title: Time Synchronization Attack in Smart Grid-Part I: Impact and Analysis

abstract: Many operations in power grids, such as fault detection and event location estimation, depend on precise timing information. In this paper, a novel Time Synchronization Attack (TSA) is proposed to attack the timing information in smart grid. Since many applications in smart grid utilize synchronous measurements and most of the measurement devices are equipped with global positioning system (GPS) for precise timing, it is highly probable to attack the measurement system by spoofing the GPS. The effectiveness of TSA is demonstrated for three applications of phasor measurement unit (PMU) in smart grid, namely transmission line fault detection, voltage stability monitoring and event locationing. The validity of TSA is demonstrated by numerical simulations.


Towards Autonomous Navigation and Assembly using Visual Recognition


Health effects of viewing landscapes – Landscape types in environmental psychology


title: The mismeasure of morals: Antisocial personality traits predict utilitarian responses to moral dilemmas

abstract: Researchers have recently argued that utilitarianism is the appropriate framework by which to evaluate moral judgment, and that individuals who endorse non-utilitarian solutions to moral dilemmas (involving active vs. passive harm) are committing an error. We report a study in which participants responded to a battery of personality assessments and a set of dilemmas that pit utilitarian and non-utilitarian options against each other. Participants who indicated greater endorsement of utilitarian solutions had higher scores on measures of Psychopathy, machiavellianism, and life meaninglessness. These results question the widely-used methods by which lay moral judgments are evaluated, as these approaches lead to the counterintuitive conclusion that those individuals who are least prone to moral errors also possess a set of psychological characteristics that many would consider prototypically immoral.


title: OLTP through the looking glass, and what we found there

abstract: Online Transaction Processing (OLTP) databases include a suite of features - disk-resident B-trees and heap files, locking-based concurrency control, support for multi-threading - that were optimized for computer technology of the late 1970's. Advances in modern processors, memories, and networks mean that today's computers are vastly different from those of 30 years ago, such that many OLTP databases will now fit in main memory, and most OLTP transactions can be processed in milliseconds or less. Yet database architecture has changed little. Based on this observation, we look at some interesting variants of conventional database systems that one might build that exploit recent hardware trends, and speculate on their performance through a detailed instruction-level breakdown of the major components involved in a transaction processing database system (Shore) running a subset of TPC-C. Rather than simply profiling Shore, we progressively modified it so that after every feature removal or optimization, we had a (faster) working system that fully ran our workload. Overall, we identify overheads and optimizations that explain a total difference of about a factor of 20x in raw performance. We also show that there is no single "high pole in the tent" in modern (memory resident) database systems, but that substantial time is spent in logging, latching, locking, B-tree, and buffer management operations.


gpdsHMM : A HIDDEN MARKOV MODEL TOOLBOX IN THE MATLAB ENVIRONMENT


title: Implementation of Cyber-Physical Production Systems for Quality Prediction and Operation Control in Metal Casting

abstract: The prediction of internal defects of metal casting immediately after the casting process saves unnecessary time and money by reducing the amount of inputs into the next stage, such as the machining process, and enables flexible scheduling. Cyber-physical production systems (CPPS) perfectly fulfill the aforementioned requirements. This study deals with the implementation of CPPS in a real factory to predict the quality of metal casting and operation control. First, a CPPS architecture framework for quality prediction and operation control in metal-casting production was designed. The framework describes collaboration among internet of things (IoT), artificial intelligence, simulations, manufacturing execution systems, and advanced planning and scheduling systems. Subsequently, the implementation of the CPPS in actual plants is described. Temperature is a major factor that affects casting quality, and thus, temperature sensors and IoT communication devices were attached to casting machines. The well-known NoSQL database, HBase and the high-speed processing/analysis tool, Spark, are used for IoT repository and data pre-processing, respectively. Many machine learning algorithms such as decision tree, random forest, artificial neural network, and support vector machine were used for quality prediction and compared with R software. Finally, the operation of the entire system is demonstrated through a CPPS dashboard. In an era in which most CPPS-related studies are conducted on high-level abstract models, this study describes more specific architectural frameworks, use cases, usable software, and analytical methodologies. In addition, this study verifies the usefulness of CPPS by estimating quantitative effects. This is expected to contribute to the proliferation of CPPS in the industry.


title: Vector-based navigation using grid-like representations in artificial agents

abstract: Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go1,2. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning3–5 failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex6. Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space7,8 and is critical for integrating self-motion (path integration)6,7,9 and planning direct trajectories to goals (vector-based navigation)7,10,11. Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types12. We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments—optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation7,10,11, demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments. Grid-like representations emerge spontaneously within a neural network trained to self-localize, enabling the agent to take shortcuts to destinations using vector-based navigation.


title: Sustainability of meat-based and plant-based diets and the environment1–3

abstract: Worldwide, an estimated 2 billion people live primarily on a meat-based diet, while an estimated 4 billion live primarily on a plant-based diet. The US food production system uses about 50% of the total US land area, 80% of the fresh water, and 17% of the fossil energy used in the country. The heavy dependence on fossil energy suggests that the US food system, whether meat-based or plant-based, is not sustainable. The use of land and energy resources devoted to an average meat-based diet compared with a lactoovovegetarian (plant-based) diet is analyzed in this report. In both diets, the daily quantity of calories consumed are kept constant at about 3533 kcal per person. The meat-based food system requires more energy, land, and water resources than the lactoovovegetarian diet. In this limited sense, the lactoovovegetarian diet is more sustainable than the average American meat-based diet.


title: A novel data glove for fingers motion capture using inertial and magnetic measurement units

abstract: A novel data glove embedded low cost MEMS inertial and magnetic measurement units, is proposed for fingers motion capture. Each unit consists of a tri-axial gyroscope, a tri-axial accelerometer and a tri-axial magnetometer. The sensor board and processor board are compactly designed, which are small enough to fit the size of our fingers. The data glove is equipped with fifteen units to measure each joint angle of the fingers. Then the calibration approach is put up to improve the accuracy of measurements by both offline and online procedures, and a fast estimation method is used to determine the three orientations of fifteen units simultaneously. The proposed algorithm is easy to be implemented, and more precise and efficient measurements can be obtained as compared with existing methods. The fingers motion capture experiments are implemented to acquire the characteristics of the fingers and teleoperate the robotic hands, which prove the effectiveness of the data glove.


title: Optical eye simulator for laser dazzle events

abstract: An optical simulator of the human eye and its application to laser dazzle events are presented. The simulator combines optical design software (ZEMAX) with a scientific programming language (MATLAB) and allows the user to implement and analyze a dazzle scenario using practical, real-world parameters. Contrary to conventional analytical glare analysis, this work uses ray tracing and the scattering model and parameters for each optical element of the eye. The theoretical background of each such element is presented in relation to the model. The overall simulator’s calibration, validation, and performance analysis are achieved by comparison with a simpler model based uponCIE disability glare data. Results demonstrate that this kind of advanced optical eye simulation can be used to represent laser dazzle and has the potential to extend the range of applicability of analytical models.


title: Induction of liver monooxygenases by beta-myrcene.

abstract: Beta-Myrcene (MYR) is an acyclic monoterpene found in the essential oils of a variety of useful plants such as lemongrass (Cymbopogon citratus), hop, verbena, bay and others. MYR and essential oils containing this olefinic monoterpene are widely used as flavoring food additives, as fragrances in cosmetics and as scents in household products. The present study was undertaken to investigate the induction of liver monooxygenases by MYR. Female Wistar rats were treated by gavage with MYR (1000 mg/kg body weight) or corn oil (vehicle) for 1 or 3 consecutive days. Activities of ethoxycoumarin-O-deethylase (ECOD) and alkoxy-resorufin O-dealkylases (methoxy- (MROD), ethoxy- (EROD), pentoxy- (PROD) and benzyloxy-resorufin-O-dealkylation (BROD)) were determined fluorimetrically in the hepatic microsomal fraction. Exposure to MYR, either for 1 or 3 days, produced marked (13-34-fold) increases in the activities of PROD and BROD and only minor changes in ECOD, EROD and MROD. Since PROD and BROD are metabolized mainly by CYP2B isoenzymes, these results suggest that MYR induces this phenobarbital-inducible P450 subfamily. The induction of CYP2B isoenzymes was confirmed by SDS-PAGE and immunoblotting. Levels of apoproteins CYP2B1/2B2 were increased 8.2-fold after treatment with MYR (1000 mg/kg body wt, 3 days). Results from this study therefore indicate that MYR is an inducer of isoenzymes belonging to CYP2B subfamily.


title: Adversarial Training for Community Question Answer Selection Based on Multi-scale Matching

abstract: Community-based question answering (CQA) websites represent an important source of information. As a result, the problem of matching the most valuable answers to their corresponding questions has become an increasingly popular research topic. We frame this task as a binary (relevant/irrelevant) classification problem, and present an adversarial training framework to alleviate label imbalance issue. We employ a generative model to iteratively sample a subset of challenging negative samples to fool our classification model. Both models are alternatively optimized using REINFORCE algorithm. The proposed method is completely different from previous ones, where negative samples in training set are directly used or uniformly down-sampled. Further, we propose using Multi-scale Matching which explicitly inspects the correlation between words and ngrams of different levels of granularity. We evaluate the proposed method on SemEval 2016 and SemEval 2017 datasets and achieves state-of-the-art or similar performance.


