title: The State of the Art in Developing Fuzzy Ontologies: A Survey

abstract: Conceptual formalism supported by typical ontologies may not be sufficient to represent uncertainty information which is caused due to the lack of clear cut boundaries between concepts of a domain. Fuzzy ontologies are proposed to offer a way to deal with this uncertainty. This paper describes the state of the art in developing fuzzy ontologies. The survey is produced by studying about 35 works on developing fuzzy ontologies from a batch of 100 articles in the field of fuzzy ontologies.


title: Waveform Diversity Extraction in Spaceborne MIMO-SAR System Based on Multidimensional Waveform Encoding

abstract: Spaceborne multi-input multi-output SAR(MIMO-SAR) system combined with multidimensional waveform encoding can obtain wider unambiguous swath width and higher azimuth resolution than traditional spaceborne SAR system.However,the mount of data of waveform diversity extraction method based on traditional digital beam-forming(DBF) techniques is seriously high and the great receive antenna gain loss from topography occurs with the recent presented scan-on-receive(SCORE) technique.A new waveform diversity extraction approach taking advantages of the SCORE technique via sub-array antennas and posterior space time processing(STP) is proposed in this paper.This approach can reduce the high date rate of SAR systems and the receiveing antenna gain loss from topography in the SCORE technique.Furthermore,this approach remains some useful information of multi-channel in elevation for other applications and it is easy to be realized.Simulation results validate the presented approach is better than the waveform diversity extraction method based on the SCORE technique.


title: Skyline: An Open Source Document Editor for Creating and Analyzing Targeted Proteomics Experiments

abstract: Summary: Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools. Availability: Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.


title: An incremental ensemble of classifiers as a technique for prediction of student's career choice

abstract: The ability to predict the career of students can be beneficial in a huge number of different techniques which are connected with the education structure. Student's marks and the result of some kind of psychometric test on students can form the training set for the supervised data mining algorithms. As the student's data in the educational systems is increasing day by day, the incremental learning properties are important for machine learning research. Against to the classical batch learning algorithm, incremental learning algorithm tries to forget unrelated information while training new instances. These days, combining classifiers is nothing but taking more than one opinion contributes a lot, to get more accurate results. Therefore, a suggestion is an incremental ensemble of three classifiers namely Naïve Bayes, K-Star, SVM using voting scheme. The ensemble technique proposed in this paper is compared with the incremental algorithms, without any ensemble concept, for the student's data set and it was found that the proposed algorithm gives better accuracy.


title: Activity-Induced DNA Breaks Govern the Expression of Neuronal Early-Response Genes

abstract: Neuronal activity causes the rapid expression of im-mediate early genes that are crucial for experience-driven changes to synapses, learning, and memory.Here, using both molecular and genome-wide next-generation sequencing methods, we report that neu-ronal activity stimulation triggers the formation ofDNA double strand breaks (DSBs) in the promotersof a subset of early-response genes, including Fos,Npas4, and Egr1. Generation of targeted DNA DSBswithin Fos and Npas4 promoters is sufﬁcient toinduce their expression even in the absence of anexternalstimulus.Activity-dependentDSBformationis likely mediated by the type II topoisomerase,Topoisomerase IIb (Topo IIb), and knockdown ofTopo IIb attenuates both DSB formation and early-response gene expression following neuronal stimu-lation. Our results suggest that DSB formation is aphysiological event that rapidly resolves topologicalconstraints to early-response gene expression inneurons.


title: Becoming more specific: Measuring and modeling teachers' perceived usefulness of ICT in the context of teaching and learning

abstract: Studies on teachers' acceptance and use of information and communication technology (ICT) have revealed perceived usefulness to be a crucial determinant for integrating ICT in classrooms. In consequence, the present study focuses on teachers' perceived usefulness of ICT for teaching and learning and is aimed at describing its structure and relations to self-efficacy, ICT use, and teachers' age. By means of Bayesian analysis, we specified confirmatory factor-analytic and structural equation models to a large-scale data set of N?=?1190 Norwegian teachers. Our results supported the hypothesized four-factor structure of teachers' perceived usefulness of ICT, signifying different facets of ICT-related teaching goals in classrooms. Moreover, it was possible to disentangle general and specific components of the construct in nested factor models. In support of existing research, we found positive relations to self-efficacy and ICT use, but a negative relation to teachers' age. Our study provides evidence on a multidimensional conceptualization of teachers' perceived usefulness of ICT for teaching and learning, and verifies the relations to teacher-related characteristics. Implications for the measurement and modeling of the construct, and future research directions are discussed. Teachers' perceived usefulness of ICT for teaching and learning is multifaceted.A general factor and specific factors can be distinguished.Bayesian models with cross-loadings represent the structure of the construct.Perceived usefulness is positively related to self-efficacy and ICT use.Perceived usefulness is negatively related to teachers' age.


On the Importance of a Safe School and Classroom Climate for Student Achievement in Reading Literacy.


title: Improving Bag-of-Features for Large Scale Image Search

abstract: This article improves recent methods for large scale image search. We first analyze the bag-of-features approach in the framework of approximate nearest neighbor search. This leads us to derive a more precise representation based on Hamming embedding (HE) and weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within an inverted file and are efficiently exploited for all images in the dataset. We then introduce a graph-structured quantizer which significantly speeds up the assignment of the descriptors to visual words. A comparison with the state of the art shows the interest of our approach when high accuracy is needed. ::: ::: Experiments performed on three reference datasets and a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints, as well as their efficiency. Estimation of the full geometric transformation, i.e., a re-ranking step on a short-list of images, is shown to be complementary to our weak geometric consistency constraints. Our approach is shown to outperform the state-of-the-art on the three datasets.


title: Fluorescence and absorption spectroscopy for warm dense matter studies and ICF plasma diagnostics

abstract: The burning core of an inertial confinement fusion (ICF) plasma produces bright x-rays at stagnation that can directly diagnose core conditions essential for comparison to simulations and understanding fusion yields. These x-rays also backlight the surrounding shell of warm, dense matter, whose properties are critical to understanding the efficacy of the inertial confinement and global morphology. We show that the absorption and fluorescence spectra of mid-Z impurities or dopants in the warm dense shell can reveal the optical depth, temperature, and density of the shell and help constrain models of warm, dense matter. This is illustrated by the example of a high-resolution spectrum collected from an ICF plasma with a beryllium shell containing native iron impurities. Analysis of the iron K-edge provides model-independent diagnostics of the shell density (2.3 × 1024 e/cm3) and temperature (10 eV), while a 12-eV red shift in Kβ and 5-eV blue shift in the K-edge discriminate among models of warm dense matter...


title: Research on Control Method of Two-wheeled Self-balancing Robot

abstract: Based on Newton dynamics mechanics theory make a study of two-wheeled self-balancing robot, a detailed mathematical model of the modeling process is provided, and then, using the reasonable method, a linear state-space equations is built up. After that, the LQR controller and state-feedback controller based on pole placement theory are both designed. After a number of simulation experiments, we get the best closed-loop poles and Q, R matrix, both of which have good simulation curves at the same disturbance force. The results of experiments prove that both of them have good dynamic performance and robustness, which also prove the system modeling and controller design are reasonable and effective via these methods. The curves from LQR controller have a better dynamic performance compare with pole placement state-feedback controller.


title: Assisted suicide by fentanyl intoxication due to excessive transdermal application

abstract: Herein, we report a case of an assisted suicide committed by application of 34 matrix-based fentanyl-containing transdermal therapeutic systems (TTS) with different release rates. The TTS were supplied by the husband but administered by the deceased herself. Besides routine systematic toxicological analysis (STA), the concentrations of fentanyl and norfentanyl were determined in the blood (femoral and heart), urine, stomach content, brain, lung tissue, musculus iliopsoas, liver, kidney, bile and in some of the used TTS by LC-MS/MS. Blood levels of fentanyl were 60.6 μg/L in femoral blood and 94.1 μg/L in heart blood. These concentrations are in good concordance with levels described in cases with accidental or lethal suicidal fentanyl patch application. The organ distribution indicates an influence of post-mortem redistribution. The levels of residual fentanyl in the TTS were also determined. STA furthermore revealed supratherapeutic levels of bromazepam. Thus, the cause of death was a combination of fentanyl and bromazepam intoxication. However, considering the determined levels of fentanyl and norfentanyl in the entire set of specimens and the high toxicity in comparison to bromazepam, fentanyl was the leading toxic noxa.


