title: Semi-autonomous arecanut tree-climbing robot

abstract: Sourcing skilled labour for agricultural sector has become a tedious job in today's time. There is a need for mechanization of the farming process in order to cope with the lack of manpower. The need of the hour is automation in the field of agriculture. This paper describes an intuitive mechanized robot, which would eliminate the need of manually climbing the Arecanut tree in order to harvest the nut itself as well as spraying the neighboring tree. Technology which is economically suited for the job specified, safe and which can be easily understood and implemented by the farmers is the goal of the final system. The robot is divided into two units. While one unit clamps itself to the tree the other one lifts itself vertically which is done using two X-bar mechanisms mounted on opposite sides. The X- bar mechanism can compress and expand which is achieved with the use of high torque DC motors with encoders. An onboard battery is the power source. The clamping involves a 4-bar mechanism. The robot has semi-autonomous control and makes the use of arduino microcontroller.


title: Semantic Matching in Search

abstract: Relevance is the most important factor to assure users' satisfaction in search and the success of a search engine heavily depends on its performance on relevance. It has been observed that most of the dissatisfaction cases in relevance are due to term mismatch between queries and documents (e.g., query "NY times" does not match well with a document only containing "New York Times"), because term matching, i.e., the bag-of-words approach, still functions as the main mechanism of modern search engines. It is not exaggerated to say, therefore, that mismatch between query and document poses the most critical challenge in search. Ideally, one would like to see query and document match with each other, if they are topically relevant. Recently, researchers have expended significant effort to address the problem. The major approach is to conduct semantic matching, i.e., to perform more query and document understanding to represent the meanings of them, and perform better matching between the enriched query and document representations. With the availability of large amounts of log data and advanced machine learning techniques, this becomes more feasible and significant progress has been made recently. This survey gives a systematic and detailed introduction to newly developed machine learning technologies for query document matching (semantic matching) in search, particularly web search. It focuses on the fundamental problems, as well as the state-of-the-art solutions of query document matching on form aspect, phrase aspect, word sense aspect, topic aspect, and structure aspect. The ideas and solutions explained may motivate industrial practitioners to turn the research results into products. The methods introduced and the discussions made may also stimulate academic researchers to find new research directions and approaches. Matching between query and document is not limited to search and similar problems can be found in question answering, online advertising, cross-language information retrieval, machine translation, recommender systems, link prediction, image annotation, drug design, and other applications, as the general task of matching between objects from two different spaces. The technologies introduced can be generalized into more general machine learning techniques, which is referred to as learning to match in this survey.


Greedy Function Approximation: A Gradient Boosting Machine


Reflections on the History of Operating Systems Research in Fault Tolerance


title: Elucidating the pre- and post-nuclear intracellular processing of 1,4-dihydropyridine based gene delivery carriers

abstract: The low transfection efficacy of non-viral gene delivery systems limits the therapeutic application of these vectors. Besides the inefficient release of the complexes or pDNA from endolysosomes into the cytoplasm or poor nuclear uptake, the nuclear and post-nuclear processing might unfavorably affect the transgene expression. Positively charged amphiphilic 1,4-dihydropyridine (1,4-DHP) derivatives were earlier proposed as a promising tool for the delivery of DNA into target cells in vitro and in vivo. However, the structure/activity relationship of these carriers is poorly understood as yet. In this work we studied the intracellular processing of complexes, composed of three structurally related 1,4-DHP derivatives, in a retinal pigment epithelial (ARPE-19) cell line. The pre- and post-nuclear processing of the complexes was quantified on the nuclear, mRNA and transgene expression level. Here we show that the interaction of 1,4-DHP complexes with the cell membrane temporarily increases the permeability of the ARPE-19 cell membrane for small molecular compounds. However, the main mechanism for internalization of 1,4-DHP complexes is endocytosis. We found that all examined derivatives are able to destabilize endosomal membranes by lipid exchange upon acidification. In addition, the buffering capacity of some of the compounds may contribute to the endosomal escape of the complexes as well through the proton sponge effect. Previously we reported that cellular uptake of 1,4-DHP complexes does not correlate with transgene expression. In this study we surprisingly revealed that there is no correlation between the amount of plasmids taken up by the cell and the amount of plasmids found in the cell nucleus. Furthermore, it was found that a high amount of plasmid in the nucleus does not ensure high mRNA expression, likely due to remaining interactions of the carrier with the plasmids. Neither did the expression of mRNA always result in the production of a functional protein, possibly due to the interaction of free carrier with intracellular components which are involved in the post-translational modification of protein and folding process. Overall, our data suggest that succeeding of both the pre- and the post-nuclear intracellular processes is equally essential for successful transgene expression.


title: Decision-making with the AHP: Why is the principal eigenvector necessary

abstract: In this paper it is shown that the principal eigenvector is a necessary representation of the priorities derived from a positive reciprocal pairwise comparison judgment matrix A=(aij) when A is a small perturbation of a consistent matrix. When providing numerical judgments, an individual attempts to estimate sequentially an underlying ratio scale and its equivalent consistent matrix of ratios. Near consistent matrices are essential because when dealing with intangibles, human judgment is of necessity inconsistent, and if with new information one is able to improve inconsistency to near consistency, then that could improve the validity of the priorities of a decision. In addition, judgment is much more sensitive and responsive to large rather than to small perturbations, and hence once near consistency is attained, it becomes uncertain which coefficients should be perturbed by small amounts to transform a near consistent matrix to a consistent one. If such perturbations were forced, they could be arbitrary and thus distort the validity of the derived priority vector in representing the underlying decision.


title: An Intelligent SPARQL Query Builder for Exploration of Various Life-science Databases

abstract: Database integration of a wide variety of life-science data is an important issue for comprehensive data analysis. Since Semantic Web technologies, such as Resource Description Framework (RDF), are expected to provide efficient data integration technologies, many life-science databases are published in RDF with SPARQL Protocol and RDF Query Language (SPARQL) endpoints as search application programming interfaces on the web. However, although SPARQL supports very useful functions for exploring and integrating various datasets, many biologists find SPARQL difficult to use. To overcome this problem, we propose an intelligent SPARQL query builder that aids users with no knowledge on SPARQL in building queries. This paper discusses the methods used by this tool, its system design and implementation. The tool assists users in generating queries for cross-database annotations based on RDF and enhances the value of life-science data by exploring and integrating these data.


title: Cooperative mapping of multiple PTZ cameras in automated surveillance systems

abstract: Due to the capacity of pan-tilt-zoom (PTZ) cameras to simultaneously cover a panoramic area and maintain high resolution imagery, researches in automated surveillance systems with multiple PTZ cameras have become increasingly important. Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the relative positioning and orientation among multiple PTZ cameras. To overcome this limitation, we propose a novel mapping algorithm that derives the relative positioning and orientation between two PTZ cameras based on a unified polynomial model. This reduces the dependence on the knowledge of intrinsic parameters of PTZ camera and relative positions. Experimental results demonstrate that our proposed algorithm presents substantially reduced computational complexity and improved flexibility at the cost of slightly decreased pixel accuracy, as compared with the work of Chen and Wang. This slightly decreased pixel accuracy can be compensated by consistent labeling approaches without added cost for the application of automated surveillance systems along with changing configurations and a larger number of PTZ cameras.


title: Gaussian process implicit surfaces for shape estimation and grasping

abstract: The choice of an adequate object shape representation is critical for efficient grasping and robot manipulation. A good representation has to account for two requirements: it should allow uncertain sensory fusion in a probabilistic way and it should serve as a basis for efficient grasp and motion generation. We consider Gaussian process implicit surface potentials as object shape representations. Sensory observations condition the Gaussian process such that its posterior mean defines an implicit surface which becomes an estimate of the object shape. Uncertain visual, haptic and laser data can equally be fused in the same Gaussian process shape estimate. The resulting implicit surface potential can then be used directly as a basis for a reach and grasp controller, serving as an attractor for the grasp end-effectors and steering the orientation of contact points. Our proposed controller results in a smooth reach and grasp trajectory without strict separation of phases. We validate the shape estimation using Gaussian processes in a simulation on randomly sampled shapes and the grasp controller on a real robot with 7DoF arm and 7DoF hand.


Fast and Reliable Browser Identification with JavaScript Engine Fingerprinting


title: Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback

abstract: Abstract Neurofeedback is a promising approach for non-invasive modulation of human brain activity with applications for treatment of mental disorders and enhancement of brain performance. Neurofeedback techniques are commonly based on either electroencephalography (EEG) or real-time functional magnetic resonance imaging (rtfMRI). Advances in simultaneous EEG–fMRI have made it possible to combine the two approaches. Here we report the first implementation of simultaneous multimodal rtfMRI and EEG neurofeedback (rtfMRI–EEG-nf). It is based on a novel system for real-time integration of simultaneous rtfMRI and EEG data streams. We applied the rtfMRI–EEG-nf to training of emotional self-regulation in healthy subjects performing a positive emotion induction task based on retrieval of happy autobiographical memories. The participants were able to simultaneously regulate their BOLD fMRI activation in the left amygdala and frontal EEG power asymmetry in the high-beta band using the rtfMRI − EEG-nf. Our proof-of-concept results demonstrate the feasibility of simultaneous self-regulation of both hemodynamic (rtfMRI) and electrophysiological (EEG) activities of the human brain. They suggest potential applications of rtfMRI–EEG-nf in the development of novel cognitive neuroscience research paradigms and enhanced cognitive therapeutic approaches for major neuropsychiatric disorders, particularly depression.