title: Unit test virtualization with VMVM

abstract: Testing large software packages can become very time intensive. To address this problem, researchers have investigated techniques such as Test Suite Minimization. Test Suite Minimization reduces the number of tests in a suite by removing tests that appear redundant, at the risk of a reduction in fault-finding ability since it can be difficult to identify which tests are truly redundant. We take a completely different approach to solving the same problem of long running test suites by instead reducing the time needed to execute each test, an approach that we call Unit Test Virtualization. With Unit Test Virtualization, we reduce the overhead of isolating each unit test with a lightweight virtualization container. We describe the empirical analysis that grounds our approach and provide an implementation of Unit Test Virtualization targeting Java applications. We evaluated our implementation, VMVM, using 20 real-world Java applications and found that it reduces test suite execution time by up to 97% (on average, 62%) when compared to traditional unit test execution. We also compared VMVM to a well known Test Suite Minimization technique, finding the reduction provided by VMVM to be four times greater, while still executing every test with no loss of fault-finding ability.


title: CODE REUSE IN OPEN SOURCE SOFTWARE

abstract: Code reuse is a form of knowledge reuse in software development that is fundamental to innovation in many fields. However, to date there has been no systematic investigation of code reuse in open source software projects. This study uses quantitative and qualitative data gathered from a sample of six open source software projects to explore two sets of research questions derived from the literature on software reuse in firms and open source software development. We find that code reuse is extensive across the sample and that open source software developers, much like developers in firms, apply tools that lower their search costs for knowledge and code, assess the quality of software components, and have incentives to reuse code. Open source software developers reuse code because they want to integrate functionality quickly, because they want to write preferred code, because they operate under limited resources in terms of time and skills, and because they can mitigate development costs through code reuse.


title: Benchmarking personal cloud storage

abstract: Personal cloud storage services are data-intensive applications already producing a significant share of Internet traffic. Several solutions offered by different companies attract more and more people. However, little is known about each service capabilities, architecture and -- most of all -- performance implications of design choices. This paper presents a methodology to study cloud storage services. We apply our methodology to compare 5 popular offers, revealing different system architectures and capabilities. The implications on performance of different designs are assessed executing a series of benchmarks. Our results show no clear winner, with all services suffering from some limitations or having potential for improvement. In some scenarios, the upload of the same file set can take seven times more, wasting twice as much capacity. Our methodology and results are useful thus as both benchmark and guideline for system design.


title: Measuring Soil Moisture with Imaging Radars

abstract: An empirical algorithm for the retrieval of soil moisture content and surface root mean square (RMS) height from remotely sensed radar data was developed using scatterometer data. The algorithm is optimized for bare surfaces and requires two copolarized channels at a frequency between 1.5 and 11 GHz. It gives best results for kh/spl les/2.5, /spl mu//sub /spl upsi///spl les/35%, and /spl theta//spl ges/30/spl deg/. Omitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplifies the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI>0.4) cause the algorithm to underestimate soil moisture and overestimate RMS height. A simple criteria based on the /spl sigma//sub hv//sup 0///spl sigma//sub vv//sup 0/ ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometer data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1991 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 4.2% soil moisture. >


title: Visual Semantic Planning Using Deep Successor Representations

abstract: A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world. In this work, we address the problem of visual semantic planning: the task of predicting a sequence of actions from visual observations that transform a dynamic environment from an initial state to a goal state. Doing so entails knowledge about objects and their affordances, as well as actions and their preconditions and effects. We propose learning these through interacting with a visual and dynamic environment. Our proposed solution involves bootstrapping reinforcement learning with imitation learning. To ensure cross task generalization, we develop a deep predictive model based on successor representations. Our experimental results show near optimal results across a wide range of tasks in the challenging THOR environment.


title: A Compact High-Performance Orthomode Transducer for the Atacama Large Millimeter Array (ALMA) Band 1 (31–45 GHz)

abstract: This paper describes a compact high-performance orthomode transducer (OMT) with a circular waveguide input and two rectangular waveguide outputs based on the superimposition of three aluminum blocks. Several prototypes operating in the band 1 (31-45 GHz) of the atacama large millimeter array have been fabricated and measured. The design is based on the use of a turnstile junction that is machined in a single block, requiring neither alignment nor a high degree of mechanical tolerances. Thus, a high repeatability of the design is possible for mass production. Across the 31-45 GHz band, the isolation is better than 50 dB and the return losses at the input and outputs of the OMT are better than -25 dB.


title: Observed versus latent features for knowledge base and text inference

abstract: In this paper we show the surprising effectiveness of a simple observed features model in comparison to latent feature models on two benchmark knowledge base completion datasets, FB15K and WN18. We also compare latent and observed feature models on a more challenging dataset derived from FB15K, and additionally coupled with textual mentions from a web-scale corpus. We show that the observed features model is most effective at capturing the information present for entity pairs with textual relations, and a combination of the two combines the strengths of both model types.


title: Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications

abstract: Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.


title: Efficient, Feature-based, Conditional Random Field Parsing

abstract: Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior feature-based dynamic programming parsers have restricted training and evaluation to artificially short sentences, we present the first general, featurerich discriminative parser, based on a conditional random field model, which has been successfully scaled to the full WSJ parsing data. Our efficiency is primarily due to the use of stochastic optimization techniques, as well as parallelization and chart prefiltering. On WSJ15, we attain a state-of-the-art F-score of 90.9%, a 14% relative reduction in error over previous models, while being two orders of magnitude faster. On sentences of length 40, our system achieves an F-score of 89.0%, a 36% relative reduction in error over a generative baseline.


title: Basic Religious Beliefs and Personality Traits

abstract: OBJECTIVE ::: Spiritual beliefs can help people find meaning of life, and can also influence their feelings, behaviors and mental health. The present research studied the relationship between basic religious beliefs (Human, Existence and God) and five personality factors: neuroticism, extraversion, openness, agreeableness, conscientiousness. ::: ::: ::: METHOD ::: One hundred seventy eight students of Islamic Azad University in Torbat-jam were randomly selected and completed the basic religious beliefs and NEO Questionnaires. ::: ::: ::: RESULTS ::: Data showed that basic religious beliefs have a significant negative correlation with neuroticism (r=-0.29),and a significant positive relationship with extraversion(r=0.28),openness(r=0.14),agreeableness (r=0.29),and conscientiousness (r=0.48). Also, the results of the regression analysis showed that basic religious beliefs can anticipate neuroticism, extraversion, agreeableness and conscientiousness, but they cannot anticipate the openness factor significantly. ::: ::: ::: CONCLUSION ::: The findings of this study demonstrate that basic religious beliefs have a positive relationship with good characteristics that help people resolve the challenges of their lives and identity crisis. Thus, the results of this study support the idea of Religious Cognitive-Emotional Theory that religiosity is correlated with positive personality traits.


Active Reward Learning


Using Background Knowledge in Multilayer Perceptron Learning


Unsupervised Adaptive Event Detection for Building-Level Energy Disaggregation


title: Lake Erie Nearshore Water Chemistry at Nanticoke, Ontario, 1969–1978

abstract: Abstract The Nanticoke Environmental Committee has monitored nearshore Lake Erie water chemistry at Nanticoke, Ontario, for 10 years (1969–1978) as part of a description of the environment existing before industrial development. Annual means for all parameters satisfied Ontario and IJC water quality objectives for at least the last 4 years. Cluster analysis showed that water chemistry was similar at all Nanticoke sampling stations but that the stations fell into offshore and nearshore groups. Discriminant analysis revealed that Secchi disk transparency and total Kjeldahl nitrogen were the principal contributors to separation of stations into groups. Levels of most parameters varied with the seasons. Least squares regression analysis showed that levels of most parameters remained the same or decreased during the last 10 years. Only phytoplankton density increased significantly since 1969.


title: Single and Multiple Illuminant Estimation Using Convolutional Neural Networks

abstract: In this paper, we present a three-stage method for the estimation of the color of the illuminant in RAW images. The first stage uses a convolutional neural network that has been specially designed to produce multiple local estimates of the illuminant. The second stage, given the local estimates, determines the number of illuminants in the scene. Finally, local illuminant estimates are refined by non-linear local aggregation, resulting in a global estimate in case of single illuminant. An extensive comparison with both local and global illuminant estimation methods in the state of the art, on standard data sets with single and multiple illuminants, proves the effectiveness of our method.


