication paradigm for sensor networks

abstract: Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.


title: Recent advances in video-based human action recognition using deep learning: A review

abstract: Video-based human action recognition has become one of the most popular research areas in the field of computer vision and pattern recognition in recent years. It has a wide variety of applications such as surveillance, robotics, health care, video searching and human-computer interaction. There are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. A large number of techniques have been proposed to address the challenges over the decades. Three different types of datasets namely, single viewpoint, multiple viewpoint and RGB-depth videos, are used for research. This paper presents a review of various state-of-the-art deep learning-based techniques proposed for human action recognition on the three types of datasets. In light of the growing popularity and the recent developments in video-based human action recognition, this review imparts details of current trends and potential directions for future work to assist researchers.


title: A 21-Gb/s 87-mW transceiver with FFE/DFE/linear equalizer in 65-nm CMOS technology

abstract: This paper presents an ultra low-power transceiver for 20-Gb/s backplane communications. Incorporating half-rate, power-saving transmitter and full-rate, high-speed receiver with 2-stage equalization, this work achieves 21 Gb/s with BER≪10−12 over a 40-cm (16-inch) regular FR4 channel, while consuming a total power of only 87 mW from a 1.2-V supply.


title: Clustering sequence data using hidden Markov model representation

abstract: This paper proposes a clustering methodology, for sequence data, using hidden Markov model (HMM) representation. The proposed methodology improves upon existing HMM-based clustering methods in two ways: (i) it enables HMMs to dynamically change its model structure, to obtain a better fit model for data during the clustering process, and (ii) it provides objective criterion function, to select the optimal clustering partition. The algorithm is presented in terms of four nested levels of searches: (i) the search for the optimal number of clusters in a partition, (ii) the search for the optimal structure for a given partition, (iii) the search for the optimal HMM structure for each cluster, and (iv) the search for the optimal HMM parameters for each HMM. Preliminary results are given to support the proposed methodology.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Metric learning on biological sequence embeddings

abstract: Embedding techniques such as word2vec [1] have gained popularity due to their ability to represent words and their semantic variants as real valued vectors. Biological sequence analysis may also leverage unsupervised feature representations, augmented with supervised learning techniques for tasks like retrieval and classification. Algorithms that rely on distance metrics are computationally efficient and can handle large datasets, however, default distances in the embedded space often yield inadequate accuracy. In this paper, we use class labels to learn a Mahalanobis distance in the embedded feature vector space and show performance improvements over the default Euclidean metric in both retrieval and classification tasks. The approach may be readily generalised, and is and applicable to a wide range of problems in sequence analysis and others involving discrete entities or segmented data streams.


title: Document type classification in online digital libraries

abstract: Online digital libraries make it easier for researchers to search for scientific information. They have been proven as powerful resources in many data mining, machine learning and information retrieval applications that require high-quality data. The quality of the data highly depends on the accuracy of classifiers that identify the types of documents that are crawled from the Web, e.g., as research papers, slides, books, etc., for appropriate indexing. These classifiers in turn depend on the choice of the feature representation. We propose novel features that result in high-accuracy classifiers for document type classification. Experimental results on several datasets show that our classifiers outperform models that are employed in current systems.


title: Reverse compilation for digital signal processors: a working example

abstract: We describe the implementation and use of a reverse compiler from Analog Devices 21xx assembler source to ANSI-C (with optional use of the language extensions for the TMS320C6x processors) which has been used to port substantial applications. The main results of this work are that reverse compilation is feasible and that some of the features that make small DSPs hard to compile for, actually assist the process of reverse compilation compared to that of a general purpose processor. The article presents statistics on the occurrence of non-statically visible features of handwritten assembler code. It also looks at the quality of the code generated by an optimising ANSI-C compiler from our reverse compiled source and compares it to code generated from conventionally authored ANSI-C programs.


title: Some Potential Issues with the Security of HTML5 IndexedDB

abstract: The new HTML5 standard provides much more access to client resources, such as user location and local data storage. Unfortunately, this greater access may create new security risks that potentially can yield new threats to user privacy and web attacks. One of these security risks lies with the HTML5 client-side database. It appears that data stored on the client file system is unencrypted. Therefore, any stored data might be at risk of exposure. This paper explains and performs a security investigation into how the data is stored on client local file systems. The investigation was undertaken using Firefox and Chrome web browsers, and Encase (a computer forensic tool), was used to examine the stored data. This paper describes how the data can be retrieved after an application deletes the client side database. Finally, based on our findings, we propose a solution to correct any potential issues and security risks, and recommend ways to store data securely on local file systems.


