title: MinT: Middleware for Cooperative Interaction of Things

abstract: This paper proposes an Internet of Things (IoT) middleware called Middleware for Cooperative Interaction of Things (MinT). MinT supports a fully distributed IoT environment in which IoT devices directly connect to peripheral devices easily construct a local or global network, and share their data in an energy efficient manner. MinT provides a sensor abstract layer, a system layer and an interaction layer. These enable integrated sensing device operations, efficient resource management, and active interconnection between peripheral IoT devices. In addition, MinT provides a high-level API to develop IoT devices easily for IoT device developers. We aim to enhance the energy efficiency and performance of IoT devices through the performance improvements offered by MinT resource management and request processing. The experimental results show that the average request rate increased by 25% compared to Californium, which is a middleware for efficient interaction in IoT environments with powerful performance, an average response time decrease of 90% when resource management was used, and power consumption decreased by up to 68%. Finally, the proposed platform can reduce the latency and power consumption of IoT devices.


title: Predicting Change in Marital Satisfaction Throughout Emotionally Focused Couple Therapy

abstract: Emotionally focused couple therapy (EFT) is an empirically validated approach to couple therapy that uses attachment theory to understand the needs and emotions of romantic partners. EFT is recognized as one of the most effective approaches to couple therapy, but to guide therapists in their use of EFT, a theoretically based model to predict change is needed. This study tested such a model by recruiting 32 couples, and 14 therapists who provided approximately 21 sessions of EFT. Couples completed self-report measures of marital satisfaction, attachment security, relationship trust, and emotional control at pre- and posttherapy and after each therapy session. Results of hierarchical linear modeling suggested that individuals higher on self-report attachment anxiety and higher levels of emotional control had greater change in marital satisfaction across EFT sessions. Assessing attachment security at the start of therapy will inform therapists of the emotion regulating strategies used by couples and may help couples achieve positive outcomes from EFT.


title: Twelve Years of Galaxy Zoo

abstract: The Galaxy Zoo (GZ) project has provided quantitative visual morphologies for over a million galaxies, and has been part of a reinvigoration of interest in the morphologies of galaxies and what they reveal about galaxy evolution. Morphological information collected by GZ has shown itself to be a powerful tool for studying galaxy evolution, and GZ continues to collect classifications - currently serving imaging from DECaLS in its main site, and running a variety of related projects hosted by the Zooniverse; the citizen science platform which came out of the early success of GZ. I highlight some of the results from the last twelve years, with a particular emphasis on linking morphology and dynamics, look forward to future projects in the GZ family, and provide a quick start guide for how you can easily make use of citizen science techniques to analysis your own large and complex data sets.


title: A Multiworld Testing Decision Service

abstract: Applications and systems are constantly faced with decisions to make, often using a policy to pick from a set of actions based on some contextual information. We create a service that uses machine learning to accomplish this goal. The service uses exploration, logging, and online learning to create a counterfactually sound system supporting a full data lifecycle. The system is general: it works for any discrete choices, with respect to any reward metric, and can work with many learning algorithms and feature representations. The service has a simple API, and was designed to be modular and reproducible to ease deployment and debugging, respectively. We demonstrate how these properties enable learning systems that are robust and safe. Our evaluation shows that the Decision Service makes decisions in real time and incorporates new data quickly into learned policies. A large-scale deployment for a personalized news website has been handling all traffic since Jan. 2016, resulting in a 25% relative lift in clicks. By making the Decision Service externally available, we hope to make optimal decision making available to all.


title: Protocol-Based Resuscitation for Septic Shock: A Meta-Analysis of Randomized Trials and Observational Studies

abstract: Owing to the recommendations of the Surviving Sepsis Campaign guidelines, protocol-based resuscitation or goal-directed therapy (GDT) is broadly advocated for the treatment of septic shock. However, the most recently published trials showed no survival benefit from protocol-based resuscitation in septic shock patients. Hence, we aimed to assess the effect of GDT on clinical outcomes in such patients.We performed a systematic review that included a meta-analysis. We used electronic search engines including PubMed, Embase, and the Cochrane database to find studies comparing protocol-based GDT to common or standard care in patients with septic shock and severe sepsis.A total of 13269 septic shock patients in 24 studies were included [12 randomized controlled trials (RCTs) and 12 observational studies]. The overall mortality odds ratio (OR) [95% confidence interval (CI)] for GDT versus conventional care was 0.746 (0.631-0.883). In RCTs only, the mortality OR (95% CI) for GDT versus conventional care in the meta-analysis was 0.93 (0.75-1.16). The beneficial effect of GDT decreased as more recent studies were added in an alternative, cumulative meta-analysis. No significant publication bias was found.The result of this meta-analysis suggests that GDT reduces mortality in patients with severe sepsis or septic shock. However, our cumulative meta-analysis revealed that the reduction of mortality risk was diminished as more recent studies were added.


title: An evaluation framework for software crowdsourcing

abstract: Recently software crowdsourcing has become an emerging area of software engineering. Few papers have presented a systematic analysis on the practices of software crowdsourcing. This paper first presents an evaluation framework to evaluate software crowdsourcing projects with respect to software quality, costs, diversity of solutions, and competition nature in crowdsourcing. Specifically, competitions are evaluated by the min-max relationship from game theory among participants where one party tries to minimize an objective function while the other party tries to maximize the same objective function. The paper then defines a game theory model to analyze the primary factors in these minmax competition rules that affect the nature of participation as well as the software quality. Finally, using the proposed evaluation framework, this paper illustrates two crowdsourcing processes, Harvard-TopCoder and AppStori. The framework demonstrates the sharp contrasts between both crowdsourcing processes as participants will have drastic behaviors in engaging these two projects.


title: Detection of Metadata Tampering Through Discrepancy Between Image Content and Metadata Using Multi-task Deep Learning

abstract: Image content or metadata editing software availability and ease of use has resulted in a high demand for automatic image tamper detection algorithms. Most previous work has focused on detection of tampered image content, whereas we develop techniques to detect metadata tampering in outdoor images using sun altitude angle and other meteorological information like temperature, humidity and weather, which can be observed in most outdoor image scenes. To train and evaluate our technique, we create a large dataset of outdoor images labeled with sun altitude angle and other meteorological data (AMOS+M2), which to our knowledge, is the largest publicly available dataset of its kind. Using this dataset, we train separate regression models for sun altitude angle, temperature and humidity and a classification model for weather to detect any discrepancy between image content and its metadata. Finally, a joint multi-task network for these four features shows a relative improvement of 15.5% compared to each of them individually. We include a detailed analysis for using these networks to detect various types of modification to location and time information in image metadata.


title: A Self-Powered and Autonomous Fringing Field Capacitive Sensor Integrated into a Micro Sprinkler Spinner to Measure Soil Water Content

abstract: We present here the design and fabrication of a self-powered and autonomous fringing field capacitive sensor to measure soil water content. The sensor is manufactured using a conventional printed circuit board and includes a porous ceramic. To read the sensor, we use a circuit that includes a 10 kHz triangle wave generator, an AC amplifier, a precision rectifier and a microcontroller. In terms of performance, the sensor's capacitance (measured in a laboratory prototype) increases up to 5% when the volumetric water content of the porous ceramic changed from 3% to 36%, resulting in a sensitivity of S = 15.5 pF per unity change. Repeatability tests for capacitance measurement showed that the θ v sensor's root mean square error is 0.13%. The average current consumption of the system (sensor and signal conditioning circuit) is less than 1.5 μ A, which demonstrates its suitability for being powered by energy harvesting systems. We developed a complete irrigation control system that integrates the sensor, an energy harvesting module composed of a microgenerator installed on the top of a micro sprinkler spinner, and a DC/DC converter circuit that charges a 1 F supercapacitor. The energy harvesting module operates only when the micro sprinkler spinner is irrigating the soil, and the supercapacitor is fully charged to 5 V in about 3 h during the first irrigation. After the first irrigation, with the supercap fully charged, the system can operate powered only by the supercapacitor for approximately 23 days, without any energy being harvested.


title: Smart city as urban innovation: focusing on management, policy, and context

abstract: This paper sees a smart city not as a status of how smart a city is but as a city's effort to make itself smart. The connotation of a smart city represents city innovation in management and policy as well as technology. Since the unique context of each city shapes the technological, organizational and policy aspects of that city, a smart city can be considered a contextualized interplay among technological innovation, managerial and organizational innovation, and policy innovation. However, only little research discusses innovation in management and policy while the literature of technology innovation is abundant. This paper aims to fill the research gap by building a comprehensive framework to view the smart city movement as innovation comprised of technology, management and policy. We also discuss inevitable risks from innovation, strategies to innovate while avoiding risks, and contexts underlying innovation and risks.