title: A Measurement Study of Internet Delay Asymmetry

abstract: RTT has been widely used as a metric for peer/server selection. However, many applications involving closest peer/server selection such as streaming, tree-based multicast services and other UDP and TCP based services would benefit more from knowing one-way delay (OWD) rather than RTT. In fact, RTT is frequently used as as an approximate solution to infer forward and reverse delays by many protocols and applications which assume forward and reverse delay to be equal to half of RTT. ::: ::: In this paper, we compare and contrast one-way delays and corresponding RTTs using a wide selection of routes in the Internet. We first measure the extent and severeness of asymmetry in forward and reverse OWD in the Internet. We then attempt to isolate the causes of OWD asymmetry by correlating OWD asymmetry with the route asymmetry. Finally, we investigate the dynamics of delay asymmetry. We find there exists a weak correlation between the fluctuation of RTT and OWD but a strong correlation between OWD change and the corresponding route change.


title: Visibility Histograms and Visibility-Driven Transfer Functions

abstract: Direct volume rendering is an important tool for visualizing complex data sets. However, in the process of generating 2D images from 3D data, information is lost in the form of attenuation and occlusion. The lack of a feedback mechanism to quantify the loss of information in the rendering process makes the design of good transfer functions a difficult and time consuming task. In this paper, we present the general notion of visibility histograms, which are multidimensional graphical representations of the distribution of visibility in a volume-rendered image. In this paper, we explore the 1D and 2D transfer functions that result from intensity values and gradient magnitude. With the help of these histograms, users can manage a complex set of transfer function parameters that maximize the visibility of the intervals of interest and provide high quality images of volume data. We present a semiautomated method for generating transfer functions, which progressively explores the transfer function space toward the goal of maximizing visibility of important structures. Our methodology can be easily deployed in most visualization systems and can be used together with traditional 1D and 2D opacity transfer functions based on scalar values, as well as with other more sophisticated rendering algorithms.


title: Using semantic data to improve cross-lingual linking of article clusters

abstract: This paper presents a system that uses semantic data to improve cross-lingual linking of news article clusters. Two approaches are compared. The first based on two different Canonical Correlation Analysis (CCA) feature vector definitions: MAX-CCA and SUM-CCA, whereas the second one has been developed using a better-performed CCA approach in combination with Entity vectors. The aim of the comparison was to determine whether taking into account the semantic aspect of news increases performance and improves linking. Evaluations of the aforementioned techniques on a news corpus, both against Google News and manual, revealed good performance of our system. The overall gain in precision and recall when using entity vectors was significant.


A Survey of Artificial Intelligence for Prognostics


title: NLP4NLP: Applying NLP to Scientific Corpora about Written and Spoken Language Processing

abstract: Analyzing the evolutions of the trends of a scientific domain in order to provide insights on its states and to establish reliable hypotheses about its future is the problem we address here. We have approached the problem by processing both the metadata and the text contents of the domain publications. Ideally, one would like to be able to automatically synthesize all the information present in the documents and their metadata. As members of the NLP community, we have applied the tools developed by our community to publications from our own domain, in what could be termed a “recursive” approach. In a first step, we have assembled a corpus of papers from NLP conferences and journals for both text and speech, covering documents produced from the 60’s up to 2015. Then , we have mined our scientific publication database to draw a picture of our field from quantitative and qualitative results according to a wide range of perspectives: ranging from sub-domains, specific communities, chronology, terminology, conceptual evolution, re-use and plagiarism, trend prediction, novelty detection and many more. We provide here an account of the corpus collection and of its processing with NLP technology, indicating for each aspect which technology was used. We conclude on the benefits brought by such corpus to the actors of the domain and on the conditions to generalize this approach to other scientific domains. Conference Topics Methods and techniques, Citation and co-citation analysis, Scientific fraud and dishonesty, Natural Language Processing


title: Methods & tools: The activity checklist: a tool for representing the “space” of context

abstract: An aluminum-silicon brazing composition containing about 0.02 to 0.20% bismuth by weight, with silicon in the range from about 7 to 14% and magnesium in the range from about 0.2 to 2%; preferably employed as a cladding layer on a magnesium-containing aluminum base core alloy.


title: Serum Interleukin-27 Level in Different Clinical Stages of Lung Cancer

abstract: BACKGROUND ::: Advanced lung cancer is indicated with rapid disease development. Interleukin 27 (IL-27) is regarded as a cytokine with anti-tumour activities. ::: ::: ::: AIM ::: Since, the impact of type of lung cancer on the level of IL-27 in patient's serum has not yet been investigated; current study evaluated the clinical stages according to American Joint Committee on Cancer (AJCC) criteria, Tumor-Node-Metastasis (TNM) stage and the lung cancer spread (localized or widespread) and it's correlation with serum IL-27.MATERIAL AND METHODS: Thirty patients with confirmed histopathological lung cancer and 30 cancer-free healthy individuals as the control group were included in the current study. Patients group were assigned to either small cell lung cancer group (SCLC) or non-small cell lung cancer (NSCLC) according to the clinical features and the results of lung biopsy specimens. Level of IL-27 was quantified with enzyme-linked immunosorbent assay (ELISA) test in serum samples. ::: ::: ::: RESULTS ::: A significant increase in serum IL-27 level was noticed in individuals with lung cancer in comparison with the control group. The level of serum IL-27 in the NSCL squamous carcinoma (NSCLC-Sc) type was significantly greater than in the NSCLC adenocarcinoma (NSCLC-Ad) type, and in both groups, this variable was more than the control group. The serum IL-27 content level was greater in stage III versus stage IV. ::: ::: ::: CONCLUSION ::: The current research confirmed the existence of the anti-tumour components in patients with NSCLC. IL-27 can be utilised in diagnosis and screening in early stages of lung cancer along with the management of patients. Different levels of IL-27 in different types of lung cancers in the current study can lead to design more comprehensive studies in the future.


title: A real-time inversion attack on the GMR-2 cipher used in the satellite phones

abstract: The GMR-2 cipher is a type of stream cipher currently being used in some inmarsat satellite phones. It has been proven that such a cipher can be cracked using only one single-frame (15 bytes) known keystream but with moderate executing time. In this paper, we present a new thorough security analysis of the GMR-2 cipher. We first study the inverse properties of the cipher’s components to reveal a bad one-way character of the cipher. By then introducing a new concept called “valid key chain” according to the cipher’s key schedule, we propose an unprecedented real-time inversion attack using a single-frame keystream. This attack comprises three phases: (1) table generation; (2) dynamic table look-up, filtration and combination; and (3) verification. Our analysis shows that, using the proposed attack, the size of the exhaustive search space for the 64-bit encryption key can be reduced to approximately 213 when a single-frame keystream is available. Compared with previous known attacks, this inversion attack is much more efficient. Finally, the proposed attack is carried out on a 3.3-GHz PC, and the experimental results thus obtained demonstrate that the 64-bit encryption-key could be recovered in approximately 0.02 s on average.


title: The neurobiological nature of syntactic hierarchies

abstract: Abstract The review focuses on the neurobiological literature concerning the specific human ability to process linguistic hierarchies. First, we will discuss current ethological studies dedicated to the comparison between human and non-human animals for the processing of different grammar types. We will inspect the functional neuroanatomical structures of human and non-human primates more closely, including human developmental data, thereby suggesting interesting phylogenetic and ontogenetic differences. We then examine the neural reality of the Merge computation, being the most fundamental mechanism regulating natural language syntax, and offer new evidence for a possible localization of Merge in the most ventral anterior portion of BA 44. We conclude that BA 44, with its strong neural connection to the posterior temporal cortex, provides a recent evolutionary neurobiological basis for the unique human faculty of language.


title: BRIDGING THE GAP BETWEEN BUSINESS STRATEGY AND SOFTWARE DEVELOPMENT Information Systems Strategy and Governance

abstract: In software -intensive organizations, a n organizational management system will not guarantee o rganization al success unless the business strategy can be translated into a set of operational sof tware goals. The Goal Question Metric (GQM) approach has proven itself useful in a variety of i ndustrial settings to support quantitative software project management. Ho wever, it does not a ddress link ing software measurement goals to higher -level goals of the organization in which the software is being developed. This linkage is important, as it helps to justify software measurement efforts and allows measurement data to contribute to higher -level decisions. In this paper , we pr opose a GQM + Strategies � measurement approach that builds on the GQM approach to plan and implement software measurement. GQM + Strategies ® provides mechanisms for explicitly linking software measureme nt goals to higher -level goals for the software organization, and further to goals and strategies at t he level of the entire business . An e xample application of the proposed method is illustrated in the context of an example mea surement initiative. Keyword s: Goal -oriented Measurement, GQM, GQM + Strategies ®


title: Sparse Multi-Modal Hashing

abstract: Learning hash functions across heterogenous high-dimensional features is very desirable for many applications involving multi-modal data objects. In this paper, we propose an approach to obtain the sparse codesets for the data objects across different modalities via joint multi-modal dictionary learning, which we call sparse multi-modal hashing (abbreviated as SM2H). In SM2H, both intra-modality similarity and inter-modality similarity are first modeled by a hypergraph, then multi-modal dictionaries are jointly learned by Hypergraph Laplacian sparse coding. Based on the learned dictionaries, the sparse codeset of each data object is acquired and conducted for multi-modal approximate nearest neighbor retrieval using a sensitive Jaccard metric. The experimental results show that SM2H outperforms other methods in terms of mAP and Percentage on two real-world data sets.