Narrative Research in Action Combining thematic and narrative analysis of qualitative interviews to understand children ’ s spatialities in Andhra Pradesh , India


title: Improvement of Internal Loop Controller for Unbalanced Control System Based on Virtual Synchronous Generator

abstract: In the case of grid voltage imbalance, the virtual synchronous generator (VSG) can not suppress the unbalanced current output by the inverter and reduce the fluctuation components of the active and reactive power output by the inverter. Therefore, an improved control strategy of virtual synchronous machine under unbalanced network voltage is proposed., which can directly obtain active current and reactive current under different control targets without using positive and negative sequence dual current inner loop control structure., reducing the PI controller The number of makes the control is relatively simple. However., in the unbalanced situation., the active and reactive current components are alternating components., which can not be tracked without error with the traditional PI controller. Therefore., the traditional PI controller is improved. A discrete PI controller is proposed to track the alternating component. The results of MATLAB simulation verify the validity and effectiveness of the proposed control strategy.


title: Factors affecting bioelectrical impedance measurements in humans.

abstract: In several groups of young healthy subjects the effect of the ingestion of a meal, of drinking normal tea or beef tea, of exercise and of the menstrual cycle on body impedance was assessed. The day-to-day reproducibility of the method was also investigated under standardized conditions. Two to four hours after ingestion of a meal, body impedance had decreased by about 13-17 Ohms in comparison with body impedance in the fasting state. Drinking 200 ml of normal tea did not result in a change of body impedance, but drinking 200 ml beef tea lowered the body impedance significantly by 4 +/- 4 Ohms. Moderate exercise on a bicycle ergometer (90 min, 100 W) did not influence body impedance, but strenuous exercise (90 min, 175 W) resulted in a decrease of 9 +/- 11 Ohms in body impedance. In general, changes in body impedance during the menstrual cycle were small, and only the difference between measurements of body impedance 1 week before the onset of the menstruation and again 1 week after menstruation (8 +/- 9 Ohms) was statistically significant. Under standardized conditions (in the morning, in the fasting state after emptying the bladder) the within-person between-day variation was found to be 2.8 per cent (13 Ohms).


title: Self-powered, ultrasensitive, flexible tactile sensors based on contact electrification.

abstract: Tactile/touch sensing is essential in developing human-machine interfacing and electronic skins for areas such as automation, security, and medical care. Here, we report a self-powered triboelectric sensor based on flexible thin-film materials. It relies on contact electrification to generate a voltage signal in response to a physical contact without using an external power supply. Enabled by the unique sensing mechanism and surface modification by polymer-nanowires, the triboelectric sensor shows an exceptional pressure sensitivity of 44 mV/Pa (0.09% Pa(-1)) and a maximum touch sensitivity of 1.1 V/Pa (2.3% Pa(-1)) in the extremely low-pressure region (<0.15 KPa). Through integration of the sensor with a signal-processing circuit, a complete tactile sensing system is further developed. Diverse applications of the system are demonstrated, explicitly indicating a variety of immediate uses in human-electronics interface, automatic control, surveillance, remote operation, and security systems.


title: Certified Normalization of Context-Free Grammars

abstract: Every context-free grammar can be transformed into an equivalent one in the Chomsky normal form by a sequence of four transformations. In this work on formalization of language theory, we prove formally in the Agda dependently typed programming language that each of these transformations is correct in the sense of making progress toward normality and preserving the language of the given grammar. Also, we show that the right sequence of these transformations leads to a grammar in the Chomsky normal form (since each next transformation preserves the normality properties established by the previous ones) that accepts the same language as the given grammar. As we work in a constructive setting, soundness and completeness proofs are functions converting between parse trees in the normalized and original grammars.


title: GridTorrent Framework: A High-performance Data Transfer and Data Sharing Framework for Scientific Computing

abstract: Large amount of data that is often stored in many thousands of files is created as part of today’s geographically distributed scientific computation and collaboration environments. Managing and transferring large volumes of data sets present a significant challenge and are often a bottleneck in the scientific computing community. In this paper, we introduce an architecture to manage data distributions in a collaborative fashion through a GridTorrent Framework (GTF) whose data transfer mechanism inspired by Bittorrent. We present performance experiment data that compares our framework to parallel TCP (PTCP) and Bittorrent. Experimental results conducted suggest that using GridTorrent for large data set has significant advantages over parallel TCP in LAN and WAN type of computer networks.


title: Melanonychia striata secondary to pigmented nail matrix fibroma simulating nodular melanoma

abstract: Melanonychia corresponds to color patterns in the nail plate, ranging from brown to black. It is a diagnostic challenge due to the fact it has several differential diagnoses, including benign and malignant entities. There are no reports of pigmented fibroma of the nail matrix causing striata (or longitudinal) melanonychia. In light of this fact, the authors report a case of melanonychia striata secondary to pigmented fibroma of the nail, with clinical examination and dermoscopic findings suggestive of nodular melanoma. Melanoniquia e a coloracao da lâmina ungueal variando do marrom ao negro. Representa um desafio diagnostico, pois ha diversos diagnosticos diferenciais incluindo entidades benignas e malignas. Nao ha relatos de fibroma da matriz ungueal pigmentado causando melanoniquia longitudinal. Diante disso, os autores relatam um caso de melanoniquia estriada secundaria a fibroma ungueal pigmentado, com achados do exame clinico e dermatoscopico sugestivos de melanoma nodular.


title: Partial Materialized Views

abstract: Early access to partial query results is highly desirable during exploration of massive data sets. However, it is challenging to provide transactionally consistent, immediate partial results without significantly increasing queries' execution time. To address this problem, this paper proposes a partial materialized view (PMV) method to cache some of the most frequently accessed results rather than all the possible results. Compared to traditional materialized views, the proposed PMVs do not require maintenance during insertion into base relations, and have much smaller storage and maintenance overhead. Upon the arrival of a query, the RDBMS first searches the PMV and returns to the user the cached partial results. Since a large portion of the PMV is cached in memory, this usually finishes within a millisecond. Then the RDBMS continues to execute the query to find the remaining results. The efficiency of our PMV method is evaluated through a simulation study, a theoretical analysis, and an initial implementation in PostgreSQL.


title: Effects of physical training on fatty acid metabolism in liver and skeletal muscle of rats fed four different high-carbohydrate diets

abstract: Abstract The adaptation of regulatory enzymes of fatty acid metabolism to endurance training and to the administration of four different diets containing fructose, sucrose, maltodextrins, or starch as carbohydrate (CH) was studied in rat liver and skeletal muscle (soleus). (i) In the soleus, physical training induced carnitine palmitoyltransferase I (CPT-I) acitivity upon complex (but not simple) CH feeding, whereas citrate synthase activity was enhanced by endurance exercise in the four different diets. No effect of diet or training was evident on glycerol 3-phosphate acyltransferase (G3PAT) activity or intramuscular triacylglycerol content. Glycogen levels were notably diminished in the soleus of trained rats fed simple-CH diets compared with their corresponding sedentary controls. (ii) In liver, CPT-I activity was depressed in trained animals fed the fructose diet, whereas citrate synthase was induced by endurance exercise in the four different diets. The activity of key lipogenic enzymes (acetyl-CoA carboxylase, fatty acid synthase, G3PAT) was induced by simple-CH feeding, and this increase was blunted by physical exercise. However, triacylglycerol accumulation and glycogen depletion ensued in liver of trained rats fed simple-CH diets. In these animals this was accompanied by a remarkable decrease of serum triacylglycerol levels and a strong increase of circulating non-esterified fatty acids (NEFA) and glycerol. Results thus suggest that in trained rats fed simple-CH diets (i) the preferential mobilization of adipose tissue triacylglycerols, by increasing the availability of circulating NEFA, may be responsible for liver triacylglycerol accumulation as well as for liver and muscle glycogen depletion, and (ii) decreased triacylglycerol secretion by the liver—but not changes in the activity of regulatory lipogenic enzymes—may be involved in hepatic triacylglycerol accumulation.


title: Relation path embedding in knowledge graphs

abstract: Large-scale knowledge graphs have currently reached impressive sizes; however, they are still far from complete. In addition, most existing methods for knowledge graph completion only consider the direct links between entities, ignoring the vital impact of the semantics of relation paths. In this paper, we study the problem of how to better embed entities and relations of knowledge graphs into different low-dimensional spaces by taking full advantage of the additional semantics of relation paths and propose a novel relation path embedding model named as RPE. Specifically, with the corresponding relation and path projections, RPE can simultaneously embed each entity into two types of latent spaces. Moreover, type constraints are extended from traditional relation-specific type constraints to the proposed path-specific type constraints and both of the two type constraints can be seamlessly incorporated into RPE. The proposed model is evaluated on the benchmark tasks of link prediction and triple classification. The results of experiments demonstrate our method outperforms all baselines on both tasks. They indicate that our model is capable of catching the semantics of relation paths, which is significant for knowledge representation learning.