title: Psychometric intelligence in patients with traumatic brain injury: utility of a new screening measure.

abstract: OBJECTIVE ::: To evaluate the validity of the General Ability Measure for Adults (GAMA) in a sample of patients with traumatic brain injury (TBI). ::: ::: ::: DESIGN ::: Comparison with criterion standards of Wechsler Adult Intelligence Scale-Revised (WAIS-R) and measures of injury severity. ::: ::: ::: SETTING ::: Regional rehabilitation center. ::: ::: ::: PARTICIPANTS ::: One-year prospective series of consecutive rehabilitation referrals, including 42 adult patients with TBI. ::: ::: ::: MAIN OUTCOME MEASURES ::: GAMA and WAIS-R IQ scores were obtained within 1 year after injury and compared with each other and with measures of injury severity. ::: ::: ::: RESULTS ::: The GAMA demonstrated statistically significant covariance with the WAIS-R and was able to discriminate severe TBI from mild/moderate TBI. The correlation between the GAMA and length of coma fell just short of statistical significance. ::: ::: ::: CONCLUSIONS ::: The GAMA is sufficiently sensitive to the presence or absence of severe TBI but may not be sufficiently sensitive to the exact degree of injury severity.


title: Deep contextualized word representations for detecting sarcasm and irony

abstract: Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.


title: The functional significance of EEG microstates—Associations with modalities of thinking

abstract: The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, attention reorientation, and subjective interoceptive-autonomic processing, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared microstate parameters between conditions. TANOVAs compared microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions concerned particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with microstate parameters except verbal cognitive style which correlated negatively with microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG microstate class parameter changes based on their hypothesized functional significance. Beyond, the associations of microstate classes A and B with visual and verbal processing, respectively and microstate class D with interoceptive-autonomic processing, our results suggest that a finely-tuned interplay between all four EEG microstate classes is necessary for the continuous formation of visual and verbal thoughts, as well as interoceptive-autonomic processing. Our results point to the possibility that the EEG microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis.


title: Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

abstract: This paper proposes KB-InfoBot -- a multi-turn dialogue agent which helps users search Knowledge Bases (KBs) without composing complicated queries. Such goal-oriented dialogue agents typically need to interact with an external database to access real-world knowledge. Previous systems achieved this by issuing a symbolic query to the KB to retrieve entries based on their attributes. However, such symbolic operations break the differentiability of the system and prevent end-to-end training of neural dialogue agents. In this paper, we address this limitation by replacing symbolic queries with an induced "soft" posterior distribution over the KB that indicates which entities the user is interested in. Integrating the soft retrieval process with a reinforcement learner leads to higher task success rate and reward in both simulations and against real users. We also present a fully neural end-to-end agent, trained entirely from user feedback, and discuss its application towards personalized dialogue agents. The source code is available at this https URL


title: To be or not to be in social media: How brand loyalty is affected by social media?

abstract: There is an ongoing debate over the activities of brands and companies in social media. Some researchers believe social media provide a unique opportunity for brands to foster their relationships with customers, while others believe the contrary. Taking the perspective of the brand community building plus the brand trust and loyalty literatures, our goal is to show how brand communities based on social media influence elements of the customer centric model (i.e., the relationships among focal customer and brand, product, company, and other customers) and brand loyalty. A survey-based empirical study with 441 respondents was conducted. The results of structural equation modeling show that brand communities established on social media have positive effects on customer/product, customer/brand, customer/company and customer/other customers relationships, which in turn have positive effects on brand trust, and trust has positive effects on brand loyalty. We find that brand trust has a fully mediating role in converting the effects of enhanced relationships in brand community to brand loyalty. The implications for marketing practice and future research are discussed.


title: Mobile Operating Systems and Application Development Platforms: A Survey

abstract: Earlier mobile communication technologies were dominated by vertically integrated service provision which are highly bounded mainly to voice and short message services that are organized in a monopolistic competition between few mobile virtual network operators, service providers and enhanced service providers. In the recent years, however, radical change driven by advancements in technology, witnessed the introduction and further development of smart phones where the user can get access to new applications and services by connecting to the device manufactures’ application stores and the like. These smart phones have added many features of a full-fledged computer: high speed processors, large storage space, multitasking, high-resolution screens and cameras, multipurpose communication hardware, and so on. However, these devices market is dominated by a number of different technological platforms, including different operating systems a (OS) and application development platforms, resulting in a variety of different competing solutions on the market driven by different actor. This paper detailed a review and comparative analysis of the features of these technological platforms. Keyword: Mobile OS, Android, iOS, Windows Phone, Black-Berry OS, webOS and Symbian.


title: Few-Shot Adversarial Domain Adaptation

abstract: This work provides a framework for addressing the problem of supervised domain adaptation with deep models. The main idea is to exploit adversarial learning to learn an embedded subspace that simultaneously maximizes the confusion between two domains while semantically aligning their embedding. The supervised setting becomes attractive especially when there are only a few target data samples that need to be labeled. In this few-shot learning scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by carefully designing a training scheme whereby the typical binary adversarial discriminator is augmented to distinguish between four different classes, it is possible to effectively address the supervised adaptation problem. In addition, the approach has a high speed of adaptation, i.e. it requires an extremely low number of labeled target training samples, even one per category can be effective. We then extensively compare this approach to the state of the art in domain adaptation in two experiments: one using datasets for handwritten digit recognition, and one using datasets for visual object recognition.


title: Standard RGB Color Spaces

abstract: This paper describes the specifications and usage of standard RGB color spaces promoted today by standard bodies and/or the imaging industry. As in the past, most of the new standard RGB color spaces were developed for specific imaging workflow and applications. They are used as interchange spaces to communicate color and/or as working spaces in imaging applications. Standard color spaces can facilitate color communication: if an image is in ‘knownRGB,’ the user, application, and/or device can unambiguously understand the color of the image, and further color manage from there if necessary. When applied correctly, a standard RGB space can minimize color space conversions in an imaging workflow, improve image reproducibility, and facilitate accountability. The digital image color workflow is examined with emphasis on when an RGB color space is appropriate, and when to apply color management by profile. An RGB space is “standard” because either it is defined in an official standards document (a de jure standard) or it is supported by commonly used tools (a de facto standard). Examples of standard RGB color spaces are ISO RGB, sRGB, ROMM RGB, Adobe RGB 98, Apple RGB, and video RGB spaces (NTSC, EBU, ITU-R BT.709). As there is no one RGB color space that is suitable for all imaging needs, factors to consider when choosing an RGB color space are discussed.


title: Inkjet Printing-Process and Its Applications

abstract: In this Progress Report we provide an update on recent developments in inkjet printing technology and its applications, which include organic thin-film transistors, light-emitting diodes, solar cells, conductive structures, memory devices, sensors, and biological/pharmaceutical tasks. Various classes of materials and device types are in turn examined and an opinion is offered about the nature of the progress that has been achieved.


Universal Dependencies v1: A Multilingual Treebank Collection


title: A framework for merging and ranking of answers in DeepQA

abstract: The final stage in the IBM DeepQA pipeline involves ranking all candidate answers according to their evidence scores and judging the likelihood that each candidate answer is correct. In DeepQA, this is done using a machine learning framework that is phase-based, providing capabilities for manipulating the data and applying machine learning in successive applications. We show how this design can be used to implement solutions to particular challenges that arise in applying machine learning for evidence-based hypothesis evaluation. Our approach facilitates an agile development environment for DeepQA; evidence scoring strategies can be easily introduced, revised, and reconfigured without the need for error-prone manual effort to determine how to combine the various evidence scores. We describe the framework, explain the challenges, and evaluate the gain over a baseline machine learning approach.


title: 3D Box Proposals from a Single Monocular Image of an Indoor Scene

abstract: Chinese Scholarship Council; CSIRO-Data61; The Program of Shanghai Subject Chief Scientist (A type) (No.15XD1502900).