title: A 200 atto farad capacitance sensing with a differential signaling method for a mutual capacitive finger-print sensors

abstract: This paper describes an atto farad capacitive sensing system for a finger-print sensor. The architecture of mutual capacitive finger-print sensor is obtained by investigating the capacitance change by the finger-print based on the Q3D Extractor model. An electrostatic simulation shows that the 200aF difference caused by the valley and the ridge in the sensor. In order to sense the atto farad capacitance in finger-print sensor, a continuous-type fully differential architecture with a differential transmitted signals is proposed to amplify a capacitance difference only with a reduced the parasitic effect caused by a large number of electrodes. The prototype IC with 0.18um CMOS achieves 300mV/200aF.


title: Pareto Multi Objective Optimization

abstract: The goal of this chapter is to give fundamental knowledge on solving multi-objective optimization problems. The focus is on the intelligent metaheuristic approaches (evolutionary algorithms or swarm-based techniques). The focus is on techniques for efficient generation of the Pareto frontier. A general formulation of MO optimization is given in this chapter, the Pareto optimality concepts introduced, and solution approaches with examples of MO problems in the power systems field are given


title: Are digital natives a myth or reality? University students' use of digital technologies

abstract: This study investigated the extent and nature of university students' use of digital technologies for learning and socialising. The findings show that students use a limited range of mainly established technologies. Use of collaborative knowledge creation tools, virtual worlds, and social networking sites was low. 'Digital natives' and students of a technical discipline (Engineering) used more technology tools when compared to 'digital immigrants' and students of a non-technical discipline (Social Work). This relationship may be mediated by the finding that Engineering courses required more intensive and extensive access to technology than Social Work courses. However, the use of technology between these groups is only quantitatively rather than qualitatively different. The study did not find evidence to support popular claims that young people adopt radically different learning styles. Their attitudes to learning appear to be influenced by lecturers' teaching approaches. Students appear to conform to traditional pedagogies, albeit with minor uses of tools delivering content. The outcomes suggest that although the calls for transformations in education may be legitimate it would be misleading to ground the arguments for such change in students' shifting patterns of learning and technology use.


title: Adversarial Examples Are a Natural Consequence of Test Error in Noise

abstract: Over the last few years, the phenomenon of adversarial examples --- maliciously constructed inputs that fool trained machine learning models --- has captured the attention of the research community, especially when the adversary is restricted to small modifications of a correctly handled input. Less surprisingly, image classifiers also lack human-level performance on randomly corrupted images, such as images with additive Gaussian noise. In this paper we provide both empirical and theoretical evidence that these are two manifestations of the same underlying phenomenon, establishing close connections between the adversarial robustness and corruption robustness research programs. This suggests that improving adversarial robustness should go hand in hand with improving performance in the presence of more general and realistic image corruptions. Based on our results we recommend that future adversarial defenses consider evaluating the robustness of their methods to distributional shift with benchmarks such as Imagenet-C.


title: Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector

abstract: Training a deep object detector for autonomous driving requires a huge amount of labeled data. While recording data via on-board sensors such as camera or LiDAR is relatively easy, annotating data is very tedious and time-consuming, especially when dealing with 3D LiDAR points or radar data. Active learning has the potential to minimize human annotation efforts while maximizing the object detector's performance. In this work, we propose an active learning method to train a LiDAR 3D object detector with the least amount of labeled training data necessary. The detector leverages 2D region proposals generated from the RGB images to reduce the search space of objects and speed up the learning process. Experiments show that our proposed method works under different uncertainty estimations and query functions, and can save up to 60% of the labeling efforts while reaching the same network performance.


title: CommunitySourcing: engaging local crowds to perform expert work via physical kiosks

abstract: Online labor markets, such as Amazon's Mechanical Turk, have been used to crowdsource simple, short tasks like image labeling and transcription. However, expert knowledge is often lacking in such markets, making it impossible to complete certain classes of tasks. In this work we introduce an alternative mechanism for crowdsourcing tasks that require specialized knowledge or skill: communitysourcing --- the use of physical kiosks to elicit work from specific populations. We investigate the potential of communitysourcing by designing, implementing and evaluating Umati: the communitysourcing vending machine. Umati allows users to earn credits by performing tasks using a touchscreen attached to the machine. Physical rewards (in this case, snacks) are dispensed through traditional vending mechanics. We evaluated whether communitysourcing can accomplish expert work by using Umati to grade Computer Science exams. We placed Umati in a university Computer Science building, targeting students with grading tasks for snacks. Over one week, 328 unique users (302 of whom were students) completed 7771 tasks (7240 by students). 80% of users had never participated in a crowdsourcing market before. We found that Umati was able to grade exams with 2% higher accuracy (at the same price) or at 33% lower cost (at equivalent accuracy) than traditional single-expert grading. Mechanical Turk workers had no success grading the same exams. These results indicate that communitysourcing can successfully elicit high-quality expert work from specific communities.


title: 8.3 M-Pixel 480-fps Global-Shutter CMOS Image Sensor with Gain-Adaptive Column ADCs and Chip-on-Chip Stacked Integration

abstract: This paper presents a 4K2K 480-fps global-shutter CMOS image sensor with a super 35-mm format for a highly realistic digital video system. The sensor employs newly developed gain-adaptive column analog-to-digital converters to obtain input-referred dark random noise of 140 $\mu \text{V}_{\mathrm {rms}}$ for an input-referred full-scale readout of 923 mV. An on-chip online correction of the error between two switchable gains maintained the nonlinearity of the output image within 0.18%. A chip-on-chip integration process realized a front-illuminated image sensor stacked with two diced logic chips through 38-K microbump interconnections. The global-shutter pixel achieved a parasitic light sensitivity of −99.6 dB. The 16-channel output interfaces with 4.752 Gbps/ch were implemented in the stacked logic chips.


title: Crowdlet: Optimal worker recruitment for self-organized mobile crowdsourcing

abstract: In this paper, we advocate Crowdlet, a novel self-organized mobile crowdsourcing paradigm, in which a mobile task requester can proactively exploit a massive crowd of encountered mobile workers at real-time for quick and high-quality results. We present a comprehensive system model of Crowdlet that defines task, worker arrival and worker ability models. Further, we introduce a service quality concept to indicate the expected service gain that a requester can enjoy when he recruits an encountered worker, by jointly taking into account worker ability, real-timeness and task reward. Based on the models, we formulate an online worker recruitment problem to maximize the expected sum of service quality. We derive an optimal worker recruitment policy through the dynamic programming principle, and show that it exhibits a nice threshold based structure. We conduct extensive performance evaluation based on real traces, and numerical results demonstrate that our policy can achieve superior performance and improve more than 30% performance gain over classic policies. Besides, our Android prototype shows that Crowdlet is cost-efficient, requiring less than 7 seconds and 6 Joule in terms of time and energy cost for policy computation in most cases.


title: Ten Simple Rules for Developing Usable Software in Computational Biology

abstract: The rise of high-throughput technologies in molecular biology has led to a massive amount of publicly available data. While computational method development has been a cornerstone of biomedical research for decades, the rapid technological progress in the wet lab makes it difficult for software development to keep pace. Wet lab scientists rely heavily on computational methods, especially since more research is now performed in silico. However, suitable tools do not always exist, and not everyone has the skills to write complex software. Computational biologists are required to close this gap, but they often lack formal training in software engineering. To alleviate this, several related challenges have been previously addressed in the Ten Simple Rules series, including reproducibility [1], effectiveness [2], and open-source development of software [3, 4]. ::: ::: Here, we want to shed light on issues concerning software usability. Usability is commonly defined as “a measure of interface quality that refers to the effectiveness, efficiency, and satisfaction with which users can perform tasks with a tool” [5]. Considering the subjective nature of this topic, a broad consensus may be hard to achieve. Nevertheless, good usability is imperative for achieving wide acceptance of a software tool in the community. In many cases, academic software starts out as a prototype that solves one specific task and is not geared for a larger user group. As soon as the developer realizes that the complexity of the problems solved by the software could make it widely applicable, the software will grow to meet the new demands. At least by this point, if not sooner, usability should become a priority. Unfortunately, efforts in scientific software development are constrained by limited funding, time, and rapid turnover of group members. As a result, scientific software is often poorly documented, non-intuitive, non-robust with regards to input data and parameters, and hard to install. For many use cases, there is a plethora of tools that appear very similar and make it difficult for the user to select the one that best fits their needs. Not surprisingly, a substantial fraction of these tools are probably abandonware; i.e., these are no longer actively developed or supported in spite of their potential value to the scientific community. ::: ::: To our knowledge, software development as part of scientific research is usually carried out by individuals or small teams with no more than two or three members. Hence, the responsibility of designing, implementing, testing, and documenting the code rests on few shoulders. Additionally, there is pressure to produce publishable results or, at least, to contribute analysis work to ongoing projects. Consequently, academic software is typically released as a prototype. We acknowledge that such a tool cannot adhere to and should not be judged by the standards that we take for granted for production grade software. However, widespread use of a tool is typically in the interest of a researcher. To this end, we propose ten simple rules that, in our experience, have a considerable impact on improving usability of scientific software.