title: Narcissism and Social Networking Behavior: A Meta-Analysis

abstract: Objective ::: The increasing popularity of social networking sites (SNS) such as Facebook and Twitter has given rise to speculations that the intensity of using these platforms is associated with narcissistic tendencies. However, recent research on this issue has been all but conclusive. ::: ::: Method ::: We present a three-level, random effects meta-analysis including 289 effect sizes from 57 studies (total N = 25,631) on the association between trait narcissism and social networking behavior. ::: ::: Results ::: The meta-analysis identified a small to moderate effect of ρ = .17 (τ = .11), 95% CI [.13, .21], for grandiose narcissism that replicated across different social networking platforms, respondent characteristics, and time. Moderator analyses revealed pronounced cultural differences, with stronger associations in power-distant cultures. Moreover, social networking behaviors geared toward self-presentation and the number of SNS friends exhibited stronger effects than usage durations. ::: ::: Conclusions ::: Overall, the study not only supported but also refined the notion of a relationship between engaging in social networking sites and narcissistic personality traits.


title: Tharwa: A Large Scale Dialectal Arabic - Standard Arabic - English Lexicon

abstract: We introduce an electronic three-way lexicon, Tharwa, comprising Dialectal Arabic, Modern Standard Arabic and English correspondents. The paper focuses on Egyptian Arabic as the first pilot dialect for the resource, with plans to expand to other dialects of Arabic in later phases of the project. We describe Tharwa’s creation process and report on its current status. The lexical entries are augmented with various elements of linguistic information such as POS, gender, rationality, number, and root and pattern information. The lexicon is based on a compilation of information from both monolingual and bilingual existing resources such as paper dictionaries and electronic, corpus-based dictionaries. Multiple levels of quality checks are performed on the output of each step in the creation process. The importance of this lexicon lies in the fact that it is the first resource of its kind bridging multiple variants of Arabic with English. Furthermore, it is a wide coverage lexical resource containing over 73,000 Egyptian entries. Tharwa is publicly available. We believe it will have a significant impact on both Theoretical Linguistics as well as Computational Linguistics research.


title: Uniform Discrete Curvelet Transform

abstract: An implementation of the discrete curvelet transform is proposed in this work. The transform is based on and has the same order of complexity as the Fast Fourier Transform (FFT). The discrete curvelet functions are defined by a parameterized family of smooth windowed functions that satisfies two conditions: i) 2π periodic; ii) their squares form a partition of unity. The transform is named the uniform discrete curvelet transform (UDCT) because the centers of the curvelet functions at each resolution are positioned on a uniform lattice. The forward and inverse transform form a tight and self-dual frame, in the sense that they are the exact transpose of each other. Generalization to M dimensional version of the UDCT is also presented. The novel discrete transform has several advantages over existing transforms, such as lower redundancy ratio, hierarchical data structure and ease of implementation.


title: Practical and lightweight domain isolation on Android

abstract: In this paper, we introduce a security framework for practical and lightweight domain isolation on Android to mitigate unauthorized data access and communication among applications of different trust levels (e.g., private and corporate). We present the design and implementation of our framework, TrustDroid, which in contrast to existing solutions enables isolation at different layers of the Android software stack: (1) at the middleware layer to prevent inter-domain application communication and data access, (2) at the kernel layer to enforce mandatory access control on the file system and on Inter-Process Communication (IPC) channels, and (3) at the network layer to mediate network traffic. For instance, (3) allows network data to be only read by a particular domain, or enables basic context-based policies such as preventing Internet access by untrusted applications while an employee is connected to the company's network. Our approach accurately addresses the demands of the business world, namely to isolate data and applications of different trust levels in a practical and lightweight way. Moreover, our solution is the first leveraging mandatory access control with TOMOYO Linux on a real Android device (Nexus One). Our evaluation demonstrates that TrustDroid only adds a negligible overhead, and in contrast to contemporary full virtualization, only minimally affects the battery's life-time.


title: A convenient nucleic acid test on the basis of the capillary convective PCR for the on-site detection of enterovirus 71.

abstract: The recent and continuing epidemic of enterovirus 71 in China has affected millions of children and resulted in thousands of deaths. Timely diagnosis and management is essential for disease control. Current enterovirus 71 molecular tests require resources that are unavailable for on-site testing. We have developed a simple-to-operate nucleic acid test, the convenient and integrated nucleic acid test, for local medical institutions. It uses a convective PCR for rapid amplification, a dipstick for visual detection of PCR products, and a simple commercial kit for nucleic acid extraction. By using a specially designed reagent and reaction tube containing a dipstick, the amplification and detection processes are well integrated and simplified. Moreover, cross contamination that may be caused by an open-tube detection system can be avoided. On the basis of the convenient and integrated nucleic acid test, an enterovirus 71 assay for on-site testing was developed. After evaluating known hand, foot, and mouth disease virus stocks of 17 strains of 11 different serotypes, this assay showed a favorable detection spectrum and no cross-reactivity. Its clinical performance was established by testing 141 clinical samples and comparing the results with a nested RT-PCR method. The assay showed a clinical sensitivity and specificity of 98.5% and 100%, respectively. Our results suggest that this convenient and integrated nucleic acid test enterovirus 71 assay may serve as an on-site diagnosis tool.


title: An SVM Based Speaker Independent Isolated Malayalam Word Recognition System

abstract: As technology advances man-machine interaction is becoming an unavoidable activity. So an effective method of communication with machines enhances the quality of life. If it is able to operate a system by simply commanding, then it will be a great blessing to the users. Speech is the most effective mode of communication used by humans. So by introducing voice user interfaces the interaction with the machines can be made more user friendly. This paper implements a speaker independent speech recognition system for limited vocabulary Malayalam Words in Raspberry Pi. Mel Frequency Cepstral Coefficients (MFCC) are the features for classification and this paper proposes Radial Basis Function (RBF) kernel in Support Vector Machine (SVM) classifier gives better accuracy in speech recognition than linear kernel. An overall accuracy of 91.8% is obtained with this work.


title: Information Seeking, Information Sharing, and going mobile: Three bridges to informal learning

abstract: Mobile access.Formal, informal learning.Information Seeking, sharing.Learner preference. This paper introduces a new perspective on information behavior in Web 2.0 environments, including the role of mobile access in bridging formal to informal learning. Kuhlthau's (1991, 2007) Information Search Process (ISP) model is identified as a theoretical basis for exploring Information Seeking attitudes and behaviors, while social learning and literacy concepts of Vygotsky (1962, 1978), Bruner (1962, 1964) and Jenkins (2010) are identified as foundations for Information Sharing. The Guided Inquiry Spaces model (Maniotes, 2005) is proposed as an approach to bridging the student's informal learning world and the curriculum-based teacher's world. Research within this framework is operationalized through a recently validated Information and Communications Technology Learning (ICTL) survey instrument measuring learners' preferences for self-expression, sharing, and knowledge acquisition interactions in technology-pervasive environments. Stepwise refinement of ICTL produced two reliable and valid psychometric scales, Information Sharing (alpha=.77) and Information Seeking (alpha=.72). Cross-validation with an established Mobile Learning Scale (Khaddage & Knezek, 2013) indicates that Information Sharing aligns significantly (p<.05) with Mobile Learning. Information Seeking, Information Sharing, and mobile access are presented as important, complimentary components important, complimentary components in the shift along the formal to informal learning continuum. Therefore, measures of these constructs can assist in understanding students' preferences for 21st century learning.


A Multi-Scale Approach to Data-Driven Mass Migration Analysis


title: High-Coverage Testing of Navigation Models in Android Applications

abstract: In this work, we present a tool that systematically discovers and tests the user-observable states of an Android application. We define an appropriate notion of test coverage, and we show the tool's potential by applying it to several publicly available applications.


title: Association mapping of seed quality traits in Brassica napus L. using GWAS and candidate QTL approaches

abstract: Single nucleotide polymorphisms (SNPs) have rapidly become the molecular marker of choice in plant and animal association mapping (AM) studies. In this work, a genome-wide association study (GWAS) and candidate quantitative trait loci (cQTL) approaches were used to identify SNP markers associated with seed quality traits, in a Brassica napus L. association panel composed of 89 adapted winter oilseed rape accessions. Six seed quality traits (oil and protein content, linolenic acid, total glucosinolates, hemicellulose and cellulose content) were evaluated in two different locations for two seasons. For GWAS, 4025 SNP markers evenly distributed along the B. napus genome were genotyped using a 6K Illumina array platform. For cQTL, 100 SNP markers previously discovered in genomic regions underlying seed quality QTL were genotyped using a competitive allele-specific PCR (KASPar). Analysis of the population structure revealed the presence of two weakly differentiated subpopulations (F ST = 0.037), with 82 % of the pairwise kinship comparisons ranging from 0 to 0.1. The GWAS approach resulted in the identification of 17 and 5 significant associations for seed glucosinolate content and seed hemicellulose content, respectively. The cQTL approach identified 4 significant associations for seed glucosinolate content and 6 significant associations for seed hemicellulose content. The associated SNPs were consistently identified across environments and were mapped to previously reported QTL. These results illustrate the suitability of AM to identify SNP markers associated with seed quality traits in B. napus.


title: Semi Supervised Preposition-Sense Disambiguation using Multilingual Data

abstract: Prepositions are very common and very ambiguous, and understanding their sense is critical for understanding the meaning of the sentence. Supervised corpora for the preposition-sense disambiguation task are small, suggesting a semi-supervised approach to the task. We show that signals from unannotated multilingual data can be used to improve supervised preposition-sense disambiguation. Our approach pre-trains an LSTM encoder for predicting the translation of a preposition, and then incorporates the pre-trained encoder as a component in a supervised classification system, and fine-tunes it for the task. The multilingual signals consistently improve results on two preposition-sense datasets.