title: Energy-efficient model inference in wireless sensing: Asymmetric data processing

abstract: The ultimate product of distributed sensing is normally a model that describes the data or a set of processes for which the data is an observation. The sensor network itself affects the collected data, often due to the need to conserve deployment costs, including energy provisioning. These effects include data compression and transmission censoring in addition to the typical noise and distortion of signal transduction. This paper describes a framework for performing data and model inference that incorporates network effects into the sophisticated data/model inference techniques used in environmental and ecological field studies. Both the monitored processes and the effects of network data gathering tend to be strongly non-linear and, with uncertainty arising at different levels, hierarchical. Hence closed-form solutions for estimation are beyond reach. Hierarchical Bayesian modeling can jointly capture models and the effects of compression/censoring, and Markov chain Monte Carol (MCMC) simulation at the data center can form posterior estimates. Integral to the inference are estimates of the uncertainty of data and parameters. As the model evolves at the data center, continuously updated estimates of uncertainty can drive adaptation of source/channel coding and data transmission policies at the sensor nodes. As an example application, this paper considers strongly asymmetric data processing to minimize computational complexity and energy cost at the sensor nodes and exploit abundant data center resources. Using an example of compression via simple requantization of the data, we show that MCMC-based inference can yield good performance even at substantial compression rates.


title: An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses

abstract: Teaching large classes remains a great challenge, primarily because it is difficult to attend to all the student needs in a timely manner. Automatic text summarization systems can be leveraged to summarize the student feedback, submitted immediately after each lecture, but it is left to be discovered what makes a good summary for student responses. In this work we explore a new methodology that effectively extracts summary phrases from the student responses. Each phrase is tagged with the number of students who raise the issue. The phrases are evaluated along two dimensions: with respect to text content, they should be informative and well-formed, measured by the ROUGE metric; additionally, they shall attend to the most pressing student needs, measured by a newly proposed metric. This work is enabled by a phrase-based annotation and highlighting scheme, which is new to the summarization task. The phrase-based framework allows us to summarize the student responses into a set of bullet points and present to the instructor promptly.


title: A high efficiency, noninverting, buck-boost DC-DC converter

abstract: Portable electronics applications often require a system voltage that is within the range of fully-charged to semidischarged batteries, for example 3.3 V output from a 2.8 V to 4.2 V lithium-ion battery input. The optimal solution to this requirement is a high-efficiency, noninverting buck-boost, DC-DC converter, providing a programmable constant output voltage, typically from single cell Li-ion, multicell NiMH, or alkaline power sources. This solution uses a control scheme which provides automatic and smooth transition through boost, buck-boost and buck modes.


title: Deep Character-Level Click-Through Rate Prediction for Sponsored Search

abstract: Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. This inevitably requires a lot of engineering efforts to define, compute, and select the appropriate features. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. Specifically, the proposed architectures only consider the textual content appearing in a query-advertisement pair as input, and produce as output a click-through rate prediction. By comparing the character-level model with the word-level model, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. Finally, by combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system.


title: 2 Function in the human connectome: Task-fMRI and individual differences in behavior

abstract: Abstract The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both group and individual levels.


title: Influence of Surface on Muscle Damage and Soreness Induced by Consecutive Drop Jumps

abstract: This study compared ::: the magnitude of muscle damage induced when consecutive ::: drop jumps (DJs) were performed on sand vs. firm (wood) ::: surfaces from a height of 0.6 m. Eight subjects performed DJs ::: on a sand surface at a depth of 0.2 m (S condition), and 8 other ::: subjects performed DJs on a wood surface (F condition). Each ::: set consisted of 20 DJs with an interval of 10 seconds between ::: jumps. Subjects performed 5 sets of DJs with 2 minutes between ::: sets. Maximal isometric force, muscle soreness, and plasma creatine ::: kinase (CK) activity were measured immediately before ::: and immediately after the DJ exercise as well as 1, 24, 48, 72, ::: and 96 hours after the DJ exercise. All measures changed significantly ::: (p , 0.05) after exercise for both conditions; however, ::: significantly (p , 0.05) smaller changes in these measures were ::: evident for the S condition than for the F condition. These results ::: show that DJs on a sand surface induce less muscle damage ::: than on a firm surface. Training on sand may improve aerobic ::: capacity or strength with a low risk of muscle damage.


title: Motif clustering and overlapping clustering for social network analysis

abstract: Motivated by applications in social network community analysis, we introduce a new clustering paradigm termed motif clustering. Unlike classical clustering, motif clustering aims to minimize the number of clustering errors associated with both edges and certain higher order graph structures (motifs) that represent “atomic units” of social organizations. Our contributions are two-fold: We first introduce motif correlation clustering, in which the goal is to agnostically partition the vertices of a weighted complete graph so that certain predetermined “important” social subgraphs mostly lie within the same cluster, while “less relevant” social subgraphs are allowed to lie across clusters. We then proceed to introduce the notion of motif covers, in which the goal is to cover the vertices of motifs via the smallest number of (near) cliques in the graph. Motif cover algorithms provide a natural solution for overlapping clustering and they also play an important role in latent feature inference of networks. For both motif correlation clustering and its extension introduced via the covering problem, we provide hardness results, algorithmic solutions and community detection results for two well-studied social networks.


title: Distinct representations of eye gaze and identity in the distributed human neural system for face perception

abstract: Face perception requires representation of invariant aspects that underlie identity recognition as well as representation of changeable aspects, such as eye gaze and expression, that facilitate social communication. Using functional magnetic resonance imaging (fMRI), we investigated the perception of face identity and eye gaze in the human brain. Perception of face identity was mediated more by regions in the inferior occipital and fusiform gyri, and perception of eye gaze was mediated more by regions in the superior temporal sulci. Eye-gaze perception also seemed to recruit the spatial cognition system in the intraparietal sulcus to encode the direction of another's gaze and to focus attention in that direction.


title: Order of the major constituents in sign languages: implications for all language

abstract: A survey of reports of sign order from forty-two sign languages leads to a handful of generalizations. Two accounts emerge, one amodal and the other modal. We argue that universal pressures are at work with respect to some generalizations, but that pressure from the visual modality is at work with respect to others. Together, these pressures conspire to make all sign languages order their major constituents SOV or SVO. This study leads us to the conclusion that the order of S with regard to VP is not determined by the grammar of a language (spoken or sign), but rather by sensorimotor system concerns.


title: CoFiDS: A Belief-Theoretic Approach for Automated Collaborative Filtering

abstract: Automated Collaborative Filtering (ACF) refers to a group of algorithms used in recommender systems, a research topic that has received considerable attention due to its e-commerce applications. However, existing techniques are rarely capable of dealing with imperfections in user-supplied ratings. When such imperfections (e.g., ambiguities) cannot be avoided, designers resort to simplifying assumptions that impair the system's performance and utility. We have developed a novel technique referred to as CoFiDS-Collaborative Filtering based on Dempster-Shafer belief-theoretic framework-that can represent a wide variety of data imperfections, propagate them throughout the decision-making process without the need to make simplifying assumptions, and exploit contextual information. With its DS-theoretic predictions, the domain expert can either obtain a "hard” decision or can narrow the set of possible predictions to a smaller set. With its capability to handle data imperfections, CoFiDS widens the applicability of ACF to such critical and sensitive domains as medical decision support systems and defense-related applications. We describe the theoretical foundation of the system and report experiments with a benchmark movie data set. We explore some essential aspects of CoFiDS' behavior and show that its performance compares favorably with other ACF systems.


title: Coordinated Control of DFIG's RSC and GSC Under Generalized Unbalanced and Distorted Grid Voltage Conditions

abstract: This paper proposes a coordinated control of the rotor-side converter (RSC) and grid-side converter (GSC) of a doubly fed induction generator (DFIG)-based wind-turbine generation system under generalized unbalanced and/or distorted grid voltage conditions. The system behaviors of the RSC and GSC during supply imbalance and distortion are investigated. To enhance the fault ride-through operation capability, the RSC is properly controlled to eliminate the torque oscillations, whereas the GSC is carefully designed to ensure constant active power output from the overall DFIG generation system. To achieve simultaneous regulation of the positive-/negative-sequence currents and fifth-/seventh-order harmonic currents for both the RSC and GSC, a novel current controller, consisting of a conventional proportional-integral regulator and a dual-frequency resonant compensator, is proposed and implemented in the positive (dq)+ reference frame. The simulation and experiment studies demonstrate the correctness of the developed model and the effectiveness of the suggested control strategy for DFIG-based wind-turbine systems under such adverse grid conditions.


title: Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges

abstract: MEC is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile RAN. MEC servers are deployed on a generic computing platform within the RAN, and allow for delay-sensitive and context-aware applications to be executed in close proximity to end users. This paradigm alleviates the backhaul and core network and is crucial for enabling low-latency, high-bandwidth, and agile mobile services. This article envisions a real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three representative use cases ranging from mobile edge orchestration, collaborative caching and processing, and multi-layer interference cancellation. We demonstrate the promising benefits of the proposed approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open research issues that need to be addressed in order to efficiently integrate MEC into the 5G ecosystem.