title: Precise and Scalable Detection of Double-Fetch Bugs in OS Kernels

abstract: During system call execution, it is common for operating system kernels to read userspace memory multiple times (multi-reads). A critical bug may exist if the fetched userspace memory is subject to change across these reads, i.e., a race condition, which is known as a double-fetch bug. Prior works have attempted to detect these bugs both statically and dynamically. However, due to their improper assumptions and imprecise definitions regarding double-fetch bugs, their multi-read detection is inherently limited and suffers from significant false positives and false negatives. For example, their approach is unable to support device emulation, inter-procedural analysis, loop handling, etc. More importantly, they completely leave the task of finding real double-fetch bugs from the haystack of multi-reads to manual verification, which is expensive if possible at all. In this paper, we first present a formal and precise definition of double-fetch bugs and then implement a static analysis system - Deadline - to automatically detect double-fetch bugs in OS kernels. Deadline uses static program analysis techniques to systematically find multi-reads throughout the kernel and employs specialized symbolic checking to vet each multi-read for double-fetch bugs. We apply Deadline to Linux and FreeBSD kernels and find 23 new bugs in Linux and one new bug in FreeBSD. We further propose four generic strategies to patch and prevent double-fetch bugs based on our study and the discussion with kernel maintainers.


title: Face anti-spoofing via hybrid convolutional neural network

abstract: Face anti-spoofing techniques have been developed several years since the face recognition systems were successfully applied. Existing approaches in this topic merely use the entire region of human face. However, different facial parts always have different structures and the full-face model maybe weaken the discrepancy of the specific parts. So training the specific model for each facial part can improve the performance of anti-spoofing. Motivated by this, in this paper, we propose a new method of face anti-spoofing leveraging hybrid convolutional neural network (CNN) for facial parts. The main procedure of our work can be summarized as follows: (i) We divide the face into several parts; (ii) Based on different parts, training the corresponding CNN model of each part, which will constitute the hybrid CNN; (iii) Concatenating the last layer of the hybrid model to train a SVM classifier; (iv) By the SVM to distinguish the real and fake faces. We tested the effectiveness of our method on two public available databases, Replay-Attack and CASIA, and the experiments show our proposed method can obtain satisfactory results with respect to the state-of-the- art methods.


title: TextFinder: An Automatic System to Detect and Recognize Text in Images

abstract: A robust system is proposed to automatically detect and extract text in images from different sources, including video, newspapers, advertisements, stock certificates, photographs, and checks. Text is first detected using multiscale texture segmentation and spatial cohesion constraints, then cleaned up and extracted using a histogram-based binarization algorithm. An automatic performance evaluation scheme is also proposed.


title: Indoor human localization with orientation using WiFi fingerprinting

abstract: Localization in indoor environment poses a fundamental challenge in ubiquitous computing compared to its well-established GPS-based outdoor environment counterpart. This study investigated the feasibility of a WiFi-based indoor positioning system to localize elderly in an elderly center focusing on their orientation. The fingerprinting method of Received Signal Strength Indication (RSSI) from WiFi Access Points (AP) has been employed to discriminate and uniquely identify a position. The discrimination process of the reference points with its orientation have been analyzed with 0.9, 1.8, and 2.7 meter resolution. The experimental result shows that the WiFi-based RSSI fingerprinting method can discriminate the location and orientation of a user within 1.8 meter resolution.


title: The Impact of the Environment on Health in Mongolia

abstract: Mongolia has significant exposure to environmental risk factors because of poor environmental management and behaviors, and children are increasingly vulnerable to these threats. This study aimed to assess levels of exposure and summarize the evidence for associations between exposures to environmental risk factors and adverse health outcomes in Mongolia, with a particular focus on children. A systematic review was conducted using the PubMed, EMBASE, Web of Science, Global Health Library, CINAHL, CABI, Scopus, and mongolmed.mn electronic databases up to April 2014 . A total of 59 studies meeting the predetermined criteria were included. Results indicate that the Mongolian population has significant exposure to outdoor and indoor air pollution, metals, environmental tobacco smoke, and other chemical toxins, and these risk factors have been linked to respiratory and cardiovascular diseases among adults and respiratory diseases and neurodevelopmental disorders among children. Well-designed epidemiological investigations in vulnerable populations especially in pregnant women and children are recommended.


title: The effect of company characteristics on working capital management: A quantitative study of Swedish listed companies

abstract: This study investigates the effect of company characteristics on the working capital management. We employed quantitative method to examine the relationship between company characteristics and the ...


title: Comparison of IoT platform architectures: A field study based on a reference architecture

abstract: The Internet of Things (IoT) is gaining increasing attention. The overall aim is to interconnect the physical with the digital world. Therefore, the physical world needs to be measured and translated into processible data. Further, data has to be translated into commands to be executed by actuators. Due to the growing awareness of IoT, the amount of offered IoT platforms rises as well. The heterogeneity of IoT platforms is the consequence of multiple different standards and approaches. This leads to problems of comprehension, which can occur during the design up to the selection of an appropriate solution. We tackle these issues by introducing an IoT reference architecture based on several state-of-the-art IoT platforms. Furthermore, the reference architecture is compared to three open-source and one proprietary IoT platform. The comparison shows that the reference architecture provides a uniform basis to understand, compare, and evaluate different IoT solutions. The considered state-of-the-art IoT platforms are OpenMTC, FIWARE, SiteWhere, and Amazon Web Services IoT.


title: Fang: a firewall analysis engine

abstract: Today, even a moderately sized corporate intranet contains multiple firewalls and routers, which are all used to enforce various aspects of the global corporate security policy. Configuring these devices to work in unison is difficult, especially if they are made by different vendors. Even testing or reverse engineering an existing configuration (say when a new security administrator takes over) is hard. Firewall configuration files are written in low level formalisms, whose readability is comparable to assembly code, and the global policy is spread over all the firewalls that are involved. To alleviate some of these difficulties, we designed and implemented a novel firewall analysis tool. Our software allows the administrator to easily discover and test the global firewall policy (either a deployed policy or a planned one). Our tool uses a minimal description of the network topology and directly parses the various vendor-specific low level configuration files. It interacts with the user through a query-and-answer session, which is conducted at a much higher level of abstruction. A typical question our tool can answer is "from which machines can our DMZ be reached and with which services?" Thus, the tool complements existing vulnerability analysis tools, as it can be used before a policy is actually deployed it operates on a more understandable level of abstraction, and it deals with all the firewalls at once.


title: Deep learning methods for protein torsion angle prediction

abstract: BackgroundDeep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins.ResultsWe design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20–21° and 29–30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method.ConclusionsOur experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.


title: Design and implementation of autonomous vehicle valet parking system

abstract: The autonomous vehicle valet parking service (AVP) enables a vehicle to drive and park without any human interaction. The driverless vehicle has to follow the scheduled route and be parked in a parking lot automatically. Most fundamental technologies for autonomous vehicle valet parking service are controlling vehicle driving and parking. For autonomous driving, the path to near goal parking lot has to be generated and control commands to follow the path have to be generated continuously. For autonomous parking, the controlling vehicle steer value has to be found to park the vehicle at slot well. And also the mobile assistant system is needed for driver to select the parking lot and monitoring the state of the vehicle in remote. In this paper, we have designed and implemented the systems for autonomous vehicle valet parking service in low speed limited parking area.


title: A multimodule parallelable series-connected PWM voltage regulator

abstract: This paper presents the analysis and design of a single-phase voltage regulator (VR) and its multinodule parallel control. The VR employs the pulsewidth modulation three-arm rectifier-inverter topology. The inverter side adjusts the load voltage with the series regulating structure aiming to minimize converter capacity and attain higher efficiency. The rectifier side regenerates the load power and executes the active power filter function to achieve unity power factor. Based on such high-performance VR, a resistive droop method combined with the P-V droop and Q-/spl delta/ shift scheme is then proposed to control the current sharing such that multiple VRs can be paralleled directly without any control interconnection. The proposed parallel control technique possesses the features of fast response, precise voltage regulation, equal fundamental and harmonic current sharing, tolerance for parameter mismatch, and so on. Two prototype 1 KVA VRs are implemented, and the effectiveness is demonstrated by some simulation and experimental results.


title: SIW Multibeam Array for 5G Mobile Devices

abstract: This paper presents a substrate integrated waveguide (SIW) multibeam slot array operating at $\sim 30$ GHz for future 5G mobile terminal applications. The multibeam forming network is realized with a Butler matrix that is composed of hybrid couplers, crossovers, and phase shifters (135° and 0°). The crossovers are formed with two cascaded hybrid couplers. In the design of 135° and 0° phase shifters, the phase compensation technique is employed. The slot array is a $2 \times 4$ type, in which each column has two slot elements that are longitudinally staggered with respect to one another (in half-wavelength). In addition, mutual couplings reduction techniques applied in the proposed slot array are also discussed. The SIW technique is adopted in case for the related components, as it can be highly integrated in mmWave circuits at low fabrication cost and has low profile characteristics. The overall dimension of the SIW multibeam slot array (including the Butler matrix feeding network) is $72 \times 27.4 \times 0.508$ mm3, and the total area of the slot array is only $10.1\times 20.4$ mm2. The measured 10 dB bandwidth was 28–32 GHz, and the measured gains at 30 GHz for each port were 10.8, 12.1, 12, and 11 dBi. The proposed slot array also possesses wide angle coverage of $\sim 40^{\circ }$ with good steerable radiation.


