title: Hidden and Under Control A Survey and Outlook on Covert Channel-internal Control Protocols

abstract: Network covert channels are policy-breaking and stealthy communication channels in computer networks. These channels can be used to bypass Internet censorship, to exfiltrate data without raising attention, to allow a safe and stealthy communication for members of political oppositions and for spies, to hide the communication of military units at the battlefield from the enemy, and to provide stealthy communication for today’s malware, especially for botnets. To enhance network covert channels, researchers started to add protocol headers, so-called micro-protocols, to hidden payload in covert channels. Such protocol headers enable fundamental features such as reliability, dynamic routing, proxy capabilities, simultaneous connections, or session management for network covert channels—features which enrich future botnet communications to become more adaptive and more stealthy than nowadays. In this survey, we provide the first overview and categorization of existing micro-protocols. We compare micro-protocol features and present currently uncovered research directions for these protocols. Afterwards, we discuss the significance and the existing means for micro-protocol engineering. Based on our findings, we propose further research directions for micro-protocols. These features include to introduce multi-layer protocol stacks, peer auto-configuration, and peer group communication based on micro-protocols, as well as to develop protocol translation in order to achieve inter-connectivity for currently separated overlay networks.


title: Laser curing of inkjet printed strain gauge structures

abstract: In this work the fabrication of strain sensors based on the resistive principle using inkjet printing of silver nanoparticles was investigated. Laser sintering was studied as an alternative sintering process. Printed on polyimide substrate silver structures were sintered using a continuous wave 405 nm diode laser. Gauge factors of printed strain gauges of around 3.6 (after laser sintering) have been achieved.


title: Coding facial expressions with Gabor wavelets

abstract: A method for extracting information about facial expressions from images is presented. Facial expression images are coded using a multi-orientation multi-resolution set of Gabor filters which are topographically ordered and aligned approximately with the face. The similarity space derived from this representation is compared with one derived from semantic ratings of the images by human observers. The results show that it is possible to construct a facial expression classifier with Gabor coding of the facial images as the input stage. The Gabor representation shows a significant degree of psychological plausibility, a design feature which may be important for human-computer interfaces.


title: Image Distortion Detection Using Convolutional Neural Network

abstract: Image distortion classification and detection is an important task in many applications. For example when compressing images, if we know the exact location of the distortion, then it is possible to re-compress images by adjusting the local compression level dynamically. In this paper, we address the problem of detecting the distortion region and classifying the distortion type of a given image. We show that our model significantly outperforms the state-of-the-art distortion classifier, and report accurate detection results for the first time. We expect that such results prove the usefulness of our approach in many potential applications such as image compression or distortion restoration.


title: Hybrid Modelling and Control of Power Electronics

abstract: Switched circuits in power electronics by their nature present hybrid behavior. Such circuits can be described by a set of discrete states with associated continuous dynamics. A control objective, usually regulation of the output in the face of disturbances in the continuous system, is accomplished by choosing among discrete states. We describe a hybrid systems perspective of several common tasks in the design and analysis of power electronics. A DC-DC boost converter circuit is presented as an illustrative example, and the extension of this circuit to a multiple output configuration is provided to show the favorable scaling properties and broad utility of the hybrid approach.


title: Expressing computer science concepts through Kodu game lab

abstract: Educational programming environments such as Microsoft Research's Kodu Game Lab are often used to introduce novices to computer science concepts and programming. Unlike many other educational languages that rely on scripting and Java-like syntax, the Kodu language is entirely event-driven and programming takes the form of "when" do' clauses. Despite this simplistic programing model, many computer science concepts can be expressed using Kodu. We identify and measure the frequency of these concepts in 346 Kodu programs created by users, and find that most programs exhibit sophistication through the use of complex control flow and boolean logic. Through Kodu's non-traditional language, we show that users express and explore fundamental computer science concepts.


title: N-gram IDF: A Global Term Weighting Scheme Based on Information Distance

abstract: This paper first reveals the relationship between Inverse Document Frequency (IDF), a global term weighting scheme, and information distance, a universal metric defined by Kolmogorov complexity. We concretely give a theoretical explanation that the IDF of a term is equal to the distance between the term and the empty string in the space of information distance in which the Kolmogorov complexity is approximated using Web documents and the Shannon-Fano coding. Based on our findings, we propose N-gram IDF, a theoretical extension of IDF for handling words and phrases of any length. By comparing weights among N-grams of any N, N-gram IDF enables us to determine dominant N-grams among overlapping ones and extract key terms of any length from texts without using any NLP techniques. To efficiently compute the weight for all possible N-grams, we adopt two string processing techniques, i.e., maximal substring extraction using enhanced suffix array and document listing using wavelet tree. We conducted experiments on key term extraction and Web search query segmentation, and found that N-gram IDF was competitive with state-of-the-art methods that were designed for each application using additional resources and efforts. The results exemplified the potential of N-gram IDF.


title: An online stair-climbing control method for a transformable tracked robot

abstract: Stair-climbing is a necessary capacity for mobile robots. This paper presents an online control method for the stair-climbing of a transformable tracked robot, Amoeba-II, and this robot is also an isomerism-modules robot with different mechanism modules. Based on the reasonable compartmentalization and kinematics analysis of the stair-climbing process, the coordination of the rotations of modules can reduce the slippage between tracks and terrain. To ensure that the robot can climb stairs with enough capability and stability, the stair-climbing criterion for the robot has been established based on the force analysis of each stage of the stair-climbing procedure. Meanwhile, the interference-avoiding criterion has been set up to avoid the interference between the non-tracked module of the robot and the stair. The experiment for the stair-climbing of the robot has been implemented to certify the validity of the online stair-climbing control method for a transformable tracked robot.


title: Sign Language Recognition using 3D convolutional neural networks

abstract: Sign Language Recognition (SLR) targets on interpreting the sign language into text or speech, so as to facilitate the communication between deaf-mute people and ordinary people. This task has broad social impact, but is still very challenging due to the complexity and large variations in hand actions. Existing methods for SLR use hand-crafted features to describe sign language motion and build classification models based on those features. However, it is difficult to design reliable features to adapt to the large variations of hand gestures. To approach this problem, we propose a novel 3D convolutional neural network (CNN) which extracts discriminative spatial-temporal features from raw video stream automatically without any prior knowledge, avoiding designing features. To boost the performance, multi-channels of video streams, including color information, depth clue, and body joint positions, are used as input to the 3D CNN in order to integrate color, depth and trajectory information. We validate the proposed model on a real dataset collected with Microsoft Kinect and demonstrate its effectiveness over the traditional approaches based on hand-crafted features.


title: An empirical study of required dimensionality for large-scale latent semantic indexing applications

abstract: The technique of latent semantic indexing is used in a wide variety of commercial applications. In these applications, the processing time and RAM required for SVD computation, and the processing time and RAM required during LSI retrieval operations are all roughly linear in the number of dimensions, k, chosen for the LSI representation space. In large-scale commercial LSI applications, reducing k values could be of significant value in reducing server costs. This paper explores the effects of varying dimensionality. The approach taken here focuses on term comparisons. Pairs of terms are considered which have strong real-world associations. The proximities of members of these pairs in the LSI space are compared at multiple values of k. The testing is carried out for collections of from one to five million documents. For the five million document collection, a value of k ≈ 400 provides the best performance. The results suggest that there is something of an 'island of stability' in the k = 300 to 500 range. The results also indicate that there is relatively little room to employ k values outside of this range without incurring significant distortions in at least some term-term correlations.


title: A Quantitative Systematic Review of Normal Values for Short-Term Heart Rate Variability in Healthy Adults

abstract: Heart rate variability (HRV) is a known risk factor for mortality in both healthy and patient populations. There are currently no normative data for short-term measures of HRV. A thorough review of short-term HRV data published since 1996 was therefore performed. Data from studies published after the 1996 Task Force report (i.e., between January 1997 and September 2008) and reporting short-term measures of HRV obtained in normally healthy individuals were collated and factors underlying discrepant values were identified. Forty-four studies met the pre-set inclusion criteria involving 21,438 participants. Values for short-term HRV measures from the literature were lower than Task Force norms. A degree of homogeneity for common measures of HRV in healthy adults was shown across studies. A number of studies demonstrate large interindividual variations (up to 260,000%), particularly for spectral measures. A number of methodological discrepancies underlined disparate values. These include a systematic failure within the literature (a) to recognize the importance of RR data recognition/editing procedures and (b) to question disparate HRV values observed in normally healthy individuals. A need for large-scale population studies and a review of the Task Force recommendations for short-term HRV that covers the full-age spectrum were identified. Data presented should be used to quantify reference ranges for short-term measures of HRV in healthy adult populations but should be undertaken with reference to methodological factors underlying disparate values. Recommendations for the measurement of HRV require updating to include current technologies.


