title: For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion

abstract: Functional neuroimaging studies examining the neural bases of the cognitive control of emotion have found increased prefrontal and decreased amygdala activation for the reduction or down-regulation of negative emotion. It is unknown, however, (1) whether the same neural systems underlie the enhancement or up-regulation of emotion, and (2) whether altering the nature of the regulatory strategy alters the neural systems mediating the regulation. To address these questions using functional magnetic resonance imaging (fMRI), participants up- and down-regulated negative emotion either by focusing internally on the self-relevance of aversive scenes or by focusing externally on alternative meanings for pictured actions and their situational contexts. Results indicated (1a) that both up- and down-regulating negative emotion recruited prefrontal and anterior cingulate regions implicated in cognitive control, (1b) that amygdala activation was modulated up or down in accord with the regulatory goal, and (1c) that up-regulation uniquely recruited regions of left rostromedial PFC implicated in the retrieval of emotion knowledge, whereas down-regulation uniquely recruited regions of right lateral and orbital PFC implicated in behavioral inhibition. Results also indicated that (2) self-focused regulation recruited medial prefrontal regions implicated in internally focused processing, whereas situation-focused regulation recruited lateral prefrontal regions implicated in externally focused processing. These data suggest that both common and distinct neural systems support various forms of reappraisal and that which particular prefrontal systems modulate the amygdala in different ways depends on the regulatory goal and strategy employed.


Lung Nodule Detection and Classification


title: Programming support and adaptive checkpointing for high-throughput data services with log-based recovery

abstract: Many applications in large-scale data mining and offline processing are organized as network services, running continuously or for a long period of time. To sustain high-throughput, these services often keep their data in memory, thus susceptible to failures. On the other hand, the availability requirement for these services is not as stringent as online services exposed to millions of users. But those data-intensive offline or mining applications do require data persistence to survive failures. This paper presents programming and runtime support called SLACH for building multi-threaded high-throughput persistent services. To keep in-memory objects persistent, SLACH employs application-assisted logging and checkpointing for log-based recovery while maximizing throughput and concurrency. SLACH adaptively adjusts checkpointing frequency based on log growth and throughput demand to balance between runtime overhead and recovery speed. This paper describes the design and API of SLACH, adaptive checkpoint control, and our experiences and experiments in using SLACH at Ask.com.


title: High-throughput determination of RNA structures

abstract: RNA performs and regulates a diverse range of cellular processes, with new functional roles being uncovered at a rapid pace. Interest is growing in how these functions are linked to RNA structures that form in the complex cellular environment. A growing suite of technologies that use advances in RNA structural probes, high-throughput sequencing and new computational approaches to interrogate RNA structure at unprecedented throughput are beginning to provide insights into RNA structures at new spatial, temporal and cellular scales.


title: Improving routing in networks of UAVs via scoped flooding and mobility prediction

abstract: Due to their specific characteristics, Unmanned Aeronautical Ad-hoc Networks (UAANETs) can be classified as a special kind of mobile ad-hoc networks. Due to the high mobility of Unmanned Aerial Vehicles (UAVs), designing a good routing protocol for UAANETs is challenging. Recently, a new protocol called Reactive-Greedy-Reactive (RGR) [1] has been proposed as a promising routing protocol in high mobility and density-variable scenarios. Although the RGR protocol improves the packet delivery ratio, the overhead and delay are higher when compared to AODV [1]. In this paper, a scoped flooding and mobility prediction based RGR protocol is proposed to improve the performance of RGR in UAANETs. Simulation results show that the new protocol can effectively enhance the performance of the RGR protocol in terms of packet delivery ratio, overhead, and delay.


Differential equations with applications


Allostratigraphy of the U.S. middle Atlantic continental margin; characteristics, distribution, and depositional history of principal unconformity-bounded upper Cretaceous and Cenozoic sedimentary units


title: A Novel Hybrid Self-Adaptive Bat Algorithm

abstract: Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction.


title: Exploiting diversity and synthetic biology for the production of algal biofuels

abstract: Modern life is intimately linked to the availability of fossil fuels, which continue to meet the world's growing energy needs even though their use drives climate change, exhausts finite reserves and contributes to global political strife. Biofuels made from renewable resources could be a more sustainable alternative, particularly if sourced from organisms, such as algae, that can be farmed without using valuable arable land. Strain development and process engineering are needed to make algal biofuels practical and economically viable.


title: Point-to-multipoint in-band mmwave backhaul for 5G networks

abstract: Cost-effective and scalable wireless backhaul solutions are essential for realizing the 5G vision of providing gigabits per second anywhere. Not only is wireless backhaul essential to support network densification based on small cell deployments, but also for supporting very low latency inter-BS communication to deal with intercell interference. Multiplexing backhaul and access on the same frequency band (in-band wireless backhaul) has obvious cost benefits from the hardware and frequency reuse perspective, but poses significant technology challenges. We consider an in-band solution to meet the backhaul and inter-BS coordination challenges that accompany network densification. Here, we present an analysis to persuade the readers of the feasibility of in-band wireless backhaul, discuss realistic deployment and system assumptions, and present a scheduling scheme for inter- BS communications that can be used as a baseline for further improvement. We show that an inband wireless backhaul for data backhauling and inter-BS coordination is feasible without significantly hurting the cell access capacities.


title: Reversible logic implementation of AES algorithm

abstract: Since the selection of the Rijndael cryptosystem as a new Advanced Encryption Standard (AES) in 2000, many hardware implementations of AES have been reported. Some of these implementations are optimized for speed, some for area, some for reconfigurability, and some for low-power applications. Again, reversible logic synthesis methodologies have drawn the attention of researchers in recent times, mainly with the prospect of quantum computing becoming a reality, and the potential of reversible logic circuits for providing ultra low-power implementations. Although many of the cryptographic primitives are inherently reversible by nature, very little work has been done towards reversible logic implementations of the same. The only published works relate to reversible implementations of Montgomery multiplication algorithm, which has applications in cryptography. The present paper, possibly for the first time, presents a reversible logic implementation of a block cipher, namely, 128-bit AES. The various AES functional blocks have been synthesized using reversible gates, using which an overall reversible architecture has been proposed. The pipelined version as suggested can only be used in the Electronic Code Book (ECB) mode. The hardware complexity of the implementation has been evaluated using the number of reversible gates required and the quantum cost.


title: A Unified Framework for Concurrent Pedestrian and Cyclist Detection

abstract: Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.


title: 2009 Special Issue: Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces

abstract: Several feature types have been used with EEG-based Brain-Computer Interfaces. Among the most popular are logarithmic band power estimates with more or less subject-specific optimization of the frequency bands. In this paper we introduce a feature called Time Domain Parameter that is defined by the generalization of the Hjorth parameters. Time Domain Parameters are studied under two different conditions. The first setting is defined when no data from a subject is available. In this condition our results show that Time Domain Parameters outperform all band power features tested with all spatial filters applied. The second setting is the transition from calibration (no feedback) to feedback, in which the frequency content of the signals can change for some subjects. We compare Time Domain Parameters with logarithmic band power in subject-specific bands and show that these features are advantageous in this situation as well.


title: Secure Border Gateway Protocol (S-BGP)

abstract: The Border Gateway Protocol (BGP), which is used to distribute routing information between autonomous systems (ASes), is a critical component of the Internet's routing infrastructure. It is highly vulnerable to a variety of malicious attacks, due to the lack of a secure means of verifying the authenticity and legitimacy of BGP control traffic. This paper describes a secure, scalable, deployable architecture (S-BGP) for an authorization and authentication system that addresses most of the security problems associated with BGP. The paper discusses the vulnerabilities and security requirements associated with BGP, describes the S-BGP countermeasures, and explains how they address these vulnerabilities and requirements. In addition, this paper provides a comparison of this architecture to other approaches that have been proposed, analyzes the performance implications of the proposed countermeasures, and addresses operational issues.


title: Successful memory encoding is associated with increased cross-frequency coupling between frontal theta and posterior gamma oscillations in human scalp-recorded EEG

abstract: Abstract Although previous studies have established that successful memory encoding is associated with increased synchronization of theta-band and gamma-band oscillations, it is unclear if there is a functional relationship between oscillations in these frequency bands. Using scalp-recorded EEG in healthy human participants, we demonstrate that cross-frequency coupling between frontal theta phase and posterior gamma power is enhanced during the encoding of visual stimuli which participants later on remember versus items which participants subsequently forget (“subsequent memory effect,” SME). Conventional wavelet analyses and source localizations revealed SMEs in spectral power of theta-, alpha-, and gamma-band. Successful compared to unsuccessful encoding was reflected in increased theta-band activity in right frontal cortex as well as increased gamma-band activity in parietal-occipital regions. Moreover, decreased alpha-band activity in prefrontal and occipital cortex was also related to successful encoding. Overall, these findings support the idea that during the formation of new memories frontal cortex regions interact with cortical representations in posterior areas.