title: Review on Dynamic Contour Tonometry and Ocular Pulse Amplitude

abstract: Intraocular pressure (IOP) measurement is the cornerstone of the management of glaucoma patients. The gold standard for assessing IOP is Goldmann applanation tonometry (GAT). Recently, the dynamic contour tonometer (DCT) has become available. While both devices provide reliable IOP measurements, the results are not interchangeable. DCT has the advantage of measuring an additional parameter: ocular pulse amplitude (OPA). OPA is defined as the difference between systolic and diastolic IOP and represents the pulsatile wave front produced by the varying amount of blood in the eye during the cardiac cycle. It has been shown to vary with ocular structural parameters, such as axial length, corneal thickness, and ocular rigidity, as well as with systemic variables like heart rate, blood pressure, and left ventricular ejection fraction. Although the existence of some of these associations is still controversial, the clinical relevance of OPA has been consistently suggested, especially in glaucoma. Further research on this intriguing parameter could not only provide insight into glaucoma pathophysiology but also help integrate this variable into clinical practice.


title: Sentiment Transfer using Seq2Seq Adversarial Autoencoders

abstract: Expressing in language is subjective. Everyone has a different style of reading and writing, apparently it all boil downs to the way their mind understands things (in a specific format). Language style transfer is a way to preserve the meaning of a text and change the way it is expressed. Progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data, use cases, and reliable evaluation metrics. In response to the challenge of lacking parallel data, we explore learning style transfer from non-parallel data. We propose a model combining seq2seq, autoencoders, and adversarial loss to achieve this goal. The key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. Considering the problem of evaluating style transfer tasks, we frame the problem as sentiment transfer and evaluation using a sentiment classifier to calculate how many sentiments was the model able to transfer. We report our results on several kinds of models.


title: A Discriminative Feature Learning Approach for Deep Face Recognition

abstract: Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state-of-the-art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss, for face recognition task. Specifically, the center loss simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers. More importantly, we prove that the proposed center loss function is trainable and easy to optimize in the CNNs. With the joint supervision of softmax loss and center loss, we can train a robust CNNs to obtain the deep features with the two key learning objectives, inter-class dispension and intra-class compactness as much as possible, which are very essential to face recognition. It is encouraging to see that our CNNs (with such joint supervision) achieve the state-of-the-art accuracy on several important face recognition benchmarks, Labeled Faces in the Wild (LFW), YouTube Faces (YTF), and MegaFace Challenge. Especially, our new approach achieves the best results on MegaFace (the largest public domain face benchmark) under the protocol of small training set (contains under 500000 images and under 20000 persons), significantly improving the previous results and setting new state-of-the-art for both face recognition and face verification tasks.


title: Numerical Study on AC Loss Properties of HTS Cable Consisting of YBCO Coated Conductor for HTS Power Devices

abstract: High-current high temperature superconducting (HTS) cables have been developed for use in HTS power devices. This paper presented the structures of HTS cables, including Conductor on Round Core (CORC) cable, Twisted Stacked-tape Conductor (TSTC) cable, and Double coaxial cable. Subsequently, three-dimensional finite element method numerical models were built to analyze the electromagnetic characteristics of the cables, and the critical current of the cables is about 380 Ampere @77 K, self-field. Using the T-A formulation, the numerical model assumed a sheet approximation for conductors, which shortened computational time. The T-A formulation were verified by experiments on a superconducting tape. Then HTS cables with different configurations were made, as functions of different transport current and background magnetic field, and different pitches of Double Coaxial Cable inner conducting layer. According to the results, the ac losses of Double coaxial cable and CORC cable decreased 40% than the TSTC cable with different transport current, and the Double coaxial cable ac loss decreased 20% than the CORC cable when background magnetic field was in the range of 20-60 mT. Conclusions obtained from this study will be helpful for understanding the ac loss properties of HTS cables and useful in design of HTS power devices (such as HTS transformer), using HTS cables.


title: Model Selection and Parameter Inference in Phylogenetics Using Nested Sampling

abstract: Bayesian inference methods rely on numerical algorithms for both model selection and parameter inference. In general, these algorithms require a high computational effort to yield reliable estimates. One of the major challenges in phylogenetics is the estimation of the marginal likelihood. This quantity is commonly used for comparing different evolutionary models, but its calculation, even for simple models, incurs high computational cost. Another interesting challenge relates to the estimation of the posterior distribution. Often, long Markov chains are required to get sufficient samples to carry out parameter inference, especially for tree distributions. In general, these problems are addressed separately by using different procedures. Nested sampling (NS) is a Bayesian computation algorithm, which provides the means to estimate marginal likelihoods together with their uncertainties, and to sample from the posterior distribution at no extra cost. The methods currently used in phylogenetics for marginal likelihood estimation lack in practicality due to their dependence on many tuning parameters and their inability of most implementations to provide a direct way to calculate the uncertainties associated with the estimates, unlike NS. In this article, we introduce NS to phylogenetics. Its performance is analysed under different scenarios and compared to established methods. We conclude that NS is a competitive and attractive algorithm for phylogenetic inference. An implementation is available as a package for BEAST 2 under the LGPL licence, accessible at https://github.com/BEAST2-Dev/nested-sampling.


title: Mental Model Updating Process in Air Traffic Control

abstract: This contribution is part of a broader research conducted at the CENA (French Research Center for Civil Aviation) to provide a better understanding of air traffic control cognitive activity while evaluating a new decision support system for air traffic controllers called Erato. Our objective is to show that the ability to handle the traffic implies for controllers to update their mental model of the situation in order to synchronize their action to the dynamic environment.


title: Evaluation of School-Based Occupational Therapy Programs: Need, Strategy, and Dissemination

abstract: This paper has identified the need for evaluation research regarding school-based occupational therapy programs, proposed a strategy for the current and future evaluation of school-based occupational therapy programs, and discussed the need and rationale for the process of dissemination as a component of such evaluation research. The purpose of evaluation research is to maintain and improve school-based occupational therapy programs. If carried out, evaluation research will not have a sudden or significant impact on school personnel, but more likely it will foster a "gradual, incremental" (Aiken & Daillak, 1979, p. 48) support for school-based occupational therapy programs.


Structure of woody riparian vegetation in Great Basin National Park


title: EGT for multiple view geometry and visual servoing: robotics vision with pinhole and panoramic cameras

abstract: The Epipolar Geometry Toolbox (EGT) for MATLAB is a software package targeted to research and education in computer vision and robotics visual servoing. It provides the user with a wide set of functions for designing multicamera systems for both pinhole and panoramic cameras. Functions include camera placement and visualization, computation, and estimation of epipolar geometry entities. The compatibility of EGT with the Robotics Toolbox enables users to address general vision-based control issues. Two applications of EGT to visual servoing tasks are examined in this article. Several epipolar geometry estimation algorithms have been implemented.


title: Enumeration versus multiple object tracking: the case of action video game players

abstract: Here, we demonstrate that action video game play enhances subjects’ ability in two tasks thought to indicate the number of items that can be apprehended. Using an enumeration task, in which participants have to determine the number of quickly flashed squares, accuracy measures showed a near ceiling performance for low numerosities and a sharp drop in performance once a critical number of squares was reached. Importantly, this critical number was higher by about two items in video game players (VGPs) than in non-video game players (NVGPs). A following control study indicated that this improvement was not due to an enhanced ability to instantly apprehend the numerosity of the display, a process known as subitizing, but rather due to an enhancement in the slower more serial process of counting. To confirm that video game play facilitates the processing of multiple objects at once, we compared VGPs and NVGPs on the multiple object tracking task (MOT), which requires the allocation of attention to several items over time. VGPs were able to successfully track approximately two more items than NVGPs. Furthermore, NVGPs trained on an action video game established the causal effect of game playing in the enhanced performance on the two tasks. Together, these studies confirm the view that playing action video games enhances the number of objects that can be apprehended and suggest that this enhancement is mediated by changes in visual short-term memory skills. q 2006 Elsevier B.V. All rights reserved.


title: ECN or Delay: Lessons Learnt from Analysis of DCQCN and TIMELY

abstract: Data center networks, and especially drop-free RoCEv2 networks require efficient congestion control protocols. DCQCN (ECN-based) and TIMELY (delay-based) are two recent proposals for this purpose. In this paper, we analyze DCQCN and TIMELY using fluid models and simulations, for stability, convergence, fairness and flow completion time. We uncover several surprising behaviors of these protocols. For example, we show that DCQCN exhibits non-monotonic stability behavior, and that TIMELY can converge to stable regime with arbitrary unfairness. We propose simple fixes and tuning for ensuring that both protocols converge to and are stable at the fair share point. Finally, using lessons learnt from the analysis, we address the broader question: are there fundamental reasons to prefer either ECN or delay for end-to-end congestion control in data center networks? We argue that ECN is a better congestion signal, due to the way modern switches mark packets, and due to a fundamental limitation of end-to-end delay-based protocols, that we derive.