title: Design of Chebyshev-type IIR filters with approximately linear phase characteristics

abstract: SUMMARY Digital filters with a linear phase characteristic are needed in many applications for signal processing. In this paper, design of a Chebyshev-type IIR filter with approximate linear phase characteristics in the passband is described. First, it is shown that a flat stopband can easily be realized by placing multiple zeros at the specified frequency points in the stopband. Next, the complex Remez exchange algorithm is applied to the passband so that the filter design problem is formulated as an eigenvalue problem. Hence, by solving the eigenvalue problem, the filter coefficients can be derived easily. Further, by means of iterative calculations, an equiripple characteristic of the error function in the passband is obtained. Finally, it is shown that a reverse Chebyshev-type IIR filter with an approximate linear phase characteristic can also be obtained by parallel connection of the proposed Chebyshev-type filter and a delay line. © 2003 Wiley Periodicals, Inc. Electron


title: A 39 dB DR CMOS log-amp RF power detector with ±1.1 dB temperature drift from −40 to 85°C

abstract: This paper presents a temperature compensated logarithmic amplifier (log-amp) RF power detector implemented in CMOS 0.18μm technology. The input power can range from -50 to +10 dBm for RF signals ranging from 100MHz to 1.5 GHz. This design attains a typical DR of 39 dB for a ±1 dB log-conformance error (LCE). Up to 900MHz the temperature drift is never larger than ±1.1 dB for all 24 measured samples over a temperature range from -40 to +85°C. The current consumption is 6.3mA from a 1.8V power supply and the chip area is 0.76mm2.


title: Quantum random access memory

abstract: A random access memory (RAM) uses n bits to randomly address N=2(n) distinct memory cells. A quantum random access memory (QRAM) uses n qubits to address any quantum superposition of N memory cells. We present an architecture that exponentially reduces the requirements for a memory call: O(logN) switches need be thrown instead of the N used in conventional (classical or quantum) RAM designs. This yields a more robust QRAM algorithm, as it in general requires entanglement among exponentially less gates, and leads to an exponential decrease in the power needed for addressing. A quantum optical implementation is presented.


title: Tightly-Secure Identity-Based Structured Aggregate Signature Scheme under the Computational Diffie-Hellman Assumption

abstract: An aggregate signature scheme is a primitive whereby each signer signs an individual document and combines them to compress data size. We propose an aggregate signature scheme which is an extension in two standpoints of structured signatures and ID-based signatures, i.e., we construct an identity-based structured aggregate signature scheme. The proposed scheme is expected to be used with consumer-generated media services. We prove the security of the proposed scheme with tight reduction under the computational Diffie-Hellman (CDH) assumption in the random oracle model. Tight reduction means that the cost of a reduction algorithm is independent of an adversary's capability, i.e., security is not downgraded by the adversary's capability. To the best of our knowledge, no structured signature scheme with tight reduction has been proposed to date because it contains complicated structures that make the reduction inefficient. Note that the security of our scheme captures the switching attack (CCS 2007, Boldyreva et al.) and the re-ordering attack (ISPEC 2007, Shao), which break several famous schemes.


title: Design of novel Multiple Valued Logic (MVL) circuits

abstract: Background: Multiple-Valued Logic (MVL) is the non-binary-valued system, in which more than two levels of information content are available, i.e., L>2. In modern technologies, the dual level binary logic circuits have normally been used. However, these suffer from several significant issues such as the interconnection considerations including the parasitics, area and power dissipation. The MVL circuits have been proved to be consisting of reduced circuitry and increased efficiency in terms of higher utilization of the circuit resources through multiple levels of voltage. Innumerable algorithms have been developed for designing such MVL circuits. Extended form is one of the algebraic techniques used in designing these MVL circuits. Voltage mode design has also been employed for constructing various types of MVL circuits. Novelty: This paper proposes a novel MVLTRANS inverter, designed using conventional CMOS and pass transistor logic based MVLPTL inverter. Binary to MVL Converter/Encoder and MVL to binary Decoder/Converter are also presented in the paper. In addition to the proposed decoder circuit, a 4-bit novel MVL Binary decoder circuit is also proposed. Tools Used: All these circuits are designed, implemented and verified using Cadence® Virtuoso tools using 180 nm technology library.


title: Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration

abstract: The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human collaborators in order to achieve good performance and improve safety and end-user adoption. This paper evaluates a human-robot collaboration scheme that combines the task allocation and motion levels of reasoning: the robotic agent uses Bayesian inference to predict the next goal of its human partner from his or her ongoing motion, and re-plans its own actions in real time. This anticipative adaptation is desirable in many practical scenarios, where humans are unable or unwilling to take on the cognitive overhead required to explicitly communicate their intent to the robot. A behavioral experiment indicates that the combination of goal inference and dynamic task planning significantly improves both objective and perceived performance of the human-robot team. Participants were highly sensitive to the differences between robot behaviors, preferring to work with a robot that adapted to their actions over one that did not.


title: Commission V Symposium, Newcastle upon Tyne, UK. 2010 UAVS FOR THE DOCUMENTATION OF ARCHAEOLOGICAL EXCAVATIONS

abstract: UAV photogrammetry experienced a growing variety of diverse applications in different scientific disciplines. Comparable early, UAVs were deployed in the Cultural Heritage and Archaeology domains, mainly for the purpose of monument, building and landscape modelling. In this paper, we will focus on the investigation of UAV application for documenting archaeological excavations. Due to the fact, that excavations are dynamic processes and therefore the objects to be acquired change significantly within few hours, UAVs can provide a suitable alternative to traditional measurement methods such as measuring tape and tachymeter in some cases. Nevertheless, the image processing steps have to be automated to a large amount as results, usually sketches, maps, orthophotos and 3D models, should be available temporally close to the related excavation event. In order to accelerate the processing workflow, an interface between the UAV ground control software and various photogrammetric software packages was developed at ETH Zurich which allows for an efficient management and transfer of orientation, trajectory and sensor data for fast project setup. The capabilities of photogrammetric methods using UAVs as a sensor platform will be demonstrated in 3 case studies: The documentation of a large archaeological site in Bhutan, an excavation of a smaller site containing ancient tombs which include several uncovered objects in the Nasca region in Peru and the Maya site of Copan in Honduras. The first and the third case study deal with the 3D modelling of buildings and their remains by means of photogrammetry, which means that accurate flight planning had to be applied and followed during the flights. In the second case study, we acquired various aerial images over the excavation area Pernil Alto near Palpa in a more simple way for quick documentation of the area of interest. In a third part, we will present our results from comparisons between the planned positions for image acquisition and the positions realized by the navigation unit during the flight for both UAV systems mentioned above. We will describe how accurate orientation data improve automated image processing if they are at hand directly after the flight and explain the workflow developed at ETH Zurich.


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title: Towards a feminist HCI methodology: social science, feminism, and HCI

abstract: With substantial efforts in ubiquitous computing, ICT4D, and sustainable interaction design, among others, HCI is increasingly engaging with matters of social change that go beyond the immediate qualities of interaction. In doing so, HCI takes on scientific and moral concerns. This paper explores the potential for feminist social science to contribute to and potentially benefit from HCI's rising interest in social change. It describes how feminist contributions to debates in the philosophy of science have helped clarify relationships among objectivity, values, data collection and interpretation, and social consequences. Feminists have proposed and implemented strategies to pursue scientific and moral agendas together and with equal rigor. In this paper, we assess the epistemologies, methodologies, and methods of feminist social science relative to prior and ongoing research efforts in HCI. We conclude by proposing an outline of a feminist HCI methodology.


title: XHAMI – extended HDFS and MapReduce interface for Big Data image processing applications in cloud computing environments

abstract: Summary ::: Hadoop distributed file system (HDFS) and MapReduce model have become popular technologies for large-scale data organization and analysis. Existing model of data organization and processing in Hadoop using HDFS and MapReduce are ideally tailored for search and data parallel applications, for which there is no need of data dependency with its neighboring/adjacent data. However, many scientific applications such as image mining, data mining, knowledge data mining, and satellite image processing are dependent on adjacent data for processing and analysis. In this paper, we identify the requirements of the overlapped data organization and propose a two-phase extension to HDFS and MapReduce programming model, called XHAMI, to address them. The extended interfaces are presented as APIs and implemented in the context of image processing application domain. We demonstrated effectiveness of XHAMI through case studies of image processing functions along with the results. Although XHAMI has little overhead in data storage and input/output operations, it greatly enhances the system performance and simplifies the application development process. Our proposed system, XHAMI, works without any changes for the existing MapReduce models and can be utilized by many applications where there is a requirement of overlapped data. Copyright © 2016 John Wiley & Sons, Ltd.


title: Cross-linguistic differences and similarities in image descriptions

abstract: Automatic image description systems are commonly trained and evaluated on large image description datasets. Recently, researchers have started to collect such datasets for languages other than English. An unexplored question is how different these datasets are from English and, if there are any differences, what causes them to differ. This paper provides a cross-linguistic comparison of Dutch, English, and German image descriptions. We find that these descriptions are similar in many respects, but the familiarity of crowd workers with the subjects of the images has a noticeable influence on description specificity.