By Humming System using Convolutional Neural Network


title: Preventing persistent Cross-Site Scripting (XSS) attack by applying pattern filtering approach

abstract: Cross-Site Scripting (XSS) vulnerability is one of the most widespread security problems for web applications, which has been haunting the web application developers for years. Various approaches to defend against attacks (that use XSS vulnerabilities) are available today but no single approach solves all the loopholes. After investigating this area, we have been motivated to propose an efficient approach to prevent persistent XSS attack by applying pattern filtering method. In this work, along with necessary background, we present case studies to show the effectiveness of our approach.


title: Candidate Selection for Large Scale Personalized Search and Recommender Systems

abstract: Modern day social media search and recommender systems require complex query formulation that incorporates both user context and their explicit search queries. Users expect these systems to be fast and provide relevant results to their query and context. With millions of documents to choose from, these systems utilize a multi-pass scoring function to narrow the results and provide the most relevant ones to users. Candidate selection is required to sift through all the documents in the index and select a relevant few to be ranked by subsequent scoring functions. It becomes crucial to narrow down the document set while maintaining relevant ones in resulting set. In this tutorial we survey various candidate selection techniques and deep dive into case studies on a large scale social media platform. In the later half we provide hands-on tutorial where we explore building these candidate selection models on a real world dataset and see how to balance the tradeoff between relevance and latency.


title: Molybdenum disulfide (MoS2) nanoelectromechanical resonators with on-chip aluminum nitride (AlN) piezoelectric excitation

abstract: We report on the first experimental demonstration of vibrating two-dimensional nanoelectromechanical systems (2D NEMS) with on-chip piezoelectric excitation. Combining a wafer-scale aluminum nitride (AlN) thin film technology with an all-dry transfer technique for atomic layer 2D semiconductors, we fabricate and piezoelectrically excite few-atomic-layer molybdenum disulfide (MoS2) NEMS resonators in the high frequency (HF) band. Multimode resonances up to 38MHz are observed, with efficient electromechanical drive from the AlN layer off the vibrating 2D NEMS device region (to avoid compromising the movable 2D device by electrodes needed for on-chip excitation and readout). The piezoelectrically excited 2D NEMS resonators may enable remotely driven, ultrasensitive transducers. Combined with on-chip electrical readout techniques (e.g., mixing), this device platform also holds promise for future radio frequency (RF) electronics and integrated systems.


title: Automated Objective and Subjective Evaluation of HTTP Adaptive Streaming Systems

abstract: Streaming audio and video content currently accounts for the majority of the internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media players and adaptation algorithms showing different behavior but lack a common framework for both objective and subjective evaluation of such systems. This paper aims to close this gap by (i) proposing such a framework, (ii) describing its architecture, (iii) providing an example evaluation, (iv) and discussing open issues.


title: Time Series Forecasting in the Presence of Concept Drift: A PSO-based Approach

abstract: Time series forecasting is a problem with many applications. However, in many domains, such as stock market, the underlying generating process of the time series observations may change, making forecasting models obsolete. This problem is known as Concept Drift. Approaches for time series forecasting should be able to detect and react to concept drift in a timely manner, so that the forecasting model can be updated as soon as possible. Despite the fact that the concept drift problem is well investigated in the literature, little effort has been made to solve this problem for time series forecasting so far. This work proposes two novel methods for dealing with the time series forecasting problem in the presence of concept drift. The proposed methods benefit from the Particle Swarm Optimization (PSO) technique to detect and react to concept drifts in the time series data stream. It is expected that the use of collective intelligence of PSO makes the proposed method more robust to false positive drift detections while maintaining a low error rate on the forecasting task. Experiments show that the methods achieved competitive results in comparison to state-of-the-art methods.


title: What's new with WhatsApp & Co.? Revisiting the Security of Smartphone Messaging Applications

abstract: In recent years mobile messaging and VoIP applications for smartphones have seen a massive surge in popularity, which has also sparked the interest in research related to the security of these applications. Various security researchers and institutions have performed in-depth analyses of specific applications or vulnerabilities. This paper gives an overview of the status quo in terms of security for a number of selected applications in comparison to a previous evaluation conducted two years ago, as well as performing an analysis on some new applications. The evaluation methods mostly focus on known vulnerabilities in connection with authentication and validation mechanisms but also describe some newly identified attack vectors. The results show a predominantly positive trend for new applications, which are mostly being developed with robust security and privacy features, while some of the older applications have shown little to no progress in this regard or have even introduced new vulnerabilities in recent versions.


title: Review on Data Mining Methods for Tuberculosis Diagnosis

abstract: Tuberculosis, which is the oldest human disease wit h the highest mortality rates among infectious diseases, continues to be the worl d's attention. Previous methods to diagnose tuberculosis are tuberculin test, Sputum-s mear microscopy and chest radiography. Unfortunately, these methods are time consuming and perform poorly. Furthermore, they require varied sensitivity, Mycob acterium tuberculosis bacilli alive, sputum which is difficult to obtain from chi ldren, trained personnel to avoid human error, and hence, high cost. Researchers keep developing accurate data mining methods for rapid Tuberculosis diagnosis to reduce the rate of growth of the world population of tuberculosis patients. This paper aims to provide state-of-the-art of data mining methods in diagnosing Tuberculosis u sing clinical symptoms as input parameters. First, it introduces tuberculosis and c urrent methods used for tuberculosis diagnosis. Then it discusses technique s for preprocessing data and data mining methods for tuberculosis diagnosis currently used. The result shows that the most frequently used variables are sweating at nigh t, more than 3 weeks of cough, fever, weight loss, age, and chest pain respectivel y. Support Vector Machine and Bayesian Network gave the highest accuracy compared to other methods.


title: Self-adaptive software: Landscape and research challenges

abstract: Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-* properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.


NASA / CR-2001-211225 ICASE Report No . 2001-28 Piezoelectric Ceramics Characterization


title: An empirical analysis of dropout in piecewise linear networks

abstract: Abstract: The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters. In this work we empirically investigate several questions related to the efficacy of dropout, specifically as it concerns networks employing the popular rectified linear activation function. We investigate the quality of the test time weight-scaling inference procedure by evaluating the geometric average exactly in small models, as well as compare the performance of the geometric mean to the arithmetic mean more commonly employed by ensemble techniques. We explore the effect of tied weights on the ensemble interpretation by training ensembles of masked networks without tied weights. Finally, we investigate an alternative criterion based on a biased estimator of the maximum likelihood ensemble gradient.


title: Analysis and design of a Salisbury screen absorber with High Impedance Ground Plane

abstract: In this paper, thin and broadband absorber of two layers consisting of high impedance ground plane (HIGP) is introduced. By controlling the phase characteristics and the resonant frequency of the HIGP, the performance of the Salisbury screen absorber can be enhanced. The equivalent circuit model is then used for results prediction and explanation of the working principles of the thin and the wideband absorbing structure.


title: Generating Text through Adversarial Training using Skip-Thought Vectors

abstract: In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.


AutoNet : Automated Network Construction and Exploration System from Domain-Specific Corpora


title: Assessing Intentional Resume Deception: Development and Nomological Network of a Resume Fraud Measure

abstract: Resume fraud is pervasive and has detrimental consequences, but researchers lack a way to study it. We develop and validate a measure for empirically investigating resume misrepresentations purposely designed to mislead recruiters. In study 1, an initial set of items designed to measure three theorized resume fraud dimensions (fabrication, embellishment, omission) are rated for content validity. In study 2, job seekers complete the measure and its factor structure is evaluated. In study 3, another sample of job seekers is surveyed to verify the measure’s factor structure and to provide evidence regarding construct validity. In study 4, working adults who recently conducted a job search are surveyed to determine which individuals are more likely to commit resume fraud and whether resume fraud relates to critical work behaviors. We confirm the three-factor structure of our measure and offer evidence of construct validity by showing that socially desirable responding, Machiavellianism, moral identity, conscientiousness, emotional stability, and agreeableness are related to resume fraud. Additionally, we find that resume fraud predicts reduced job performance and increased workplace deviance beyond deceptive interviewing behavior. Resume fraud is rarely studied despite the negative impact it can have on job-related outcomes. Researchers can use this measure to explore further the antecedents and outcomes of resume fraud and to advise recruiters on how to minimize it. We develop a measure focusing on intentional resume misrepresentations designed to deceive recruiters. This is one of the first studies to examine the antecedents and outcomes of resume fraud.