title: A decision model for evaluating third-party logistics providers using fuzzy analytic hierarchy process

abstract: As a consequence of an increasing trend toward the outsourcing of logistics activities, shippers have been faced with the inevitability of selecting an appropriate third-party logistics (3PL) provider. The selection process for the identification of a 3PL provider that best fits user requirements involves multiple criteria and alternatives and may be one of the most complex decisions facing logistics users. In this regard, this study proposes an evaluation framework and methodology for selecting a suitable 3PL provider and illustrates the process of evaluation and selection through a case study. It is expected that the results of this study will provide a practical reference for logistics managers who want to engage the best 3PL provider. Future research using different datasets is warranted to verify the generalizability of the findings.


title: Attribute-Based Signatures: Achieving Attribute-Privacy and Collusion-Resistance

abstract: We introduce a new and versatile cryptographic primitive called Attribute-Based Signatures (ABS), in which a signature attests not to the identity of the individual who endorsed a message, but instead to a (possibly complex) claim regarding the attributes she posseses. ABS offers: – A strong unforgeability guarantee for the verifier, that the signature was produced by a single party whose attributes satisfy the claim being made; i.e., not by a collusion of individuals who pooled their attributes together. – A strong privacy guarantee for the signer, that the signature reveals nothing about the identity or attributes of the signer beyond what is explicitly revealed by the claim being made. We formally define the security requirements of ABS as a cryptographic primitive, and then describe an efficient ABS construction based on groups with bilinear pairings. We prove that our construction is secure in the generic group model. Finally, we illustrate several applications of this new tool; in particular, ABS fills a critical security requirement in attribute-based messaging (ABM) systems. A powerful feature of our ABS construction is that unlike many other attribute-based cryptographic primitives, it can be readily used in a multi-authority setting, wherein users can make claims involving combinations of attributes issued by independent and mutually distrusting authorities.


title: Double-Sided Parallel-Strip Line With an Inserted Conductor Plane and Its Applications

abstract: In this paper, a novel multilayer structure using the double-sided parallel-strip line with an inserted conductor plane is investigated. A conductor plane is inserted into the middle of the substrate of the double-sided parallel-strip line in the proposed structure, which can function as virtual ground to isolate the nonidentical circuits on the top and bottom layers effectively. On the other hand, the conductor plane can be negligible for the identical circuits. Based on the new structure, a dual-band stub can be realized using the isolation characteristic of the conductor plane, where the stub can be treated as a combination of two back-to-back microstrip stubs operating at different frequencies. The common ground of the two microstrip stubs is provided by the inserted conductor plane. For demonstrating this idea, a bandpass filter with two transmission zeros is optimized, fabricated, and tested. The simulated and measured results are presented and show good agreement. The size of this filter is reduced approximately by half, as compared with that of the similar type of microstrip filters.


title: Leveraging prior ratings for recommender systems in e-commerce

abstract: User ratings are the essence of recommender systems in e-commerce. Lack of motivation to provide ratings and eligibility to rate generally only after purchase restrain the effectiveness of such systems and contribute to the well-known data sparsity and cold start problems. This article proposes a new information source for recommender systems, called prior ratings. Prior ratings are based on users’ experiences of virtual products in a mediated environment, and they can be submitted prior to purchase. A conceptual model of prior ratings is proposed, integrating the environmental factor presence whose effects on product evaluation have not been studied previously. A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments. A method is proposed to show how to leverage prior ratings in collaborative filtering. Experimental results indicate the effectiveness of prior ratings in improving predictive performance.


Improvised Architecture for Distributed Web Crawling


title: Tumours of salivary tissue.

abstract: A clinical study of 401 cases of tumour of salivary tissue has been made and the results reported, together with a review of the literature. The histological variations of such tumours are classified and discussed.


title: Parsing to stanford dependencies: Trade-offs between speed and accuracy

abstract: We investigate a number of approaches to generating Stanford Dependencies, a widely used semantically-oriented dependency representation. We examine algorithms specifically designed for dependency parsing (Nivre, Nivre Eager, Covington, Eisner, and RelEx) as well as dependencies extracted from constituent parse trees created by phrase structure parsers (Charniak, Charniak-Johnson, Bikel, Berkeley and Stanford). We found that phrase structure parsers systematically outperform algorithms designed specifically for dependency parsing. The most accurate method for generating dependencies is the Charniak-Johnson reranking parser, with 89% (labeled) attachment F1 score. The fastest methods are Nivre, Nivre Eager, and Covington. When used with a linear classifier to make local parsing decisions, these methods can parse the entire Penn Treebank development set (section 22) in less than 10 seconds on an Intel Xeon E5520. However, this speed comes with a substantial drop in F1 score (about 76% for labeled attachment) compared to competing methods. By tuning how much of the search space is explored by the Charniak-Johnson parser, we are able to arrive at a balanced configuration that is both fast and nearly as good as the most accurate approaches.


title: Choosy But Not Chaste: Multiple Mating in Human Females

abstract: When Charles Darwin set out to relate his theory of evolution by natural selection to humans he discovered that a complementary explanation was needed to properly understand the great variation seen in human behavior. The resulting work, The Descent of Man and Selection in Relation to Sex, laid out the defining principles and evidence of sexual selection. In brief, this work is best known for illuminating the typically male strategy of intrasexual competition and the typically female response of intersexual choice. While these sexual stereotypes were first laid out by Darwin, they grew in importance when, years later, A. J. Bateman, in a careful study of Drosophila mating strategies, noted that multiple mating appeared to provide great benefit to male reproductive success, but to have no such effect on females. As a result, female choice soon became synonymous with being coy, and only males were thought to gain from promiscuous behavior. However, the last thirty years of research have served to question much of the traditional wisdom about sex differences proposed by Darwin and Bateman, illuminating the many ways that women (and females more generally) can and do engage in multiple mating. © 2013 Wiley Periodicals, Inc.


title: Biological pattern formation: from basic mechanisms to complex structures

abstract: The reliable development of highly complex organisms is an intriguing and fascinating problem. The genetic material is, as a rule, the same in each cell of an organism. How then do cells, under the influence of their common genes, produce spatial patterns? Simple models are discussed that describe the generation of patterns out of an initially nearly homogeneous state. They are based on nonlinear interactions of at least two chemicals and on their diffusion. The concepts of local autocatalysis and of long-range inhibition play a fundamental role. Numerical simulations show that the models account for many basic biological observations such as the regeneration of a pattern after excision of tissue or the production of regular (or nearly regular) arrays of organs during (or after) completion of growth. Very complex patterns can be generated in a reproducible way by hierarchical coupling of several such elementary reactions. Applications to animal coats and to the generation of polygonally shaped patterns are provided. It is further shown how to generate a strictly periodic pattern of units that themselves exhibit a complex and polar fine structure. This is illustrated by two examples: the assembly of photoreceptor cells in the eye of Drosophila and the positioning of leaves and axillary buds in a growing shoot. In both cases, the substructures have to achieve an internal polarity under the influence of some primary pattern-forming system existing in the fly's eye or in the plant. The fact that similar models can describe essential steps in organisms as distantly related as animals and plants suggests that they reveal some universal mechanisms.


Constraints on Hypothesis Selection in Causal Learning


title: 3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection

abstract: The goal of this paper is to perform 3D object detection in the context of autonomous driving. Our method first aims at generating a set of high-quality 3D object proposals by exploiting stereo imagery. We formulate the problem as minimizing an energy function that encodes object size priors, placement of objects on the ground plane as well as several depth informed features that reason about free space, point cloud densities and distance to the ground. We then exploit a CNN on top of these proposals to perform object detection. In particular, we employ a convolutional neural net (CNN) that exploits context and depth information to jointly regress to 3D bounding box coordinates and object pose. Our experiments show significant performance gains over existing RGB and RGB-D object proposal methods on the challenging KITTI benchmark. When combined with the CNN, our approach outperforms all existing results in object detection and orientation estimation tasks for all three KITTI object classes. Furthermore, we experiment also with the setting where LIDAR information is available, and show that using both LIDAR and stereo leads to the best result.


title: The craniopagus malformation: classification and implications for surgical separation

abstract: Craniopagus twins (CPT) are an uncommon, highly fascinating accident of nature. The clinical pathology of this complex entity is reviewed and placed in perspective. A logical classification aids understanding of the anomaly, and is essential to gauge outcome from separation attempts. ‘Partial forms’ lack significant shared dural venous sinuses (SDVS) and ‘Total forms’ with SDVS also exhibit more severe compressional brain distortion. Our classification consists of Partial Angular (PA), Partial Vertical (PV), Total Angular (TA) and Total Vertical (TV, formerly O’Connell Types I‐III). Total vertical has a continuous cranium, and inter-twin axial facial rotation <40 o (Type I), 140‐180 o (Type II) or intermediate (Type III). The term ‘Angular’ denotes an inter-twin longitudinal angle below 140 o , regardless of axial rotation. Our review categorized 64 well-delineated CPT, including 41 operative separation attempts in small children since initial success in 1952. Just over one-half were TV, almost one-third TA, and partial forms accounted for the remaining one-sixth. About 30% of CPT had shared or fused brain tissue, and a similar percentage of TA twins shared a posterior fossa. Partial forms had significantly higher birth weights, were separated at an earlier age (6 versus 11 months) and had lower mortality and better outcome compared with Total forms. A multi-staged surgical separation for Total CPT had a significantly better mortality than single-staged separation. Discussion emphasizes embryological, anatomical and clinical aspects of the malformation, with emphasis upon obstacles to a successful outcome.