Intersection types for explicit substitution with resource control


title: Linking Effective Whole Life Cycle Cost Data Requirements to Parametric Building Information Models Using BIM Technologies

abstract: This paper demonstrates the capabilities of BIM Building Information Modeling in leveraging Whole Life Cycle Cost WLCC data requirements to perform WLCC calculations and produce WLLC estimates. The research determines the extent to which WLCC data, such as time, interest rates, escalation rates and real costs can be attached to parametric BIM data to be used effectively to create speedier and more accurate real-time WLLC analysis. Without incorporating WLCC data in the BIM, a complete picture of a construction project's WLCC cannot be formed from the default outputs of the model. BIM 5D applications such as CostX utilise the parametric properties of the model, providing users with the ability to generate information and quantities from the BIM to be used in a formatted cost plan. The benefit of the 5D process is that selected quantity surveying information in the BIM can be live linked from the model to the cost plan providing a real-time analysis of WLLC. The authors demonstrate in this paper how they leverage BIM, by incorporating WLCC data and calculations in a customised CostX workbook, thus providing the authors with the ability to live link the output values from the model to the values in the workbook to perform WLCC. This paper demonstrates the practical application of this process on a pilot project in order to complete a WLCC analysis.


title: Say It Ain't So, Norm! Reflections on Who We Are

abstract: Norm, an ABC sitcom that first aired in spring 1999 featuring a "social worker" as the lead character, set off a not-surprising reaction among professional social workers. Social work listservs and Web sites have been abuzz with commentary, most of which is highly negative, about this portrayal of a social worker ("Members Say," 1999). There is reason to be concerned about the image of social work, not because of this sitcom, per se (which maintains high audience ratings in its second season), but because Norm epitomizes the massive public misunderstanding about who social workers are and what they do. Professional social workers would probably argue that Norm is not a social worker (and if he were, he would quickly be subject to charges of ethical breaches!). His training is as a hockey player. He is working in his current job as a form of community service; his option was community service for tax fraud, or jail. Norm chose to become a social worker and is apparently paid for his work. (Note that in the real world community service is uncompensated.) Beyond the criteria established by individual states for licensing and the qualifications specified for membership in the NASW, the classification of social workers is largely outside of the profession's control. Norm occupies a social work-titled position, carries out functions associated with social work, and receives remuneration for this job. Thus, according to the number-one source of data on occupations, the U.S. Bureau of Labor Statistics (BLS), Norm is a social worker. Clarifying how the government collects and maintains data about social workers can shed light on public perceptions and misperceptions about the profession. How the Title of Social Worker Is Determined Data on employment by occupation available through the Current Population Survey (CPS) for 1998 reveal that the number of people in social work-titled positions was 749,000. This number is in contrast to the current NASW membership of approximately 150,000. The CPS does include a variable related to level of educational attainment, but this variable is not incorporated in the analysis of occupational data. The CPS occupational information is based on self-reports of workers or their "proxy" respondents--that is, another member of the household (such as a spouse) who answers the CPS questions on behalf of himself or herself and everyone else in the household. Two principal questions in the CPS are used to determine a worker's occupation: 1. What kind of work (do/does/did) (name/you) do--that is, what (is/was) (your/his/ her) occupation (for example, plumber, typist, farmer)? 2. What are (your/his/her) usual activities or duties at this job (for example, typing, keeping account books, filing, selling cars, laying brick)? (BLS, 1998a) This is how the two questions work: Suppose a respondent's answer to the first question is "sanitation engineer," a nonexistent category in the BLS occupational classification system. To classify this person properly, more information is needed, and the second question helps clarify what the person actually does. If the respondent says, "I design sewage treatment facilities and oversee their construction," he or she probably will be classified as a civil engineer. If the respondent instead replies, "I pick up people's trash, throw it on the truck, and ride the truck to the dump," he or she would probably be classified as a garbage collector. If the response is "I drive the garbage truck," the respondent would be classified as a truck driver (personal communication with J. Meisenheimer, economist, U.S. Department of Labor, Bureau of Labor Statistics, March 19, 1999). This example helps explain how Norm lays claim to being a social worker. He would respond to the question about occupation by referencing his job title. In response to the second question, Norm might say that he counsels individuals and children with a variety of problems. …


title: Unsupervised Low-Dimensional Vector Representations for Words, Phrases and Text that are Transparent, Scalable, and produce Similarity Metrics that are Complementary to Neural Embeddings

abstract: Neural embeddings are a popular set of methods for representing words, phrases or text as a low dimensional vector (typically 50-500 dimensions). However, it is difficult to interpret these dimensions in a meaningful manner, and creating neural embeddings requires extensive training and tuning of multiple parameters and hyperparameters. We present here a simple unsupervised method for representing words, phrases or text as a low dimensional vector, in which the meaning and relative importance of dimensions is transparent to inspection. We have created a near-comprehensive vector representation of words, and selected bigrams, trigrams and abbreviations, using the set of titles and abstracts in PubMed as a corpus. This vector is used to create several novel implicit word-word and text-text similarity metrics. The implicit word-word similarity metrics correlate well with human judgement of word pair similarity and relatedness, and outperform or equal all other reported methods on a variety of biomedical benchmarks, including several implementations of neural embeddings trained on PubMed corpora. Our implicit word-word metrics capture different aspects of word-word relatedness than word2vec-based metrics and are only partially correlated (rho = ~0.5-0.8 depending on task and corpus). The vector representations of words, bigrams, trigrams, abbreviations, and PubMed title+abstracts are all publicly available from http://arrowsmith.psych.uic.edu for release under CC-BY-NC license. Several public web query interfaces are also available at the same site, including one which allows the user to specify a given word and view its most closely related terms according to direct co-occurrence as well as different implicit similarity metrics.


title: SoC It to EM: ElectroMagnetic Side-Channel Attacks on a Complex System-on-Chip

abstract: Increased complexity in modern embedded systems has presented various important challenges with regard to side-channel attacks. In particular, it is common to deploy SoC-based target devices with high clock frequencies in security-critical scenarios; understanding how such features align with techniques more often deployed against simpler devices is vital from both destructive (i.e., attack) and constructive (i.e., evaluation and/or countermeasure) perspectives. In this paper, we investigate electromagnetic-based leakage from three different means of executing cryptographic workloads (including the general purpose ARM core, an on-chip co-processor, and the NEON core) on the AM335x SoC. Our conclusion is that addressing challenges of the type above is feasible, and that key recovery attacks can be conducted with modest resources.


title: A study of SSL Proxy attacks on Android and iOS mobile applications

abstract: According to recent articles in popular technology websites, some mobile applications function in an insecure manner when presented with untrusted SSL certificates. These non-browser based applications seem to, in the absence of a standard way of alerting a user of an SSL error, accept any certificate presented to it. This paper intends to research these claims and show whether or not an invisible proxy based SSL attack can indeed steal user’s credentials from mobile applications, and which types applications are most likely to be vulnerable to this attack vector. To ensure coverage of the most popular platforms, applications on both Android 4.2 and iOS 6 are tested. The results of our study showed that stealing credentials is indeed possible using invisible proxy man in the middle attacks. Keywords-Mobile Devices, SSL, TLS, Security, Proxy, Manin-the-middle, Android, iOS.


title: A shared control architecture for human-in-the-loop robotics applications

abstract: We propose a shared control architecture to enable the modeling of human-in-the-loop cyber physical systems (HiLCPS) in robotics applications. We identify challenges that currently hinder ideas and concepts from cross-domain applications to be shared among different implementation of HiLCPS. The presented architecture is developed with the intent to help bridge the gap between different communities developing HiLCPS by providing a common framework, associated metrics, and associated language to describe individual elements. We provide examples from two different domains, disaster robotics and assistive robotics, to demonstrate the structure of the architecture.


title: Energy efficient cross-layer routing protocol in Wireless Sensor Networks based on fuzzy logic

abstract: Resources are scarce in Wireless Sensor Networks (WSN) and one of the challenges is to design a lightweight communication protocol to support efficient and uniform power consumption among nodes. In this paper, we propose an energy aware routing scheme based on a cross-layer approach for WSNs with the objective to minimize the overall consumed energy; thus, maximizing the network lifetime. The remaining battery reserve capacity, link quality and transmission power for nodes within the local communication range are taken into consideration to determine the next hop relay node to reach the network sink. Parameters from different stack layers (i.e., physical, MAC, and network) are presented to a fuzzy logic system controller which makes a next hop routing decision. The performance of the proposed cross-layer algorithm is evaluated using discrete event simulation (OMNET++ Modeler).


title: Low Levels of Empathic Concern Predict Utilitarian Moral Judgment

abstract: Is it permissible to harm one to save many? Classic moral dilemmas are often defined by the conflict between a putatively rational response to maximize aggregate welfare (i.e., the utilitarian judgment) and an emotional aversion to harm (i.e., the non-utilitarian judgment). Here, we address two questions. First, what specific aspect of emotional responding is relevant for these judgments? Second, is this aspect of emotional responding selectively reduced in utilitarians or enhanced in non-utilitarians? The results reveal a key relationship between moral judgment and empathic concern in particular (i.e., feelings of warmth and compassion in response to someone in distress). Utilitarian participants showed significantly reduced empathic concern on an independent empathy measure. These findings therefore reveal diminished empathic concern in utilitarian moral judges.