title: OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images

abstract: In this paper, we propose an OCR (optical character recognition)-based localization system called OCRAPOSE II, which is applicable in a number of indoor scenarios including office buildings, parkings, airports, grocery stores, etc. In these scenarios, characters (i.e. texts or numbers) can be used as suitable distinctive landmarks for localization. The proposed system takes advantage of OCR to read these characters in the query still images and provides a rough location estimate using a floor plan. Then, it finds depth and angle-of-view of the query using the information provided by the OCR engine in order to refine the location estimate. We derive novel formulas for the query angle-of-view and depth estimation using image line segments and the OCR box information. We demonstrate the applicability and effectiveness of the proposed system through experiments in indoor scenarios. It is shown that our system demonstrates better performance compared to the state-of-the-art benchmarks in terms of location recognition rate and average localization error specially under sparse database condition.


title: Strategies for Evaluating Software Usability

abstract: This paper presents a usage analysis and taxonomy of methods which are used to evaluate the usability of computer systems. To accommodate the analysis and taxonomy, a matrix of strategies which can be used for effective usability evaluation is presented. Such an analysis, taxonomy and strategies support human-computer interaction (HCI) professionals who have the responsibility for ensuring computer system usability. The strategies outlined are named Virtual Engineering, Soft Modelling, Hard Review and Real World. This paper also uses a composite set of existing popular generic evaluation methods which can be used as part of these strategies. The methods used are observation, questionnaire, interview, empirical methods, user groups, cognitive walkthroughs, heuristic methods, review methods and model methods. The paper continues by presenting a Usage Analysis Table of these methods and concludes by grouping them into a Taxonomy of Usability Evaluation Methods. A key emphasis of this paper is the appropriateness of individual methods to lifecycle timing.


title: Wireless sensor network for river water quality monitoring in India

abstract: Water is an important natural resource which needs constant quality monitoring for ensuring its safe use. This paper introduces a river water quality monitoring system based on wireless sensor network which helps in continuous and remote monitoring of the water quality data in India. The wireless sensor node in the system is designed for monitoring the pH of water, which is one of the main parameters that affect the quality of water. The proposed sensor node design mainly comprises of a signal conditioning module, processing module, wireless communication module and the power module. The sensed pH value will be wirelessly transmitted to the base station using Zigbee communication after the required signal conditioning and processing techniques. The circuit for the sensor node is designed, simulated and the hardware prototype is developed using the appropriate components which minimize the power requirement of the system and provides a cost effective platform for monitoring water quality.


title: Transperineal Sonography Evaluation of Muscles and Vascularity in the Male Pelvic Floor

abstract: Idiopathic chronic male pelvic pain is difficult to diagnose and treat. Currently, diagnosis relies on subjective symptoms; objective measures of neuromuscular mechanisms have not been investigated. Sonographic imaging has been used to investigate these neuromuscular mechanisms in the female pelvic floor, but neither research nor books describe sonography evaluation of the male pelvic floor. The purpose of this study was to develop and evaluate a perineal sonographic technique for the examination of the male pelvic floor muscles. Anatomic landmarks were identified with images collected from two subjects, one with intermittent reports of pelvic pain and one with no history of pain in the pelvic region. A description of the equipment settings, the examination protocol, and the resulting comparative image analysis is included. A validated protocol such as this may be useful in documenting differences in the soft tissue structures between asymptomatic individuals and patients with chronic pelvic pain to aid in diagnosis and treatment. This is the first known study to report sonographic findings of the individual muscles in the male pelvic floor, and additional research is needed to validate the techniques that have been deemed feasible.


title: Performance of emerging multi-carrier waveforms for 5G asynchronous communications

abstract: This paper presents an extensive and fair comparison among the most promising waveform contenders for the 5G air interface. The considered waveform contenders, namely filter-bank multi-carrier (FBMC), universal-filtered multi-carrier (UFMC), generalized frequency-division multiplexing (GFDM) and resource-block filtered orthogonal frequency-division multiplexing (RB-F-OFDM) are compared to OFDM used in 4G in terms of spectral efficiency, numerical complexity, robustness towards multi-user interference (MUI) and resilience to power amplifier non-linearity. FBMC shows the best spectral containment and reveals to be almost insensitive to multi-user interference. It however suffers from its bad spectral efficiency for short bursts and from its poor multiple input multiple output (MIMO) compatibility. GFDM reveals to be the most promising contender, with the best spectral efficiency and the smallest complexity overhead compared to OFDM. It is also the most resilient to multi-user interference after FBMC and is MIMO compatible as soon as the interference can be managed. UFMC and RB-F-OFDM are finally the closest to OFDM and benefit therefore from a better compatibility with existing systems, even if their performance is generally lower than FBMC and GFDM.


title: Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning

abstract: In this paper, we propose a discriminative representation for patterned fabric defect detection when only limited negative samples are available. Fabric patches are efficiently classified into defectless and defective categories by Fisher criterion-based stacked denoising autoencoders (FCSDA). First, fabric images are divided into patches of the same size, and both defective and defectless samples are utilized to train FCSDA. Second, test patches are classified through FCSDA into defective and defectless categories. Finally, the residual between the reconstructed image and defective patch is calculated, and the defect is located by thresholding. Experimental results demonstrate the effectiveness of the proposed scheme in the defect detection for periodic patterned fabric and more complex jacquard warp-knitted fabric.


title: Supervised Learning with Quantum-Inspired Tensor Networks

abstract: Tensor networks are efficient representations of high-dimensional tensors which have been very successful for physics and mathematics applications. We demonstrate how algorithms for optimizing such networks can be adapted to supervised learning tasks by using matrix product states (tensor trains) to parameterize models for classifying images. For the MNIST data set we obtain less than 1% test set classification error. We discuss how the tensor network form imparts additional structure to the learned model and suggest a possible generative interpretation.


title: News Classification from Social Media Using Twitter-based Doc2Vec Model and Automatic Query Expansion

abstract: News classification is among essential needs for people to organize, better understand, and utilize information from the Internet. This motivates the authors to propose a novel method to classify news from social media. First, we propose to vectorize an article with TD2V, our pre-trained Twitter-based universal document representation following Doc2Vec approach. We then define Modified Distance to better measure the semantic distance between two document vectors. Finally, we apply retrieval and automatic query expansion to get the most relevant labeled documents in a corpus to determine the category for a new article. As our TD2V is created from 297 million words in 420,351 news articles from more than one million tweets in Twitters from 2010 to 2017, it can be used as one of the efficient pre-trained models for English document representation in various applications. Experiments on datasets from different online sources show that our method achieves the classification accuracy better than existing methods, specifically 98.4±0.3% (BBC dataset), 98.9±0.7% (BBC Sport dataset), 94.1±0.2% (Amazon4 dataset), and 78.6% (20NewsGroup dataset). Furthermore, in the classification training process, we just encode all articles in the training set with TD2V, not to train a dedicated classification model for each of these datasets.


title: Sparsely connected autoencoder

abstract: This work proposes to learn autoencoders with sparse connections. Prior studies on autoencoders enforced sparsity on the neuronal activity; these are different from our proposed approach - we learn sparse connections. Sparsity in connections helps in learning (and keeping) the important relations while trimming the irrelevant ones. We have tested the performance of our proposed method on two tasks - classification and denoising. For classification we have compared against stacked autneencoders, contractive autoencoders, deep belief network, sparse deep neural network and optimal brain damage neural network; the denoising performance was compared against denoising autoencoder and sparse (activity) autoencoder. In both the tasks our proposed method yields superior results.


title: SANet: Structure-Aware Network for Visual Tracking

abstract: Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction. Most existing CNN-based trackers treat tracking as a classification problem. However, these trackers are sensitive to similar distractors because their CNN models mainly focus on inter-class classification. To address this problem, we use self-structure information of object to distinguish it from distractors. Specifically, we utilize recurrent neural network (RNN) to model object structure, and incorporate it into CNN to improve its robustness to similar distractors. Considering that convolutional layers in different levels characterize the object from different perspectives, we use multiple RNNs to model object structure in different levels respectively. Extensive experiments on three benchmarks, OTB100, TC-128 and VOT2015, show that the proposed algorithm outperforms other methods. Code is released at www.dabi.temple.edu/hbling/code/SANet/SANet.html.