title: Information fusion for automotive applications - An overview

abstract: This article focusses on the fusion of information from various automotive sensors like radar, video, and lidar for enhanced safety and traffic efficiency. Fusion is not restricted to data from sensors onboard the same vehicle but vehicular communication systems allow to propagate and fuse information with sensor data from other vehicles or from the road infrastructure as well. This enables vehicles to perceive information from regions that are hardly accessible otherwise and represents the basis for cooperative driving maneuvers. While the Bayesian framework builds the basis for information fusion, automobile environments are characterized by their a priori unknown topology, i.e., the number, type, and structure of the perceived objects is highly variable. Multi-object detection and tracking methods are a first step to cope with this challenge. Obviously, the existence or non-existence of an object is of paramount importance for safe driving. Such decisions are highly influenced by the association step that assigns sensor measurements to object tracks. Methods that involve multiple sequences of binary assignments are compared with soft-assignment strategies. Finally, fusion based on finite set statistics that (theoretically) avoid an explicit association are discussed.


title: The Intuitive Eating Scale-2: item refinement and psychometric evaluation with college women and men.

abstract: The 21-item Intuitive Eating Scale (IES; Tylka, 2006) measures individuals' tendency to follow their physical hunger and satiety cues when determining when, what, and how much to eat. While its scores have demonstrated reliability and validity with college women, the IES-2 was developed to improve upon the original version. Specifically, we added 17 positively scored items to the original IES items (which were predominantly negatively scored), integrated an additional component of intuitive eating (Body― Food Choice Congruence), and evaluated its psychometric properties with 1,405 women and 1,195 men across three studies. After we deleted 15 items (due to low item―factor loadings, high cross-loadings, and redundant content), the results supported the psychometric properties of the IES-2 with women and men. The final 23-item IES-2 contained 11 original items and 12 added items. Exploratory and second-order confirmatory factor analyses upheld its hypothesized 4-factor structure (its original 3 factors, plus Body―Food Choice Congruence) and a higher order factor. The IES-2 was largely invariant across sex, although negligible differences on 1 factor loading and 2 item intercepts were detected. Demonstrating validity, the IES-2 total scores and most IES-2 subscale scores were (a) positively related to body appreciation, self-esteem, and satisfaction with life; (b) inversely related to eating disorder symptomatology, poor interoceptive awareness, body surveillance, body shame, body mass index, and internalization of media appearance ideals; and (c) negligibly related to social desirability. IES-2 scores also garnered incremental validity by predicting psychological well-being above and beyond eating disorder symptomatology. The IES-2's applications for empirical research and clinical work are discussed.


title: H-MAS architecture and reinforcement learning method for autonomous robot path planning

abstract: This work provides a novel collaborative Q-Learning method using a Holonic Multi Agent System (H-MAS), with the use of two types of Q-table; Q-Master Table (QMT) and Q-Embedded Table (QET) instead of the classical use. The QMT is updated by a collaborative policy between head holons. Thus the QET is updated based on the individual decision of each agent, so that each and every robot in the environment can use and profit not only from its own experiences but also from the experiences of other agents. This method is supported by a H-MAS architecture based on holonic and internal agents, and a Fuzzy Logic System, in order to solve the problem of path planning for a mobile robot in a partially or completely unknown environment.


A Review of Vision-Based Hand Gestures


title: Reverse engineering feature models

abstract: Feature models describe the common and variable characteristics of a product line. Their advantages are well recognized in product line methods. Unfortunately, creating a feature model for an existing project is time-consuming and requires substantial effort from a modeler. We present procedures for reverse engineering feature models based on a crucial heuristic for identifying parents - the major challenge of this task. We also automatically recover constructs such as feature groups, mandatory features, and implies/excludes edges. We evaluate the technique on two large-scale software product lines with existing reference feature models--the Linux and eCos kernels--and FreeBSD, a project without a feature model. Our heuristic is effective across all three projects by ranking the correct parent among the top results for a vast majority of features. The procedures effectively reduce the information a modeler has to consider from thousands of choices to typically five or less.


title: Conceptual Data Structures for Personal Knowledge Management

abstract: Purpose – The purpose of the paper is to design a model and tools that are capable of representing and handling personal knowledge in different degrees of structuredness and formalisation, and usable and extensible by end‐users.Design/methodology/approach – This paper presents the results of analysing literature and various data models and formalisms used to structure information on the desktop.Findings – The unified data model (CDS) is capable of representing structures from various information tools, including documents, file systems, hypertext, tagging and mind maps. The five knowledge axes of CDS are identity, order, hierarchy, annotation and linking.Research limitations/implications – The CDS model is based on text. Extensions for multimedia annotations have not been investigated.Practical implications – Future personal knowledge management (PKM) tools should take the mentioned shortcoming of existing PKM tools into account. Implementing the CDS model can be a way to make PKM tools interoperable.Orig...


title: A novel hybrid excited synchronous machine for (H)EV applications

abstract: Numerous different motor types have been examined recently to be used as main traction drives for EV applications. The aims are typically to increase power density and efficiency, fulfill safety requirements and to save costs. Due to price fluctuations of rare earth magnets in the past, such magnets are identified as unpredictable future costs and their content in a motor should be minimized. In contrast, traction motor concepts without rare earth magnets typically cannot achieve a comparable power and torque density. In this paper, a new hybrid excited synchronous motor concept is presented, combining aspects of permanent magnet (PM) machines and electrically excited synchronous motors. The benefits of each concept are combined while overcoming the individual drawbacks. After a presentation of the concept, the prototype design and manufacturing are explained. Test bench results proof the functionality of the concept. The highlights and benefits of this concept are summarized.


title: Field-stop layer optimization for 1200V FS IGBT operating at 200˚C

abstract: This paper is concerned with design considerations for enabling the operation of Field-Stop Insulated Gate Bipolar Transistors (FS IGBTs) at 200C. It is found that through a careful optimization of the Field-Stop layer doping profile the device has a low leakage current and delivers a favorable tradeoff between the on-state voltage (Von) and turn-off loss (Eoff). An investigation of the adverse effects of increasing the junction temperature on the temperature-dependent properties of the FS IGBTs is also discussed herein. © 2014 IEEE.


title: SNX11 Identified as an Essential Host Factor for SFTS Virus Infection by CRISPR Knockout Screening

abstract: Severe fever with thrombocytopenia syndrome virus (SFTSV) is a highly pathogenic tick-borne bunyavirus that causes lethal infectious disease and severe fever with thrombocytopenia syndrome (SFTS) in humans. The molecular mechanisms and host cellular factors required for SFTSV infection remain uncharacterized. Using a genome-wide CRISPR-based screening strategy, we identified a host cellular protein, sorting nexin 11 (SNX11) which is involved in the intracellular endosomal trafficking pathway, as an essential cell factor for SFTSV infection. An SNX11-KO HeLa cell line was established, and SFTSV replication was significantly reduced. The glycoproteins of SFTSV were detected and remained in later endosomal compartments but were not detectable in the endoplasmic reticulum (ER) or Golgi apparatus. pH values in the endosomal compartments of the SNX11-KO cells increased compared with the pH of normal HeLa cells, and lysosomal-associated membrane protein 1 (LAMP1) expression was significantly elevated in the SNX11-KO cells. Overall, these results indicated that penetration of SFTSV from the endolysosomes into the cytoplasm of host cells was blocked in the cells lacking SNX11. Our study for the first time provides insight into the important role of the SNX11 as an essential host factor in the intracellular trafficking and penetrating process of SFTSV infection via potential regulation of viral protein sorting, membrane fusion, and other endocytic machinery.


title: Real-time air quality monitoring through mobile sensing in metropolitan areas

abstract: Traditionally, pollution measurements are performed using expensive equipment at fixed locations or dedicated mobile equipment laboratories. This is a coarse-grained and expensive approach where the pollution measurements are few and far in-between. In this paper, we present a vehicular-based mobile approach for measuring fine-grained air quality in real-time. We propose two cost effective data farming models -- one that can be deployed on public transportation and the second a personal sensing device. We present preliminary prototypes and discuss implementation challenges and early experiments.