Long-term Follow-up of Longitudinal Melanonychia in Children and Adolescents Using an Objective Discrimination Index


title: Ethanol production using corn, switchgrass, and wood; biodiesel production using soybean and sunflower

abstract: In this analysis, the most recent scientific data for corn, switchgrass, and wood, for fermentation/distillation were used. All current fossil energy inputs used in corn production and for the fermentation/distillation were included to determine the entire energy cost of ethanol production. Additional costs to consumers include federal and state subsidies, plus costs associated with environmental pollution and/or degradation that occur during the entire production process. In addition, an investigation was made concerning the conversion of soybeans into biodiesel fuel.


title: Mixture Matrix Completion

abstract: Completing a data matrix X has become an ubiquitous problem in modern data science, with applications in recommender systems, computer vision, and networks inference, to name a few. One typical assumption is that X is low-rank. A more general model assumes that each column of X corresponds to one of several low-rank matrices. This paper generalizes these models to what we call mixture matrix completion (MMC): the case where each entry of X corresponds to one of several low-rank matrices. MMC is a more accurate model for recommender systems, and brings more flexibility to other completion and clustering problems. We make four fundamental contributions about this new model. First, we show that MMC is theoretically possible (well-posed). Second, we give its precise information-theoretic identifiability conditions. Third, we derive the sample complexity of MMC. Finally, we give a practical algorithm for MMC with performance comparable to the state-of-the-art for simpler related problems, both on synthetic and real data.


title: On the Use, the Misuse, and the Very Limited Usefulness of Cronbach’s Alpha

abstract: This discussion paper argues that both the use of Cronbach’s alpha as a reliability estimate and as a measure of internal consistency suffer from major problems. First, alpha always has a value, which cannot be equal to the test score’s reliability given the interitem covariance matrix and the usual assumptions about measurement error. Second, in practice, alpha is used more often as a measure of the test’s internal consistency than as an estimate of reliability. However, it can be shown easily that alpha is unrelated to the internal structure of the test. It is further discussed that statistics based on a single test administration do not convey much information about the accuracy of individuals’ test performance. The paper ends with a list of conclusions about the usefulness of alpha.


title: Bijoux: Data Generator for Evaluating ETL Process Quality

abstract: Obtaining the right set of data for evaluating the fulfillment of different quality standards in the extract-transform-load (ETL) process design is rather challenging. First, the real data might be out of reach due to different privacy constraints, while providing a synthetic set of data is known as a labor-intensive task that needs to take various combinations of process parameters into account. Additionally, having a single dataset usually does not represent the evolution of data throughout the complete process lifespan, hence missing the plethora of possible test cases. To facilitate such demanding task, in this paper we propose an automatic data generator (i.e., Bijoux). Starting from a given ETL process model, Bijoux extracts the semantics of data transformations, analyzes the constraints they imply over data, and automatically generates testing datasets. At the same time, it considers different dataset and transformation characteristics (e.g., size, distribution, selectivity, etc.) in order to cover a variety of test scenarios. We report our experimental findings showing the effectiveness and scalability of our approach.


title: Implementing openflow based distributed firewall

abstract: SDN is an emerging technology which is going to drive next generation networks. Lot of companies and organizations has started using SDN applications. It is giving network administrators the flexibility in implementing their networks. But at the same time, it is bringing new security issues. To secure SDN networks, we need strong firewall application. Already some firewall applications are there but they suffer from certain shortcomings. One of the main drawbacks of existing firewall solutions is that they suffer from single point of failure due to their centralized nature and overloading of rules in single device. Other drawback of existing firewall is that they are mostly layer 2 firewalls. In this paper, we are implementing Distributed Firewall where every OpenFlow switch in a network can acts as a firewall. Plus this firewall will be capable of handling TCP, UDP and ICMP Traffic. We have tested this firewall using Mininet Emulator installed in Ubuntu 14.04 Linux installed under VirtualBox virtualization solution. We are using python based POX controller. This work is extension of our earlier work on programmable firewalls.


title: Input-agreement: a new mechanism for collecting data using human computation games

abstract: Since its introduction at CHI 2004, the ESP Game has inspired many similar games that share the goal of gathering data from players. This paper introduces a new mechanism for collecting labeled data using "games with a purpose." In this mechanism, players are provided with either the same or a different object, and asked to describe that object to each other. Based on each other's descriptions, players must decide whether they have the same object or not. We explain why this new mechanism is superior for input data with certain characteristics, introduce an enjoyable new game called "TagATune" that collects tags for music clips via this mechanism, and present findings on the data that is collected by this game.


title: A.: On the automatic assemblage of arbitrary broken solid artefacts

abstract: Abstract Presented here is a fast method that combines curve matching techniques with a surface matching algorithm to estimate the positioning and respective matching error for the joining of three-dimensional fragmented objects. Furthermore, this paper describes how multiple joints are evaluated and how the broken artefacts are clustered and transformed to form potential solutions of the assemblage problem.


title: IoT’s Tiny Steps towards 5G: Telco’s Perspective

abstract: The numerous and diverse applications of the Internet of Things (IoT) have the potential to change all areas of daily life of individuals, businesses, and society as a whole. The vision of a pervasive IoT spans a wide range of application domains and addresses the enabling technologies needed to meet the performance requirements of various IoT applications. In order to accomplish this vision, this paper aims to provide an analysis of literature in order to propose a new classification of IoT applications, specify and prioritize performance requirements of such IoT application classes, and give an insight into state-of-the-art technologies used to meet these requirements, all from telco’s perspective. A deep and comprehensive understanding of the scope and classification of IoT applications is an essential precondition for determining their performance requirements with the overall goal of defining the enabling technologies towards fifth generation (5G) networks, while avoiding over-specification and high costs. Given the fact that this paper presents an overview of current research for the given topic, it also targets the research community and other stakeholders interested in this contemporary and attractive field for the purpose of recognizing research gaps and recommending new research directions.


title: Adversarial Autoencoders

abstract: In this paper, we propose the"adversarial autoencoder"(AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution. Matching the aggregated posterior to the prior ensures that generating from any part of prior space results in meaningful samples. As a result, the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution. We show how the adversarial autoencoder can be used in applications such as semi-supervised classification, disentangling style and content of images, unsupervised clustering, dimensionality reduction and data visualization. We performed experiments on MNIST, Street View House Numbers and Toronto Face datasets and show that adversarial autoencoders achieve competitive results in generative modeling and semi-supervised classification tasks.


title: A Semantic-Aware Profile Updating Model for Text Recommendation

abstract: Content-based recommender systems (CBRSs) rely on user-item similarities that are calculated between user profiles and item representations. Appropriate representation of each user profile based on the user's past preferences can have a great impact on user's satisfaction in CBRSs. In this paper, we focus on text recommendation and propose a novel profile updating model based on previously recommended items as well as semantic similarity of terms calculated using distributed representation of words. We evaluate our model using two standard text recommendation datasets: TREC-9 Filtering Track and CLEF 2008-09 INFILE Track collections. Our experiments investigate the importance of both past recommended items and semantic similarities in recommendation performance. The proposed profile updating method significantly outperforms the baselines, which confirms the importance of incorporating semantic similarities in the profile updating task.


title: A wheelchair with lever propulsion control for climbing up and down stairs

abstract: This study proposes a novel stair-climbing wheelchair based on lever propulsion control using the human upper body. Wheelchairs are widely used as supporting locomotion devices for people with acquired lower limb disabilities. However, steps and stairs are critical obstacles to locomotion, which restrict their activities when using wheelchairs. Previous research focused on power-assisted, stair-climbing wheelchairs, which were large and heavy due to its large actuators and mechanisms. In the previous research, we proposed a wheelchair with lever propulsion mechanism and presented its feasibility of climbing up the stairs. The developed stair-climbing wheelchair consists of manual wheels with casters for planar locomotion and a rotary-leg mechanism based on lever propulsion that is capable of climbing up stairs. The wheelchair also has a passive mechanism powered by gas springs for posture transition to shift the user's center of gravity between the desired positions for planar locomotion and stair-climbing. In this paper, we present an advanced study on both climbing up and going down using lever propulsion control by the user's upper body motion. For climbing down the stairs, we reassembled one-way clutches used for the rotary-leg mechanism to help a user climb down the stairs through lever operation. We also equipped the wheelchair with sufficient torque dampers. The frontal wheels were fixed while climbing down the stairs to ensure safety. Relevant experiments were then performed to investigate its performance and verify that the wheelchair users can operate the proposed lever propulsion mechanism.