title: Self-taught clustering

abstract: This paper focuses on a new clustering task, called self-taught clustering. Self-taught clustering is an instance of unsupervised transfer learning, which aims at clustering a small collection of target unlabeled data with the help of a large amount of auxiliary unlabeled data. The target and auxiliary data can be different in topic distribution. We show that even when the target data are not sufficient to allow effective learning of a high quality feature representation, it is possible to learn the useful features with the help of the auxiliary data on which the target data can be clustered effectively. We propose a co-clustering based self-taught clustering algorithm to tackle this problem, by clustering the target and auxiliary data simultaneously to allow the feature representation from the auxiliary data to influence the target data through a common set of features. Under the new data representation, clustering on the target data can be improved. Our experiments on image clustering show that our algorithm can greatly outperform several state-of-the-art clustering methods when utilizing irrelevant unlabeled auxiliary data.


title: Equilibrium interfacial tension and the influence of extreme dilutions of uni-univalent salts: An expression of the “Jones–Ray effect”

abstract: Abstract There are inevitable low levels of salt content in qualified waters which can alter accurate estimations of water interfacial tension with organic immiscible liquids. In this study, the equilibrium interfacial tension variation of extremely diluted (0.001 mmol · dm −3 to 0.050 mmol · dm −3 ) aqueous solutions of NaCl, KCl and KI, and toluene at different temperatures of (20.0 to 50.0) °C was studied. Measurements were performed by the drop-weight method. Interfacial tension becomes reduced by (0.41 to 0.86) mN · m −1 at the highest salt concentrations used and different temperatures. A linear decrease in this parameter was also observed with increasing temperature. The order of this salt effect on the interfacial tension follows: KI > KCl ≈ NaCl at all temperatures except 50.0 °C where KCl has a stronger effect than NaCl. Assuming that inorganic ions do not penetrate the non-polar organic phase, the Petersen–Saykally model for surface tension which predicts the descending trend of variations and is based on the well-known “Jones–Ray effect” was used for modelling the data provided. Good agreement was found with three adjustable parameters for each salt and temperature.


title: Improving cyberbullying detection with user context

abstract: The negative consequences of cyberbullying are becoming more alarming every day and technical solutions that allow for taking appropriate action by means of automated detection are still very limited. Up until now, studies on cyberbullying detection have focused on individual comments only, disregarding context such as users' characteristics and profile information. In this paper we show that taking user context into account improves the detection of cyberbullying.


title: Transition-based Dependency Parsing Using Recursive Neural Networks

abstract: In this work, we present a general compositional vector framework for transitionbased dependency parsing. The ability to use transition-based algorithms allows for the application of vector composition to a large set of languages where only dependency treebanks are available, as well as handling linguistic phenomena such as non-projectivities which pose problems for previously proposed methods. We introduce the concept of a Transition Directed Acyclic Graph that allows us to apply Recursive Neural Networks for parsing with existing transition-based algorithms. Our framework captures semantic relatedness between phrases similarly to a constituency-based counterpart from the literature, for example predicting that “a financial crisis”, “a cash crunch” and “a bear market” are semantically similar. Currently, a parser based on our framework is capable of achieving 86.25% in Unlabelled Attachment Score for a well-established dependency dataset using only word representations as input, falling less than 2% points short of a previously proposed comparable feature-based model.


title: Epidemiology and risk factors for typhoid fever in Central Division, Fiji, 2014–2017: A case-control study

abstract: BACKGROUND ::: Typhoid fever is endemic in Fiji, with high reported annual incidence. We sought to identify the sources and modes of transmission of typhoid fever in Fiji with the aim to inform disease control. ::: ::: ::: METHODOLOGY/PRINCIPAL FINDINGS ::: We identified and surveyed patients with blood culture-confirmed typhoid fever from January 2014 through January 2017. For each typhoid fever case we matched two controls by age interval, gender, ethnicity, and residential area. Univariable and multivariable analysis were used to evaluate associations between exposures and risk for typhoid fever. We enrolled 175 patients with typhoid fever and 349 controls. Of the cases, the median (range) age was 29 (2-67) years, 86 (49%) were male, and 84 (48%) lived in a rural area. On multivariable analysis, interrupted water availability (odds ratio [OR] = 2.17; 95% confidence interval [CI] 1.18-4.00), drinking surface water in the last 2 weeks (OR = 3.61; 95% CI 1.44-9.06), eating unwashed produce (OR = 2.69; 95% CI 1.48-4.91), and having an unimproved or damaged sanitation facility (OR = 4.30; 95% CI 1.14-16.21) were significantly associated with typhoid fever. Frequent handwashing after defecating (OR = 0.57; 95% CI 0.35-0.93) and using soap for handwashing (OR = 0.61; 95% CI 0.37-0.95) were independently associated with a lower odds of typhoid fever. ::: ::: ::: CONCLUSIONS ::: Poor sanitation facilities appear to be a major source of Salmonella Typhi in Fiji, with transmission by drinking contaminated surface water and consuming unwashed produce. Improved sanitation facilities and protection of surface water sources and produce from contamination by human feces are likely to contribute to typhoid control in Fiji.


title: Impact of the new global environment on US national security planning - challenges to the OR community

abstract: Recent events have changed the world political/military environment from a relatively stable bi-polar one to one with much less regional stability. This paper will describe the impacts of this change on US national security planning. It summarizes the global and national security environment changes, corresponding changes in the analysis activity to support national security planning, some approaches used to conduct analyses in the new environment, and some emerging results.


title: Diabetes Mellitus and the β Cell: The Last Ten Years

abstract: Diabetes is a major global problem. During the past decade, the genetic basis of various monogenic forms of the disease, and their underlying molecular mechanisms, have been elucidated. Many genes that increase type 2 diabetes (T2DM) risk have also been identified, but how they do so remains enigmatic. Nevertheless, defective insulin secretion emerges as the main culprit in both monogenic and polygenic diabetes, with environmental and lifestyle factors, via obesity, accounting for the current dramatic increase in T2DM. There also have been significant advances in therapy, particularly for some monogenic disorders. We review here what ails the β cell and how its function may be restored.


title: ATTITUDES AND SOCIAL COGNITION Spontaneous Facial Expressions of Emotion of Congenitally and Noncongenitally Blind Individuals David Matsumoto

abstract: The study of the spontaneous expressions of blind individuals offers a unique opportunity to understand basic processes concerning the emergence and source of facial expressions of emotion. In this study, the authors compared the expressions of congenitally and noncongenitally blind athletes in the 2004 Paralympic Games with each other and with those produced by sighted athletes in the 2004 Olympic Games. The authors also examined how expressions change from 1 context to another. There were no differences between congenitally blind, noncongenitally blind, and sighted athletes, either on the level of individual facial actions or in facial emotion configurations. Blind athletes did produce more overall facial activity, but these were isolated to head and eye movements. The blind athletes’ expressions differentiated whether they had won or lost a medal match at 3 different points in time, and there were no cultural differences in expression. These findings provide compelling evidence that the production of spontaneous facial expressions of emotion is not dependent on observational learning but simultaneously demonstrates a learned component to the social management of expressions, even among blind individuals.


title: Enabling online learning in lithography hotspot detection with information-theoretic feature optimization

abstract: With the continuous shrinking of technology nodes, lithography hotspot detection and elimination in the physical verification phase is of great value. Recently machine learning and pattern matching based methods have been extensively studied to overcome runtime overhead problem of expensive full-chip lithography simulation. However, there is still much room for improvement in terms of accuracy and Overall Detection and Simulation Time (ODST). In this paper, we propose a unified machine learning based hotspot detection framework, where feature extraction and optimization is guided by an information-theoretic approach and solved by a dynamic programming model. More importantly, our framework can be naturally extended to online learning scenario, where some newly detected and verified layout patterns are integrated into the learning model. Experimental results show that the proposed batch detection model outperforms all state-of-the-art methods with 3.47% of accuracy improvement and 58.88% of ODST reduction on ICCAD-2012 contest benchmark suite. More importantly, equipped with online learning, our framework can further improve both accuracy and ODST.


title: Are There Levels of Consciousness?

abstract: The notion of a level of consciousness is a key construct in the science of consciousness. Not only is the term employed to describe the global states of consciousness that are associated with post-comatose disorders, epileptic absence seizures, anaesthesia, and sleep, it plays an increasingly influential role in theoretical and methodological contexts. However, it is far from clear what precisely a level of consciousness is supposed to be. This paper argues that the levels-based framework for conceptualizing global states of consciousness is untenable and develops in its place a multidimensional account of global states.