1 Experienced Utility and Objective Happiness: A Moment-Based Approach


Towards a Dataset for Natural Language Requirements Processing


title: Truthful incentives in crowdsourcing tasks using regret minimization mechanisms

abstract: What price should be offered to a worker for a task in an online labor market? How can one enable workers to express the amount they desire to receive for the task completion? Designing optimal pricing policies and determining the right monetary incentives is central to maximizing requester's utility and workers' profits. Yet, current crowdsourcing platforms only offer a limited capability to the requester in designing the pricing policies and often rules of thumb are used to price tasks. This limitation could result in inefficient use of the requester's budget or workers becoming disinterested in the task. In this paper, we address these questions and present mechanisms using the approach of regret minimization in online learning. We exploit a link between procurement auctions and multi-armed bandits to design mechanisms that are budget feasible, achieve near-optimal utility for the requester, are incentive compatible (truthful) for workers and make minimal assumptions about the distribution of workers' true costs. Our main contribution is a novel, no-regret posted price mechanism, BP-UCB, for budgeted procurement in stochastic online settings. We prove strong theoretical guarantees about our mechanism, and extensively evaluate it in simulations as well as on real data from the Mechanical Turk platform. Compared to the state of the art, our approach leads to a 180% increase in utility.


title: To Do or Not To Do: Distill crowdsourced negative caveats to augment api documentation

abstract: Negative caveats of application programming interfaces (APIs) are about “how not to use an API,” which are often absent from the official API documentation. When these caveats are overlooked, programming errors may emerge from misusing APIs, leading to heavy discussions on Q&A websites like Stack Overflow. If the overlooked caveats could be mined from these discussions, they would be beneficial for programmers to avoid misuse of APIs. However, it is challenging because the discussions are informal, redundant, and diverse. For this, for example, we propose Disca, a novel approach for automatically Distilling desirable API negative caveats from unstructured Q&A discussions. Through sentence selection and prominent term clustering, Disca ensures that distilled caveats are context‐independent, prominent, semantically diverse, and nonredundant. Quantitative evaluation in our experiments shows that the proposed Disca significantly outperforms four text‐summarization techniques. We also show that the distilled API negative caveats could greatly augment API documentation through qualitative analysis.


title: Bulk Disambiguation of Speculative Threads in Multiprocessors

abstract: Transactional Memory (TM), Thread-Level Speculation (TLS), and Checkpointed multiprocessors are three popular architectural techniques based on the execution of multiple, cooperating speculative threads. In these environments, correctly maintaining data dependences across threads requires mechanisms for disambiguating addresses across threads, invalidating stale cache state, and making committed state visible. These mechanisms are both conceptually involved and hard to implement. In this paper, we present Bulk, a novel approach to simplify these mechanisms. The idea is to hash-encode a thread's access information in a concise signature, and then support in hardware signature operations that efficiently process sets of addresses. Such operations implement the mechanisms described. Bulk operations are inexact but correct, and provide substantial conceptual and implementation simplicity. We evaluate Bulk in the context of TLS using SPECint2000 codes and TM using multithreaded Java workloads. Despite its simplicity, Bulk has competitive performance with more complex schemes. We also find that signature configuration is a key design parameter.


title: A Framework for Multiple-Instance Learning

abstract: Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem.


Digital Preservation of New Media Art Through Exploration of Established Symbolic Representation Systems


title: Multiscale probability transformation of basic probability assignment

abstract: Decision making is still an open issue in the application of Dempster-Shafer evidence theory. A lot of works have been presented for it. In the transferable belief model (TBM), pignistic probabilities based on the basic probability as- signments are used for decision making. In this paper, multiscale probability transformation of basic probability assignment based on the belief function and the plausibility function is proposed, which is a generalization of the pignistic probability transformation. In the multiscale probability function, a factor q based on the Tsallis entropy is used to make the multiscale prob- abilities diversified. An example is shown that the multiscale probability transformation is more reasonable in the decision making.


title: Scalability! but at what cost?

abstract: We offer a new metric for big data platforms, COST, or the Configuration that Outperforms a Single Thread. The COST of a given platform for a given problem is the hardware configuration required before the platform outperforms a competent single-threaded implementation. COST weighs a system's scalability against the overheads introduced by the system, and indicates the actual performance gains of the system, without rewarding systems that bring substantial but parallelizable overheads. ::: ::: We survey measurements of data-parallel systems recently reported in SOSP and OSDI, and find that many systems have either a surprisingly large COST, often hundreds of cores, or simply underperform one thread for all of their reported configurations.


title: Printed Annular Ring with Circular Patch Monopole UWB Antenna

abstract: A New type of Ultra Wide-Band printed monopole antenna comprising of circular patch inside an annular ring structure has been proposed. The antenna with full annular ring and circular patch yields 5:1 simulated impedance -- bandwidth ratio for VSWR


title: Fine-grained mapping of mouse brain functional connectivity with resting-state fMRI

abstract: Abstract Understanding the intrinsic circuit-level functional organization of the brain has benefited tremendously from the advent of resting-state fMRI (rsfMRI). In humans, resting-state functional network has been consistently mapped and its alterations have been shown to correlate with symptomatology of various neurological or psychiatric disorders. To date, deciphering the mouse brain functional connectivity (MBFC) with rsfMRI remains a largely underexplored research area, despite the plethora of human brain disorders that can be modeled in this specie. To pave the way from pre-clinical to clinical investigations we characterized here the intrinsic architecture of mouse brain functional circuitry, based on rsfMRI data acquired at 7 T using the Cryoprobe technology. High-dimensional spatial group independent component analysis demonstrated fine-grained segregation of cortical and subcortical networks into functional clusters, overlapping with high specificity onto anatomical structures, down to single gray matter nuclei. These clusters, showing a high level of stability and reliability in their patterning, formed the input elements for computing the MBFC network using partial correlation and graph theory. Its topological architecture conserved the fundamental characteristics described for the human and rat brain, such as small-worldness and partitioning into functional modules. Our results additionally showed inter-modular interactions via “network hubs”. Each major functional system (motor, somatosensory, limbic, visual, autonomic) was found to have representative hubs that might play an important input/output role and form a functional core for information integration. Moreover, the rostro-dorsal hippocampus formed the highest number of relevant connections with other brain areas, highlighting its importance as core structure for MBFC.


title: A multimodal hybrid robot with transformable wheels

abstract: This paper presents a novel multimodal hybrid robot that consists of a robot chassis and four transformable wheels. Compared to most robot wheels that have a rigid round rim, the transformable wheel with a foldable rim can switch to track mode for higher efficiency locomotion on swampy terrain or leg mode for better over-obstacle capability on rugged road. In detail, the wheel rim of this robot is cut into four end-to-end arc rims to make it capable of folding or unfolding between a circular ring with a elongated ring (just like “O” and “ ”) to change the contact type between transformable wheels with the ground. The detailed structure design and transformation principles between different locomotion modes are explained. The driving methods and motion strategies on various terrains of the hybrid robot are analyzed. Meanwhile, an initial experiment of the transformable wheel is conducted to verify the design.


title: .Net library for SMS spam detection using machine learning: A cross platform solution

abstract: Short Message Service is now-days the most used way of communication in the electronic world. While many researches exist on the email spam detection, we haven't had the insight knowledge about the spam done within the SMS's. This might be because the frequency of spam in these short messages is quite low than the emails. This paper presents different ways of analyzing spam for SMS and a new pre-processing way to get the actual dataset of spam messages. This dataset was then used on different algorithm techniques to find the best working algorithm in terms of both accuracy and recall. Random Forest algorithm was then implemented in a real world application library written in C# for cross platform .Net development. This library is capable of using a prebuild model for classifying a new dataset for spam and ham.


title: Design of spoke-type ferrite interior permanent magnet synchronous machines considering the number of rotor poles

abstract: Spoke type is one of the most popular rotor configurations in ferrite interior permanent magnet synchronous machines. In such configuration, the focusing factor of permanent magnet flux is of essential importance to achieve high performance. Large number of rotor poles is usually preferred to boost the air-gap flux density from the low-energy ferrite permanent magnets. Besides, multi-layer magnet configuration is commonly employed to increase the rotor saliency in interior permanent magnet synchronous machines. However, such rotor saliency of interior permanent magnet synchronous machine is also heavily influenced by the number of rotor poles. The novel two-layer ferrite magnet spoke-type structure can harness considerable reluctance torque and enhance the torque density. In this paper, the designs of both conventional one-layer and novel two-layer spoke-type ferrite interior permanent magnet synchronous machines are investigated with particular emphasis on the influences of rotor pole numbers. The machine performances are compared in terms of torque capability, electromagnetic losses, and efficiency. Finally, a prototype machine with such novel two-layer ferrite magnet structure is built and tested to validate the advantageous performances.


title: A Compact Single Conductor Transmission Line Launcher for Telemetry in Borehole Drilling

abstract: A very compact and conformal launcher for single conductor transmission lines (SCTLs) at the HF-band is presented. Also the concept of using drill pipes as the conductor of SCTL is introduced for the first time to satisfy the need for cost-effective and real-time data communication in drilling process. It is shown that a TM surface wave can be launched effectively making use of the drilling mud around the drill pipe. Provision of TM surface wave launchers that can fit within the borehole at both ends makes it feasible to transmit data over the drill pipe from downhole to the surface and vice versa. The launcher is composed of an open ended helical conductor which concentrically surrounds the drill pipe with appropriate pitch and length forming a compact surface wave launcher. Keeping its dimension smaller than $0.005\lambda \times 0.005\lambda \times 0.03\lambda $ , the proposed launcher provides 2% fractional bandwidth at low HF-band.