title: Prospective Trial of Customized Adherence Enhancement Plus Long-Acting Injectable Antipsychotic Medication in Homeless or Recently Homeless Individuals With Schizophrenia or Schizoaffective Disorder

abstract: Background: Treatment nonadherence in people with schizophrenia is associated with relapse and homelessness. Building on the usefulness of long-acting medication and our work in psychosocial interventions to enhance adherence, we conducted a prospective uncontrolled trial of customized adherence enhancement (CAE) plus long-acting injectable antipsychotic (LAI) using haloperidol decanoate in 30 homeless or recently homeless individuals with DSM-IV–defined schizophrenia or schizoaffective disorder. Method: Participants received monthly CAE and LAI (CAE-L) for 6 months. Primary outcomes were adherence, as measured by the Tablets Routine Questionnaire, and housing status. Secondary outcomes included psychiatric symptoms, functioning, side effects, and hospitalizations. The study was conducted from July 2010 to December 2012. Results: The mean age of participants was 41.8 years (SD = 8.6); they were mainly minorities (90%, n = 27 AfricanAmerican) and mainly single/never married (70%, n = 21). Most (97%, n = 29) had past or current substance abuse and had been incarcerated (97%, n = 29). Ten individuals (33%) terminated the study prematurely. CAE-L was associated with good adherence to LAI (at 6 months, 76%) and dramatic improvement in oral medication adherence, which changed from missing 46% of medication at study enrollment to missing only 10% at study end (P = .03). There were significant improvements in psychiatric symptoms (P < .001) and functioning (P < .001). Akathisia was a major side effect with LAI. Conclusions: While interpretation of findings must be tempered by the methodological limitations, CAE-L appears to be associated with improved adherence, symptoms, and functioning in homeless or recently homeless individuals with schizophrenia or schizoaffective disorder. Additional research is needed on effective and practical approaches to improving health outcomes for homeless people with serious mental illness. Trial Registration: ClinicalTrials.gov identifier: NCT01152697


BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY


title: Study on Personality Types of Dentists in different Disciplines of Dentistry

abstract: AIM ::: The purpose of this study was to determine the most common personality type among dentists in seven selected clinical dentistry specialties using the Myers-Briggs Type Indicator (MBTI) and to compare between these different types of personalities. ::: ::: ::: MATERIALS AND METHODS ::: A survey containing the MBTI and demographic and practice questions was used to assess the personality styles of 243 dental specialists in Riyadh, Saudi Arabia. The survey results were analyzed using descriptive statistics. ::: ::: ::: RESULTS ::: The results of the MBTI for 243 specialist dentists revealed, generally, a higher percentage of scoring for introversion (I) with an average of 65% than extroversion (E). The study identified 10 common personality types among these specialists: ISTJ, ISFJ, INFJ, ISTP, INFP, INTP, ENFP, ENTP, ENFJ, and ENTJ (extraversion-introversion (E-I), sensing-intuition (S-N), thinking-feeling (T-F), and judging-perception (J-P)). The dominant personality type in all seven clinical specialties in dentistry was ISTJ, with an average of 54%. ::: ::: ::: CONCLUSION ::: The personality types showed variation among the seven clinical dentistry specialties. However, among these seven clinician's specialties in dentistry, more than 50% of the individuals shared one common type of personality (ISTJ). ::: ::: ::: CLINICAL SIGNIFICANCE ::: The identification of the personality type might help in their association with coworkers, students, and patients as well as knowing the individual preferences toward different specialties in dentistry.


title: Exoshoe: A sensory system to measure foot pressure in industrial exoskeleton

abstract: This paper presents a novel sensor fusion methodology to dynamically detect weight variations and the position of an exoskeleton system. The proposed methodology is intended for tasks of lifting and lowering heavy weights with an industrial exoskeleton to substantially reduce spinal loads during these activities.


title: Makeup Lamps: Live Augmentation of Human Faces via Projection

abstract: We propose the first system for live dynamic augmentation of human faces. Using projector-based illumination, we alter the appearance of human performers during novel performances. The key challenge of live augmentation is latency - an image is generated according to a specific pose, but is displayed on a different facial configuration by the time it is projected. Therefore, our system aims at reducing latency during every step of the process, from capture, through processing, to projection. Using infrared illumination, an optically and computationally aligned high-speed camera detects facial orientation as well as expression. The estimated expression blendshapes are mapped onto a lower dimensional space, and the facial motion and non-rigid deformation are estimated, smoothed and predicted through adaptive Kalman filtering. Finally, the desired appearance is generated interpolating precomputed offset textures according to time, global position, and expression. We have evaluated our system through an optimized CPU and GPU prototype, and demonstrated successful low latency augmentation for different performers and performances with varying facial play and motion speed. In contrast to existing methods, the presented system is the first method which fully supports dynamic facial projection mapping without the requirement of any physical tracking markers and incorporates facial expressions.


title: An Evaluation of Different Types of Blended Learning Activities in Higher Education

abstract: Blended learning has provided benefits to higher education. Therefore, universities tend to introduce blended learning activities to improve their teaching-learning practices. There are different types of blended learning activities such as forum based discussions, online games, and assignments. In order to investigates students' satisfaction towards different types of blended learning activities and their usefulness for students to score well in examinations, an action research was conducted over a period of three years from 2015-2017. The results of the study informed that students preferred to engage with activities that started in classroom and continued online. Also, students found online discussion were less useful than the other types of blended learning activities. Based on the finding of the research paper discusses how blended learning activities should be designed to make them more useful and effective.


title: A Vision and Speech Enabled, Customizable, Virtual Assistant for Smart Environments

abstract: Recent developments in smart assistants and smart home automation are lately attracting the interest and curiosity of consumers and researchers. Speech enabled virtual assistants (often named smart speakers) offer a wide variety of network-oriented services and, in some cases, can connect to smart environments, thus enhancing them with new and effective user interfaces. However, such devices also reveal new needs and some weaknesses. In particular, they represent faceless and blind assistants, unable to show a face, and therefore an emotion, and unable to ‘see’ the user. As a consequence, the interaction is impaired and, in some cases, ineffective. Moreover, most of those devices heavily rely on cloud-based services, thus transmitting potentially sensitive data to remote servers. To overcome such issues, in this paper we combine some of the most advanced techniques in computer vision, deep learning, speech generation and recognition, and artificial intelligence, into a virtual assistant architecture for smart home automation systems. The proposed assistant is effective and resource-efficient, interactive and customizable, and the realized prototype runs on a low-cost, small-sized, Raspberry PI 3 device. For testing purposes, the system was integrated with an open source home automation environment and ran for several days, while people were encouraged to interact with it, and proved to be accurate, reliable and appealing.


title: Intelligent Crime Anomaly Detection in Smart Cities Using Deep Learning

abstract: The quick and accurate identification of criminal activity is paramount to securing any residence. With the rapid growth of smart cities, the integration of crime detection systems seeks to improve this security. In the past a strong reliance has been put on standard video surveillance in order to achieve this goal. This often creates a backlog of video data that must be monitored by a supervising official. For large urban areas, this creates a increasingly large workload for supervising officials which leads to an increase in error rate. Solutions have been implemented to help reduce the workload. Currently, auto regressive models have been used to better forecast criminal acts, but also have a list of shortcomings. We propose a solution of using neural networks in combination with a Hybrid Deep Learning algorithm to analyze video stream data. Our system will be able to quickly identify and assess criminal activity which will in turn reduce workloads on the supervising officials. When implemented across smart city infrastructure it will allow for a efficient and adaptable crime detection system.


title: Multi-Target Modelling for Embedded Software Development for Automotive Applications

abstract: Manual ‘porting” of source code is often required in order to “reuse” control software in different applications with different target hardware. This process is not cost effective. Maintaining multiple “versions” of the same software also causes problems. This paper describes a way in which multiple target source code can be generated from a single model. A custom data class is developed so that it can be used to define both signal and parameter data types necessary for data dictionarydriven models. This capability allows a single model to be used to generate code for multiple target hardware architectures. A software development process using a generic model to support multiple hardware targets is compared with the hand porting process (e.g. floatingpoint to/from fixed-point). Auto code generation from a sample multi-target feature model will be presented. The efficiency of the auto code will also be discussed.


title: The neighbor-joining method: a new method for reconstructing phylogenetic trees.

abstract: A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [ =neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Far-r-is’s method, Sattath and Tversky’s method, Li’s method, and Tateno et al.‘s modified Fan-is method. The new, neighbor-joining method and Sattath and Tversky’s method are shown to be generally better than the other methods.


title: Guarding Against the Erosion of Competitive Advantage: A Knowledge Leakage Mitigation Model

abstract: A critical objective of knowledge-intensive organizations is to prevent erosion of their competitive knowledge base through leakage. Our review of the literature highlights the need for a more refined conceptualization of perceived leakage risk. We propose a Knowledge Leakage Mitigation (KLM) model to explain the incongruity between perceived high-risk of leakage and lack of protective actions. We argue that an organization's perceived risk of leakage increases if competitors can benefit from leakage incidents. Further, perceived leakage risk decreases if the organization is shielded from impact due to their diversity of knowledge assets and their ability to reconfigure knowledge resources to refresh their competitive knowledge base. We describe our approach to the design of a large-scale survey instrument that has been tested and refined in two stakeholder communities: 1) knowledge managers responsible for organizational strategy, and 2) Information security management consultants.


title: The Media and Technology Usage and Attitudes Scale: An empirical investigation

abstract: Current approaches to measuring people's everyday usage of technology-based media and other computer-related activities have proved to be problematic as they use varied outcome measures, fail to measure behavior in a broad range of technology-related domains and do not take into account recently developed types of technology including smartphones. In the present study, a wide variety of items, covering a range of up-to-date technology and media usage behaviors. Sixty-six items concerning technology and media usage, along with 18 additional items assessing attitudes toward technology, were administered to two independent samples of individuals, comprising 942 participants. Factor analyses were used to create 11 usage subscales representing smartphone usage, general social media usage, Internet searching, e-mailing, media sharing, text messaging, video gaming, online friendships, Facebook friendships, phone calling, and watching television in addition to four attitude-based subscales: positive attitudes, negative attitudes, technological anxiety/dependence, and attitudes toward task-switching. All subscales showed strong reliabilities and relationships between the subscales and pre-existing measures of daily media usage and Internet addiction were as predicted. Given the reliability and validity results, the new Media and Technology Usage and Attitudes Scale was suggested as a method of measuring media and technology involvement across a variety of types of research studies either as a single 60-item scale or any subset of the 15 subscales.