title: DIVE A multi-user virtual reality system

abstract: The Distributed Interactive Virtual Environment (DIVE) is a heterogeneous distributed virtual reality system based on UNIX and Internet networking protocols. Each participating process has a copy of a replicated database and changes are propagated to the other processes with reliable multicast protocols. DIVE provides a dynamic virtual environment where applications and users can enter and leave the environment on demand. Several user-related abstractions have been introduced to ease the task of application and user interface construction. >


title: Turning the Waiting Room into a Classroom: Weekly Classes Using a Vegan or a Portion-Controlled Eating Plan Improve Diabetes Control in a Randomized Translational Study

abstract: Abstract Background In research settings, plant-based (vegan) eating plans improve diabetes management, typically reducing weight, glycemia, and low-density lipoprotein (LDL) cholesterol concentrations to a greater extent than has been shown with portion-controlled eating plans. Objective The study aimed to test whether similar benefits could be found using weekly nutrition classes in a typical endocrinology practice, hypothesizing that a vegan eating plan would improve glycemic control, weight, lipid concentrations, blood pressure, and renal function and would do so more effectively than a portion-controlled eating plan. Design In a 20-week trial, participants were randomly assigned to a low-fat vegan or portion-controlled eating plan. Participants/setting Individuals with type 2 diabetes treated in a single endocrinology practice in Washington, DC, participated (45 starters, 40 completers). Intervention Participants attended weekly after-hours classes in the office waiting room. The vegan plan excluded animal products and added oils and favored low-glycemic index foods. The portion-controlled plan included energy intake limits for weight loss (typically a deficit of 500 calories/day) and provided guidance on portion sizes. Main outcome measures Body weight, hemoglobin A1c (HbA1c), plasma lipids, urinary albumin, and blood pressure were measured. Statistical analyses performed For normally distributed data, t tests were used; for skewed outcomes, rank-based approaches were implemented (Wilcoxon signed-rank test for within-group changes, Wilcoxon two-sample test for between-group comparisons, and exact Hodges-Lehmann estimation to estimate effect sizes). Results Although participants were in generally good metabolic control at baseline, body weight, HbA1c, and LDL cholesterol improved significantly within each group, with no significant differences between the two eating plans (weight: −6.3 kg vegan, −4.4 kg portion-controlled, between-group P=0.10; HbA1c, −0.40 percentage point in both groups, P=0.68; LDL cholesterol −11.9 mg/dL vegan, −12.7 mg/dL portion-controlled, P=0.89). Mean urinary albumin was normal at baseline and did not meaningfully change. Blood pressure changes were not significant. Conclusions Weekly classes, integrated into a clinical practice and using either a low-fat vegan or portion-controlled eating plan, led to clinical improvements in individuals with type 2 diabetes.


title: Hindi handwritten character recognition using multiple classifiers

abstract: Humans can easily recognize handwritten words, after gaining basic knowledge of languages. This knowledge needs to be transferred to computers for automatic character recognition. The work proposed in this paper tries to automate recognition of handwritten hindi isolated characters using multiple classifiers. For feature extraction, it uses histogram of oriented gradients as one feature and profile projection histogram as another feature. The performance of various classifiers has been evaluated using theses features experimentally and quadratic SVM has been found to produce better results.


title: Teaching for understanding in medical classrooms using multimedia design principles.

abstract: Objectives In line with a recent report entitled Effective Use of Educational Technology in Medical Education from the Association of American Medical Colleges Institute for Improving Medical Education (AAMC-IME), this study examined whether revising a medical lecture based on evidence-based principles of multimedia design would lead to improved long-term transfer and retention in Year 3 medical students. A previous study yielded positive effects on an immediate retention test, but did not investigate long-term effects. ::: ::: Methods In a pre-test/post-test control design, a cohort of 37 Year 3 medical students at a private, midwestern medical school received a bullet point-based PowerPoint™ lecture on shock developed by the instructor as part of their core curriculum (the traditional condition group). Another cohort of 43 similar medical students received a lecture covering identical content using slides redesigned according to Mayer’s evidence-based principles of multimedia design (the modified condition group). ::: ::: Results Findings showed that the modified condition group significantly outscored the traditional condition group on delayed tests of transfer given 1 week (d = 0.83) and 4 weeks (d = 1.17) after instruction, and on delayed tests of retention given 1 week (d = 0.83) and 4 weeks (d = 0.79) after instruction. The modified condition group also significantly outperformed the traditional condition group on immediate tests of retention (d = 1.49) and transfer (d = 0.76). ::: ::: Conclusions This study provides the first evidence that applying multimedia design principles to an actual medical lecture has significant effects on measures of learner understanding (i.e. long-term transfer and long-term retention). This work reinforces the need to apply the science of learning and instruction in medical education.


title: Compiler optimization-space exploration

abstract: To meet the demands of modern architectures, optimizing compilers must incorporate an ever larger number of increasingly complex transformation algorithms. Since code transformations may often degrade performance or interfere with subsequent transformations, compilers employ predictive heuristics to guide optimizations by predicting their effects a priori. Unfortunately, the unpredictability of optimization interaction and the irregularity of today's wide-issue machines severely limit the accuracy of these heuristics. As a result, compiler writers may temper high variance optimization with overly conservative heuristics or may exclude these optimizations entirely. While this process results in a compiler capable of generating good average code quality across the target benchmark set, it is at the cost of missed optimization opportunities in individual code segments.To replace predictive heuristics, researchers have proposed compilers which explore many optimization options, selecting the best one a posteriori. Unfortunately, these existing iterative compilation techniques are not practical for reasons of compile time and applicability. In this paper, we present the Optimization-Space Exploration (OSE) compiler organization, the first practical iterative compilation strategy applicable to optimizations in general-purpose compilers. Instead of replacing predictive heuristics, OSE uses the compiler writer's knowledge encoded in the heuristics to select a small number of promising optimization alternatives for a given code segment. Compile time is limited by evaluating only these alternatives for hot code segments using a general compiletime performance estimator. An OSE-enhanced version of lntel's highly-tuned, aggressively optimizing production compiler for IA-64 yields a significant performance improvement, more than 20% in some cases, on Itanium for SPEC codes.


title: Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome

abstract: The translation of the growing increase of findings emerging from basic nutritional science into meaningful and clinically relevant dietary advices represents nowadays one of the main challenges of clinical nutrition. From nutrigenomics to deep phenotyping, many factors need to be taken into account in designing personalized and unbiased nutritional solutions for individuals or population sub-groups. Likewise, a concerted effort among basic, clinical scientists and health professionals will be needed to establish a comprehensive framework allowing the implementation of these new findings at the population level. In a world characterized by an overwhelming increase in the prevalence of obesity and associated metabolic disturbances, such as type 2 diabetes and cardiovascular diseases, tailored nutrition prescription represents a promising approach for both the prevention and management of metabolic syndrome. This review aims to discuss recent works in the field of precision nutrition analyzing most relevant aspects affecting an individual response to lifestyle/nutritional interventions. Latest advances in the analysis and monitoring of dietary habits, food behaviors, physical activity/exercise and deep phenotyping will be discussed, as well as the relevance of novel applications of nutrigenomics, metabolomics and microbiota profiling. Recent findings in the development of precision nutrition are highlighted. Finally, results from published studies providing examples of new avenues to successfully implement innovative precision nutrition approaches will be reviewed.


title: A rational account of pedagogical reasoning: Teaching by, and learning from, examples

abstract: Much of learning and reasoning occurs in pedagogical situations— situations in which a person who knows a concept chooses examples for the purpose of helping a learner acquire the concept. We introduce a model of teaching and learning in pedagogical settings that predicts which examples teachers should choose and what learners should infer given a teacher’s examples. We present three experiments testing the model predictions for rule-based, prototype, and causally structured concepts. The model shows good quantitative and qualitative fits to the data across all three experiments, predicting novel qualitative phenomena in each case. We conclude by discussing implications for understanding concept learning and implications for theoretical claims about the role of pedagogy in human learning.


title: CRM Strategies for A Small-Sized Online Shopping Mall Based on Association Rules and Sequential Patterns.

abstract: As dot-com bubble burst in 2002, an uncountable number of small-sized online shopping malls have emerged every day due to many good characteristics of online marketplace, including significantly reduced search costs and menu cost for products or services and easily accessing products or services in the world. However, all the online shopping malls have not continuously flourished. Many of them even vanished because of the lack of customer relationship management (CRM) strategies that fit them. The objective of this paper is to propose CRM strategies for small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customers in terms of recency, frequency and monetary (RFM) value. Then, we developed a model which classifies customers into VIP or non-VIP, using various data mining techniques such as decision tree, artificial neural network, logistic regression and bagging with each of these as a base classifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall.