title: Digital forensic analysis of cloud storage data in IDrive and Mega cloud drive

abstract: In this technological world one of the general method for user to save their data is cloud. Most of the cloud storage company provides some storage space as free to its users. Both individuals and corporate are storing their files in the cloud infrastructure so it becomes a problem for a forensics analyst to perform evidence acquisition and examination. One reason that makes evidence acquisition more difficult is user data always saved in remote computer on cloud. Various cloud companies available in the market serving storage as one of their services and everyone delivering different kinds of features and facilities in the storage technology. One area of difficulty is the acquisition of evidential data associated to a cybercrime stored in a different cloud company service. Due to lack of understanding about the location of evidence data regarding which place it is saved could also affect an analytical process and it take a long time to speak with all cloud service companies to find whether data is saved within their cloud. By analyzing two cloud service companies (IDrive and Mega cloud drive) this study elaborates the various steps involved in the activity of obtaining evidence on a user account through a browser and then via cloud software application on a Windows 7 machine. This paper will detail findings for both the Mega cloud drive and IDrive client software, to find the different evidence that IDrive and the mega cloud drive leaves behind on a user computer. By establishing the artifacts on a user machine will give an overall idea regarding kind of evidence residue in user computer for investigators. Key evidences discovered on this investigation comprises of RAM memory captures, registry files application logs, file time and date values and browser artifacts are acquired from these two cloud companies on a user windows machine.


title: Improving Credit Card Fraud Detection using a Meta- Classification Strategy

abstract: of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These "false alarms" delay the detection of fraudulent transactions and can cause unnecessary concerns for customers. In this study, over 1 million unique credit card transactions from 11 months of data from a large Canadian bank were analyzed. A meta-classifier model was applied to the transactions after being analyzed by the Bank's existing neural network based fraud detection algorithm. This meta-classifier model consists of 3 base classifiers constructed using the decision tree, naive Bayesian, and k-nearest neighbour algorithms. The naive Bayesian algorithm was also used as the meta-level algorithm to combine the base classifier predictions to produce the final classifier. Results from the research show that when a meta- classifier was deployed in series with the Bank's existing fraud detection algorithm improvements of up to 28% to their existing system can be achieved.


title: An on-board differential Bunny - Ear Antenna design for 60 GHz applications

abstract: A design of differential Bunny-Ear Antenna (BEA) for the application of 60 GHz frequency band is presented. This antenna is fed by differential microstrip transmission line (MTL) and to be connected to the transceiver chip by bondwire. The bondwire introduces inductance and high impedance which destroys the matching, so the compensation structure for the interconnect is included in the design. In the simulation, the return loss is better than −10 dB. The radiation patterns are similar from the very low frequency of 57 GHz to high frequency of 65 GHz, with a 3-dB beamwidth of about 28 degrees in the principle planes. The measurement verifies the simulated radiation characteristics and shows that a gain of more than 9.7 dBi can be achieved by using this antenna.


Are emoji a poor substitute for words?: Sentence processing with emoji substitutions


title: A framework for narrative adaptation in interactive story-based learning environments

abstract: A key functionality provided by interactive narrative systems is narrative adaptation: augmenting story experiences in response to users' actions, and tailoring story elements to individual users' preferences and needs. However, the task of interactive narrative adaptation is not yet well defined. There is little consensus about the key features of a general model for narrative adaptation, which results in a limited ability to draw comparisons between alternate solutions. This paper aims to synthesize a diverse range of approaches to narrative adaptation, as well as related experience management techniques from intelligent tutoring systems, in order to devise a framework for more clearly conceptualizing the general task of interactive narrative adaptation. The framework has three components: plot adaptation, discourse adaptation, and user tailoring. The conceptual framework is illustrated with examples from Crystal Island, an interactive narrative-centered learning environment for middle school microbiology. The framework is being used to inform preliminary development efforts on an interactive narrative director agent for the Crystal Island learning environment, and aims to provide a first step toward establishing a general model of interactive narrative.


Kamuee Zero : the Design and Implementation of Route Table for High-Performance Software Router


title: Detection of Duplicate Defect Reports Using Natural Language Processing

abstract: Defect reports are generated from various testing and development activities in software engineering. Sometimes two reports are submitted that describe the same problem, leading to duplicate reports. These reports are mostly written in structured natural language, and as such, it is hard to compare two reports for similarity with formal methods. In order to identify duplicates, we investigate using natural language processing (NLP) techniques to support the identification. A prototype tool is developed and evaluated in a case study analyzing defect reports at Sony Ericsson mobile communications. The evaluation shows that about 2/3 of the duplicates can possibly be found using the NLP techniques. Different variants of the techniques provide only minor result differences, indicating a robust technology. User testing shows that the overall attitude towards the technique is positive and that it has a growth potential.


title: Learning to recognition from Bing Clickture data

abstract: MSR Image Recognition Challenge (IRC) 2016 tries to deal with designing a dog breeds recognition system based on Clickture-Dog dataset. To address the task, we proposed an effective method with systematic strategies as follows. We presented a data cleaning method using faster-rcnn to learn a dog detector. Besides, we ensembeled a series of CNN models to enhance the robustness. A dense evaluation strategy was carried out to boost the test accuracy. Finally, we achieved the first prize of this challenge with 89.65% top-5 accuracy.


title: Higher-order clustering in networks

abstract: A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order structures beyond triangles are crucial to understanding complex networks, and the clustering behavior with respect to such higher-order patterns is not well understood. Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network structures and provide a more comprehensive view of how the edges of complex networks cluster. Our higher-order clustering coefficients are a natural generalization of the traditional clustering coefficient. We derive several properties about higher-order clustering coefficients and analyze them under common random graph models. Finally, we use higher-order clustering coefficients to gain new insights into the structure of real-world networks from several domains.


title: Analysis, control and comparison of DC/DC boost converter topologies for fuel cell hybrid electric vehicle applications

abstract: Interleaving techniques are widely used to reduce input/output ripples, to increase the efficiency and to increase the power capacity of the boost converters. This paper presents an analysis, design and implementation of a high-power multileg interleaved DC/DC boost converter with a digital signal processor (DSP) based controller. This research focuses on non-isolated DC/DC converter that interfaces the fuel cell to the powertrain of the hybrid electric vehicles. In this paper, two-phase interleaved boost converter (IBC) with digital phase-shift control scheme is proposed in order to reduce the input current ripples, to reduce the output voltage ripples and to reduce the size of passive components with high efficiency for high power applications. The digital control based on DSP is proposed to solve the associated synchronization problem with interleaving converters. In addition, the real time workshop (RTW) is used for automatic real-time code generation. The proposed converter is compared with other topologies, such as conventional boost converter (BC) and multi-device boost converter (MDBC) in order to examine its performance. Moreover, a generalized small-signal model with complete parameters of these DC/DC converters is derived. The PWM DC/DC converter topologies and their control are simulated and investigated by using MATLAB/SIMULINK. Experimentally, a dual-loop average current control implemented in TMS320F2808 DSP is employed to achieve the fast transient response. Furthermore, the simulation and experimental results are provided.


title: Process Model Generation from Natural Language Text

abstract: Business process modeling has become an important tool for managing organizational change and for capturing requirements of software. A central problem in this area is the fact that the acquisition of as-is models consumes up to 60% of the time spent on process management projects. This is paradox as there are often extensive documentations available in companies, but not in a ready-to-use format. In this paper, we tackle this problem based on an automatic approach to generate BPMN models from natural language text. We combine existing tools from natural language processing in an innovative way and augmented them with a suitable anaphora resolution mechanism. The evaluation of our technique shows that for a set of 47 text-model pairs from industry and textbooks, we are able to generate on average 77% of the models correctly.


title: An Evidence-Based Guideline for Introducing Oral Feeding to Healthy Preterm Infants

abstract: The ability of a preterm infant to make the transition from gavage to oral nipple feeding depends on the infant’s neurodevelopment in relation to behavioral organization, to a rhythmic suck-swallow-breathe pattern, and to cardiorespiratory regulation. Research-based knowledge about infant neurodevelopment in these three areas has led to the creation of a semidemand feeding method to aid in this transition. The method combines the use of nonnutritive sucking to promote awake behavior for feeding, use of behavioral assessment to identify readiness for feeding, and systematic observation of and response to infant behavior cues to regulate frequency, length, and volume of oral feedings. Semidemand feeding may be individualized for healthy preterm infants. This article discusses both the relevant knowledge about neurodevelopment and the semidemand feeding method itself.


title: VARAN the Unbelievable: An Efficient N-version Execution Framework

abstract: With the widespread availability of multi-core processors, running multiple diversified variants or several different versions of an application in parallel is becoming a viable approach for increasing the reliability and security of software systems. The key component of such N-version execution (NVX) systems is a runtime monitor that enables the execution of multiple versions in parallel. Unfortunately, existing monitors impose either a large performance overhead or rely on intrusive kernel-level changes. Moreover, none of the existing solutions scales well with the number of versions, since the runtime monitor acts as a performance bottleneck. In this paper, we introduce Varan, an NVX framework that combines selective binary rewriting with a novel event-streaming architecture to significantly reduce performance overhead and scale well with the number of versions, without relying on intrusive kernel modifications. Our evaluation shows that Varan can run NVX systems based on popular C10k network servers with only a modest performance overhead, and can be effectively used to increase software reliability using techniques such as transparent failover, live sanitization and multi-revision execution.