DESIGN AND VLSI IMPLEMENTATION OF ANTI- COLLISION ENABLED ROBOT PROCESSOR USING RFID TECHNOLOGY


title: RescueDP: Real-time spatio-temporal crowd-sourced data publishing with differential privacy

abstract: Nowadays gigantic crowd-sourced data collected from mobile phone users have become widely available, which enables the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of these data to the public will pose a considerable threat to mobile users' privacy. To solve this problem, the notion of differential privacy has been proposed to provide privacy with theoretical guarantee, and recently it has been applied in streaming data publishing. However, most of the existing literature focus on either event-level privacy on infinite streams or user-level privacy on finite streams. In this paper, we investigate the problem of real-time spatiotemporal crowd-sourced data publishing with privacy preservation. Specifically, we consider continuous publication of population statistics for monitoring purposes and design RescueDP — an online aggregate monitoring scheme over infinite streams with privacy guarantee. RescueDP's key components include adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, which are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. We show that RescueDP can achieve w-event privacy over data generated and published periodically by crowd users. We evaluate our scheme with real-world as well as synthetic datasets and compare it with two w-event privacy-assured representative benchmarks. Experimental results show that our solution outperforms the existing methods and improves the utility with strong privacy guarantee.


title: A Differentiable Physics Engine for Deep Learning in Robotics

abstract: An important field in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. When gradient-based methods are used, models are kept small or rely on finite difference approximations for the Jacobian. This method quickly grows expensive with increasing numbers of parameters, such as found in deep learning. We propose the implementation of a modern physics engine, which can differentiate control parameters. This engine is implemented for both CPU and GPU. Firstly, this paper shows how such an engine speeds up the optimization process, even for small problems. Furthermore, it explains why this is an alternative approach to deep Q-learning, for using deep learning in robotics. Finally, we argue that this is a big step for deep learning in robotics, as it opens up new possibilities to optimize robots, both in hardware and software.


title: Practicing Oblivious Access on Cloud Storage: the Gap, the Fallacy, and the New Way Forward

abstract: To understand the gap between theory and practice for oblivious cloud storage, we experimentally evaluate four representative Oblivious RAM (ORAM) designs on Amazon S3. We replay realistic application traces to these ORAMs in order to understand whether they can meet the demands of various real applications using cloud storage as a backend. We find that metrics traditionally used in the ORAM literature, e.g., bandwidth overhead, fail to capture the practical needs of those applications. With a new understanding of the desirable properties, relevant metrics, and observations about the cloud services and their applications, we propose CURIOUS, a new modular partition-based ORAM framework, and show experimentally that it is thus far the most promising approach.


title: 59-dBOmega 68-GHz Variable Gain-Bandwidth Differential Linear TIA in 0.7-µm InP DHBT for 400-Gb/s Optical Communication Systems

abstract: We report a variable-gain and variable-bandwidth differential linear transimpedance amplifier (TIA-VGA) realised in a 0.7-μm indium phosphide (InP) double heterojunction bipolar transistor (DHBT) technology. The circuits yields 59-dBΩ / 900-Ω differential transimpedance gain with 20-dB control and 68-GHz -3-dB bandwidth with 30-GHz control. Output-referred 1-dB-compression single-ended amplitude is larger than -4-dBm (400mVpp) up to 60 GHz. High quality On-Off-Keying (OOK) eye diagrams are measured at 64 GBd. Consuming less than 475mW, this circuit exhibits the largest bandwidth for a linear TIA-VGA, to our knowledge, along with wide-range gain and bandwidth controls. This makes it a particularly suitable and flexible candidate for high-symbol-rate photoreceivers dedicated to next generation 400-Gb/s and 1-Tb/s optical communication systems.


title: Terrain-adaptive obstacle detection

abstract: Reliable detection and avoidance of obstacles is a crucial prerequisite for autonomously navigating robots as both guarantee safety and mobility. To ensure safe mobility, the obstacle detection needs to run online, thereby taking limited resources of autonomous systems into account. At the same time, robust obstacle detection is highly important. Here, a too conservative approach might restrict the mobility of the robot, while a more reckless one might harm the robot or the environment it is operating in. In this paper, we present a terrain-adaptive approach to obstacle detection that relies on 3D-Lidar data and combines computationally cheap and fast geometric features, like step height and steepness, which are updated with the frequency of the lidar sensor, with semantic terrain information, which is updated with at lower frequency. We provide experiments in which we evaluate our approach on a real robot on an autonomous run over several kilometers containing different terrain types. The experiments demonstrate that our approach is suitable for autonomous systems that have to navigate reliable on different terrain types including concrete, dirt roads and grass.


title: A module and LabVIEW-based Mho offset Directional distance relay development for education

abstract: The main purpose of this study is to introduce a new software and hardware simulation method for a four zones Mho Directional digital distance relay, having an offset characteristic and allowing both forward and reverse power flows. The relay model is first developed using Lab VIEW, tested and validated using MATLAB/SIMULINK package for six operating and fault conditions. Lastly, a laboratory-scale model is implemented through data acquisition, short-circuit and lumped transmission line modules. Furthermore, Lab VIEW Front Panel serves as an improved Graphical User Interface (GUI) which leads to a more user friendly environment, better interaction and understanding in power system protection especially for inexperienced electrical engineers and provide hands-on training to students towards Project Based Learning (PBL). From impedance calculation point of view, the relay model is able to identify the proper zone of operation and determine the fault location as expected.


title: Low-Profile RFID Tag Antenna Using Compact AMC Substrate for Metallic Objects

abstract: A low-profile (aplambda/100) passive radio frequency identification (RFID) tag antenna incorporated with a compact artificial magnetic conductor (AMC) substrate is proposed. A novel modified rectangular patch type AMC substrate with offset vias is employed to significantly reduce the planar dimensions of a tag antenna, which is about 0.1lambda times 0.2lambda. The effect of a ground plane size and the number of AMC unit cells on the antenna performance such as return loss and gain is investigated with a view of tag installation. It is demonstrated through experiments that the proposed tag antenna shows relatively tolerant reading distances from 2.4 to 4.8 m under various platform environments such as a conductive plate or a plastic water container.


title: An empirical investigation of socio-technical code review metrics and security vulnerabilities

abstract: One of the guiding principles of open source software development is to use crowds of developers to keep a watchful eye on source code. Eric Raymond declared Linus'' Law as "many eyes make all bugs shallow", with the socio-technical argument that high quality open source software emerges when developers combine together their collective experience and expertise to review code collaboratively. Vulnerabilities are a particularly nasty set of bugs that can be rare, difficult to reproduce, and require specialized skills to recognize. Does Linus' Law apply to vulnerabilities empirically? In this study, we analyzed 159,254 code reviews, 185,948 Git commits, and 667 post-release vulnerabilities in the Chromium browser project. We formulated, collected, and analyzed various metrics related to Linus' Law to explore the connection between collaborative reviews and vulnerabilities that were missed by the review process. Our statistical association results showed that source code files reviewed by more developers are, counter-intuitively, more likely to be vulnerable (even after accounting for file size). However, files are less likely to be vulnerable if they were reviewed by developers who had experience participating on prior vulnerability-fixing reviews. The results indicate that lack of security experience and lack of collaborator familiarity are key risk factors in considering Linus’ Law with vulnerabilities.


title: Wireless sensor networks for permanent health monitoring of historic buildings

abstract: This paper describes the application of a wireless sensor network to a 31 meter-tall medieval tower located in the city of Trento, Italy. The effort is motivated by preservation of the integrity of a set of frescoes decorating the room on the second floor, representing one of most important International Gothic artworks in Europe. The specific application demanded development of customized hardware and software. The wireless module selected as the core platform allows reliable wireless communication at low cost with a long service life. Sensors include accelerometers, deformation gauges, and thermometers. A multi-hop data collection protocol was applied in the software to improve the system`s flexibility and scalability. The system has been operating since September 2008, and in recent months the data loss ratio was estimated as less than 0.01%. The data acquired so far are in agreement with the prediction resulting a priori from the 3-dimensional FEM. Based on these data a Bayesian updating procedure is employed to real-time estimate the probability of abnormal condition states. This first period of operation demonstrated the stability and reliability of the system, and its ability to recognize any possible occurrence of abnormal conditions that could jeopardize the integrity of the frescos.