Professional Ethics in Accounting and Auditing


title: Predicting pragmatic reasoning in language games

abstract: One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have been few successes in making precise, quantitative predictions about pragmatic reasoning. We examined judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative and that listeners use Bayesian inference to recover speakers’ intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that the use of information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication.


title: Smooth Nonnegative Matrix Factorization for Unsupervised Audiovisual Document Structuring

abstract: This paper introduces a new paradigm for unsupervised audiovisual document structuring. In this paradigm, a novel Nonnegative Matrix Factorization (NMF) algorithm is applied on histograms of counts (relating to a bag of features representation of the content) to jointly discover latent structuring patterns and their activations in time. Our NMF variant employs the Kullback-Leibler divergence as a cost function and imposes a temporal smoothness constraint to the activations. It is solved by a majorization-minimization technique. The approach proposed is meant to be generic and is particularly well suited to applications where the structuring patterns may overlap in time. As such, it is evaluated on two person-oriented video structuring tasks (one using the visual modality and the second the audio). This is done using a challenging database of political debate videos. Our results outperform reference results obtained by a method using Hidden Markov Models. Further, we show the potential that our general approach has for audio speaker diarization.


title: Single-Step, Solvent-Free Enzymatic Route to α,ω-Functionalized Polypentadecalactone Macromonomers

abstract: A straightforward enzymatic single-step route toward the synthesis of alpha,omega-functionalized polypentadecalactone (PPDL) macromonomers containing dithiol, thiol-acrylate, diacrylate, or dimetha ...


title: A 0.4V ultra low-power UWB CMOS LNA employing noise cancellation

abstract: This paper presents an ultra low voltage (ULV), ultra low power (ULP) and ultra wideband CMOS low noise amplifier with noise cancelling. A design methodology for optimizing the trade-off between power consumption and RF performance for a MOS transistor is employed. A current-reuse technique is used to lower the power consumption, and an inductive gm-boosting technique is exploited to increase the gain and improve input matching at high frequencies. The circuit is implemented in a 90nm TSMC CMOS technology. Simulation results demonstrate a 15dB gain, a 6.8GHz bandwidth and a 4.5-5.3dB noise figure. The power consumption is only 410μW at a 0.4V supply.


title: Deep contextual residual network for electron microscopy image segmentation in connectomics

abstract: The goal of connectomics research is to manifest the mechanisms and functions of neural system by using electron microscopy (EM). One of the biggest challenges in connectomic reconstruction is developing reliable neuronal membranes segmentation method to reduce the burden on manual neurite labeling and validation. In this paper, we put forward an effective deep learning approach to realize neuronal membranes segmentation in EM image stacks, which utilizes spatially efficient residual network and multilevel representations of contextual cues to achieve accurate segmentation performance. Furthermore, multicut is used as post-processing to optimize the outputs of network. Experimental results on the public dataset of ISBI 2012 EM Segmentation Challenge demonstrate the effectiveness of our approach in neuronal membranes segmentation. Our method now ranks top 3 among 88 teams and yields 0.98356 Rand Score as well as 0.99063 Information Score, which outperforms most of state-of-the-art methods.


title: Bottester: Testing Conversational Systems with Simulated Users

abstract: Recently, conversation agents have attracted the attention of many companies such as IBM, Facebook, Google, and Amazon which have focused on developing tools or API (Application Programming Interfaces) for developers to create their own chat-bots. In this paper, we focus on new approaches to evaluate such systems presenting some recommendations resulted from evaluating a real chatbot use case. Testing conversational agents or chatbots is not a trivial task due to the multitude aspects/tasks (e.g., natural language understanding, dialog management and, response generation) which must be considered separately and as a mixture. Also, the creation of a general testing tool is a challenge since evaluation is very sensitive to the application context. Finally, exhaustive testing can be a tedious task for the project team what creates a need for a tool to perform it automatically. This paper opens a discussion about how conversational systems testing tools are essential to ensure well-functioning of such systems as well as to help interface designers guiding them to develop consistent conversational interfaces.


title: Theory and Experiment of Two-Section Two-Resistor Wilkinson Power Divider With Two Arbitrary Frequency Bands

abstract: In this paper, a novel design theory of two-section two-resistor Wilkinson power divider (WPD) is introduced. By selecting two different physical lengths of two-section transmission lines (TLs), two arbitrary frequency band WPD can be achieved. Compared with the former work, the designable frequency ratio range $u$ can be extremely extended from $1 to $1 , general design equations for characteristic impedances, physical lengths, absorption resistors, and frequency ratio ranges are newly derived and proved from even- and odd-mode analysis. Because of no capacitors or inductors, the proposed WPD can be used for high-frequency applications. Spurious band of $S_{21}$ appears between two passbands under the condition of two different physical lengths; therefore, a $\Pi$ -type dual-band transformer is newly introduced to replace single TL for spurious band suppression. We proved that $\Pi$ -type structure could effectively suppress spurious band of $S_{11}$ , maintain two arbitrary frequency passband of $S_{21}$ , and provide an extra isolation band of $S_{32}$ at center frequency. Finally, three proposed WPD examples with $\Pi$ -type structure are selected with different frequency ratios, where frequency ratio $u = 4$ in Example A, $u = 6$ in Example B, and $u = 20$ in Example C are designed and fabricated in the experiment. Measured results show good agreement with the theoretical results.


title: A multisource and multivariate dataset for indoor localization methods based on WLAN and geo-magnetic field fingerprinting

abstract: Indoor localization is a key topic for the Ambient Intelligence (AmI) research community. In this scenarios, recent advancements in wearable technologies, particularly smartwatches with built-in sensors, and personal devices, such as smartphones, are being seen as the breakthrough for making concrete the envisioned Smart Environment (SE) paradigm. In particular, scenarios devoted to indoor localization represent a key challenge to be addressed. Many works try to solve the indoor localization issue, but the lack of a common dataset or frameworks to compare and evaluate solutions represent a big barrier to be overcome in the field. The unavailability and uncertainty of public datasets hinders the possibility to compare different indoor localization algorithms. This constitutes the main motivation of the proposed dataset described herein. We collected Wi-Fi and geo-magnetic field fingerprints, together with inertial sensor data during two campaigns performed in the same environment. Retrieving sincronized data from a smartwatch and a smartphone worn by users at the purpose of create and present a public available dataset is the goal of this work.


title: 2007) "Automated criminal link analysis based on domain knowledge

abstract: Link (association) analysis has been used in the criminal justice domain to search large datasets for associations between crime entities in order to facilitate crime investigations. However, link analysis still faces many challenging problems, such as information overload, high search complexity, and heavy reliance on domain knowledge. To address these challenges, this article proposes several techniques for automated, effective, and efficient link analysis. These techniques include the co-occurrence analysis, the shortest path algorithm, and a heuristic approach to identifying associations and determining their importance. We developed a prototype system called CrimeLink Explorer based on the proposed techniques. Results of a user study with 10 crime investigators from the Tucson Police Department showed that our system could help subjects conduct link analysis more efficiently than traditional single-level link analysis tools. Moreover, subjects believed that association paths found based on the heuristic approach were more accurate than those found based solely on the co-occurrence analysis and that the automated link analysis system would be of great help in crime investigations.


title: Metropolis procedural modeling

abstract: Procedural representations provide powerful means for generating complex geometric structures. They are also notoriously difficult to control. In this article, we present an algorithm for controlling grammar-based procedural models. Given a grammar and a high-level specification of the desired production, the algorithm computes a production from the grammar that conforms to the specification. This production is generated by optimizing over the space of possible productions from the grammar. The algorithm supports specifications of many forms, including geometric shapes and analytical objectives. We demonstrate the algorithm on procedural models of trees, cities, buildings, and Mondrian paintings.


title: Seismic Performance Evaluation for Steel Moment Frames

abstract: A performance prediction and evaluation procedure based on nonlinear dynamics and reliability theory is presented. It features full integration over the three key stochastic models: ground motion hazard curve, nonlinear dynamic displacement demand, and displacement capacity. Further, both epistemic and aleatory uncertainties are evaluated and carried through the analysis. A suite of uncertainty analyses are input to the procedure such as period, live load, material properties, damping, analysis procedure, and orientation of the structure. Two limit states are defined instead of the traditional single state. The procedure provides a simple method for estimating the confidence level for satisfying the performance level for a given hazard. The confidence level of a post- and a pre-Northridge nine-story building for a given hazard level is calculated using the procedure described in the paper. New steel moment frame buildings are expected to perform much better during major earthquakes than existing buildings designed and built with older technologies.


title: CONCEPTS OF SERVICE QUALITY MEASUREMENT IN HOTEL INDUSTRY

abstract: The quality of service in hotel industry is an important factor of successful business. The existing trend of complete quality management in hotel industry ensures the achievement of competitive advantage of hotel companies and is therefore the subject of contemporary research into service quality in hotel industry. The concept and the conceptual model of service quality is indispensable if we wish to understand the genesis of service quality and potential gaps in quality. The aim of this paper is to show the importance of service quality in hotel industry from both the conceptual standpoint and that of service quality measurement. The paper describes the most common criteria for measuring service quality, namely the model of internal service quality and the SERVQUAL model. The shown results are those of quantitive and qualitative application of such models in hotels.