title: Cyber-physical security of Wide-Area Monitoring, Protection and Control in a smart grid environment

abstract: Smart grid initiatives will produce a grid that is increasingly dependent on its cyber infrastructure in order to support the numerous power applications necessary to provide improved grid monitoring and control capabilities. However, recent findings documented in government reports and other literature, indicate the growing threat of cyber-based attacks in numbers and sophistication targeting the nation’s electric grid and other critical infrastructures. Specifically, this paper discusses cyber-physical security of Wide-Area Monitoring, Protection and Control (WAMPAC) from a coordinated cyber attack perspective and introduces a game-theoretic approach to address the issue. Finally, the paper briefly describes how cyber-physical testbeds can be used to evaluate the security research and perform realistic attack-defense studies for smart grid type environments.


title: A High Step-Up Converter With a Voltage Multiplier Module for a Photovoltaic System

abstract: A novel high step-up converter is proposed for a front-end photovoltaic system. Through a voltage multiplier module, an asymmetrical interleaved high step-up converter obtains high step-up gain without operating at an extreme duty ratio. The voltage multiplier module is composed of a conventional boost converter and coupled inductors. An extra conventional boost converter is integrated into the first phase to achieve a considerably higher voltage conversion ratio. The two-phase configuration not only reduces the current stress through each power switch, but also constrains the input current ripple, which decreases the conduction losses of metal-oxide-semiconductor field-effect transistors (MOSFETs). In addition, the proposed converter functions as an active clamp circuit, which alleviates large voltage spikes across the power switches. Thus, the low-voltage-rated MOSFETs can be adopted for reductions of conduction losses and cost. Efficiency improves because the energy stored in leakage inductances is recycled to the output terminal. Finally, the prototype circuit with a 40-V input voltage, 380-V output, and 1000- W output power is operated to verify its performance. The highest efficiency is 96.8%.


title: Hexahedral mesh generation constraints

abstract: For finite element analyses within highly elastic and plastic structural domains, hexahedral meshes have historically offered some benefits over tetrahedral finite element meshes in terms of reduced error, smaller element counts, and improved reliability. However, hexahedral finite element mesh generation continues to be difficult to perform and automate, with hexahedral mesh generation taking several orders of magnitude longer than current tetrahedral mesh generators to complete. Thus, developing a better understanding of the underlying constraints that make hexahedral meshing difficult could result in dramatic reductions in the amount of time necessary to prepare a hexahedral finite element model for analysis. In this paper, we present a survey of constraints associated with hexahedral meshes (i.e., the conditions that must be satisfied to produce a hexahedral mesh). In presenting our formulation of these constraints, we will utilize the dual of a hexahedral mesh. We also discuss how incorporation of these constraints into existing hexahedral mesh generation algorithms could be utilized to extend the class of geometries to which these algorithms apply. We also describe a list of open problems in hexahedral mesh generation and give some context for future efforts in addressing these problems.


title: Adaptive near-duplicate detection via similarity learning

abstract: In this paper, we present a novel near-duplicate document detection method that can easily be tuned for a particular domain. Our method represents each document as a real-valued sparse k-gram vector, where the weights are learned to optimize for a specified similarity function, such as the cosine similarity or the Jaccard coefficient. Near-duplicate documents can be reliably detected through this improved similarity measure. In addition, these vectors can be mapped to a small number of hash-values as document signatures through the locality sensitive hashing scheme for efficient similarity computation. We demonstrate our approach in two target domains: Web news articles and email messages. Our method is not only more accurate than the commonly used methods such as Shingles and I-Match, but also shows consistent improvement across the domains, which is a desired property lacked by existing methods.


title: The Pursuit of Self-Esteem: Contingencies of Self-Worth and Self-Regulation

abstract: Successful self-regulation is defined as the willingness to exert effort toward one's most important goals, while taking setbacks and failures as opportunities to learn, identify weaknesses and address them, and develop new strategies toward achieving those goals. Contingencies of self-worth can facilitate self-regulation because people are highly mo- tivated to succeed and avoid failure in domains of contingency. However, because boosts in self-esteem are pleasurable and drops in self-esteem are painful, protection, maintenance, and enhancement of self-esteem can become the overriding goal. Several pitfalls for self-regulation can result, especially when tasks are difficult and failure is likely. In this article, we describe a program of research examining these self-regulation pitfalls associated with contingent self-worth and suggest that learning orienta- tions, particularly the willingness to embrace failure for the learning it affords, foster successful self-regulation even in people with highly contingent self-esteem.


title: A Multi-objective Genetic Algorithm for Build Order Optimization in StarCraft II

abstract: This article presents a modified version of the multi-objective genetic algorithm NSGA II in order to find optimal opening strategies in the real-time strategy game StarCraft II. Based on an event-driven simulator capable of performing an accurate estimate of in-game build-times the quality of different build lists can be judged. These build lists are used as chromosomes within the genetic algorithm. Procedural constraints e.g. given by the Tech-Tree or other game mechanisms, are implicitly encoded into them. Typical goals are to find the build list producing most units of one or more certain types up to a certain time (Rush) or to produce one unit as early as possible (Tech-Push). Here, the number of entries in a build list varies and the objective values have in contrast to the search space a very small diversity. We introduce our game simulator including its graphical user interface, the modifications necessary to fit the genetic algorithm to our problem, test our algorithm on different Tech-Pushes and Rushes for all three races, and validate it with empirical data of expert StarCraft II players.


title: Anchor Cascade for Efficient Face Detection

abstract: Face detection is essential to facial analysis tasks, such as facial reenactment and face recognition. Both cascade face detectors and anchor-based face detectors have translated shining demos into practice and received intensive attention from the community. However, cascade face detectors often suffer from a low detection accuracy, while anchor-based face detectors rely heavily on very large neural networks pre-trained on large-scale image classification datasets such as ImageNet, which is not computationally efficient for both training and deployment. In this paper, we devise an efficient anchor-based cascade framework called anchor cascade. To improve the detection accuracy by exploring contextual information, we further propose a context pyramid maxout mechanism for anchor cascade. As a result, anchor cascade can train very efficient face detection models with a high detection accuracy. Specifically, compared with a popular convolutional neural network (CNN)-based cascade face detector MTCNN, our anchor cascade face detector greatly improves the detection accuracy, e.g., from 0.9435 to 0.9704 at $1k$ false positives on FDDB, while it still runs in comparable speed. Experimental results on two widely used face detection benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of the proposed framework.


title: Absolute phase estimation: adaptive local denoising and global unwrapping

abstract: The paper attacks absolute phase estimation with a two-step approach: the first step applies an adaptive local denoising scheme to the modulo-2 pi noisy phase; the second step applies a robust phase unwrapping algorithm to the denoised modulo-2 pi phase obtained in the first step. The adaptive local modulo-2 pi phase denoising is a new algorithm based on local polynomial approximations. The zero-order and the first-order approximations of the phase are calculated in sliding windows of varying size. The zero-order approximation is used for pointwise adaptive window size selection, whereas the first-order approximation is used to filter the phase in the obtained windows. For phase unwrapping, we apply the recently introduced robust (in the sense of discontinuity preserving) PUMA unwrapping algorithm [IEEE Trans. Image Process.16, 698 (2007)] to the denoised wrapped phase. Simulations give evidence that the proposed algorithm yields state-of-the-art performance, enabling strong noise attenuation while preserving image details.


title: Hermes: A Privacy-Preserving Approximate Search Framework for Big Data

abstract: We propose a sampling-based framework for privacy-preserving approximate data search in the context of big data. The framework is designed to bridge multi-target query needs from users and the data platform, including required query accuracy, timeliness, and query privacy constraints. A novel privacy metric, $(\varepsilon,\delta)$ -approximation, is presented to uniformly measure accuracy, efficiency and privacy breach risk. Based on this, we employ bootstrapping to efficiently produce approximate results that meet the preset query requirements. Moreover, we propose a quick response mechanism to deal with homogeneous queries, and discuss the reusage of results when appending data. Theoretical analyses and experimental results demonstrate that the framework is capable of effectively fulfilling multi-target query requirements with high efficiency and accuracy.


title: Indoor tracking of multiple persons with a 77 GHz MIMO FMCW radar

abstract: In this paper, we tackle the task of multi-target tracking of humans in an indoor setting using a low power 77 GHz MIMO CMOS radar. A drawback of such a highresolution and low-power device is the higher sensitivity to noise, which makes the analysis of signals more challenging. Therefore, a pipeline is proposed to address both pre-processing of the radar signal and multi-target tracking. In the pre-processing phase, we focus on handling the low Signal-to-Noise Ratio (SNR) and eliminating so-called ghost targets. The tracking method we propose is based on Markov Chain Monte Carlo Data Association (MCMCDA), thus taking a combinatorial approach towards the task of tracking. The pipeline is tested on a number of real-world scenarios and shows promising results, overcoming the significant amount of noise associated with embedded radar devices.


title: Verificator: Educational Tool for Learning Programming

abstract: The paper introduces Verificator, our learning programming interface aimed for learning programming in C++ at the university beginners' level. In teaching programming some specific problems concerning the teaching itself as well as the organization of the teaching process need to be considered. One of the biggest problems is that students tend to adopt certain bad programming habits in their attempt to more easily deal with their examinations, such as trying to write programs without any syntax and logical checking. It is very hard to help them correct those errors once they are deeply rooted. Our students' web questionnaire and its results show that the majority of prob- lems in learning programming among our students arise from the gap between the understanding of programming language syntax and problem-solving algorithms. Verificator prevents students from making a lot of errors they are likely to make in learning programming and helps them to learn programming language syntax and adopt good programming habits.


title: Wireless power transmission trends

abstract: Wireless power transmission (WPT) has been attracting a wide range of subjects in various fields and also become a highly active research area because of their potential in providing high technology to our daily lives. The wireless power transmission will be mandatory to use in the near future because this technology enables the transmission of electrical energy from a power source to an electrical load across an air gap without interconnecting wires. In this paper, we carry out a pilot study to present the existing technologies of wireless power transmission, their recent technology as well as its future trends. Furthermore, we also describe plenty of applications in wireless transmission.