title: Intelligent traffic signal control system for ambulance using RFID and cloud

abstract: Road traffic congestion becomes a major issues for highly crowded metropolitan cities like, Chennai. Ambulance service is one of the major services which gets affected by traffic jams. To smoothen the ambulance movement this paper have come up with the solution of “Intelligent automatic traffic control for ambulance”. The proposed system creates a android app that connects both the ambulance and the traffic signal station using cloud network. This system makes uses RFID (radio frequency identification) technology to implement the Intelligent traffic signal control. The basic idea behind the proposed system is, if the Ambulance halts on the way due to a traffic signal, RFID installed at the traffic signal tracks the RFID tagged ambulance and sends the data to the cloud. After the acknowledgment for the user through the mobile app, the particular signal is made Green for some time and after the ambulance passes by, it regains its original flow of sequence of signaling If, this scheme is fully automated, it finds the ambulance spot, controls the traffic lights. This system control the traffic lights and save the time in emergency periods. Thus it acts as a life saver project.


title: The Multiphasic and Dynamic Nature of Flow in Adventure Experiences

abstract: This study evaluated phases of adventure experiences by identifying flow and reversal theory states over a 3-day white-water river surfing course. Data were col lected with novice river surfers (n = S) via in-depth qualitative interviews using head-mounted video cameras. Findings suggested that "opposing" experiential phases (i.e., telic and paratelic) may be symbiotic in adventure experiences and may facilitate flow experiences. These results may account for the dynamic nature of enjoyment, flow, and motivational states within adventure experiences. Future research should seek to validate the phasic models presented herein and evaluate their potential applicability to other adventurous activities.


title: In-service and pre-service early childhood teachers' views and intentions about ICT use in early childhood settings: A comparative study

abstract: This paper regards a comparative study which investigates in-service and pre-service Greek early childhood teachers' views and intentions about integrating and using computers in early childhood settings. Views and intentions were investigated via a questionnaire administered to 240 in-service and 428 pre-service early childhood teachers. Confirmatory Factor Analysis showed that the one-factor structure of the questionnaire holds in both populations. Measurement partial invariance between the two populations was confirmed. Comparing the two populations with regard to the degree of adopting positive views-intentions and the level of computer self-efficacy, teachers expressed more positive views-intentions and students reported higher computer self-efficacy. Implications for teacher training are discussed.


title: Adaptive bidirectional associative memories

abstract: Bidirectionality, forward and backward information flow, is introduced in neural networks to produce two-way associative search for stored stimulus-response associations (A(i),B(i)). Two fields of neurons, F(A) and F(B), are connected by an n x p synaptic marix M. Passing information through M gives one direction, passing information through its transpose M(T) gives the other. Every matrix is bidirectionally stable for bivalent and for continuous neurons. Paired data (A(i),B(i)) are encoded in M by summing bipolar correlation matrices. The bidirectional associative memory (BAM) behaves as a two-layer hierarchy of symmetrically connected neurons. When the neurons in F(A) and F(B) are activated, the network quickly evolves to a stable state of twopattern reverberation, or pseudoadaptive resonance, for every connection topology M. The stable reverberation corresponds to a system energy local minimum. An adaptive BAM allows M to rapidly learn associations without supervision. Stable short-term memory reverberations across F(A) and F(B) gradually seep pattern information into the long-term memory connections M, allowing input associations (A(i),B(i)) to dig their own energy wells in the network state space. The BAM correlation encoding scheme is extended to a general Hebbian learning law. Then every BAM adaptively resonates in the sense that all nodes and edges quickly equilibrate in a system energy local minimum. A sampling adaptive BAM results when many more training samples are presented than there are neurons in F(B) and F(B), but presented for brief pulses of learning, not allowing learning to fully or nearly converge. Learning tends to improve with sample size. Sampling adaptive BAMs can learn some simple continuous mappings and can rapidly abstract bivalent associations from several noisy gray-scale samples.


Domain independent automatic keyphrase indexing with small training sets


title: Database and tools for analysis of topographic organization and map transformations in major projection systems of the brain

abstract: Integration of dispersed and complicated information collected from the brain is needed to build new knowledge. But integration may be hampered by rigid presentation formats, diversity of data formats among laboratories, and lack of access to lower level data. We have addressed some of the fundamental issues related to this challenge at the level of anatomical data, by producing a coordinate based digital atlas and database application for a major projection system in the rat brain: the cerebro-ponto-cerebellar system. This application, Functional Anatomy of the Cerebro-Cerebellar System in rat (FACCS), is available via the Rodent Brain WorkBench (http://www.rbwb.org/). The data included are x,y,z-coordinate lists describing exact distributions of tissue elements (axonal terminal fields of axons, or cell bodies) that are labeled with axonal tracing techniques. All data are translated to a common local coordinate system to facilitate across animal comparison. A search capability allows queries based on, e.g. location of tracer injection sites, tracer category, size of the injection sites, and contributing author. A graphic search tool allows the user to move a volume cursor inside a coordinate system to detect particular injection sites having connections to a specific tissue volume at chosen density levels. Tools for visualization and analysis of selected data are included, as well as an option to download individual data sets for further analysis. With this application, data and metadata from different experiments are mapped into the same information structure and made available for re-use and re-analysis in novel combinations. The application is prepared for future handling of data from other projection systems as well as other data categories.


title: Variable Perturbation Size Adaptive P&O MPPT Algorithm for Sudden Changes in Irradiance

abstract: In this paper, a variable perturbation size adaptive perturb and observe (P&O) maximum power point tracking (MPPT) algorithm is proposed to track the maximum power under sudden changes in irradiance. The proposed method consists of three algorithms, namely current perturbation algorithm (CPA), adaptive control algorithm (ACA), and variable perturbation algorithm (VPA). CPA always tries to operate the photovoltaic (PV) panel at maximum power point (MPP). ACA sets the operating point closer to MPP, only if the operating limits are violated. These operating limits are expressed in terms of the operating current range of the PV panel and the sudden changes in irradiance. VPA dynamically reduces the perturbation size based on polarity of change in power. Two-stage variable size perturbation is proposed in this paper. The proposed algorithm is realized using a boost converter. The effectiveness of proposed algorithm in terms of dynamic performance and improved stability is validated by detailed simulation and experimental studies.


title: AQuPR: Attention based Query Passage Retrieval

abstract: Search queries issued over the Web increasingly look like questions, especially as the domain becomes more specific. Finding good response to such queries amounts to finding relevant passages from Web documents. Traditional information retrieval based Web search still matches the query to the words in the entire document. With the advent of machine reading comprehension techniques, Web search is moving more towards identifying the best sentence / group of sentences in the document. We present AQuPR an A ttention based Qu ery P assage R etrieval system to find human acceptable answer containing passages to technology queries issued over the Web. We train character level embeddings for the query and passage pairs, train a deep recurrent network with a novel simplified attention mechanism and incorporate additional signals present in Web documents to improve the performance of such a system. We collect a database of human issued queries along with their answer passages and learn an end to end system to enable automated query resolution. We present results for answering human issued search queries which show considerable promise against basic versions of current generation question answering systems.


title: Design of vehicle bus data acquisition and fault diagnosis system

abstract: This paper introduces the overall design of vehicle bus data acquisition and fault diagnosis system on the basis of OBD, focusing on its lower computer system and the upper computer system design principles. This system is based on the widely used CAN bus technology, to extract the vehicle's status or fault information. The CAN bus adopts the SAE J1939 protocol standards, maximize the superiority of it. The system acquires and analyses the data from the CAN bus under the SAEJ1939 protocol, which provides about 400 faults. The system displays the faults on the LCD screen in the way of the fault tree, so the users can check them easily. It can send data to the upper computer system or management center by General Packet Radio Service (GPRS), to view the vehicle status or fault information far away, and to achieve the remote monitoring of the vehicle and fault diagnosis.


title: Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning

abstract: Collaborative technologies support group work in project-based environments. In this study, we enhance the technology acceptance model to explain the factors that influence the acceptance of Google Applications for collaborative learning. The enhanced model was empirically evaluated using survey data collected from 136 students enrolled in a full-time degree program that used Google Applications to support project work. According to the research results, determinants of the technology acceptance model are the major factors influencing the adoption of the technology. In addition, the subjective norm represented by peers is found to significantly moderate the relationship between attitude and intention toward the technology. However, our results do not show a significant effect of subjective norms represented by instructors and mass media on students' intentions to use the technology. The ability to share information in the collaborative learning environment is found to influence intention and behavior toward the Google Applications platform.