title: LoRa™ wide-area networks from an Internet of Things perspective

abstract: In recent years, there are many discussions about the IoT (Internet of Things) concept. LoRa (Long Range) modulation together with LoRaWAN (LoRa Wide-Area Networks) communication protocol can represent a suitable candidate ensuring a high level of performance. The main contribution of the paper is the performance evaluation of the LoRa technology considering the requirements of IoT. In the first part of this paper LoRa technology is summarized by reviewing some aspects regarding the architecture and the security of the technology, meanwhile in the second part of the paper are presented some simulation scenarios. From the obtained results, by simply varying the SF (Spreading Factor) parameter the communication distance and robustness to interferences can be increased, meanwhile the data rate is reduced.


title: Intraoperative chemonucleolysis as an adjunct to lumbar discectomy.

abstract: STUDY DESIGN ::: This is a report of a series of 104 patients who underwent lumbar discectomy and chemonucleolysis at the University of Virginia between 1985 and 1989. ::: ::: ::: OBJECTIVE ::: The primary objective was to determine the safety of this procedure. A secondary objective was to begin studying its efficacy. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: This is the first report on the use of chymopapain during open surgery. ::: ::: ::: METHODS ::: Patients with low back pain and radicular symptoms not cured with conservative therapy were selected for participation in the study. Intraoperatively, disc spaces were injected with 2 ml of chymopapain. Patients were followed-up for an average of 5 years. Patients were surveyed regarding pain relief, and failures of the procedure were analyzed. ::: ::: ::: RESULTS ::: Seventy percent of patients reported good or excellent pain relief. Operative failures, characterized by reoperation at the same level, totaled 9.6%. Recurrent disc herniation at the time of reoperation occurred in 4.8%. ::: ::: ::: CONCLUSION ::: The procedure is safe and the relief of pain was similar to that in other published series. The suggestion is that the rate of recurrent disc herniation is reduced.


title: Three stages of facial expression processing: ERP study with rapid serial visual presentation

abstract: Abstract Electrophysiological correlates of the processing facial expressions were investigated in subjects performing the rapid serial visual presentation (RSVP) task. The peak latencies of the event-related potential (ERP) components P1, vertex positive potential (VPP), and N170 were 165, 240, and 240 ms, respectively. The early anterior N100 and posterior P1 amplitudes elicited by fearful faces were larger than those elicited by happy or neutral faces, a finding which is consistent with the presence of a ‘negativity bias.’ The amplitude of the anterior VPP was larger when subjects were processing fearful and happy faces than when they were processing neutral faces; it was similar in response to fearful and happy faces. The late N300 and P300 not only distinguished emotional faces from neutral faces but also differentiated between fearful and happy expressions in lag2. The amplitudes of the N100, VPP, N170, N300, and P300 components and the latency of the P1 component were modulated by attentional resources. Deficient attentional resources resulted in decreased amplitude and increased latency of ERP components. In light of these results, we present a hypothetical model involving three stages of facial expression processing.


title: Intelligent Range-Only Mapping and Navigation for Mobile Robots

abstract: Addressing the navigation (localization and motion control) problem of a mobile robot, coupled with its mapping problem, remains a significant challenge to date. The well-known simultaneous localization and mapping problem of mobile robots has been addressed in the literature without specifically taking into account the robot's motion control tasks. Moreover, its implementation can cost more than the robot itself. Robot motion control strategies developed in the literature either 1) rely on sophisticated hardware platforms, 2) assume noise-free environment, or 3) are based on abstract theories, which are validated using computer simulations only. The current work solves the navigation and mapping problems of mobile robots using open-source hardware and range-only measurements from a network of radio sources. The hardware platform used in this work is customized such that it is cost-effective and easy-to-implement, addressing the aforementioned issues in developing motion control strategies. The current strategy permits the robot to estimate its position and orientation and builds a map of its operating environment using radio frequency signals received from radio sources. It then navigates through a path defined by a set of two-dimensional points on the ground using a motion control strategy in cooperation with a computational intelligence tool. The proposed robot navigation and mapping scheme was tested in an indoor laboratory environment and its performance was compared with the simulation counterpart using a commercial robot simulator.


title: Learning Trans-Dimensional Random Fields with Applications to Language Modeling

abstract: To describe trans-dimensional observations in sample spaces of different dimensions, we propose a probabilistic model, called the trans-dimensional random field (TRF) by explicitly mixing a collection of random fields. In the framework of stochastic approximation (SA), we develop an effective training algorithm, called augmented SA, which jointly estimates the model parameters and normalizing constants while using trans-dimensional mixture sampling to generate observations of different dimensions. Furthermore, we introduce several statistical and computational techniques to improve the convergence of the training algorithm and reduce computational cost, which together enable us to successfully train TRF models on large datasets. The new model and training algorithm are thoroughly evaluated in a number of experiments. The word morphology experiment provides a benchmark test to study the convergence of the training algorithm and to compare with other algorithms, because log-likelihoods and gradients can be exactly calculated in this experiment. For language modeling, our experiments demonstrate the superiority of the TRF approach in being computationally more efficient in computing data probabilities by avoiding local normalization and being able to flexibly integrate a richer set of features, when compared with n-gram models and neural network models.


title: The Failure of Null Hypothesis Significance Testing When Studying Incremental Changes, and What to Do About It:

abstract: A standard mode of inference in social and behavioral science is to establish stylized facts using statistical significance in quantitative studies. However, in a world in which measurements are no...


title: Exponential Discriminative Metric Embedding in Deep Learning

abstract: Abstract With the remarkable success achieved by the Convolutional Neural Networks (CNNs) in object recognition recently, deep learning is being widely used in the computer vision community. Deep Metric Learning (DML), integrating deep learning with conventional metric learning, has set new records in many fields, especially in classification task. In this paper, we propose a replicable DML method, called Include and Exclude (IE) loss, to force the distance between a sample and its designated class center away from the mean distance of this sample to other class centers with a large margin in the exponential feature projection space. With the supervision of IE loss, we can train CNNs to enhance the intra-class compactness and inter-class separability, leading to great improvements on several public datasets ranging from object recognition to face verification. We conduct a comparative study of our algorithm with several typical DML methods on three kinds of networks with different capacity. Extensive experiments on three object recognition datasets and two face recognition datasets demonstrate that IE loss is always superior to other mainstream DML methods and approach the state-of-the-art results.


title: Prognostics and Health Management A Data-Driven Approach to Supporting the F-35 Lightning II

abstract: The Joint Strike Fighter (JSF) Program has developed an aggressive vision for and has specified a very stringent set of requirements for a comprehensive Prognostics and Health Management (PHM) system. This vision and the associated specified requirements has resulted in the development of perhaps the most advanced and comprehensive set of diagnostic, prognostic, and health management capabilities yet to be applied to an aviation platform. These PHM capabilities are currently being developed and integrated into the JSF Air System design. This paper will provide a top level overview summary of the current PHM concept, design, and architecture; provide a general capabilities description; and discuss overall program status. Some specific examples of system benefits, design trade-off decisions, and system integration challenges will be discussed. Particular examples of some subsystem prognostic capabilities will also be discussed.


title: ‘There’s a network out there you might as well tap’: Exploring the benefits of and barriers to exchanging informational and support-based resources on Facebook

abstract: Research has established a positive relationship between Facebook use and perceptions of social capital, a construct that describes the total resources − both potential and actual − available in one’s social network. However, the process through which social capital conversions occur is unclear. This study presents results from semi-structured interviews with 18 adult Facebook users (ages 25 to 55) about their Facebook use, focusing on how participants use the site to request and provide social support (associated with bonding social capital) and information (associated with bridging social capital). Findings describe how Facebook use facilitates interactions related to social capital and users’ beliefs about the potential negative outcomes of these interactions, providing insight into how users negotiate potential benefits and risks when making decisions about site use.


Reversion of advanced Ebola virus disease in nonhuman primates with ZMapp™


title: Material-based Design Computation An Inquiry into Digital Simulation of Physical Material Properties as Design Generators

abstract: The paper demonstrates the association between geometry and material behavior, specifically the elastic properties of resin impregnated latex membranes, by means of homogenizing protocols which translate physical properties into geometrical functions. Resin-impregnation patterns are applied to 2-D pre-stretched form-active tension systems to induce 3-D curvature upon release. This method enables form-finding based on material properties, organization and behavior. Some theoretical foundations for material-computation are outlined. A digital tool developed in the Processing (JAVA coded) environment demonstrates the simulation of material behavior and its prediction under specific environmental conditions. Finally, conclusions are drawn from the physical and digital explorations which redefine generative material-based design computation, supporting a synergetic approach to design integrating form, structure, material and environment.


title: A comparison of statistical significance tests for information retrieval evaluation

abstract: Information retrieval (IR) researchers commonly use three tests of statistical significance: the Student's paired t-test, the Wilcoxon signed rank test, and the sign test. Other researchers have previously proposed using both the bootstrap and Fisher's randomization (permutation) test as non-parametric significance tests for IR but these tests have seen little use. For each of these five tests, we took the ad-hoc retrieval runs submitted to TRECs 3 and 5-8, and for each pair of runs, we measured the statistical significance of the difference in their mean average precision. We discovered that there is little practical difference between the randomization, bootstrap, and t tests. Both the Wilcoxon and sign test have a poor ability to detect significance and have the potential to lead to false detections of significance. The Wilcoxon and sign tests are simplified variants of the randomization test and their use should be discontinued for measuring the significance of a difference between means.


title: Predicting User Behavior in E-commerce Based on Psychology Model

abstract: Web sites are making great effort to understand the user’s behavior in order to make the web sites easy to use and further increase their profits. This paper presents a method to predict the user’s buying behavior based on psychology model. We employ the method to analyze online store data and treat the clicking and buying as user’s attitude and behavior. Then, a new model, that is used to predict the user’s future buying behavior, is built based on the attitude-behavior relationship theory. We then verify the model and simultaneously estimate its parameters by path analysis. Our method is evaluated by comparing with traditional naive bayes classification algorithm. Experiments results show that our model is more effective in predicting buying behavior and finding out users who are more profitable to web sites.