Pain mechanisms: a new theory


title: Rational argument, rational inference

abstract: Reasoning researchers within cognitive psychology have spent decades examining the extent to which human inference measures up to normative standards. Work here has been dominated by logic, but logic has little to say about most everyday, informal arguments. Empirical work on argumentation within psychology and education has studied the development and improvement of argumentation skills, but has been theoretically limited to broad structural characteristics. Using the catalogue of informal reasoning fallacies established over the centuries within the realms of philosophy, Hahn and Oaksford (2007a) recently demonstrated how Bayesian probability can provide a normative standard by which to evaluate quantitatively the strength of a wide range of everyday arguments. This broadens greatly the potential scope of reasoning research beyond the rather narrow set of logical and inductive arguments that have been studied; it also provides a framework for the normative assessment of argument content that has been la...


title: Smartphone-Based Vehicle-to-Driver/Environment Interaction System for Motorcycles

abstract: This letter concerns the definition and the implementation of an add-on interaction system for motorcycles. The system consists of a vehicle-to-driver and a vehicle-to-environment communication mechanism, which is based on a smartphone core and on a wireless Bluetooth medium. The system is focused to increase the safety level of a motorcycle and it is constituted by a vehicle with a CAN bus, a compact embedded electronic unit implementing a CAN-to-Bluetooth gateway, a smartphone and a Bluetooth helmet. The driver-to-vehicle interaction is based on an audio interaction located at helmet level. The driver-to-environment is represented by communication via the HTTP protocol. The vocal synthesis, the speech recognition, and the gateway to the web server are implemented on the smartphone. This letter presents the hardware architecture, the software architecture and some case-study implementation issues. An evaluation of the critical aspects of the system is also provided.


title: Image-driven view management for augmented reality browsers

abstract: In this paper, we introduce a novel view management technique for placing labels in Augmented Reality systems. A common issue in many Augmented Reality applications is the absence of knowledge of the real environment, limiting the efficient representation and optimal layout of the digital information augmented onto the real world. To overcome this problem, we introduce an image-based approach, which combines a visual saliency algorithm with edge analysis to identify potentially important image regions and geometric constraints for placing labels. Our proposed solution also includes adaptive rendering techniques that allow a designer to control the appearance of depth cues. We describe the results obtained from a user study considering different scenarios, which we performed for validating our approach. Our technique will provide special benefits to Augmented Reality browsers that usually lack scene knowledge, but also to many other applications in the domain of Augmented Reality such as cultural heritage and maintenance applications.


abstract: Words Mark the Nerds: Computational Models of Personality Recognition through Language Franc¸ois Mairesse (F.Mairesse@sheffield.ac.uk) Department of Computer Science, University of Sheffield 211 Portobello Street, Sheffield S1 4DP, United Kingdom Marilyn Walker (M.A.Walker@sheffield.ac.uk) Department of Computer Science, University of Sheffield 211 Portobello Street, Sheffield S1 4DP, United Kingdom Abstract ent parties involved. Personality traits influence many aspects of individual behaviour, such as the attitude toward machines (Sigurdsson, 1991), overall job performance (Furnham, Jack- son & Miller, 1999), as well as academic motivation (Komar- raju & Karau, 2005); this last finding suggests that training systems would be more efficient if they could adapt to the learner’s personality. Our goal is to develop models to recognize the user’s per- sonality, and use them to modify the output generation of a dialogue system. This paper presents non-linear statistical models of the five most essential personality traits, learned automatically via machine learning on different sources of data. Results show that extraversion, emotional stability and conscientiousness are easier to model, and recognition mod- els based on observed personality perform significantly bet- ter than a baseline returning the average personality score, as well as better than models using self-reports. An analysis of decision trees confirms previous findings linking language and personality, while revealing many new linguistic markers. User models in human-computer interfaces have focused on various user characteristics, but there is little user modeling of the most fundamental dimension of variation between hu- mans: personality traits. We explore here the possibility of automatically acquiring such models by simply observing the user’s language. We automatically learn models for personality recognition from different corpora and sources of personality evaluation. The models are completely transparent, i.e. they can run in the background evaluating the user at every con- versational turn, and provide input for the system to adapt to the user. Results show that recognition models based on ob- served personality perform significantly better than a baseline of the average personality score, as well as better than mod- els using self-reports. An analysis of decision trees confirms previous findings linking language and personality, while re- vealing many new linguistic markers. Keywords: Personality; Statistical model; Machine learn- ing; Language; Conversation; EAR; Big Five; Pragmatics; Five factor model; Dialogue systems Introduction Personality traits and language Many scientists have focused on how to optimize our inter- actions with machines, by finding the best way to select and communicate information. However, because users behave in different ways, computer systems need to adapt to their con- versational partner, as humans do (Funder & Sneed, 1993; McLarney-Vesotski et al., in press); this is the purpose of user models in dialogue systems (Zukerman & Litman, 2001). One approach to user modeling is to elicit user preferences by asking the user (Linden, Hanks & Lesh, 1997), but other work develops such models through observation, i.e. by ana- lyzing the user’s language. Rich (1979) successfully maps user’s keywords to content selection and language genera- tion parameters, while Maloor & Chai (2000) estimate the user’s expertise using dialogue features. Researchers have also learned user models from data, e.g. for sentence plan- ning (Mairesse & Walker, 2005) and dialogue management (Litman et al., 2000). Rather than modeling specific user preferences, we pro- pose modeling personality traits. By definition, personal- ity is the highest level variable characterizing individuals. Computer users can detect a machine’s personality and pre- fer working with a computer exhibiting the same personality as theirs (Reeves & Nass, 1996). Personalities of real estate agents were used as a basis for the design of an e-commerce website (Fuchs, 2001). In addition, many studies have shown that the success of interpersonal tasks depends on the personalities of the differ- Trait identification Personality can be described as a set of attributes characteriz- ing an individual. The number of those attributes seems to be extremely large. Indeed, when talking about a close friend, one can usually come up with many adjectives describing his or her behaviour. To be able to reason about personality, psy- chologists have tried to identify the most essential traits, re- ferred to as the Big Five (Norman, 1963): • Extraversion (sociability, activity, assertiveness) • Emotional stability (as opposed to neuroticism) • Agreeableness to other people • Conscientiousness (discipline) • Openness to experience (intellect) Such traits were obtained by doing a factor analysis over the adjectives used in personality description questionnaires. They are therefore based on the assumption that the most rel- evant individual differences are encoded into the language, and the more important the difference, the more likely it is to be expressed as a single word. This is referred to as the Lexical Hypothesis. While there are known limits to the Big Five model (Eysenck, 1991; Paunonen & Jackson, 2000), we shall use it in our models, as it provides a very general framework for reasoning about individual differences, and it has become a standard in psychology as it was replicated many times (Nor- man, 1963; Peabody & Goldberg, 1989).


title: Anthropometric 3D Face Recognition

abstract: We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel technique for automatically detecting 10 anthropometric facial fiducial points that are associated with these discriminatory anthropometric features. We isolate and employ unique textural and/or structural characteristics of these fiducial points, along with the established anthropometric facial proportions of the human face for detecting them. Lastly, we develop a completely automatic face recognition algorithm that employs facial 3D Euclidean and geodesic distances between these 10 automatically located anthropometric facial fiducial points and a linear discriminant classifier. On a database of 1149 facial images of 118 subjects, we show that the standard deviation of the Euclidean distance of each automatically detected fiducial point from its manually identified position is less than 2.54 mm. We further show that the proposed Anthroface 3D recognition algorithm performs well (equal error rate of 1.98% and a rank 1 recognition rate of 96.8%), out performs three of the existing benchmark 3D face recognition algorithms, and is robust to the observed fiducial point localization errors.