title: A Novel Generator of Ultra-wideband Pulse

abstract: A novel Ultra-Wideband (UWB) picoseconds pulse generator is described in this paper. The basis of the generator is an avalanche transistor and a unique pulse forming circuit, which forms ultra-wideband balanced Gaussian pulses. The unique pulse forming circuit utilizes the performance of the SRD effectively to generate a couple of balanced narrow pulses with high pulse repetition frequency (PRF) up to 1MHz. A simple avalanche transistor driver is also described, which transforms a trigger signal to a driving pulse with the timing and amplitude parameters required by the SRD. After optimizing the circuit parameters and the bias conditions of the pulse generator, the design can generate a couple of balanced ultra-short electrical pulses with the peak output voltages of approximately 30V at 100 kHz PRF and the output pulse width FWHM (Full-Width at Half-Maximum) of approximately 250 ps. As the power dissipation of this generator is very low that it is used for application in near-field UWB radar sensors.


title: LQR-Fuzzy Control for Double Inverted Pendulum

abstract: According to the Lagrange equation, the mathematical model for the double inverted pendulum is first presented. For the fuzzy controller, the dimension of input varieties of fuzzy controller is depressed by designing a fusion function using optimization control theory, and it can reduce the rules of fuzzy sharply, ‘rule explosion’ problem is solved. The result shows that the LQR-Fuzzy logic controller not only has quite good stability, but also be able to make the cart of the pendulum moving to the designed in advance place along the orbit.


ON-LINE COARSE ORE GRANULOMETRIC ANALYZER USING NEURAL NETWORKS


title: SECURITY CHALLENGES AND CRYPTOGRAPHY IN EMBEDDED SYSTEMS

abstract: Embedded systems now days are integral part of our lives. They are used for various industrial automation purposes, service oriented applications like power, water distribution, healthcare and low cost systems like smart cards and PDA’s. Today, already more than 98% of all manufactured microprocessors are employed in embedded applications rather than in traditional personal computers. Embedded processors are already an integral part of many communications devices and their importance will continue to increase. Embedded systems exchange data and information both locally and globally using networking and wireless communication, thus making them vulnerable to various kinds of active and passive attacks. In this paper we provide an overview of the security requirements, security challenges and need of light weight cryptography in embedded systems.


title: Disconnecting outcomes and evaluations: The role of negotiator focus

abstract: Three experiments explored the role of negotiator focus in disconnecting negotiated outcomes and evaluations. Negotiators who focused on their target prices, the ideal outcome they could obtain, achieved objectively superior outcomes compared with negotiators who focused on their lower bound (e.g., reservation price). Those negotiators who focused on their targets, however, were less satisfied with their objectively superior outcomes. In the final experiment, when negotiators were reminded of their lower bound after the negotiation, the satisfaction of those negotiators who had focused on their target prices was increased, with outcomes and evaluations becoming connected rather than disconnected. The possible negative effects of setting high goals and the temporal dimensions of the disconnection and reconnection between outcomes and evaluations are discussed.


title: Achieving efficient conjunctive keyword searches over encrypted data

abstract: We present two provably secure and efficient schemes for performing conjunctive keyword searches over symmetrically encrypted data. Our first scheme is based on Shamir Secret Sharing and provides the most efficient search technique in this context to date. Although the size of its trapdoors is linear in the number of documents being searched, we empirically show that this overhead remains reasonable in practice. Nonetheless, to address this limitation we provide an alternative based on bilinear pairings that yields constant size trapdoors. This latter construction is not only asymptotically more efficient than previous secure conjunctive keyword search schemes in the symmetric setting, but incurs significantly less storage overhead. Additionally, unlike most previous work, our constructions are proven secure in the standard model.


title: How to Construct Polar Codes

abstract: A method for efficiently constructing polar codes is presented and analyzed. Although polar codes are explicitly defined, straightforward construction is intractable since the resulting polar bit-channels have an output alphabet that grows exponentially with the code length. Thus, the core problem that needs to be solved is that of faithfully approximating a bit-channel with an intractably large alphabet by another channel having a manageable alphabet size. We devise two approximation methods which “sandwich” the original bit-channel between a degraded and an upgraded version thereof. Both approximations can be efficiently computed and turn out to be extremely close in practice. We also provide theoretical analysis of our construction algorithms, proving that for any fixed e > 0 and all sufficiently large code lengths n, polar codes whose rate is within e of channel capacity can be constructed in time and space that are both linear in n.


title: Inkjet printed ultra wideband spiral antenna using integrated balun on liquid crystal polymer (LCP)

abstract: In this paper, an ultra wideband spiral antenna with a planar, integrated balun is inkjet printed on liquid crystal polymer (LCP) substrate and operates successfully from 1 GHz up to 8 GHz. An exponentially tapered spiral microstrip balun is used for, the matching of the 130 Ω input impedance, the characteristic impedance (Z 0 ) of 50 Ω, at the feed point. The equiangular spiral antenna is planar and low profile since a bulky balun is not required. Both the microstrip balun and the spiral arms are inkjet printed on LCP. The fabricated antenna covers a wideband frequency range (1∼8GHz) that corresponds to L, S, and C bands.