Complexity Thinking & Leadership : How Nonlinear Models of Human Organizing Dynamics Can Inform Management Practice


title: Character-Word LSTM Language Models

abstract: We present a Character-Word Long Short-Term Memory Language Model which both reduces the perplexity with respect to a baseline word-level language model and reduces the number of parameters of the model. Character information can reveal structural (dis)similarities between words and can even be used when a word is out-of-vocabulary, thus improving the modeling of infrequent and unknown words. By concatenating word and character embeddings, we achieve up to 2.77% relative improvement on English compared to a baseline model with a similar amount of parameters and 4.57% on Dutch. Moreover, we also outperform baseline word-level models with a larger number of parameters.


title: Recurrent Mixture Density Network for Spatiotemporal Visual Attention

abstract: In many computer vision tasks, the relevant information to solve the problem at hand is mixed to irrelevant, distracting information. This has motivated researchers to design attentional models that can dynamically focus on parts of images or videos that are salient, e.g., by down-weighting irrelevant pixels. In this work, we propose a spatiotemporal attentional model that learns where to look in a video directly from human fixation data. We model visual attention with a mixture of Gaussians at each frame. This distribution is used to express the probability of saliency for each pixel. Time consistency in videos is modeled hierarchically by: 1) deep 3D convolutional features to represent spatial and short-term time relations and 2) a long short-term memory network on top that aggregates the clip-level representation of sequential clips and therefore expands the temporal domain from few frames to seconds. The parameters of the proposed model are optimized via maximum likelihood estimation using human fixations as training data, without knowledge of the action in each video. Our experiments on Hollywood2 show state-of-the-art performance on saliency prediction for video. We also show that our attentional model trained on Hollywood2 generalizes well to UCF101 and it can be leveraged to improve action classification accuracy on both datasets.


title: Low power XOR gate design and its applications

abstract: With advent of technology scaling, the prime objective of design i.e. low power consumption can be easily acquired. For any digital logic design the power consumption depends on; Supply voltage, number of transistors incorporated in circuit and scaling ratios of the same. As CMOS technology supports inversion logic designs; NAND & NOR structures are useful for converting any logic equation into physical level design that comprises of PMOS and NMOS transistors. In similar way, logic can be implemented in other styles as well, with the difference in number of transistors required. The conventional CMOS design for XOR logic can be possible with 8 or more than 8 transistors, with the methodology discussed in this paper, the same design for XOR logic can be made possible with 6 transistors. The proposed methodology consists of Pass transistor logic and Single feedback topology. This design consumes 50% less power than that of conventional XOR logic design with CMOS technology. Since the design for XOR logic, is useful for variety of applications such as Data encryption, Arithmetic circuits, Binary to Gray encoding etc. the XOR logic has been selected for design. The design explained in this paper is simulated with Cadence 90nm technology.


title: RGBD salient object detection: A benchmark and algorithms

abstract: Although depth information plays an important role in the human vision system, it is not yet well-explored in existing visual saliency computational models. In this work, we first introduce a large scale RGBD image dataset to address the problem of data deficiency in current research of RGBD salient object detection. To make sure that most existing RGB saliency models can still be adequate in RGBD scenarios, we continue to provide a simple fusion framework that combines existing RGB-produced saliency with new depth-induced saliency, the former one is estimated from existing RGB models while the latter one is based on the proposed multi-contextual contrast model. Moreover, a specialized multi-stage RGBD model is also proposed which takes account of both depth and appearance cues derived from low-level feature contrast, mid-level region grouping and high-level priors enhancement. Extensive experiments show the effectiveness and superiority of our model which can accurately locate the salient objects from RGBD images, and also assign consistent saliency values for the target objects.


title: Monte Carlo sampling of solutions to inverse problems

abstract: Probabilistic formulation of inverse problems leads to the definition of a probability distribution in the model space. This probability distribution combines a priori information with new information obtained by measuring some observable parameters (data). As, in the general case, the theory linking data with model parameters is nonlinear, the a posteriori probability in the model space may not be easy to describe (it may be multimodal, some moments may not be defined, etc.). When analyzing an inverse problem, obtaining a maximum likelihood model is usually not sucient, as we normally also wish to have infor


title: Adaptive Neural Network Control of a Self-Balancing Two-Wheeled Scooter

abstract: This paper presents an adaptive control using radial-basis-function neural networks (RBFNNs) for a two-wheeled self-balancing scooter. A mechatronic system structure of the scooter driven by two dc motors is briefly described, and its mathematical modeling incorporating two frictions between the wheels and the motion surface is derived. By decomposing the overall system into two subsystems (yaw motion and mobile inverted pendulum), one proposes two adaptive controllers using RBFNN to achieve self-balancing and yaw control. The performance and merit of the proposed adaptive controllers are exemplified by conducting several simulations and experiments on a two-wheeled self-balancing scooter.


title: Light Source Detection in Photographs

abstract: Common tasks related to image processing or augmented reality include rendering new objects into existing images, or matching objects with unknown illumination. To facilitate such algorithms, it is often necessary to infer from which directions a scene was illuminated, even if only a photograph is available. For this purpose, we present a novel light source detection algorithm that, contrary to the current state-of-the-art, is able to detect multiple light sources with sufficient accuracy. 3D measures are not required, only the input image and a very small amount of unskilled user interaction.


title: Mixed Membership Models of Scientific Publications

abstract: PNAS is one of world's most cited multidisciplinary scientific journals. The PNAS official classification structure of subjects is reflected in topic labels submitted by the authors of articles, largely related to traditionally established disciplines. These include broad field classifications into physical sciences, biological sciences, social sciences, and further subtopic classifications within the fields. Focusing on biological sciences, we explore an internal soft-classification structure of articles based only on semantic decompositions of abstracts and bibliographies and compare it with the formal discipline classifications. Our model assumes that there is a fixed number of internal categories, each characterized by multinomial distributions over words (in abstracts) and references (in bibliographies). Soft classification for each article is based on proportions of the article's content coming from each category. We discuss the appropriateness of the model for the PNAS database as well as other features of the data relevant to soft classification.


title: Nissen vs. Toupet Fundoplication in the Treatment of Gastroesophageal Reflux Disease

abstract: Gastroesophageal reflux disease (GERD) is a growing phenomenon in pediatric patients that requires much attention. The prevalence of GERD in infants has been reported as high as 20% to 40% (Keady, 2007), with a higher prevalence in premature infants and infants with medical comorbidities (Riffe, Sayre, Waller, Brinker, & Roberts, 2007). Although most cases of infant GERD self-resolve by 1 year of age, if left untreated, GERD can lead to a number of complications, varying from esophagitis, dental erosion, otitis media, bronchitis, recurrent pneumonia, persistent asthma, and failure to thrive (Riffe et al., 2007). These complications can impede normal development and can lead to multiple hospitalizations and medical conditions involving chronic pulmonary and nutrition disorders, as well as dental problems and hearing loss. With better treatment of GERD, the rates and costs of hospitalizations associated with complications can decrease, and a better quality of life for patients and families dealing with GERD can be achieved. Gastroesophageal reflux (GER) is


Robust speech recognition with speech enhanced deep neural networks.


title: The Generalized A* Architecture

abstract: We consider the problem of computing a lightest derivation of a global structure using a set of weighted rules. A large variety of inference problems in AI can be formulated in this framework. We generalize A* search and heuristics derived from abstractions to a broad class of lightest derivation problems. We also describe a new algorithm that searches for lightest derivations using a hierarchy of abstractions. Our generalization of A* gives a new algorithm for searching AND/OR graphs in a bottom-up fashion. ::: ::: We discuss how the algorithms described here provide a general architecture for addressing the pipeline problem -- the problem of passing information back and forth between various stages of processing in a perceptual system. We consider examples in computer vision and natural language processing. We apply the hierarchical search algorithm to the problem of estimating the boundaries of convex objects in grayscale images and compare it to other search methods. A second set of experiments demonstrate the use of a new compositional model for finding salient curves in images.