A technique for determination of usual daily energy expenditure in the field


title: Parallel Grid Pooling for Data Augmentation

abstract: Convolutional neural network (CNN) architectures utilize downsampling layers, which restrict the subsequent layers to learn spatially invariant features while reducing computational costs. However, such a downsampling operation makes it impossible to use the full spectrum of input features. Motivated by this observation, we propose a novel layer called parallel grid pooling (PGP) which is applicable to various CNN models. PGP performs downsampling without discarding any intermediate feature. It works as data augmentation and is complementary to commonly used data augmentation techniques. Furthermore, we demonstrate that a dilated convolution can naturally be represented using PGP operations, which suggests that the dilated convolution can also be regarded as a type of data augmentation technique. Experimental results based on popular image classification benchmarks demonstrate the effectiveness of the proposed method. Code is available at: this https URL


title: Rasch model analysis of the Depression, Anxiety and Stress Scales (DASS)

abstract: BackgroundThere is a growing awareness of the need for easily administered, psychometrically sound screening tools to identify individuals with elevated levels of psychological distress. Although support has been found for the psychometric properties of the Depression, Anxiety and Stress Scales (DASS) using classical test theory approaches it has not been subjected to Rasch analysis. The aim of this study was to use Rasch analysis to assess the psychometric properties of the DASS-21 scales, using two different administration modes.MethodsThe DASS-21 was administered to 420 participants with half the sample responding to a web-based version and the other half completing a traditional pencil-and-paper version. Conformity of DASS-21 scales to a Rasch partial credit model was assessed using the RUMM2020 software.ResultsTo achieve adequate model fit it was necessary to remove one item from each of the DASS-21 subscales. The reduced scales showed adequate internal consistency reliability, unidimensionality and freedom from differential item functioning for sex, age and mode of administration. Analysis of all DASS-21 items combined did not support its use as a measure of general psychological distress. A scale combining the anxiety and stress items showed satisfactory fit to the Rasch model after removal of three items.ConclusionThe results provide support for the measurement properties, internal consistency reliability, and unidimensionality of three slightly modified DASS-21 scales, across two different administration methods. The further use of Rasch analysis on the DASS-21 in larger and broader samples is recommended to confirm the findings of the current study.


title: A VARIANCE DECOMPOSITION FOR STOCK RETURNS

abstract: This paper shows that unexpected stock returns must be associated with changes in expected future dividends or expected future returns A vector autoregressive method is used to break unexpected stock returns into these two components. In U.S. monthly data in 1927-88, one-third of the variance of unexpected returns is attributed to the variance of changing expected dividends, one-third to the variance of changing expected returns, and one-third to the covariance of the two components. Changing expected returns have a large effect on stock prices because they are persistent: a 1% innovation in the expected return is associated with a 4 or 5% capital loss. Changes in expected returns are negatively correlated with changes in expected dividends, increasing the stock market reaction to dividend news. In the period 1952-88, hanging expected. returns account for a larger fraction of stock return variation than they do in the period 1927-51.


title: Text Classification from Labeled and Unlabeled Documents using EM

abstract: This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. ::: ::: We introduce an algorithm for learning from labeled and unlabeled documents based on the combination of Expectation-Maximization (EM) and a naive Bayes classifier. The algorithm first trains a classifier using the available labeled documents, and probabilistically labels the unlabeled documents. It then trains a new classifier using the labels for all the documents, and iterates to convergence. This basic EM procedure works well when the data conform to the generative assumptions of the model. However these assumptions are often violated in practice, and poor performance can result. We present two extensions to the algorithm that improve classification accuracy under these conditions: (1) a weighting factor to modulate the contribution of the unlabeled data, and (2) the use of multiple mixture components per class. Experimental results, obtained using text from three different real-world tasks, show that the use of unlabeled data reduces classification error by up to 30%.


title: Towards Responsible Hydropower Development through Contentious Multi-stakeholder Negotiations: The Case of India

abstract: This chapter explores reforms in environmental and resettlement policies in India and the influence of domestic and external actors on the reform process. It also analyses the ways in which environment and resettlement policies have been implemented in a number of hydropower projects. At macro level, the study begins by describing the multilevel processes that govern dam decision-making. It then considers changes regarding environmental clearance and resettlement, and the role which state and non-state actors played in the last three decades. At project level the analysis focuses on how state and non-state actors influence decision-making on the introduction of superior environmental and social standards through changes in policies and laws. The dam projects selected are the Allain Duhangan Project, the Lower Subansiri Project and the Dibang Multipurpose Project. The study argues that, at macro level, it is civil society which has been the major driver of change in the area of resettlement over the last three decades. In the environment arena the changes are the outcome of competing demands from civil society and growth-oriented ministries and departments of the Government of India. At project level, superior social and environmental standards are primarily driven by social movements. International actors like the International Finance Corporation also catalyse the reform process.


title: On the Convergence of SGD Training of Neural Networks

abstract: Neural networks are usually trained by some form of stochastic gradient descent (SGD)). A number of strategies are in common use intended to improve SGD optimization, such as learning rate schedules, momentum, and batching. These are motivated by ideas about the occurrence of local minima at different scales, valleys, and other phenomena in the objective function. Empirical results presented here suggest that these phenomena are not significant factors in SGD optimization of MLP-related objective functions, and that the behavior of stochastic gradient descent in these problems is better described as the simultaneous convergence at different rates of many, largely non-interacting subproblems


title: Binary RSA encryption algorithm

abstract: A cryptographic system uses two dissimilar keys — a public key known to all and a private key known only to the receiver of the message. Encryption of the data uses public key while decryption uses both public and private keys. So in this method all parties need a key pair for conversing with any other interactive parties. RSA is a generally known asymmetric cryptography algorithm. Modified RSA is the improved version of RSA, enhancing better security. To advance the security of RSA, this paper is proposing a new cryptography algorithm called Binary RSA Encryption Algorithm (BREA). In BREA, the security is further improved by converting the cipher text which is encrypted by MREA into binary code format. The first section of this paper is the introduction and second section contains the literature survey of various modifications of RSA. Section three describes about the Modified RSA Encryption Algorithm that we have selected. The fourth section is details about the Binary RSA Encryption Algorithm (BREA). The fifth section is the comparison between RSA, MREA, and BREA. The sixth section is the conclusion and seventh section is the References.


title: Alcohol Marketing on Twitter and Instagram: Evidence of Directly Advertising to Youth/Adolescents

abstract: AIMS ::: Assess whether alcohol companies restrict youth/adolescent access, interaction, and exposure to their marketing on Twitter and Instagram. ::: ::: ::: METHODS ::: Employed five fictitious male and female Twitter (n = 10) and Instagram (n = 10) user profiles aged 13, 15, 17, 19 and/or 21. Using cellular smartphones, we determined whether profiles could (a) interact with advertising content-e.g. retweet, view video or picture content, comment, share URL; and/or (b) follow and directly receive advertising material updates from the official Instagram and Twitter pages of 22 alcohol brands for 30 days. ::: ::: ::: RESULTS ::: All user profiles could fully access, view, and interact with alcohol industry content posted on Instagram and Twitter. Twitter's age-gate, which restricts access for those under 21, successfully prevented underage profiles from following and subsequently receiving promotional material/updates. The two 21+ profiles collectively received 1836 alcohol-related tweets within 30 days. All Instagram profiles, however, were able to follow all alcohol brand pages and received an average of 362 advertisements within 30 days. The quantity of promotional updates increased throughout the week, reaching their peak on Thursday and Friday. Representatives/controllers of alcohol brand Instagram pages would respond directly to our underage user's comments. ::: ::: ::: CONCLUSION ::: The alcohol industry is in violation of their proposed self-regulation guidelines for digital marketing communications on Instagram. While Twitter's age-gate effectively blocked direct to phone updates, unhindered access to post was possible. Everyday our fictitious profiles, even those as young as 13, were bombarded with alcohol industry messages and promotional material directly to their smartphones.


title: Tone-Mapped Mean-Shift Based Environment Map Sampling

abstract: In this paper, we present a novel approach for environment map sampling, which is an effective and pragmatic technique to reduce the computational cost of realistic rendering and get plausible rendering images. The proposed approach exploits the advantage of adaptive mean-shift image clustering with aid of tone-mapping, yielding oversegmented strata that have uniform intensities and capture shapes of light regions. The resulted strata, however, have unbalanced importance metric values for rendering, and the strata number is not user-controlled. To handle these issues, we develop an adaptive split-and-merge scheme that refines the strata and obtains a better balanced strata distribution. Compared to the state-of-the-art methods, our approach achieves comparable and even better rendering quality in terms of SSIM, RMSE and HDRVDP2 image quality metrics. Experimental results further show that our approach is more robust to the variation of viewpoint, environment rotation, and sample number.