title: Workshop on Smart Object Security, in conjunction with IETF83, Paris 23 March 2012 Security Considerations around End-to-End Security in the IP-based Internet of Things

abstract: The IP-based Internet of Things refers to the interconnection of smart objects in a Low-power and Lossy Network (LLN) with the Internetby means of protocols such as 6LoWPAN or CoAP. The provisioning of an end-to-end security connection is the key to ensure basic functionalities such as software updates or network access. This is, however, very challenging due to the asymmetry of the devices in the system(smart objects are resource constrained when compared with traditional Internet devices), and the interaction of possibly different security protocols such as TLS and DTLS. This paper describes the security and threat model for this scenario to arrive at the following conclusion: end-to-end security in the IP-based Internet of Things ismore than a simple end-to-end handshake and requires additional measures to protect the LLN while performing the handshake. We further describe some simple solutions and give hints for further work.


title: Deep Joint Entity Disambiguation with Local Neural Attention

abstract: We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combines benefits of deep learning with more traditional approaches such as graphical models and probabilistic mention-entity maps. Extensive experiments show that we are able to obtain competitive or state-of-the-art accuracy at moderate computational costs.


title: A Study of Cryptographic Backdoors in Cryptographic Primitives

abstract: Since the revelations of Edward Snowden in 2013, the topic of cryptographic backdoors has been popular in both cyber security and popular culture. However, such backdoors were well known in the cryptology community well before 2013. There are a variety of mechanisms for modifying cryptographic primitives so that the resultant modified algorithm is more easily compromised, but the change in the algorithm is not immediately obvious. This paper provides a review of several modifications to traditional cryptographic primitives, that result in backdoors. Then general principles and counter measures are extrapolated from those studies to provide a generalized understanding of cryptographic backdoors and counter measures.


title: Communication Technologies and Architectures for Space Network and Interplanetary Internet

abstract: Future space exploration demands a Space Network that will be able to connect spacecrafts with one another and in turn with Earth's terrestrial Internet and hence efficiently transfer data back and forth. Currently there are hundreds of active space programs around the world that communicate in outer space. However, the concept of an Interplanetary Internet (IPN) is only in its incubation stage. Considerable amount of common standards and research is required before widespread deployment occurs to make IPN feasible. This paper presents a concise picture of the current space networking technologies and architectures. It discusses the Interplanetary Internet and Delay Tolerant Networking (DTN) concepts along with the various space networks that are currently deployed. The paper also identifies the significant areas of space network design and operation that still require extensive research and development.


title: Age differences in credibility judgments of online health information

abstract: To better support older adults' consumption of high quality health information on the Internet, it is important to understand how older adults make credibility judgments with online health information. For this purpose, we conducted two laboratory studies to explore how the credibility cues in message contents, website features, and user reviews could differentially impact younger and older adults' credibility judgments. Results from the first experiment showed that older adults, compared to younger ones, were less sensitive to the credibility cues in message contents, as well as those in the website features. Results from the second experiment showed that user reviews that were consistent with the credibility cues in message contents could reinforce older adults' credibility judgments. Older adults, compared to younger adults, seemed to be less swayed by user reviews that were inconsistent with the message contents. These results provided implications for designing health information websites that better support older adults' credibility judgments.


title: Dual-Mode Power-Split Transmission for Hybrid Electric Vehicles

abstract: In this paper, an innovative power-split device (PSD) is introduced, and its application in a hybrid powertrain system is studied. The new PSD is a mechanism that allows operation in two different power-split modes through locking/unlocking of two clutches. In one mode, the PSD operates similar to a standard planetary gear unit, and in the other mode, it works the same as a compound planetary set. A well-known analogous system is the Toyota Hybrid System (THS) and is used for comparison purposes. It is demonstrated that by the new system, the transmission losses are reduced by a considerable extent, and thus, the efficiency is improved. A controller is designed based on fuzzy logic, which receives the battery state of charge (SOC), the vehicle speed, and the power that is requested at the wheels to coordinate each component in such a way as to optimize the entire system efficiency. A numerical optimization algorithm is applied to sustain the SOC in high regions and shift engine operating points to higher efficiency regions. Simulation results demonstrate notable improvements in fuel economy and performance characteristics.


title: A ubiquitous English vocabulary learning system: Evidence of active/passive attitudes vs. usefulness/ease-of-use

abstract: English vocabulary learning and ubiquitous learning have separately received considerable attention in recent years. However, research on English vocabulary learning in ubiquitous learning contexts has been less studied. In this study, we develop a ubiquitous English vocabulary learning (UEVL) system to assist students in experiencing a systematic vocabulary learning process in which ubiquitous technology is used to develop the system, and video clips are used as the material. Afterward, the technology acceptance model and partial least squares approach are used to explore students' perspectives on the UEVL system. The results indicate that (1) both the system characteristics and the material characteristics of the UEVL system positively and significantly influence the perspectives of all students on the system; (2) the active students are interested in perceived usefulness; (3) the passive students are interested in perceived ease of use.


title: Analysis of security threats and vulnerability for cyber-physical systems

abstract: Governments, enterprises and research institutions have participated the research and development of cyber-physical systems (CPS). However, the development of cyber-physical systems will be restricted by security threats and vulnerabilities. We summarize security threats and to the cyber-physical systems to provide a theoretical reference for research on cyber-physical systems and to provide effective security measures. The architecture of cyber-physical systems is used to classify threats in the physical layer, network layer and application layer. This paper presents the security threats in the three layers. Then it lists the vulnerabilities of cyber-physical systems from the three aspects of management and policy, platform and network. Correspondingly, it also provides some security measures and recommendations for the security threats and vulnerabilities in each section.


title: 6.2 A 60Gb/s 288mW NRZ transceiver with adaptive equalization and baud-rate clock and data recovery in 65nm CMOS technology

abstract: The demand for ultra-high speed transceivers continues to explode, and while the data-rate for high-speed I/O standards has increased accordingly, the historically constant or even decreasing power budgets available for the transceivers push these designs to be extremely energy efficient. To begin addressing this need, recently published NRZ transceivers operating up to 56.5Gb/s with a 1-tap DFE and/or CTLE [1,2] in 28–40nm CMOS processes have been demonstrated for 20dB channel loss, implemented in a 65nm CMOS technology (Fig. 6.2.1). The full transceiver (including baud-rate CDR) achieves 60Gb/s and 4.8pJ/b energy efficiency over a 0.7m Twinax cable.


title: A 57.5-to-90.1GHz magnetically-tuned multi-mode CMOS VCO

abstract: A magnetically-tuned multi-mode VCO featuring an ultra-wide frequency tuning range is presented. By changing the magnetic coupling coefficient between the primary and secondary coils in the transformer tank, the frequency tuning range of a dual-band VCO is greatly increased to continuously cover the whole E-band. Fabricated in a 65-nm CMOS process, the presented VCO measures a tuning range of 44.2% from 57.5 to 90.1 GHz while consuming 7mA to 9mA at 1.2V supply. The measured phase noises at 10MHz offset from carrier frequencies of 72.2, 80.5 and 90.1 GHz are −111.8, −108.9 and −105 dBc/Hz, respectively, which corresponds to a FOM T between −192.2 and −184.2dBc/Hz.


title: THE SUPPLY CHAIN MANAGEMENT PROCESSES

abstract: Increasingly, supply chain management is being recognized as the management of key business processes across the network of organizations that comprise the supply chain. While many have recognized the benefits of a process approach to managing the business and the supply chain, most are vague about what processes are to be considered, what sub‐processes and activities are contained in each process, and how the processes interact with each other and with the traditional functional silos. In this paper, we provide strategic and operational descriptions of each of the eight supply chain processes identified by members of The Global Supply Chain Forum, as well as illustrations of the interfaces among the processes and an example of how a process approach can be implemented within an organization. Our aim is to provide managers with a framework to be used in implementing supply chain management, instructors with material useful in structuring a supply chain management course, and researchers with a set of opportunities for further development of the field.


title: Generalized Wiener filtering with fractional power spectrograms

abstract: In the recent years, many studies have focused on the single-sensor separation of independent waveforms using so-called soft-masking strategies, where the short term Fourier transform of the mixture is multiplied element-wise by a ratio of spectrogram models. When the signals are wide-sense stationary, this strategy is theoretically justified as an optimal Wiener filtering: the power spectrograms of the sources are supposed to add up to yield the power spectrogram of the mixture. However, experience shows that using fractional spectrograms instead, such as the amplitude, yields good performance in practice, because they experimentally better fit the additivity assumption. To the best of our knowledge, no probabilistic interpretation of this filtering procedure was available to date. In this paper, we show that assuming the additivity of fractional spectrograms for the purpose of building soft-masks can be understood as separating locally stationary α-stable harmonizable processes, α-harmonizable in short, thus justifying the procedure theoretically.


title: Vector Approximate Message Passing

abstract: The standard linear regression (SLR) problem is to recover a vector $\mathbf{x}^0$ from noisy linear observations $\mathbf{y}=\mathbf{Ax}^0+\mathbf{w}$. The approximate message passing (AMP) algorithm recently proposed by Donoho, Maleki, and Montanari is a computationally efficient iterative approach to SLR that has a remarkable property: for large i.i.d.\ sub-Gaussian matrices $\mathbf{A}$, its per-iteration behavior is rigorously characterized by a scalar state-evolution whose fixed points, when unique, are Bayes optimal. AMP, however, is fragile in that even small deviations from the i.i.d.\ sub-Gaussian model can cause the algorithm to diverge. This paper considers a"vector AMP"(VAMP) algorithm and shows that VAMP has a rigorous scalar state-evolution that holds under a much broader class of large random matrices $\mathbf{A}$: those that are right-rotationally invariant. After performing an initial singular value decomposition (SVD) of $\mathbf{A}$, the per-iteration complexity of VAMP can be made similar to that of AMP. In addition, the fixed points of VAMP's state evolution are consistent with the replica prediction of the minimum mean-squared error recently derived by Tulino, Caire, Verd\'u, and Shamai. The effectiveness and state evolution predictions of VAMP are confirmed in numerical experiments.