title: A Perception-Driven Approach to Supervised Dimensionality Reduction for Visualization

abstract: Dimensionality reduction (DR) is a common strategy for visual analysis of labeled high-dimensional data. Low-dimensional representations of the data help, for instance, to explore the class separability and the spatial distribution of the data. Widely-used unsupervised DR methods like PCA do not aim to maximize the class separation, while supervised DR methods like LDA often assume certain spatial distributions and do not take perceptual capabilities of humans into account. These issues make them ineffective for complicated class structures. Towards filling this gap, we present a perception-driven linear dimensionality reduction approach that maximizes the perceived class separation in projections. Our approach builds on recent developments in perception-based separation measures that have achieved good results in imitating human perception. We extend these measures to be density-aware and incorporate them into a customized simulated annealing algorithm, which can rapidly generate a near optimal DR projection. We demonstrate the effectiveness of our approach by comparing it to state-of-the-art DR methods on 93 datasets, using both quantitative measure and human judgments. We also provide case studies with class-imbalanced and unlabeled data.


title: A real‐world study of the effectiveness of DBT in the UK National Health Service

abstract: Objectives.Dialectical behavioral therapy (DBT) has gained widespread popularity as a treatment for borderline personality disorder (BPD), and its efficacy has been demonstrated in several trials. The aim of this study was to evaluate the effectiveness of DBT delivered by staff with a level of train


title: Understanding and Improving the Latency of DRAM-Based Memory Systems

abstract: Over the past two decades, the storage capacity and access bandwidth of main memory have improved tremendously, by 128x and 20x, respectively. These improvements are mainly due to the continuous technology scaling of DRAM (dynamic random-access memory), which has been used as the physical substrate for main memory. In stark contrast with capacity and bandwidth, DRAM latency has remained almost constant, reducing by only 1.3x in the same time frame. Therefore, long DRAM latency continues to be a critical performance bottleneck in modern systems. Increasing core counts, and the emergence of increasingly more data-intensive and latency-critical applications further stress the importance of providing low-latency memory access. ::: In this dissertation, we identify three main problems that contribute significantly to long latency of DRAM accesses. To address these problems, we present a series of new techniques. Our new techniques significantly improve both system performance and energy efficiency. We also examine the critical relationship between supply voltage and latency in modern DRAM chips and develop new mechanisms that exploit this voltage-latency trade-off to improve energy efficiency. ::: The key conclusion of this dissertation is that augmenting DRAM architecture with simple and low-cost features, and developing a better understanding of manufactured DRAM chips together lead to significant memory latency reduction as well as energy efficiency improvement. We hope and believe that the proposed architectural techniques and the detailed experimental data and observations on real commodity DRAM chips presented in this dissertation will enable development of other new mechanisms to improve the performance, energy efficiency, or reliability of future memory systems.


title: Where to wear it: functional, technical, and social considerations in on-body location for wearable technology 20 years of designing for wearability

abstract: One of the first questions a researcher or designer of wearable technology has to answer in the design process is where on the body the device should be worn. It has been almost 20 years since Gemperle et al. wrote "Design for Wearability" [17], and although much of her initial guidelines on humans factors surrounding wearability still stand, devices and use cases have changed over time. This paper is a collection of literature and updated guidelines and reasons for on-body location depending on the use of the wearable technology and the affordances provided by different locations on the body.


title: Introducing a transdisciplinary curriculum to foster student citizenship: a challenge beyond curricula reform

abstract: The article discusses the implementation of new curricula in the Engineering School of the Autonomous Metropolitan University of Mexico City, Campus Azcapotzalco. The curricula aim at fostering student citizenship, and at training transdisciplinary practices. First, the concepts of citizenship and transdisciplinarity are introduced and discussed, with an emphasis on Nicolescu's concept of transdisciplinarity that emphasises the integration of formal, experiential and direct knowing. The article makes a plea for such integration in curricula so they become like rich ecologies incorporating various ways of knowing. Then, the implementation process is analysed, addressing resistance encountered and changes made, up to the final curricula acceptance. The article concludes that designing transdisciplinary curricula implies the introduction of a new paradigm that: 1) needs to be broadly discussed; 2) involves training of the academic staff; 3) asks seeing curricula in a real systemic way placing each course within the context of the broader whole.


title: Ten-year Retrospective Study on the Efficacy of a Manual Physical Therapy to Treat Female Infertility

abstract: BackgroundFemale infertility is a complex issue encompassing a wide variety of diagnoses, many of which are caused or affected by adhesions. ObjectivesThe study intended to examine the rates of successful treatment of infertile women using a protocol of manual physical therapy to address underlying adhesive disease leading to infertility. MethodsThe research team designed a retrospective chart review. SettingThe study took place in a private physical therapy clinic. ParticipantsParticipants were 1392 female patients who were treated at the clinic between the years of 2002 and 2011. They had varying diagnoses of infertility, including occluded fallopian tubes, hormonal dysfunction, and endometriosis, and some women were undergoing in vitro fertilization (IVF). InterventionAll patients underwent whole-body, patient- centered treatments that used a protocol of manual physical therapy, which focused on restoring mobility and motility to structures affecting reproductive function. Outcome MeasuresImprovements demonstrated in the condition(s) causing infertility were measured by improvements in tubal patency and/or improved hormone levels or by pregnancy. ResultsThe results included a 60.85% rate of clearing occluded fallopian tubes, with a 56.64% rate of pregnancy in those patients. Patients with endometriosis experienced a 42.81% pregnancy rate. The success rate was 49.18% for lowering elevated levels of follicle stimulating hormone (FSH), with a 39.34% pregnancy rate in that group, and 53.57% of the women with polycystic ovarian syndrome (PCOS) achieved pregnancy. The reported pregnancy rate for patients who underwent IVF after the therapy was 56.16%. The results also suggested that the treatment was effective for patients with premature ovarian failure (POF). ConclusionThe manual physical therapy represented an effective, conservative treatment for women diagnosed as infertile due to mechanical causes, independent of the specific etiology. (Altern Ther Health Med. 2015;21(3):32- 40.)


title: Towards a model of safety culture

abstract: Organisational culture is a concept often used to describe shared corporate values that affect and influence members' attitudes and behaviours. Safety culture is a sub-facet of organisational culture, which is thought to affect members' attitudes and behaviour in relation to an organisation's ongoing health and safety performance. However, the myriad of definitions of 'organisational culture' and 'safety culture' that abound in both the management and safety literature suggests that the concept of business-specific cultures is not clear-cut. Placing such 'culture' constructs into a goal-setting paradigm appears to provide greater clarity than has hitherto been the case. Moreover, as yet there is no universally accepted model with which to formulate testable hypotheses that take into account antecedents, behaviour(s) and consequence(s). A reciprocal model of safety culture drawn from Social Cognitive Theory (Bandura, 1986. Social Foundation of Thought and Action: A Social Cognitive Theory. Prentice Hall, Englewood Cliffs, NJ.) is offered so as to provide both a theoretical and practical framework with which to measure and analyse safety culture. Implications for future research to establish the model's utility and validity are addressed.


title: Recognizing Emotional Body Language Displayed by a Human-like Social Robot

abstract: Natural social human–robot interactions (HRIs) require that robots have the ability to perceive and identify complex human social behaviors and, in turn, be able to also display their own behaviors using similar communication modes. Recently, it has been found that body language plays an important role in conveying information about changes in human emotions during human–human interactions. Our work focuses on extending this concept to robotic affective communication during social HRI. Namely, in this paper, we explore the design of emotional body language for our human-like social robot, Brian 2.0. We develop emotional body language for the robot using a variety of body postures and movements identified in human emotion research. To date, only a handful of researchers have focused on the use of robotic body language to display emotions, with a significant emphasis being on the display of emotions through dance. Such emotional dance can be effective for small robots with large workspaces, however, it is not as appropriate for life-sized robots such as Brian 2.0 engaging in one-on-one interpersonal social interactions with a person. Experiments are presented to evaluate the feasibility of the robot’s emotional body language based on human recognition rates. Furthermore, a unique comparison study is presented to investigate the perception of human body language features displayed by the robot with respect to the same body language features displayed by a human actor.


title: A scalable pipelined architecture for real-time computation of MLP-BP neural networks

abstract: In this paper a novel architecture for implementing multi-layer perceptron (MLP) neural networks on field programmable gate arrays (FPGA) is presented. The architecture presents a new scalable design that allows variable degrees of parallelism in order to achieve the best balance between performance and FPGA resources usage. Performance is enhanced using a highly efficient pipelined design. Extensive analysis and simulations have been conducted on four standard benchmark problems. Results show that a minimum performance boost of three orders of magnitude (O^3) over software implementation is regularly achieved. We report performance of 2-67GCUPS for these simple problems, and performance reaching over 1TCUPS for larger networks and different single FPGA chips. To our knowledge, this is the highest speed reported to date for any MLP network implementation on FPGAs.


title: Linguistic meaning, communicated meaning and cognitive pragmatics

abstract: Within the philosophy of language, pragmatics has tended to be seen as an adjunct to, and a means of solving problems in, semantics. A cognitive-scientific conception of pragmatics as a mental processing system responsible for interpreting ostensive communicative stimuli (specifically, verbal utterances) has effected a transformation in the pragmatic issues pursued and the kinds of explanation offered. Taking this latter perspective, I compare two distinct proposals on the kinds of processes, and the architecture of the system(s), responsible for the recovery of speaker meaning (both explicitly and implicitly communicated meaning).