title: Low cost planar coil structure for inductive sensors to measure absolute angular position

abstract: There are many applications that need to sense angular displacement/position, such as automotive electronic steering system, throttle and brake pedal position, motors, robotic arm, etc. Contactless sensors are becoming increasingly popular in these kinds of applications due to their robustness, reliability and longevity. The paper proposes a new contactless variable reluctance (VR) resolver that can be built on a PCB as coil traces. The coil layout consists of an excitation coil and two sense coils laid on a single plane of a PCB. A circular metallic disk with a sector aperture alters the induced voltage in the sense coils. The induced voltage in the sense coils is a sine and cosine function of the angular position of disk. The absolute angular position can be measured with high resolution based on sense coil voltages. The design is validated using an ASIC to process the sensor signals and compute the angular position. The linearity of the coil design has been validated through experimental results.


title: Parametric nonlinear dimensionality reduction using kernel t-SNE

abstract: Abstract Novel non-parametric dimensionality reduction techniques such as t-distributed stochastic neighbor embedding (t-SNE) lead to a powerful and flexible visualization of high-dimensional data. One drawback of non-parametric techniques is their lack of an explicit out-of-sample extension. In this contribution, we propose an efficient extension of t-SNE to a parametric framework, kernel t-SNE, which preserves the flexibility of basic t-SNE, but enables explicit out-of-sample extensions. We test the ability of kernel t-SNE in comparison to standard t-SNE for benchmark data sets, in particular addressing the generalization ability of the mapping for novel data. In the context of large data sets, this procedure enables us to train a mapping for a fixed size subset only, mapping all data afterwards in linear time. We demonstrate that this technique yields satisfactory results also for large data sets provided missing information due to the small size of the subset is accounted for by auxiliary information such as class labels, which can be integrated into kernel t-SNE based on the Fisher information.


title: Temporal expert finding through generalized time topic modeling

abstract: This paper addresses the problem of semantics-based temporal expert finding, which means identifying a person with given expertise for different time periods. For example, many real world applications like reviewer matching for papers and finding hot topics in newswire articles need to consider time dynamics. Intuitively there will be different reviewers and reporters for different topics during different time periods. Traditional approaches used graph-based link structure by using keywords based matching and ignored semantic information, while topic modeling considered semantics-based information without conferences influence (richer text semantics and relationships between authors) and time information simultaneously. Consequently they result in not finding appropriate experts for different time periods. We propose a novel Temporal-Expert-Topic (TET) approach based on Semantics and Temporal Information based Expert Search (STMS) for temporal expert finding, which simultaneously models conferences influence and time information. Consequently, topics (semantically related probabilistic clusters of words) occurrence and correlations change over time, while the meaning of a particular topic almost remains unchanged. By using Bayes Theorem we can obtain topically related experts for different time periods and show how experts' interests and relationships change over time. Experimental results on scientific literature dataset show that the proposed generalized time topic modeling approach significantly outperformed the non-generalized time topic modeling approaches, due to simultaneously capturing conferences influence with time information.


Assessment in Education: Principles, Policy & Practice


title: Robust Visual Knowledge Transfer via EDA

abstract: We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine based cross-domain network learning framework, that is called Extreme Learning Machine (ELM) based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the l_(2,1)-norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as base classifiers. The network output weights cannot only be analytically determined, but also transferrable. Additionally, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semi-supervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition, demonstrate that our EDA methods outperform existing cross-domain learning methods.


title: Investing in the transition to sustainable agriculture

abstract: Abstract Ecological impacts of industrial agriculture include significant greenhouse gas emissions, loss of biodiversity, widespread pollution by fertilizers and pesticides, soil loss and degradation, declining pollinators, and human health risks, among many others. A rapidly growing body of scientific research, however, suggests that farming systems designed and managed according to ecological principles can meet the food needs of society while addressing these pressing environmental and social issues. The promise of such systems implies an urgent need for increasing the scope and scale of this area of research – agroecology. Notably, agroecological systems have been shown to reduce input dependency and therefore related research is unlikely to be supported by the private sector. Yet, the amount of federal funding available for agroecology has remained unclear. To address this gap in knowledge, we identified projects beginning in 2014 from the USDA Current Research Information System (CRIS) database and searched key sections of project reports for major components emphasizing sustainable agriculture, including agroecology. Components were grouped into four levels according to their focus on: improving system efficiency to reduce the use of inputs (L1), substituting more sustainable inputs and practices into farming systems (L2), redesigning systems based on ecological principles (L3: agroecology), or reestablishing connections between producers and consumers to support a socio-ecological transformation of the food system (L4: social dimensions of agroecology). We identified 824 projects, which accounted for $294 million dollars: just over 10% of the entire 2014 USDA Research, Extension, and Economics (REE) budget. Using a highly conservative classification protocol, we found that the primary focus of many projects was unrelated to sustainable agriculture at any level, but the majority of projects had at least one relevant component (representing 52–69% of analyzed funds, depending on whether projects focused exclusively on increasing yields were included). Of the total $294 million of analyzed funds, 18–36% went to projects that included a L1 component. Projects including components in L2, L3, or L4 received just 24%, 15%, and 14% of analyzed funds, respectively. Systems-based projects that included both agroecological farming practices (L3) and support for socioeconomic sustainability (L4) were particularly poorly funded (4%), as were L3 projects that included complex rotations (3%), spatially diversified farms (3%), rotational or regenerative grazing (1%), integrated crop-livestock systems (1%), or agroforestry (


title: Power Characterization of Isolated Bidirectional Dual-Active-Bridge DC–DC Converter With Dual-Phase-Shift Control

abstract: Compared to the traditional single-phase-shift control, dual-phase-shift (DPS) control can greatly improve the performance of the isolated bidirectional dual-active-bridge dc-dc converter (IBDC). This letter points out some wrong knowledge about transmission power of IBDC under DPS control in the earlier studies. On this basis, this letter gives the detailed theoretical and experimental analyses of the transmission power of IBDC under DPS control. And the experimental results showed agreement with theoretical analysis.


title: From HMMs to Segment Models: A Unified View of Stochastic Modeling for Speech Recognition

abstract: Many alternative models have been proposed to address some of the shortcomings of the hidden Markov model (HMM), which is currently the most popular approach to speech recognition. In particular, a variety of models that could be broadly classified as segment models have been described for representing a variable-length sequence of observation vectors in speech recognition applications. Since there are many aspects in common between these approaches, including the general recognition and training problems, it is useful to consider them in a unified framework. The paper describes a general stochastic model that encompasses most of the models proposed in the literature, pointing out similarities of the models in terms of correlation and parameter tying assumptions, and drawing analogies between segment models and HMMs. In addition, we summarize experimental results assessing different modeling assumptions and point out remaining open questions.


title: Pelvic Exercise and Gait in Hemiplegia

abstract: The purpose of this study was to describe and compare the gait of 20 patients with hemiplegia secondary to cerebrovascular accident (CVA) before and after a treatment regimen of resisted pelvic motions. Ten women and 10 men were studied, with a mean age of 48 years and a mean duration post-CVA of two months. Nine subjects (45%) were right hemiplegic, and 11 subjects (55%) were left hemiplegic. Treatment consisted of four sets of five repetitions each of manually resisted pelvic anterior-elevation and posterior-depression movements on the involved side. An insole footswitch system, knee electrogoniometer, and force walking aid were used in gait analysis performed before treatment, immediately after treatment (posttest 1), and 30 minutes after treatment (posttest 2). Results showed significant overall improvement in gait in posttest 1 ( p < .005) compared with the pretest. This improvement, however, was not maintained in posttest 2. Ten patients improved overall in posttest 1; only 4 patients also showed improvement in posttest 2. The major improvements seen immediately after treatment were obseved in stance stability and limb advancement in the involved limb. More research is needed to identify an optimum treatment with carry-over using this technique.


title: Exposure to RF EMF From Array Antennas in 5G Mobile Communication Equipment

abstract: In this paper, radio-frequency (RF) electromagnetic field (EMF) exposure evaluations are conducted in the frequency range 10–60 GHz for array antennas intended for user equipment (UE) and low-power radio base stations in 5G mobile communication systems. A systematic study based on numerical power density simulations considering effects of frequency, array size, array topology, distance to exposed part of human body, and beam steering range is presented whereby the maximum transmitted power to comply with RF EMF exposure limits specified by the International Commission on Non-Ionizing Radiation Protection, the US Federal Communications Commission, and the Institute of Electrical and Electronics Engineers is determined. The maximum transmitted power is related to the maximum equivalent isotropically radiated power to highlight the relevance of the output power restrictions for a communication channel. A comparison between the simulation and measurement data is provided for a canonical monopole antenna. For small distances, with the antennas transmitting directly toward the human body, it is found that the maximum transmitted power is significantly below the UE power levels used in existing third and fourth generation mobile communication systems. Results for other conceivable exposure scenarios based on technical solutions that could allow for larger output power levels are also discussed. The obtained results constitute valuable information for the design of future mobile communication systems and for the standardization of EMF compliance assessment procedures of 5G devices and equipment.