title: A novel STFT-ranking feature of multi-channel EMG for motion pattern recognition

abstract: STFT-ranking feature is efficient for multi-channel EMG signal analysis.STFT-ranking feature can characterize relationships between multi-channel signals.Recognition accuracy over 90% was achieved applying the STFT-ranking feature.The performance of STFT-ranking feature is superior to conventional features.STFT-ranking feature can be applied to other multi-channel signals applications. Electromyography (EMG) is widely applied for neural engineering. For motion pattern recognition, many features of multi-channel EMG signals were investigated, but the relationships between muscles were not considered. In this study, a novel STFT-ranking feature based on short-time Fourier transform (STFT) is proposed. The novelty of STFT-ranking features is considering and covering the relationship information between EMG signals and multiple muscles in a motion pattern. With an exoskeleton robot arm, two series of motion patterns corresponding to the shoulder and elbow in the sagittal plane were investigated. EMG signals from six muscles were acquired in arm motion patterns when participants worn the robot arm. Four types of feature combinations, including seven conventional features, were compared with the STFT-ranking feature. The principal component analysis (PCA) and support vector machine (SVM) were used to build the motion recognition model. With the STFT-ranking feature, the recognition performance (93.9%) is superior to the conventional features (33.3-90.8%). The recognition variation is smaller (SD=4.3%) than the other features tested (SD=5.9-13.8%). These achievements will contribute to the advancement of control method of exoskeleton robots or power orthoses based on multi-channel EMG signals in the future. Based on the principle of STFT-ranking feature, the method also has potential for other multi-channel signal applications, such as electroencephalography (EEG) signal processing, speech recognition, and acoustic analysis.


title: Going Deeper with Contextual CNN for Hyperspectral Image Classification

abstract: In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark datasets: the Indian Pines dataset, the Salinas dataset and the University of Pavia dataset. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three datasets.


title: Osteoarthritis Classification Scales: Interobserver Reliability and Arthroscopic Correlation.

abstract: BACKGROUND ::: Osteoarthritis of the knee is commonly diagnosed and monitored with radiography. However, the reliability of radiographic classification systems for osteoarthritis and the correlation of these classifications with the actual degree of confirmed degeneration of the articular cartilage of the tibiofemoral joint have not been adequately studied. ::: ::: ::: METHODS ::: As the Multicenter ACL (anterior cruciate ligament) Revision Study (MARS) Group, we conducted a multicenter, prospective longitudinal cohort study of patients undergoing revision surgery after anterior cruciate ligament reconstruction. We followed 632 patients who underwent radiographic evaluation of the knee (an anteroposterior weight-bearing radiograph, a posteroanterior weight-bearing radiograph made with the knee in 45° of flexion [Rosenberg radiograph], or both) and arthroscopic evaluation of the articular surfaces. Three blinded examiners independently graded radiographic findings according to six commonly used systems-the Kellgren-Lawrence, International Knee Documentation Committee, Fairbank, Brandt et al., Ahlbäck, and Jäger-Wirth classifications. Interobserver reliability was assessed with use of the intraclass correlation coefficient. The association between radiographic classification and arthroscopic findings of tibiofemoral chondral disease was assessed with use of the Spearman correlation coefficient. ::: ::: ::: RESULTS ::: Overall, 45° posteroanterior flexion weight-bearing radiographs had higher interobserver reliability (intraclass correlation coefficient = 0.63; 95% confidence interval, 0.61 to 0.65) compared with anteroposterior radiographs (intraclass correlation coefficient = 0.55; 95% confidence interval, 0.53 to 0.56). Similarly, the 45° posteroanterior flexion weight-bearing radiographs had higher correlation with arthroscopic findings of chondral disease (Spearman rho = 0.36; 95% confidence interval, 0.32 to 0.39) compared with anteroposterior radiographs (Spearman rho = 0.29; 95% confidence interval, 0.26 to 0.32). With respect to standards for the magnitude of the reliability coefficient and correlation coefficient (Spearman rho), the International Knee Documentation Committee classification demonstrated the best combination of good interobserver reliability and medium correlation with arthroscopic findings. ::: ::: ::: CONCLUSIONS ::: The overall estimates with the six radiographic classification systems demonstrated moderate (anteroposterior radiographs) to good (45° posteroanterior flexion weight-bearing radiographs) interobserver reliability and medium correlation with arthroscopic findings. The International Knee Documentation Committee classification assessed with use of 45° posteroanterior flexion weight-bearing radiographs had the most favorable combination of reliability and correlation. ::: ::: ::: LEVEL OF EVIDENCE ::: Diagnostic Level I. See Instructions for Authors for a complete description of levels of evidence.


title: Towards global aerobic activity monitoring

abstract: With recent progress in wearable sensing it becomes reasonable for individuals to wear different sensors all day, thus global activity monitoring is establishing. The goals in global activity monitoring systems are amongst others to tell the type of activity that was performed, the duration and the intensity. With the information obtained this way, the individual's daily routine can be described in detail. One of the strong motivations to achieve these goals comes from healthcare: to be able to tell if individuals were performing enough physical activity to maintain or even promote their health. This paper focuses on the monitoring of aerobic activities, and targets two main goals: to estimate the intensity of activities, and to identify basic/recommended physical activities and postures. For these purposes, a dataset with 8 subjects and 14 different activities was recorded, including the basic activities and postures, but also examples of household (ironing, vacuum cleaning), sports (playing soccer, rope jumping) and everyday activities (ascending and descending stairs). Data from 3 accelerometers --- placed on lower arm, chest and foot --- and a heart rate monitor were analyzed. In this paper, first results are shown on both the intensity estimation and activity recognition tasks, with a performance of 87, 54% and 86, 80%, respectively.


title: Experimental validation of FEM based design method for synchronous reluctance machine

abstract: This paper presents a design methodology of a synchronous reluctance motor (SynRM) based on the equivalent electrical parameters model. The parameters of the model are calculated using FEM 2D simulations and corroborated with experimental results. In addition, the experimental test procedure applied to the prototype is presented.


title: ThinkLets: achieving predictable, repeatable patterns of group interaction with group support systems (GSS)

abstract: Over the past decade, Group Support Systems (GSS) has shown that, under certain circumstances, teams using GSS can be far more productive than teams who do not use GSS. However, research results are not unequivocal; they have been ambiguous, and sometimes conflicting which makes it difficult for GSS research to inform GSS practice. One cause of the conflict and ambiguity in GSS research results may be the result of focusing on a less-than-useful level of abstraction: GSS itself. This paper argues that in GSS research, the thinkLet may be a more useful unit of comparison than the GSS. A thinkLet encapsulates three components of a GSS stimulus: The tool, its configuration, and the script. Field experience shows that thinkLets may be used to create repeatable, predictable patterns of thinking among people making an effort toward a goal. To date we have documented about 60 thinkLets that map to seven basic patterns of thinking: Diverge, Converge, Organize, Elaborate, Abstract, Evaluate, and Build Consensus. Each thinkLet creates some unique variation on its basic pattern. By focusing research on thinkLets, rather than GSS, field and laboratory research may be more controllable, more replicable, and better able to inform GSS development and use.


title: Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

abstract: Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). However, it is also a challenging task due to the diversity of traffic scene and the storage, power and computing source limitations of the platforms for traffic scene applications. This paper presents a generalized Haar filter based deep network which is suitable for the object detection tasks in traffic scene. In this approach, we first decompose a object detection task into several easier local regression tasks. Then, we handle the local regression tasks by using several tiny deep networks which simultaneously output the bounding boxes, categories and confidence scores of detected objects. To reduce the consumption of storage and computing resources, the weights of the deep networks are constrained to the form of generalized Haar filter in training phase. Additionally, we introduce the strategy of sparse windows generation to improve the efficiency of the algorithm. Finally, we perform several experiments to validate the performance of our proposed approach. Experimental results demonstrate that the proposed approach is both efficient and effective in traffic scene compared with the state-of-the-art.


title: Extension of tunneling quality index and rock mass rating systems for tunnel support design through back calculations in highly stressed jointed rock mass: An empirical approach based on tunneling data from Himalaya

abstract: Abstract A preliminary support design is the basic output of an empirical rock mass classification system. Tunneling quality index (Q) and rock mass rating (RMR) systems are used specifically for tunnel support in moderately jointed and massive rocks; however, they do not provide any information/guidance for jointed rock masses in a highly stressed environment. Hence, this study focuses on the already supported drill and blast tunnel sections mapping data of four tunneling projects in Pakistan. The study extends the application of these systems to the tunnel support design for highly stressed jointed rock mass through an empirical approach. In each empirical approach, the parameters for the stress condition are suggested. Rock mass quality (Q or Qc) is determined from back calculations using an installed support and tunnel span. Empirical equations and charts are proposed for the stress reduction factor (SRF) characterization. In the proposed equations and charts, the SRF is a function of intact rock strength, relative block size, and strength–stress ratio. In the case of the RMR system, approximately 90% of the sections show that the actual supports are heavier than the suggested supports by RMR89. The RMR was determined from the installed support for each tunnel section through the back calculations. To select the adjustment rating factor for stress, three hypotheses are considered based on the intact rock strength to the major principal stress ratio. The RMR14 suggested support revealed that all sections are heavily supported. A strong correlation exists between RMR89 and RMR14. Three hypotheses are considered for the stress adjustment factor rating selection for RMR14 based on the correlation equation between RMR89 and RMR14. For the evaluation, the application of modified Q and RMR systems are used in the tunnel support in a case study. The exploration reports show that the tunnel will pass through a jointed rock mass under high in-situ stress environments. The comparison shows that heavy support is recommended from the modified systems for tunnel stability.