title: Managing Multigranular Linguistic Distribution Assessments in Large-Scale Multiattribute Group Decision Making

abstract: Linguistic large-scale group decision making (LGDM) problems are more and more common nowadays. In such problems a large group of decision makers are involved in the decision process and elicit linguistic information that are usually assessed in different linguistic scales with diverse granularity because of decision makers' distinct knowledge and background. To keep maximum information in initial stages of the linguistic LGDM problems, the use of multi-granular linguistic distribution assessments seems a suitable choice, however to manage such multigranular linguistic distribution assessments, it is necessary the development of a new linguistic computational approach. In this paper it is proposed a novel computational model based on the use of extended linguistic hierarchies, which not only can be used to operate with multi-granular linguistic distribution assessments, but also can provide interpretable linguistic results to decision makers. Based on this new linguistic computational model, an approach to linguistic large-scale multi-attribute group decision making is proposed and applied to a talent selection process in universities.


title: Wearable skin vibration sensor using a PVDF film

abstract: This paper aims to develop a wearable tactile sensor for measuring skin vibrations using a polyvinylidene fluoride (PVDF) film, which is a polymer piezo material. The sensor is worn on the finger pad where is remote from contact fingertip and detects skin-propagated vibrations when fingertip touches an object. The proposed sensor allows users to touch with bare fingers and to conduct active touch. A transfer function from vibrations applied on the fingertip to the sensor output is expressed by using a finger model, a sensor model, and an electric model of the PVDF film. On the basis of the transfer function, frequency response of the sensor is measured and estimation of vibrations is tested. Furthermore, the sensor output is investigated for three materials with different textures. Results show the validity and availability of the sensor.


Entropy-based algorithms for best basis selection


Intensive Lipid Lowering with Simvastatin and Ezetimibe in Aortic Stenosis


title: On Two Parameters for Denoising With Non-Local Means

abstract: Non-local means (NLM) provides a very efficient procedure to denoise digital images. We study the influence of two important parameters on this algorithm: the size of the searching window and the weight given to the central patch. We verify numerically the common knowledge that the searching zone can be advantageously limited and we propose an efficient modification of the central weight based on the Stein's unbiased risk estimate principle.


Findings of the WMT 2018 Shared Task on Automatic Post-Editing


title: Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks

abstract: In this paper we propose the utterance-level Permutation Invariant Training (uPIT) technique. uPIT is a practically applicable, end-to-end, deep learning based solution for speaker independent multi-talker speech separation. Specifically, uPIT extends the recently proposed Permutation Invariant Training (PIT) technique with an utterance-level cost function, hence eliminating the need for solving an additional permutation problem during inference, which is otherwise required by frame-level PIT. We achieve this using Recurrent Neural Networks (RNNs) that, during training, minimize the utterance-level separation error, hence forcing separated frames belonging to the same speaker to be aligned to the same output stream. In practice, this allows RNNs, trained with uPIT, to separate multi-talker mixed speech without any prior knowledge of signal duration, number of speakers, speaker identity or gender. We evaluated uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks and found that uPIT outperforms techniques based on Non-negative Matrix Factorization (NMF) and Computational Auditory Scene Analysis (CASA), and compares favorably with Deep Clustering (DPCL) and the Deep Attractor Network (DANet). Furthermore, we found that models trained with uPIT generalize well to unseen speakers and languages. Finally, we found that a single model, trained with uPIT, can handle both two-speaker, and three-speaker speech mixtures.


title: An Efficient, Distributed Stochastic Gradient Descent Algorithm for Deep-Learning Applications

abstract: Parallel and distributed processing is employed to accelerate training for many deep-learning applications with large models and inputs. As it reduces synchronization and communication overhead by tolerating stale gradient updates, asynchronous stochastic gradient descent (ASGD), derived from stochastic gradient descent (SGD), is widely used. Recent theoretical analyses show ASGD converges with linear asymptotic speedup over SGD.Oftentimes glossed over in theoretical analysis are communication overhead and practical learning rates that are critical to the performance of ASGD. After analyzing the communication performance and convergence behavior of ASGD using the Downpour algorithm as an example, we demonstrate the challenges for ASGD to achieve good practical speedup over SGD. We propose a distributed, bulk-synchronous stochastic gradient descent algorithm that allows for sparse gradient aggregation from individual learners. The communication cost is amortized explicitly by a gradient aggregation interval, and global reductions are used instead of a parameter server for gradient aggregation. We prove its convergence and show that it has superior communication performance and convergence behavior over popular ASGD implementations such as Downpour and EAMSGD for deep-learning applications.


title: A Kabbalah System Theory Modeling Framework for Knowledge Based Behavioral Economics and Finance

abstract: Kabbalah and its Tree of Life integrate the cognitive, behavioral/emotional and action levels of human existence, explaining the relations between these and their unity. This makes it an ideal framework for behavioral economics and finance where cognitive and emotional biases and heuristics play a central role. In Burstein and Negoita [9], we began to develop a Kabbalah system theory, modeling the Tree of Life as a hierarchical three level feedback control system corresponding to the cognitive, behavioral/emotional and action levels. We will further develop this here by focusing on system dynamics, in order to create a Kabbalah system theory modeling framework for a knowledge based behavioral economics and finance. In this new framework, emotional intelligence theory [19–21, 40] and Polanyi personal tacit/explicit knowledge theory [36, 37] are used to model the emotional and cognitive level processes. Here we are connecting these theories with behavioral economics and finance. While behavioral finance focuses on the impact of knowledge/cognition and emotional factors, the intrinsic dynamics of these factors is not considered in depth. This is why behavioral economics and finance can benefit from integrating emotional intelligence and knowledge theory in their modeling. Our Kabbalah system theory creates the modeling framework for that. In particular, it allows connecting the recent knowledge based economic theory attempts with behavioral economics in a unified knowledge based behavioral economics. We apply this in particular to Kabbalah behavioral, knowledge based asset pricing modeling.


title: 5G multi-antenna technology and experimental trials

abstract: In order to provide a wide variety of mobile services and applications, the fifth-generation (5G) mobile communication system has attracted much attention to improve system capacity much more than the 4G system. The drastic improvement is mainly realized by small/semi-macro cell deployment with much wider bandwidth in higher frequency bands. To cope with larger pathloss in the higher frequency bands, Massive MIMO is one of key technologies to acquire beamforming (BF) in addition to spatial multiplexing. This paper introduces 5G Massive MIMO technologies including high-performance hybrid BF and novel digital BF schemes in addition to distributed Massive MIMO concept with flexible antenna deployment. The latest 5G experimental trials using the Massive MIMO technologies are also shown briefly.


title: Impact of certain plants and synthetic molluscicides on some fresh water snails and fish.

abstract: The LC50 (78, 85 ppm) and LC90 (88, 135 ppm) of Anagalis arvensis and Calendula micrantha respectively against Biomphalaria alexandrina were higher than those of the non-target snails, Physa acuta, Planorbis planorbis, Helisoma duryi and Melanoides tuberculata. In contrast, the LC50 of Niclosamide (0.11 ppm) and Copper sulphate (CuSO4) (0.42 ppm) against B. alexandrina were lower than those of the non-target snails. The mortalities percentage among non-target snails ranged between 0.0 & 20% when sublethal concentrations of CuSO4 against B. alexandrina mixed with those of C. micrantha and between 0.0 & 40% when mixed with A. arvensis. Mortalities ranged between 0.0 & 50% when Niclosamide was mixed with each of A. arvensis and C. micrantha. A. arvensis induced 100% mortality on Oreochromis niloticus after 48 hrs exposure and after 24 hrs for Gambusia affinis. C. micrantha was non-toxic to the fish. The survival rate of O. niloticus and G. affinis after 48 hrs exposure to 0.11 ppm of Niclosamide were 83.3% & 100% respectively. These rates were 91.7% & 93.3% respectively when each of the two fish species was exposed to 0.42 ppm of CuSO4. Mixture of sub-lethal concentrations of A. arvensis against B. alexandrina and those of Niclosamide or CuSO4 at ratios 10:40 & 25:25 induced 66.6% mortalities on O. niloticus and 83.3% at 40:10. These mixtures caused 100% mortalities on G. affinis at all ratios. A. arvensis CuSO4 mixtures at 10:40 induced 83.3% & 40% mortalities on O. niloticus and G. affinis respectively and 100% mortalities on both fish species at ratios 25:25 & 40:10. A mixture of sub-lethal concentrations of C. micrantha against B. alexandrina and of Niclosamide or CuSO4 caused mortalities of O. niloticus between 0.0 & 33.3% and between 5% & 35% of G. affinis. The residue of Cu in O. niloticus were 4.69, 19.06 & 25.37 mg/1kgm fish after 24, 48 & 72 hrs exposure to LC0 of CuSO4 against B. alexandrina respectively.


title: Gut flora in health and disease

abstract: The human gut is the natural habitat for a large and dynamic bacterial community, but a substantial part of these bacterial populations are still to be described. However, the relevance and effect of resident bacteria on a host's physiology and pathology has been well documented. Major functions of the gut microflora include metabolic activities that result in salvage of energy and absorbable nutrients, important trophic effects on intestinal epithelia and on immune structure and function, and protection of the colonised host against invasion by alien microbes. Gut flora might also be an essential factor in certain pathological disorders, including multisystem organ failure, colon cancer, and inflammatory bowel diseases. Nevertheless, bacteria are also useful in promotion of human health. Probiotics and prebiotics are known to have a role in prevention or treatment of some diseases.