A Study of Various Attacks and Intrusion Detection System in Cloud


title: Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks

abstract: This letter presents an effective method to encode the spatiotemporal information of a skeleton sequence into color texture images, referred to as skeleton optical spectra, and employs convolutional neural networks (ConvNets) to learn the discriminative features for action recognition. Such spectrum representation makes it possible to use a standard ConvNet architecture to learn suitable “dynamic” features from skeleton sequences without training millions of parameters afresh and it is especially valuable when there is insufficient annotated training video data. Specifically, the encoding consists of four steps: mapping of joint distribution, spectrum coding of joint trajectories, spectrum coding of body parts, and joint velocity weighted saturation and brightness. Experimental results on three widely used datasets have demonstrated the efficacy of the proposed method.


title: Emulation of computer mouse control with a noninvasive brain-computer interface

abstract: Brain?computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user's task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.


title: Universal Successor Representations for Transfer Reinforcement Learning

abstract: The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. In this work, we focus on the transfer scenario where the dynamics among tasks are the same, but their goals differ. Although general value function (Sutton et al., 2011) has been shown to be useful for knowledge transfer, learning a universal value function can be challenging in practice. To attack this, we propose (1) to use universal successor representations (USR) to represent the transferable knowledge and (2) a USR approximator (USRA) that can be trained by interacting with the environment. Our experiments show that USR can be effectively applied to new tasks, and the agent initialized by the trained USRA can achieve the goal considerably faster than random initialization.


title: Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans

abstract: Several stories in the popular media have speculated that it may be possible to infer from the brain which word a person is speaking or even thinking. While recent studies have demonstrated that brain signals can give detailed information about actual and imagined actions, such as different types of limb movements or spoken words, concrete experimental evidence for the possibility to 'read the mind', i.e. to interpret internally-generated speech, has been scarce. In this study, we found that it is possible to use signals recorded from the surface of the brain (electrocorticography) to discriminate the vowels and consonants embedded in spoken and in imagined words, and we defined the cortical areas that held the most information about discrimination of vowels and consonants. The results shed light on the distinct mechanisms associated with production of vowels and consonants, and could provide the basis for brain-based communication using imagined speech.


title: Human Missense Variation is Constrained by Domain Structure and Highlights Functional and Pathogenic Residues

abstract: Human genome sequencing has generated population variant datasets containing millions of variants from hundreds of thousands of individuals1-3. The datasets show the genomic distribution of genetic variation to be influenced on genic and sub-genic scales by gene essentiality,1,4,5 protein domain architecture6 and the presence of genomic features such as splice donor/acceptor sites.2 However, the variant data are still too sparse to provide a comparative picture of genetic variation between individual protein residues in the proteome.1,6 Here, we overcome this sparsity for ∼25,000 human protein domains in 1,291 domain families by aggregating variants over equivalent positions (columns) in multiple sequence alignments of sequence-similar (paralagous) domains7,8. We then compare the resulting variation profiles from the human population to residue conservation across all species9 and find that the same tertiary structural and functional pressures that affect amino acid conservation during domain evolution constrain missense variant distributions. Thus, depletion of missense variants at a position implies that it is structurally or functionally important. We find such positions are enriched in known disease-associated variants (OR = 2.83, p ≈ 0) while positions that are both missense depleted and evolutionary conserved are further enriched in disease-associated variants (OR = 1.85, p = 3.3×10-17) compared to those that are only evolutionary conserved (OR = 1.29, p = 4.5×10-19). Unexpectedly, a subset of evolutionary Unconserved positions are Missense Depleted in human (UMD positions) and these are also enriched in pathogenic variants (OR = 1.74, p = 0.02). UMD positions are further differentiated from other unconserved residues in that they are enriched in ligand, DNA and protein binding interactions (OR = 1.59, p = 0.003), which suggests this stratification can identify functionally important positions. A different class of positions that are Conserved and Missense Enriched (CME) show an enrichment of ClinVar risk factor variants (OR = 2.27, p = 0.004). We illustrate these principles with the G-Protein Coupled Receptor (GPCR) family, Nuclear Receptor Ligand Binding Domain family and In Between Ring-Finger (IBR) domains and list a total of 343 UMD positions in 211 domain families. This study will have broad applications to: (a) providing focus for functional studies of specific proteins by mutagenesis; (b) refining pathogenicity prediction models; (c) highlighting which residue interactions to target when refining the specificity of small-molecule drugs.


title: Ranking Graph Embedding for Learning to Rerank

abstract: Dimensionality reduction is a key step to improving the generalization ability of reranking in image search. However, existing dimensionality reduction methods are typically designed for classification, clustering, and visualization, rather than for the task of learning to rank. Without using of ranking information such as relevance degree labels, direct utilization of conventional dimensionality reduction methods in ranking tasks generally cannot achieve the best performance. In this paper, we show that introducing ranking information into dimensionality reduction significantly increases the performance of image search reranking. The proposed method transforms graph embedding, a general framework of dimensionality reduction, into ranking graph embedding (RANGE) by modeling the global structure and the local relationships in and between different relevance degree sets, respectively. The proposed method also defines three types of edge weight assignment between two nodes: binary, reconstruction, and global. In addition, a novel principal components analysis based similarity calculation method is presented in the stage of global graph construction. Extensive experimental results on the MSRA-MM database demonstrate the effectiveness and superiority of the proposed RANGE method and the image search reranking framework.


title: Vulnerability Assessment of OAuth Implementations in Android Applications

abstract: Enforcing security on various implementations of OAuth in Android apps should consider a wide range of issues comprehensively. OAuth implementations in Android apps differ from the recommended specification due to the provider and platform factors, and the varied implementations often become vulnerable. Current vulnerability assessments on these OAuth implementations are ad hoc and lack a systematic manner. As a result, insecure OAuth implementations are still widely used and the situation is far from optimistic in many mobile app ecosystems. To address this problem, we propose a systematic vulnerability assessment framework for OAuth implementations on Android platform. Different from traditional OAuth security analyses that are experiential with a restrictive three-party model, our proposed framework utilizes an systematic security assessing methodology that adopts a five-party, three-stage model to detect typical vulnerabilities of popular OAuth implementations in Android apps. Based on this framework, a comprehensive investigation on vulnerable OAuth implementations is conducted at the level of an entire mobile app ecosystem. The investigation studies the Chinese mainland mobile app markets (e.g., Baidu App Store, Tencent, Anzhi) that covers 15 mainstream OAuth service providers. Top 100 relevant relying party apps (RP apps) are thoroughly assessed to detect vulnerable OAuth implementations, and we further perform an empirical study of over 4,000 apps to validate how frequently developers misuse the OAuth protocol. The results demonstrate that 86.2% of the apps incorporating OAuth services are vulnerable, and this ratio of Chinese mainland Android app market is much higher than that (58.7%) of Google Play.


title: Towards trajectory anonymization: a generalization-based approach

abstract: Trajectory datasets are becoming popular due to the massive usage of GPS and location-based services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques.


title: Monolayer WS2 Nanopores for DNA Translocation with Light-Adjustable Sizes

abstract: Two-dimensional materials are promising for a range of applications, as well as testbeds for probing the physics of low-dimensional systems. Tungsten disulfide (WS2) monolayers exhibit a direct band gap and strong photoluminescence (PL) in the visible range, opening possibilities for advanced optoelectronic applications. Here, we report the realization of two-dimensional nanometer-size pores in suspended monolayer WS2 membranes, allowing for electrical and optical response in ionic current measurements. A focused electron beam was used to fabricate nanopores in WS2 membranes suspended on silicon-based chips and characterized using PL spectroscopy and aberration-corrected high-resolution scanning transmission electron microscopy. It was observed that the PL intensity of suspended WS2 monolayers is ∼10–15 times stronger when compared to that of substrate-supported monolayers, and low-dose scanning transmission electron microscope viewing and drilling preserves the PL signal of WS2 around the pore. We establ...