title: Transfer Learning with Neural AutoML

abstract: We reduce the computational cost of Neural AutoML with transfer learning. AutoML relieves human effort by automating the design of ML algorithms. Neural AutoML has become popular for the design of deep learning architectures, however, this method has a high computation cost. To address this we propose Transfer Neural AutoML that uses knowledge from prior tasks to speed up network design. We extend RL-based architecture search methods to support parallel training on multiple tasks and then transfer the search strategy to new tasks. On language and image classification tasks, Transfer Neural AutoML reduces convergence time over single-task training by over an order of magnitude on many tasks.


title: Special Digital Monies: The Design of Alipay and WeChat Wallet for Mobile Payment Practices in China

abstract: While research studies of digital and mobile payment systems in HCI have pointed out design opportunities situated within informal and nuanced mobile contexts, we have not yet understood how we can design digital monies to allow users to use monies more easily in these contexts. In this study, we examined the design of Alipay and WeChat Wallet, two successful mobile payment apps in China, which have been used by Chinese users for purposes such as playing, gifting, and ceremonial practices. Through semi-structured interviews with 24 Chinese users and grounded theory coding, we identified five contexts in which the flexibility and extensive functions of these payment apps have allowed these users to adaptively use digital monies in highly flexible ways. Finally, our analysis arrived at our conceptual frame—special digital monies—to highlight how digital monies, by allowing users to alter and define their transactional rules and pathways, could vastly expand the potential of digital monies to support users beyond standard retail contexts.


title: Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation

abstract: The L1-regularized maximum likelihood estimation problem has recently become a topic of great interest within the machine learning, statistics, and optimization communities as a method for producing sparse inverse covariance estimators. In this paper, a proximal gradient method (G-ISTA) for performing L1-regularized covariance matrix estimation is presented. Although numerous algorithms have been proposed for solving this problem, this simple proximal gradient method is found to have attractive theoretical and numerical properties. G-ISTA has a linear rate of convergence, resulting in an O(log e) iteration complexity to reach a tolerance of e. This paper gives eigenvalue bounds for the G-ISTA iterates, providing a closed-form linear convergence rate. The rate is shown to be closely related to the condition number of the optimal point. Numerical convergence results and timing comparisons for the proposed method are presented. G-ISTA is shown to perform very well, especially when the optimal point is well-conditioned.


title: Online anomaly detection on the webscope S5 dataset: A comparative study

abstract: An unresolved challenge for all kind of temporal data is the reliable anomaly detection, especially when adaptability is required in the case of non-stationary time series or when the nature of future anomalies is unknown or only vaguely defined. Most of the current anomaly detection algorithms follow the general idea to classify an anomaly as a significant deviation from the prediction. In this paper we present a comparative study where several online anomaly detection algorithms are compared on the large Yahoo Webscope S5 anomaly benchmark. We show that a relatively Simple Online Regression Anomaly Detector (SORAD) is quite successful compared to other anomaly detectors. We discuss the importance of several adaptive and online elements of the algorithm and their influence on the overall anomaly detection accuracy.


Equihash: Asymmetric Proof-of-Work Based on the Generalized Birthday Problem


title: Performance Evaluation of Container-Based Virtualization for High Performance Computing Environments

abstract: The use of virtualization technologies in high performance computing (HPC) environments has traditionally been avoided due to their inherent performance overhead. However, with the rise of container-based virtualization implementations, such as Linux VServer, OpenVZ and Linux Containers (LXC), it is possible to obtain a very low overhead leading to near-native performance. In this work, we conducted a number of experiments in order to perform an in-depth performance evaluation of container-based virtualization for HPC. We also evaluated the trade-off between performance and isolation in container-based virtualization systems and compared them with Xen, which is a representative of the traditional hypervisor-based virtualization systems used today.


title: Semantic Annotation: The Mainstay of Semantic Web

abstract: Given that semantic Web realization is based on the critical mass of metadata accessibility and the representation of data with formal knowledge, it needs to generate metadata that is specific, easy to understand and well-defined. However, semantic annotation of the web documents is the successful way to make the Semantic Web vision a reality. This paper introduces the Semantic Web and its vision (stack layers) with regard to some concept definitions that helps the understanding of semantic annotation. Additionally, this paper introduces the semantic annotation categories, tools, domains and models.


title: Road Environment Recognition Using On-vehicle LIDAR

abstract: In this paper, we present the road environment recognition algorithm using on-vehicle LIDAR. Although our 2-dimensional scanning and high-resolution LIDAR can detect the forward vehicle, other objects and the lane mark at the same time, we have only applied the LIDAR to ACC system by recognizing only vehicles. To pursue the potential of the LIDAR, DENSO has developed the road environment recognition algorithm using only the LIDAR as a forward object detection sensor with compensation of the ego-vehicle motion. As a result, we constructed a model-based algorithm, which could recognize various objects on the absolute coordinate system. Moreover we show the possibility of the coordination with LIDAR and map or GPS for high accurate navigation.


title: Enhanced smart doorbell system based on face recognition

abstract: In recent years considerable progress has been made in the area of face recognition. Through the work of computer science engineers, computers can now outperform humans in many face recognition tasks, particularly those in which large databases of faces must be searched. A system with the ability to detect and recognize faces has many potential applications including crowd and airport surveillance, private security and improved human-computer interaction. An automatic face recognition system is perfectly suited to fix security issues and offer flexibility to smart house control. This project aims to replace costly image processing boards using Raspberry pi board with ARMv7 Cortex-A7 as the core within Opencv library. This project is mainly based on image processing by porting the Opencv library to the Raspberry Pi board. Algorithm for face recognition, based on principal component analysis (PCA), is programmed and implemented on the platform. The system is based on the criteria of low power consumption, resources optimization, and improved operation speed. This paper reviews the related work in the field of home automation systems and presents the system design, software algorithm, implementation and results.


title: The Hmong Diaspora : Preserved South-East Asian genetic ancestry in French Guianese Asians La Diaspora Hmong : un patrimoine génétique conservé chez la population asiatique de Guyane française

abstract: The Hmong Diaspora is one of the widest modern human migrations. Mainly localised in South-East Asia, the United States of America, and metropolitan France, a small community has also settled the Amazonian forest of French Guiana. We have biologically analysed 62 individuals of this unique Guianese population through three complementary genetic markers: mitochondrial DNA (HVS-I/II and coding region SNPs), Y-chromosome (SNPs and STRs), and the Gm allotypic system. All genetic systems showed a high conservation of the Asian gene pool (Asian ancestry: mtDNA=100.0%; NRY=99.1%; Gm=96.6%), without a trace of founder effect. When compared across various Asian populations, the highest correlations were observed with Hmong-Mien groups still living in South-East Asia (Fst<0.05; P-value<0.05). Despite a long history punctuated by exodus, the French Guianese Hmong have maintained their original genetic diversity.


Characterization and Pharmacologic Targeting of EZH2, a Fetal Retinal Protein and Epigenetic Regulator, in Human Retinoblastoma


title: A Socio-Behavioral Study of Home Computer Users' Intention to Practice Security

abstract: Home computer users play a crucial role in securing the cyberspace, but the protection of home computers is left to the initiative of the users. In this study, we focus on the sociobehavioral perspective, as the behavior of home computer users on security issues is one of the most important factors in securing the cyberspace. The decomposed Theory of Planned Behavior is adapted to investigate the factors that influence a user’s intention to practice home computer security. A survey instrument was developed and administered to 233 home computer users. The findings revealed that attitude and subjective norm determined intention to practice computer security, and perceived usefulness, family and peer influence, mass media influence and self-efficacy are important factors that influence a home computer user’s intention to practice computer security. Findings also provide an impetus for researchers to conduct future studies in this domain.


title: Implementation of EMG- and Force-Based Control Interfaces in Active Elbow Supports for Men With Duchenne Muscular Dystrophy: A Feasibility Study

abstract: While there is an extensive number of studies on the development and evaluation of electromyography (EMG)- and force-based control interfaces for assistive devices, no studies have focused on testing these control strategies for the specific case of adults with Duchenne muscular dystrophy (DMD). This paper presents a feasibility study on the use of EMG and force as control interfaces for the operation of active arm supports for men with DMD. We have built an experimental active elbow support, with a threefold objective: 1) to investigate whether adult men with DMD could use EMG- and force-based control interfaces; 2) to evaluate their performance during a discrete position-tracking task; and 3) to examine users' acceptance of the control methods. The system was tested in three adults with DMD (21-22 years). Although none of the three participants had performed any voluntary movements with their arms for the past 3-5 years, all of them were 100% successful in performing the series of tracking tasks using both control interfaces (mean task completion time EMG: [Formula: see text] , force: [Formula: see text] ). While movements with the force-based control were considerably smoother in Subject 3 and faster in Subject 1, EMG based-control was perceived as less fatiguing by all three subjects. Both EMG- and force-based interfaces are feasible solutions for the control of active elbow supports in adults with DMD and should be considered for further investigations on multi-DOF control.