title: CoARX: a coprocessor for ARX-based cryptographic algorithms

abstract: Cryptographic coprocessors are inherent part of modern System-on-Chips. It serves dual purpose - efficient execution of cryptographic kernels and supporting protocols for preventing IP-piracy. Flexibility in such coprocessors is required to provide protection against emerging cryptanalytic schemes and to support different cryptographic functions like encryption and authentication. In this context, a novel crypto-coprocessor, named CoARX, supporting multiple cryptographic algorithms based on Addition (A), Rotation (R) and eXclusive-or (X) operations is proposed. CoARX supports diverse ARX-based cryptographic primitives. We show that compared to dedicated hardware implementations and general-purpose microprocessors, it offers excellent performance-flexibility trade-off including adaptability to resist generic cryptanalysis.


title: Network Virtualization in Multi-tenant Datacenters

abstract: Multi-tenant datacenters represent an extremely challenging networking environment. Tenants want the ability to migrate unmodified workloads from their enterprise networks to service provider datacenters, retaining the same networking configurations of their home network. The service providers must meet these needs without operator intervention while preserving their own operational flexibility and efficiency. Traditional networking approaches have failed to meet these tenant and provider requirements. Responding to this need, we present the design and implementation of a network virtualization solution for multi-tenant datacenters.


title: Extracting keyword and keyphrase from online privacy policies

abstract: One of the key components of constructing an ontology is a taxonomy. Creating a comprehensive taxonomy involves extracting keywords and keyphrases from the domain corpus. It is a time consuming endeavour that involves domain expertise and syntactic and structural knowledge of the corpus in question. In this paper we explore different keyword and keyphrase extraction algorithms for the domain of online privacy policies. To do this we used a variety of well-known techniques such as TF-IDF, RAKE, TextRank, and AlchemyAPI, benchmarked against manual annotation. We then further evaluated the performances of various algorithms over a large corpus of 631 privacy policies. Due to the inconsistent language of privacy policies algorithms evaluating single documents (RAKE, TextRank, AlchemyAPI) outperformed the one evaluating the entire corpus (TF-IDF).


title: Modelling of transmission line loaded with BCI probe using circuit concept approach

abstract: An estimation method of the induced current of a transmission line under the bulk current injection (BCI) test is presented. In this method, the influence of the current injected by the BCI probe is expressed using the equivalent voltage and current sources. By modelling the transmission line loaded with the BCI probe as a 3-port network, an absolute value of the induced current of transmission line can be estimated analytically without using numerical simulation. To validate the proposed method, the calculated results are shown and compared with the experimental results.


title: Healthcare Robotics

abstract: Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.


Combining Software Games with Education: Evaluation of its Educational Effectiveness


title: The persona effect: affective impact of animated pedagogical agents

abstract: Animated pedagogical agents that inhabit interactive learning environments can exhibit strikingly lifelike behaviors. In addition to providing problem-solving advice in response to students’ activities in the learning environment, these agents may also be able to play a powerful motivational role. To design the most effective agent-based learning environment software, it is essential to understand how students perceive an animated pedagogical agent with regard to affective dimensions such as encouragement, utility, credibility, and clarity. This paper describes a study of the affective impact of animated pedagogical agents on students’ learning experiences. One hundred middle school students interacted with animated pedagogical agents to assess their perception of agents’ affective characteristics. The study revealed the persona eflecr, which is that the presence of a lifelike character in an interactive learning environment~ven one that is not expressive— can have a strong positive effect on student’s perception of their learning experience. The study also demonstrates the interesting effect of multiple types of explanatory behaviors on both affective perception and learning performance.


title: A case study in the gamification of a university-level games development course

abstract: Gamification is the application of game mechanics and player incentives to non-game environments. When designed correctly, gamification has been found to increase engagement and encourage targeted behaviours among users. This paper presents the gamification of a university course in Computer Games Development using an online learning management tool, including how this might generalize to other courses. Our goal with gamification was to improve lecture attendance, content understanding, problem solving skills and general engagement. The success of this intervention was measured using course marks, lecturer evaluations, lecture attendance, and a questionnaire; all with strongly positive results. However, this must be balanced against the costs, both monetary and time, required to successfully implement gamification.


title: NoSQL databases: a step to database scalability in web environment

abstract: The paper is focused on so called NoSQL databases. In context of cloud computing, architectures and basic features of these databases are studied, particularly their horizontal scalability and concurrency model, that is mostly weaker than ACID transactions in relational SQL-like database systems. Some characteristics like a data model and querying capabilities are discussed in more detail. The paper also contains an overview of some representatives of NoSQL databases.


title: Cytoarchitecture of the cerebral cortex—More than localization

abstract: Abstract The present paper reviews that macroanatomical landmarks are problematic for a reliable and sufficiently precise localization of clusters of activation obtained by functional imaging because sulcal and gyral patterns are extremely variable and macroanatomical landmarks do not match (in nearly all cases) architectonically defined borders. It argues that cytoarchitectonic probabilistic maps currently offer the most precise tool for the localization of brain functions as obtained from functional imaging studies. Finally, it provides some examples that cytoarchitecture is more than localization with respect to a particular brain region because it reflects the inner organization of cortical areas and, furthermore, functional principles of the brain.


title: Design 2. 4 GHz 130nm CMOS Low Noise Amplifier Design for Wireless Network

abstract: A low noise amplifier is one of the most commonly used components in analog and digital circuit designs. Low voltage, low power and low noise amplifier design has become an increasingly interesting subject as many applications switch to portable battery powered operations. An amplifier is linear electronic circuits that may used amplify an input signal and provide an output signal that is a magnified replica of the input signal. The need for design techniques to allow amplifiers to maintain an acceptable level of performance when the supply voltages are decreased is immense for maintain high gain and as low as possible noise. Popular LNA topologies are the inductive source degeneration common-source low noise amplifier. The common source low noise amplifier is commonly used for narrow-band applications due to its ease of input matching, high gain and low noise. Using this method, overall gain can be increased and the noise figure of the low noise amplifier can be decreased.. This work we presents a design of a low noise CMOS amplifier realized in a standard 130 nm CMOS technology with 1.3V supply voltage and consumes power less than 200uW with 18dB on given frequency range also have good input and output matching.


title: Late Reverberation Suppression Using Recurrent Neural Networks with Long Short-Term Memory

abstract: Human speech is usually distorted by room reverberation. These corruptions degrade speech quality and intelligibility, especially under a long reverberation time, and they also pose a serious problem for many speech-related applications such as automatic speech recognition. In this paper, we propose a supervised speech dereverberation algorithm that models late reverberation using a recurrent neural network (RNN) with long short-term memory (LSTM). By taking advantage of LSTM's ability to capture a long history, late reverberation can be effectively removed by the proposed approach. Systematic evaluations indicate that our approach improves the quality of reverberant speech in a wide range of reverberant conditions. Moreover, the proposed system is a causal system, which can be applied in real-time applications.


title: Ten key considerations for the successful implementation and adoption of large-scale health information technology

abstract: The implementation of health information technology interventions is at the forefront of most policy agendas internationally. However, such undertakings are often far from straightforward as they require complex strategic planning accompanying the systemic organizational changes associated with such programs. Building on our experiences of designing and evaluating the implementation of large-scale health information technology interventions in the USA and the UK, we highlight key lessons learned in the hope of informing the on-going international efforts of policymakers, health directorates, healthcare management, and senior clinicians.


title: CMM in uncertain environments

abstract: Injecting ancient Chinese philosophy into modern science and technology provides a unique perspective on uncertainty and rapid change.


title: Cloud for e-Learning: Determinants of Its Adoption by University Students in a Developing Country

abstract: Cloud computing is an innovative technology that can provide huge benefits to universities to improve their teaching and learning processes. While it has been widely adopted for e-Learning across universities in developed countries, its adoption in developing countries is very low and there is a lack of empirical studies that investigate the factors that influence its adoption by university students. In this paper, we identify and examine the factors that influence university students' intention to adopt cloud services in a developing country, Saudi Arabia. An empirical case study on a Software as a Service (SaaS) was conducted across two universities and a survey questionnaire modified from Technology Acceptance Model 3 (TAM3) was administered to 527 students. The results show that perceived usefulness and perceived ease of use are the main determinants of students' behavioural intention to adopt the provided cloud computing service. Detailed analysis of the determinants provides recommendations that can guide decision makers in universities to improve students' adoption by facilitating successful implementation of cloud services and by making students realise the relevance of cloud services to their e-Learning activities and outcomes. The study also offers cloud service providers recommendations on tailoring their services in order to build a successful e-Business model for universities in developing countries.