title: AdaCluster : Adaptive Clustering for Heterogeneous Data

abstract: Clustering algorithms start with a fixed divergence, which captures the possibly asymmetric distance between a sample and a centroid. In the mixture model setting, the sample distribution plays the same role. When all attributes have the same topology and dispersion, the data are said to be homogeneous. If the prior knowledge of the distribution is inaccurate or the set of plausible distributions is large, an adaptive approach is essential. The motivation is more compelling for heterogeneous data, where the dispersion or the topology differs among attributes. We propose an adaptive approach to clustering using classes of parametrized Bregman divergences. We first show that the density of a steep exponential dispersion model (EDM) can be represented with a Bregman divergence. We then propose AdaCluster, an expectation-maximization (EM) algorithm to cluster heterogeneous data using classes of steep EDMs. We compare AdaCluster with EM for a Gaussian mixture model on synthetic data and nine UCI data sets. We also propose an adaptive hard clustering algorithm based on Generalized Method of Moments. We compare the hard clustering algorithm with k-means on the UCI data sets. We empirically verified that adaptively learning the underlying topology yields better clustering of heterogeneous data.


title: On the use of MapReduce for imbalanced big data using Random Forest

abstract: In this age, big data applications are increasingly becoming the main focus of attention because of the enormous increment of data generation and storage that has taken place in the last years. This situation becomes a challenge when huge amounts of data are processed to extract knowledge because the data mining techniques are not adapted to the new space and time requirements. Furthermore, real-world data applications usually present a class distribution where the samples that belong to one class, which is precisely the main interest, are hugely outnumbered by the samples of the other classes. This circumstance, known as the class imbalance problem, complicates the learning process as the standard learning techniques do not correctly address this situation.In this work, we analyse the performance of several techniques used to deal with imbalanced datasets in the big data scenario using the Random Forest classifier. Specifically, oversampling, undersampling and cost-sensitive learning have been adapted to big data using MapReduce so that these techniques are able to manage datasets as large as needed providing the necessary support to correctly identify the underrepresented class. The Random Forest classifier provides a solid basis for the comparison because of its performance, robustness and versatility.An experimental study is carried out to evaluate the performance of the diverse algorithms considered. The results obtained show that there is not an approach to imbalanced big data classification that outperforms the others for all the data considered when using Random Forest. Moreover, even for the same type of problem, the best performing method is dependent on the number of mappers selected to run the experiments. In most of the cases, when the number of splits is increased, an improvement in the running times can be observed, however, this progress in times is obtained at the expense of a slight drop in the accuracy performance obtained. This decrement in the performance is related to the lack of density problem, which is evaluated in this work from the imbalanced data point of view, as this issue degrades the performance of classifiers in the imbalanced scenario more severely than in standard learning.


Recursive implementation of optimal time VLSi integer multipliers


title: ResFeats: Residual Network Based Features for Image Classification

abstract: Deep residual networks have recently emerged as the state-of-the-art architecture in image segmentation and object detection. In this paper, we propose new image features (called ResFeats) extracted from the last convolutional layer of deep residual networks pre-trained on ImageNet. We propose to use ResFeats for diverse image classification tasks namely, object classification, scene classification and coral classification and show that ResFeats consistently perform better than their CNN counterparts on these classification tasks. Since the ResFeats are large feature vectors, we propose to use PCA for dimensionality reduction. Experimental results are provided to show the effectiveness of ResFeats with state-of-the-art classification accuracies on Caltech-101, Caltech-256 and MLC datasets and a significant performance improvement on MIT-67 dataset compared to the widely used CNN features.


title: Recognizing Textual Entailment Using Probabilistic Inference

abstract: Recognizing Text Entailment (RTE) plays an important role in NLP applications including question answering, information retrieval, etc. In recent work, some research explore “deep” expressions such as discourse commitments or strict logic for representing the text. However, these expressions suffer from the limitation of inference inconvenience or translation loss. To overcome the limitations, in this paper, we propose to use the predicate-argument structures to represent the discourse commitments extracted from text. At the same time, with the help of the YAGO knowledge, we borrow the distant supervision technique to mine the implicit facts from the text. We also construct a probabilistic network for all the facts and conduct inference to judge the confidence of each fact for RTE. The experimental results show that our proposed method achieves a competitive result compared to the previous work.


title: Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace.

abstract: Gradient-based meta-learning has been shown to be expressive enough to approximate any learning algorithm. While previous such methods have been successful in meta-learning tasks, they resort to simple gradient descent during meta-testing. Our primary contribution is the {\em MT-net}, which enables the meta-learner to learn on each layer's activation space a subspace that the task-specific learner performs gradient descent on. Additionally, a task-specific learner of an {\em MT-net} performs gradient descent with respect to a meta-learned distance metric, which warps the activation space to be more sensitive to task identity. We demonstrate that the dimension of this learned subspace reflects the complexity of the task-specific learner's adaptation task, and also that our model is less sensitive to the choice of initial learning rates than previous gradient-based meta-learning methods. Our method achieves state-of-the-art or comparable performance on few-shot classification and regression tasks.


title: Stochastic game net and applications in security analysis for enterprise network

abstract: Stochastic game theoretic framework has been used in many fields of networks with interactive behaviors. However, further use of this framework is limited due to the following reasons. Firstly, it is difficult to build comprehensive and rigorous models for complex network structures by the state-based game model. Secondly, solving and extending the dynamic behaviors of participators of the network are nearly impossible, because of the complexity of state transitions. Last but not least, general game model is not able to describe and analyze specific events and behaviors in some kinds of networks, like enterprise networks. In this paper, we propose a new modeling paradigm (stochastic game net, or SGN) for stochastic games representation with Petri nets. Based on our graphical tool, stochastic game problems can be described clearly, and the model can be solved and extended easily. Moreover, this paper puts forth a series of methods for modeling and analyzing the competitive game by SGN, which is the main contribution of this work. Our achievements are applied to the security analysis for enterprise networks. The analysis results prove the powerful ability of our achievements in solving the complicated and dynamic game problems. Furthermore, our approaches can be used to calculate the existence and the value of an equilibrium point.


title: Cyber Scanning: A Comprehensive Survey

abstract: Cyber scanning refers to the task of probing enterprise networks or Internet wide services, searching for vulnerabilities or ways to infiltrate IT assets. This misdemeanor is often the primarily methodology that is adopted by attackers prior to launching a targeted cyber attack. Hence, it is of paramount importance to research and adopt methods for the detection and attribution of cyber scanning. Nevertheless, with the surge of complex offered services from one side and the proliferation of hackers' refined, advanced, and sophisticated techniques from the other side, the task of containing cyber scanning poses serious issues and challenges. Furthermore recently, there has been a flourishing of a cyber phenomenon dubbed as cyber scanning campaigns - scanning techniques that are highly distributed, possess composite stealth capabilities and high coordination - rendering almost all current detection techniques unfeasible. This paper presents a comprehensive survey of the entire cyber scanning topic. It categorizes cyber scanning by elaborating on its nature, strategies and approaches. It also provides the reader with a classification and an exhaustive review of its techniques. Moreover, it offers a taxonomy of the current literature by focusing on distributed cyber scanning detection methods. To tackle cyber scanning campaigns, this paper uniquely reports on the analysis of two recent cyber scanning incidents. Finally, several concluding remarks are discussed.


title: Negative Capacitance Carbon Nanotube FETs

abstract: As continued scaling of silicon FETs grows increasingly challenging, alternative paths for improving digital system energy efficiency are being pursued. These paths include replacing the transistor channel with emerging nanomaterials (such as carbon nanotubes), as well as utilizing negative capacitance effects in ferroelectric materials in the FET gate stack, e.g., to improve sub-threshold slope beyond the 60 mV/decade limit. However, which path provides the largest energy efficiency benefits—and whether these multiple paths can be combined to achieve additional energy efficiency benefits—is still unclear. Here, we experimentally demonstrate the first negative capacitance carbon nanotube FETs (CNFETs), combining the benefits of both carbon nanotube channels and negative capacitance effects. We demonstrate negative capacitance CNFETs, achieving sub-60 mV/decade sub-threshold slope with an average sub-threshold slope of 55 mV/decade at room temperature. The average ON-current ( ${I}_{ \mathrm{ON}}$ ) of these negative capacitance CNFETs improves by $2.1\times $ versus baseline CNFETs, (i.e., without negative capacitance) for the same OFF-current ( ${I}_{ \mathrm{OFF}}$ ). This work demonstrates a promising path forward for future generations of energy-efficient electronic systems.