Scrybe : A Blockchain Ledger for Clinical Trials


title: A requirements monitoring framework for enterprise systems

abstract: Requirements compliant software is becoming a necessity. Fewer and fewer organizations will run their critical transactions on software that has no visible relationship to its requirements. Businesses wish to see their software being consistent with their policies. Moreover, partnership agreements are pressuring less mature organizations to improve their systems. Businesses that rely on web services, for example, are vulnerable to the problems of their web service providers. While electronic commerce has increased the speed of on-line transactions, the technology for monitoring requirements compliance—especially for transactions—has lagged behind. To address the requirements monitoring problem for enterprise information systems, we integrate techniques for requirements analysis and software execution monitoring. Our framework assists analysts in the development of requirements monitors for enterprise services. The deployed system raises alerts when services succeed or fail to satisfy their specified requirements, thereby making software requirements visible. The framework usage is demonstrated with an analysis of ebXML marketplace specifications. An analyst applies goal analysis to discover potential service obstacles, and then derives requirements monitors and a distributed monitoring system. Once deployed, the monitoring system provides alerts when obstacles occur. A summary of the framework implementation is presented, along with analysis of two monitor component implementations. We conclude that the approach implemented in the framework, called ReqMon, provides real-time feedback on requirements satisfaction, and thereby provides visibility into requirements compliance of enterprise information systems.


title: Magnetic induction for MWD telemetry system

abstract: Nowadays, many methods have been applied for data transmission of MWD system. Magnetic induction is one of the alternative technique. In this paper, detailed discussion on magnetic induction communication system is provided. The optimal coil configuration is obtained by theoretical analysis and software simulations. Based on this coil arrangement, communication characteristics of path loss and bit error rate are derived.


title: Local auxiliary-color maximum vector pattern: A new feature descriptor for image indexing and retrieval

abstract: A new feature descriptor, local auxiliary color maximum vector pattern (LACMVP) has been proposed in this paper for image indexing and retrieval. The presented method synergize the color and texture information by taking a cardinal (red, green, blue) and an auxiliary channel (value) from two different color spaces (RGB and HSV). A vector pattern comprising of magnitude, sign and position patterns are calculated for the maximum local difference between the center pixel and its neighbor from the auxiliary channel. In essence LACMVP converts the image into local vectors along the maximum edge of the inter-chromatic texture pattern. The performance evaluation of proposed method has been done by performing natural image retrieval on Corel-10K and texture retrieval on MIT VisTex dataset. The results when compared with existing state-of-the-art techniques using standard performance evaluation measure like precision, recall, F1-score and G-score, showed a substantial improvement.


title: RFID in textile and clothing manufacturing: technology and challenges

abstract: In the field of textile and clothing, radio frequency identification (RFID), which is one of the most promising technological innovations, is used in manufacturing, inventory control, warehousing, distribution, logistics, automatic object tracking and supply chain management. Various retailers and manufacturers (of clothing as well as consumer goods) such as CVS, Tesco, Prada, Benetten, Wal-mart and Procter & Gamble, are now implementing the technology and exploring the impact of the technology on their business. RFID technologies may improve the potential benefits of supply chain management through reduction of inventory losses, increase of the efficiency and speed of processes and improvement of information accuracy. The basic of success lies in understanding the technology and other features to minimize the potential problems. Although the technology existed for several years, the technological challenges and cost issues are the major hurdles for the widespread use of RFID. In this paper, the authors have addressed the technology of RFID and various applications related to inventory management, production control, retail management, brand segregation etc. in textile and clothing industry. In addition, the disadvantages, challenges and future directions of RFID technology have also been highlighted.


title: A 28-GHz Low-Power Phased-Array Receiver Front-End With 360° RTPS Phase Shift Range

abstract: A low-power 28-GHz phased-array receiver (RX) front end is presented that incorporates a low-power low-noise amplifier (LNA) and a passive reflection-type phase shifter (RTPS) capable of 360° phase shift with 5-b phase resolution and low gain variation. Passive phase shifters are limited by tradeoffs between phase resolution, insertion loss, and phase shift range. The proposed RTPS load design and optimization approach leads to a 28-GHz RTPS achieving the state-of-the-art insertion loss with 360° phase shift range and low loss variation across the phase shift. The LNA adopts a transformer-coupled neutralization architecture that increases available gain, enabling lower power consumption. The phased-array front end is designed for Ka -band applications and has been implemented in 65-nm CMOS. The measured RTPS achieves 360° phase shift with 7.75 ± 0.3 dB insertion loss and an rms phase error of 0.3° at 28 GHz. The low-power phased-array RX front end has an overall gain of 9.5 ± 0.4 dB and noise figure <5.5 dB at 28 GHz. The RX front end consumes 10 mW from a 0.9-V supply with phase shifter and an LNA active area of 0.16 and 0.32 mm2, respectively, in 65-nm CMOS, demonstrating its suitability for low-power phased-array RX for emerging wireless links.


title: Spelling Error Correction Using a Nested RNN Model and Pseudo Training Data

abstract: We propose a nested recurrent neural network (nested RNN) model for English spelling error correction and generate pseudo data based on phonetic similarity to train it. The model fuses orthographic information and context as a whole and is trained in an end-to-end fashion. This avoids feature engineering and does not rely on a noisy channel model as in traditional methods. Experiments show that the proposed method is superior to existing systems in correcting spelling errors.


title: The social side of gaming: How playing online computer games creates online and offline social support

abstract: Online gaming has gained millions of users around the globe, which have been shown to virtually connect, to befriend, and to accumulate online social capital. Today, as online gaming has become a major leisure time activity, it seems worthwhile asking for the underlying factors of online social capital acquisition and whether online social capital increases offline social support. In the present study, we proposed that the online game players' physical and social proximity as well as their mutual familiarity influence bridging and bonding social capital. Physical proximity was predicted to positively influence bonding social capital online. Social proximity and familiarity were hypothesized to foster both online bridging and bonding social capital. Additionally, we hypothesized that both social capital dimensions are positively related to offline social support. The hypotheses were tested with regard to members of e-sports clans. In an online survey, participants (N=811) were recruited via the online portal of the Electronic Sports League (ESL) in several countries. The data confirmed all hypotheses, with the path model exhibiting an excellent fit. The results complement existing research by showing that online gaming may result in strong social ties, if gamers engage in online activities that continue beyond the game and extend these with offline activities.


title: Visualizing similarity data with a mixture of maps

abstract: A perpetual calendar that has a flat frame with two cavities. When cards are arranged in each cavity of the frame it will display a specific month. To display a particular number of days in a month, select the proper 31 day card and arrange the vertical slits on the card with the horizontal slit on the frame to obtain 28, 29, 30 or 31 days. To display a particular month choose the name of the month card to be viewed and insert it in the position of the frame provided for it. The few cards that are not used will remain in the frame cavities behind the month being displayed. This perpetual calendar can also display three consecutive months at one time.


title: The Suffocation of Marriage: Climbing Mount Maslow Without Enough Oxygen

abstract: This article distills insights from historical, sociological, and psychological perspectives on marriage to develop the suffocation model of marriage in America. According to this model, contemporary Americans are asking their marriage to help them fulfill different sets of goals than in the past. Whereas they ask their marriage to help them fulfill their physiological and safety needs much less than in the past, they ask it to help them fulfill their esteem and self-actualization needs much more than in the past. Asking the marriage to help them fulfill the latter, higher level needs typically requires sufficient investment of time and psychological resources to ensure that the two spouses develop a deep bond and profound insight into each other’s essential qualities. Although some spouses are investing sufficient resources—and reaping the marital and psychological benefits of doing so—most are not. Indeed, they are, on average, investing less than in the past. As a result, mean levels of marital quality and personal well-being are declining over time. According to the suffocation model, spouses who are struggling with an imbalance between what they are asking from their marriage and what they are investing in it have several promising options for corrective action: intervening to optimize their available resources, increasing their investment of resources in the marriage, and asking less of the marriage in terms of facilitating the fulfillment of spouses’ higher needs. Discussion explores the implications of the suffocation model for understanding dating and courtship, sociodemographic variation, and marriage beyond American’s borders.


Liver Patient Classification Using Intelligent Techniques