Gun Ownership and Attitudes Toward Gun Control in Older Adults : Re-examining Self Interest Theory


title: Copper ion liquid-like thermoelectrics

abstract: Advanced thermoelectric technology offers a potential for converting waste industrial heat into useful electricity, and an emission-free method for solid state cooling. Worldwide efforts to find materials with thermoelectric figure of merit, zT values significantly above unity, are frequently focused on crystalline semiconductors with low thermal conductivity. Here we report on Cu_(2−x)Se, which reaches a zT of 1.5 at 1,000 K, among the highest values for any bulk materials. Whereas the Se atoms in Cu_(2−x)Se form a rigid face-centred cubic lattice, providing a crystalline pathway for semiconducting electrons (or more precisely holes), the copper ions are highly disordered around the Se sublattice and are superionic with liquid-like mobility. This extraordinary ‘liquid-like’ behaviour of copper ions around a crystalline sublattice of Se in Cu_(2−x)Se results in an intrinsically very low lattice thermal conductivity which enables high zT in this otherwise simple semiconductor. This unusual combination of properties leads to an ideal thermoelectric material. The results indicate a new strategy and direction for high-efficiency thermoelectric materials by exploring systems where there exists a crystalline sublattice for electronic conduction surrounded by liquid-like ions.


title: Observer Design for Stochastic Nonlinear Systems via Contraction-Based Incremental Stability

abstract: This paper presents a new design approach to nonlinear observers for Ito stochastic nonlinear systems with guaranteed stability. A stochastic contraction lemma is presented which is used to analyze incremental stability of the observer. A bound on the mean-squared distance between the trajectories of original dynamics and the observer dynamics is obtained as a function of the contraction rate and maximum noise intensity. The observer design is based on a non-unique state-dependent coefficient (SDC) form, which parametrizes the nonlinearity in an extended linear form. The observer gain synthesis algorithm, called linear matrix inequality state-dependent algebraic Riccati equation (LMI-SDARE), is presented. The LMI-SDARE uses a convex combination of multiple SDC parametrizations. An optimization problem with state-dependent linear matrix inequality (SDLMI) constraints is formulated to select the coefficients of the convex combination for maximizing the convergence rate and robustness against disturbances. Two variations of LMI-SDARE algorithm are also proposed. One of them named convex state-dependent Riccati equation (CSDRE) uses a chosen convex combination of multiple SDC matrices; and the other named Fixed-SDARE uses constant SDC matrices that are pre-computed by using conservative bounds of the system states while using constant coefficients of the convex combination pre-computed by a convex LMI optimization problem. A connection between contraction analysis and L_2 gain of the nonlinear system is established in the presence of noise and disturbances. Results of simulation show superiority of the LMI-SDARE algorithm to the extended Kalman filter (EKF) and state-dependent differential Riccati equation (SDDRE) filter.


title: The effect of early visual deprivation on the development of face detection

abstract: The expertise of adults in face perception is facilitated by their ability to rapidly detect that a stimulus is a face. In two experiments, we examined the role of early visual input in the development of face detection by testing patients who had been treated as infants for bilateral congenital cataract. Experiment 1 indicated that, at age 9 to 20, patients’ accuracy and response times on a Mooney face detection task were normal. Experiment 2 revealed that the neural mechanisms underlying face detection in a similar group of adult patients are abnormal: the amplitude of both the P100 and N170 event-related potential were larger in patients than in visually normal controls, and the extent of augmentation was related to the duration of deprivation. Thus, early visual experience is necessary for the establishment of normal neural networks for face detection; abnormalities at these early processing stages may contribute to the deficits we previously reported in configural face processing for this patient cohort.


title: A U.S. Research Roadmap for Human Computation

abstract: The Web has made it possible to harness human cognition en masse to achieve new capabilities. Some of these successes are well known; for example Wikipedia has become the go-to place for basic information on all things; Duolingo engages millions of people in real-life translation of text, while simultaneously teaching them to speak foreign languages; and fold.it has enabled public-driven scientific discoveries by recasting complex biomedical challenges into popular online puzzle games. These and other early successes hint at the tremendous potential for future crowd-powered capabilities for the benefit of health, education, science, and society. In the process, a new field called Human Computation has emerged to better understand, replicate, and improve upon these successes through scientific research. Human Computation refers to the science that underlies online crowd-powered systems and was the topic of a recent visioning activity in which a representative cross-section of researchers, industry practitioners, visionaries, funding agency representatives, and policy makers came together to understand what makes crowd-powered systems successful. Teams of experts considered past, present, and future human computation systems to explore which kinds of crowd-powered systems have the greatest potential for societal impact and which kinds of research will best enable the efficient development of new crowd-powered systems to achieve this impact. This report summarize the products and findings of those activities as well as the unconventional process and activities employed by the workshop, which were informed by human computation research.


title: Challenges in multimodal gesture recognition

abstract: This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the Kinect™ revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.


title: Optimization of asynchronous graph processing on GPU with hybrid coloring model

abstract: Modern GPUs have been widely used to accelerate the graph processing for complicated computational problems regarding graph theory. Many parallel graph algorithms adopt the asynchronous computing model to accelerate the iterative convergence. Unfortunately, the consistent asynchronous computing requires locking or the atomic operations, leading to significant penalties/overheads when implemented on GPUs. To this end, coloring algorithm is adopted to separate the vertices with potential updating conflicts, guaranteeing the consistency/correctness of the parallel processing. We propose a light-weight asynchronous processing framework called Frog with a hybrid coloring model. We find that majority of vertices (about 80%) are colored with only a few colors, such that they can be read and updated in a very high degree of parallelism without violating the sequential consistency. Accordingly, our solution will separate the processing of the vertices based on the distribution of colors.


Interpolation of Parametric CNC Machine Tool Path Under Confined Jounce 1 )


title: Utility of STaT for the identification of recent intimate partner violence.

abstract: Intimate partner violence (IPV) is an important issue with far-reaching health consequences. This study investigates the utility of STaT, a three-question IPV screening tool, for recent IPV identification in a sample of adult women in an innercity urgent care clinic. STaT score was calculated as the total number of affirmative responses to the three questions. Efficacy of STaT as an IPV screen was estimated by computing the sensitivity and specificity at possible cut points, based on participant's STaT score, and using Index of Spouse Abuse scores as a comparison standard. The sensitivities of STaT were 94.9%, 84.8% and 62% with the cut points set at scores of 1, 2 and 3, respectively. Thus, with the criterion for a positive screen set at a cut-point score of 1, STaT can be used to facilitate the identification of abused women in busy public healthcare settings.


title: Common-Ground-Type Transformerless Inverters for Single-Phase Solar Photovoltaic Systems

abstract: This paper proposes a family of novel flying capacitor transformerless inverters for single-phase photovoltaic (PV) systems. Each of the new topologies proposed is based on a flying capacitor principle and requires only four power switches and/or diodes, one capacitor, and a small filter at the output stage. A simple unipolar sinusoidal pulse width modulation technique is used to modulate the inverter to minimize the switching loss, output current ripple, and the filter requirements. In general, the main advantages of the new inverter topologies are: 1) the negative polarity of the PV is directly connected to the grid, and therefore, no leakage current; 2) reactive power compensation capability; and 3) the output ac voltage peak is equal to the input dc voltage (unlike neutral-point-clamped and derivative topologies, which requires twice the magnitude of the peak ac voltage). A complete description of the operating principle with modulation techniques, design guidelines, and comprehensive comparisons is presented to reveal the properties and limitations of each topology in detail. Finally, experimental results of 1-kVA prototypes are presented to prove the concept and theoretical analysis of the proposed inverter family for practical applications.


title: Evolution of Attacks, Threat Models, and Solutions for Virtualized Systems

abstract: Virtualization technology enables Cloud providers to efficiently use their computing services and resources. Even if the benefits in terms of performance, maintenance, and cost are evident, however, virtualization has also been exploited by attackers to devise new ways to compromise a system. To address these problems, research security solutions have evolved considerably over the years to cope with new attacks and threat models. In this work, we review the protection strategies proposed in the literature and show how some of the solutions have been invalidated by new attacks, or threat models, that were previously not considered. The goal is to show the evolution of the threats, and of the related security and trust assumptions, in virtualized systems that have given rise to complex threat models and the corresponding sophistication of protection strategies to deal with such attacks. We also categorize threat models, security and trust assumptions, and attacks against a virtualized system at the different layers—in particular, hardware, virtualization, OS, and application.


title: PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth

abstract: Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of A priori which may substantially reduce the number of combinations to be examined. Howeve6 Apriori still encounters problems when a sequence database is large andor when sequential patterns to be mined are numerous ano we propose a novel sequential pattern mining method, called Prefixspan (i.e., Prefix-projected - Ettern_ mining), which explores prejxprojection in sequential pattern mining. Prefixspan mines the complete set of patterns but greatly reduces the efforts of candidate subsequence generation. Moreover; prefi-projection substantially reduces the size of projected databases and leads to efJicient processing. Our performance study shows that Prefixspan outperforms both the Apriori-based GSP algorithm and another recently proposed method; Frees pan, in mining large sequence data bases.


title: Learning spectral mapping for speech dereverberation and denoising

abstract: In real-world environments, human speech is usually distorted by both reverberation and background noise, which have negative effects on speech intelligibility and speech quality. They also cause performance degradation in many speech technology applications, such as automatic speech recognition. Therefore, the dereverberation and denoising problems must be dealt with in daily listening environments. In this paper, we propose to perform speech dereverberation using supervised learning, and the supervised approach is then extended to address both dereverberation and denoising. Deep neural networks are trained to directly learn a spectral mapping from the magnitude spectrogram of corrupted speech to that of clean speech. The proposed approach substantially attenuates the distortion caused by reverberation, as well as background noise, and is conceptually simple. Systematic experiments show that the proposed approach leads to significant improvements of predicted speech intelligibility and quality, as well as automatic speech recognition in reverberant noisy conditions. Comparisons show that our approach substantially outperforms related methods.


title: Recognizing primes in random polynomial time

abstract: This paper is the first in a sequence of papers which will prove the existence of a random polynomial time algorithm for the set of primes. The techniques used are from arithmetic algebraic geometry and to a lesser extent algebraic and analytic number theory. The result complements the well known result of Strassen and Soloway that there exists a random polynomial time algorithm for the set of composites.


title: Intimate Partner Violence as a Risk Factor for Mental Disorders: A Meta-Analysis

abstract: This article reviews literature on the prevalence of mental health problems among women with a history of intimate partner violence. The weighted mean prevalence of mental health problems among battered women was 47.6% in 18 studies of depression, 17.9% in 13 studies of suicidality, 63.8% in 11 studies of posttraumatic stress disorder (PTSD), 18.5% in 10 studies of alcohol abuse, and 8.9% in four studies of drug abuse. These were typically inconsistent across studies. Weighted mean odds ratios representing associations of these problems with violence ranged from 3.55 to 5.62, and were typically consistent across studies. Variability was accounted for by differences in sampling frames. Dose-response relationships of violence to depression and PTSD were observed. Although research has not addressed many criteria for causal inferences, the existing research is consistent with the hypothesis that intimate partner violence increases risk for mental health problems. The appropriate way to conceptualize these problems deserves careful attention.


title: Arav: monitoring a cloud's virtual routers

abstract: Virtual Routers (VRs) are increasingly common in cloud environments. VRs route traffic between network segments and support network services. Routers, including VRs, have been the target of several recent high-profile attacks, emphasizing the need for more security measures, including security monitoring. However, existing agent-based monitoring systems are incompatible with a VR's temporary nature, stripped-down operating system, and placement in the cloud. As a result, VRs are often not monitored, leading to undetected security incidents. This paper proposes a new security monitoring design that leverages virtualization instead of in-guest agents. Its hypervisor-based system, Arav, scrutinizes VRs by novel application of Virtual Machine Introspection (VMI) breakpoint injection. Arav monitored and addressed security-related events in two common VRs, pfSense and VyOS, and detected four attacks against two popular VR services, Quagga and OpenVPN. Arav's performance overhead is negligible, less than 0.63%, demonstrating VMI's utility in monitoring virtual machines unsuitable for traditional security monitoring.


title: Mosaic: A low-cost mobile sensing system for urban air quality monitoring

abstract: Air quality monitoring has attracted a lot of attention from governments, academia and industry, especially for PM2.5 due to its significant impact on our respiratory systems. In this paper, we present the design, implementation, and evaluation of Mosaic, a low cost urban PM2.5 monitoring system based on mobile sensing. In Mosaic, a small number of air quality monitoring nodes are deployed on city buses to measure air quality. Current low-cost particle sensors based on light-scattering, however, are vulnerable to airflow disturbance on moving vehicles. In order to address this problem, we build our air quality monitoring nodes, Mosaic-Nodes, with a novel constructive airflow-disturbance design based on a carefully tuned airflow structure and a GPS-assisted filtering method. Further, the buses used for system deployment are selected by a novel algorithm which achieves both high coverage and low computation overhead. The collected sensor data is also used to calculate the PM2.5 of locations without direct measurements by an existing inference model. We apply the Mosaic system in a testing urban area which includes more than 70 point-of-interests. Results show that the Mosaic system can accurately obtain the urban air quality with high coverage and low cost.


title: Parameter estimation of q-Gaussian Radial Basis Functions Neural Networks with a Hybrid Algorithm for binary classification

abstract: A classification problem is a decision-making task that many researchers have studied. A number of techniques have been proposed to perform binary classification. Neural networks are one of the artificial intelligence techniques that has had the most successful results when applied to this problem. Our proposal is the use of q-Gaussian Radial Basis Function Neural Networks (q-Gaussian RBFNNs). This basis function includes a supplementary degree of freedom in order to adapt the model to the distribution of data. A Hybrid Algorithm (HA) is used to search for a suitable architecture for the q-Gaussian RBFNN. The use of this type of more flexible kernel could greatly improve the discriminative power of RBFNNs. In order to test performance, the RBFNN with the q-Gaussian basis functions is compared to RBFNNs with Gaussian, Cauchy and Inverse Multiquadratic RBFs, and to other recent neural networks approaches. An experimental study is presented on 11 binary-classification datasets taken from the UCI repository. Moreover, aerial imagery taken in mid-May, mid-June and mid-July was used to evaluate the potential of the methodology proposed for discriminating Ridolfia segetum patches (one of the most dominant and harmful weeds in sunflower crops) in two naturally infested fields in southern Spain.


title: Grammar Argumented LSTM Neural Networks with Note-Level Encoding for Music Composition.

abstract: Creating any aesthetically pleasing piece of art, like music, has been a long time dream for artificial intelligence research. Based on recent success of long-short term memory (LSTM) on sequence learning, we put forward a novel system to reflect the thinking pattern of a musician. For data representation, we propose a note-level encoding method, which enables our model to simulate how human composes and polishes music phrases. To avoid failure against music theory, we invent a novel method, grammar argumented (GA) method. It can teach machine basic composing principles. In this method, we propose three rules as argumented grammars and three metrics for evaluation of machine-made music. Results show that comparing to basic LSTM, grammar argumented model's compositions have higher contents of diatonic scale notes, short pitch intervals, and chords.


title: Analysis of vibration of rotors in unmanned aircraft

abstract: In the paper, solution of the problem of vibrations which appears during the maneuvers of quadrocopters is presented. Those special kinds of vibration aren't related to mounting or manufacturing faults. In this kinds of flying machines the main sources of vibration comes from motors and/or propellers unbalance. The paper shows analytical description of the source of this problem. Mathematical description of rotor which was subjected of additional rotation had been modeled in Matlab - Simulink environment. It was shown, that correct set of parameters lead to total elimination of this kind of vibration. In paper is shown also investigation on real object and influence of results on vibration reduction.


title: A simple planar high-directivity Yagi-Uda antenna with a concave parabolic reflector

abstract: A simple planar Yagi-Uda antenna with a single director and a concave parabolic reflector on a thin dielectric substrate is proposed. Through the innovative design, the simple Yagi-Uda antenna can achieve the high directivity of 9.0 dBi, front-to-back ratio of 14.5 dB, cross-polarization level of −38.7 dB, bandwidth of 6.0%, and the radiation efficiency of 85.1%, i.e., better than −1 dBi in terms of the 3D average gain. This proposed antenna is especially suitable for the mobile devices with GPS functions.


title: Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search

abstract: Similarity indices for high-dimensional data are very desirable for building content-based search systems for feature-rich data such as audio, images, videos, and other sensor data. Recently, locality sensitive hashing (LSH) and its variations have been proposed as indexing techniques for approximate similarity search. A significant drawback of these approaches is the requirement for a large number of hash tables in order to achieve good search quality. This paper proposes a new indexing scheme called multi-probe LSH that overcomes this drawback. Multi-probe LSH is built on the well-known LSH technique, but it intelligently probes multiple buckets that are likely to contain query results in a hash table. Our method is inspired by and improves upon recent theoretical work on entropy-based LSH designed to reduce the space requirement of the basic LSH method. We have implemented the multi-probe LSH method and evaluated the implementation with two different high-dimensional datasets. Our evaluation shows that the multi-probe LSH method substantially improves upon previously proposed methods in both space and time efficiency. To achieve the same search quality, multi-probe LSH has a similar time-efficiency as the basic LSH method while reducing the number of hash tables by an order of magnitude. In comparison with the entropy-based LSH method, to achieve the same search quality, multi-probe LSH uses less query time and 5 to 8 times fewer number of hash tables.


title: Fast algorithms for recovering a corrupted low-rank matrix

abstract: This paper studies algorithms for solving the problem of recovering a low-rank matrix with a fraction of its entries arbitrarily corrupted. This problem can be viewed as a robust version of classical PCA, and arises in a number of application domains, including image processing, web data ranking, and bioinformatic data analysis. It was recently shown that under surprisingly broad conditions, it can be exactly solved via a convex programming surrogate that combines nuclear norm minimization and l1-norm minimization. This paper develops and compares two complementary approaches for solving this convex program. The first is an accelerated proximal gradient algorithm directly applied to the primal; while the second is a gradient algorithm applied to the dual problem. Both are several orders of magnitude faster than the previous state-of-the-art algorithm for this problem, which was based on iterative thresholding. Simulations demonstrate the performance improvement that can be obtained via these two algorithms, and clarify their relative merits.


title: A Survey of Insider Attack Detection Research

abstract: This paper surveys proposed solutions for the problem of insider attack detection appearing in the computer security research literature. We distinguish between masqueraders and traitors as two distinct cases of insider attack. After describing the challenges of this problem and highlighting current approaches and techniques pursued by the research community for insider attack detection, we suggest directions for future research.


title: Bakebot: Baking Cookies with the PR2

abstract: We present BakeBot, a PR2 robot system that bakes cookies autonomously, from mise en place presentation through baking in a toaster oven. The baking planning and control system is implemented as a hierarchical finite state machine. We developed parameterized motion primitives for baking. The motion primitives utilize the existing sensing and manipulation capabilities of the PR2 platform and also our new compliant control techniques to address environmental uncertainty. The system was tested through 27 baking attempts, 16 of which successfully resulted in edible cookies.


title: RFID Technology and Applications

abstract: Radio frequency identification is a wireless communication technology that lets computers read the identity of inexpensive electronic tags from a distance without requiring a battery in the tags. As RFID technology matures, it will likely unleash a new wave of applications that will exploit inexpensive and highly available automatic identification.