title: Design Optimization and Test of a Radially Magnetized Magnetic Screw With Discretized PMs

abstract: A magnetic screw is a new type of high force density linear actuator. One of the key challenges for realization of the magnetic screw concept is the manufacturing process of its helical permanent-magnet (PM) poles. Structure simplification and simple assembly process are essential in promoting the development of the magnetic screw. This paper studies several PM configurations employed to realize the magnetic screw and proposes a new structure, which can well approximate the helical magnetic poles in a very simple way. The electromagnetic performances are assessed analytically and by time-stepping finite-element analysis (FEA). Finally, both the analytical model and the FE results are validated by experiments based on a prototype machine.


title: A 12.4-kHz on-chip RC oscillator with comparator offset cancellation for PVT variation tolerance

abstract: This paper presents a low-power on-chip RC oscillator with compensation for temperature and supply voltage variation. This circuit is based on a conventional on-chip oscillator, with only a capacitor and a comparator to avoid mismatch. Multiple current sources flow through the same resistor to generate a reference voltage. This is complemented with the dynamic element matching technique to reduce the inherent mismatch between current sources. A comparator offset compensation scheme is also proposed in this circuit. Simulation results show that the circuit can generate a stable frequency of 12.4 kHz. The power consumption is 9.3 µW. The temperature coefficient is ±0.12% across −40°C to 120°C and the supply voltage variation coefficient is ±0.08% ranging from 1 V to 2 V.


title: Overhead of deoptimization checks in the V8 javascript engine

abstract: Just-in-time compilation for dynamic languages uses type speculation to generate optimized code for frequently executed code sections. However, even the high-performance code requires checks to ensure that assumptions made during code generation are not violated. If the assumptions are violated then the program reverts to executing unoptimized code. This process is called deoptimization, and the checks are called deoptimization checks. We analyzed the frequency of conditional deoptimization checks and the performance impact of the associated conditional branch using the V8 Javascript engine running the Octane benchmarks. Over half of the benchmarks have at least 5 checks per 100 instructions, and 1 benchmark has as many as 19 checks per 100 instructions. The performance impact of the checks varies significantly depending on the microarchitecture of the CPU that is running the code. On average the conditional branch used by the checks accounts for over 6.2% of the benchmark's dynamic instructions. Removing these checks provides only a 2.2% performance improvement when running on a high performance Intel CPU, but a 4.6% performance improvement on an Intel CPU optimized for low power consumption.


Aircraft in Your Head: How Air Traffic Controllers Mentally Organize Air Traffic


title: Design Guidelines for Mobile Augmented Reality: User Experience

abstract: In designing for mobile augmented reality applications it is very important to understand the context in which the information has to be presented. Past research on information presentation on small form factor computing has highlighted the importance of presenting the right information in the right way to effectively engage the user. This chapter provides an overview of the design challenges in developing persuasive usage model interactions for a great user experience on mobile devices. When designing for a mobile augmented reality application it is important to understand the usage scenario, the interaction modalities and the device form factor.


title: Flexible SDN control in tactical ad hoc networks

abstract: Abstract Modern tactical operations have complex communication and computing requirements that cannot be supported by today’s mobile ad hoc networks. The emerging Software Defined Networking (SDN) paradigm has the potential to enable the redesign and successful deployment of these systems. An SDN-based approach, however, will also bring new challenges since the SDN architecture was not designed to accommodate the requirements of an ad hoc network environment. Unreliability and dynamism may fragment the tactical network making the centralized SDN controller unsafe. To address these issues, in this paper, we propose flexible protocols that split the control of the ad hoc network between the centralized SDN controller and the data plane nodes. The latter can dynamically decide whether to follow the controller instructions or adapt to network changes in a distributed manner. We implement a proof-of-concept prototype of a flexible SDN ad hoc system and perform experiments to measure its performance benefits over traditional OpenFlow. Going a step further, we study theoretical, yet practical, methods of managing the overheads of flexible control which are crucial for the successful large-scale deployment of these systems. Evaluations on a real tactical ad hoc network dataset demonstrate that up to 50% more packets can be routed to their destinations if flexible SDN control is enabled, while the extra overheads are one order of magnitude lower than the control message overheads of traditional OpenFlow systems.


title: Multi-stage beamforming codebook for 60GHz WPAN

abstract: Beamforming(BF) based on codebook is regarded as an attractive solution to resolve the poor link budget of millimeter-wave 60GHz wireless communication. Because the number of the elements of antenna array in 60GHz increases, the beam patterns generated are more than common BF, and it causes long set-up time during beam patterns searching. In order to reduce the set-up time, three stages protocol, namely the device (DEV) to DEV linking, sector-level searching and beam-level searching has been adopted by the IEEE 802.15.3c as an optional functionality to realize Gbps communication systems. However, it is still a challenge to create codebook of different patterns to support three stages protocol from common codebook of beam pattern. In this paper, we proposes a multi-stage codebook design and the realization architecture to support three stages BF. The multi-stage codebook can create different granularity of beam patterns and realize progressive searching. Simulation results for eight elements uniform linear array (ULA) show that this design can divide the beam searching to three stages searching without increasing the system complexity.


BEOWULF: A Parallel Workstation for Scientific Computation


title: Active + Semi-supervised Learning = Robust Multi-View Learning

abstract: In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept. Semi-supervised, multi-view algorithms, which reduce the amount of labeled data required for learning, rely on the assumptions that the views are compatible and uncorrelated (i.e., every example is identically labeled by the target concepts in each view; and, given the label of any example, its descriptions in each view are independent). As these assumptions are unlikely to hold in practice, it is crucial to understand the behavior of multi-view algorithms on problems with incompatible, correlated views. We address this issue by studying several algorithms on a parameterized family of text classification problems in which we control both view correlation and incompatibility. We first show that existing semi-supervised algorithms are not robust over the whole spectrum of parameterized problems. Then we introduce a new multi-view algorithm, Co-EMT, which combines semi-supervised and active learning. Co-EMT outperforms the other algorithms both on the parameterized problems and on two additional real world domains. Our experiments suggest that Co-EMT’s robustness comes from active learning compensating for the correlation of the views.


Background: Physics and Math of Shading


title: Carbohydrate Metabolism in Experimental Salicylism.∗

abstract: Summary Fasting rats given large doses of sodium salicylate fail to deposit liver glycogen even when they are given a major stimulus for such deposition in the form of Compound F. Yet such animals continue to break down body protein at an accelerated rate as judged by the nitrogen balances. The level of muscle glycogen salicylate-treated rats is reduced and this effect is countered by concurrent treatment with Compound F. The significance of these findings is briefly discussed.


title: Early (M170) activation of face-specific cortex by face-like objects

abstract: The tendency to perceive faces in random patterns exhibiting configural properties of faces is an example of pareidolia. Perception of ‘real’ faces has been associated with a cortical response signal arising at approximately 170 ms after stimulus onset, but what happens when nonface objects are perceived as faces? Using magnetoencephalography, we found that objects incidentally perceived as faces evoked an early (165 ms) activation in the ventral fusiform cortex, at a time and location similar to that evoked by faces, whereas common objects did not evoke such activation. An earlier peak at 130 ms was also seen for images of real faces only. Our findings suggest that face perception evoked by face-like objects is a relatively early process, and not a late reinterpretation cognitive phenomenon.


title: Neuromorphic Processors with Memristive Synapses: Synaptic Interface and Architectural Exploration

abstract: Due to their nonvolatile nature, excellent scalability, and high density, memristive nanodevices provide a promising solution for low-cost on-chip storage. Integrating memristor-based synaptic crossbars into digital neuromorphic processors (DNPs) may facilitate efficient realization of brain-inspired computing. This article investigates architectural design exploration of DNPs with memristive synapses by proposing two synapse readout schemes. The key design tradeoffs involving different analog-to-digital conversions and memory accessing styles are thoroughly investigated. A novel storage strategy optimized for feedforward neural networks is proposed in this work, which greatly reduces the energy and area cost of the memristor array and its peripherals.


title: All learning is Local: Multi-agent Learning in Global Reward Games

abstract: In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algorithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world from a single agent's limited perspective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and learn an effective policy.


title: Ultra-wideband slot-loaded antipodal Vivaldi antenna array

abstract: A slot loading exploited as a novel termination of conventional Vivaldi antennas is introduced. This named slot-loaded Vivaldi antenna has been demonstrated to have the impedance bandwidth of over 25∶1. In addition, an 1×4 H-plane phased array is constructed to demonstrate maximum 30 degree scan angle operated at the ultra-wide bandwidth of 4–16 GHz.


title: How can CSR affect company performance? : A qualitative study of CSR and its effects

abstract: In today’s society, there is a growing interest in, and demand for Corporate Social Responsibility (CSR). Reasons for this can be multinational corporations’ increasing influence on world economy a ...


title: Defining the Ethereum Virtual Machine for Interactive Theorem Provers

abstract: Smart contracts in Ethereum are executed by the Ethereum Virtual Machine (EVM). We defined EVM in Lem, a language that can be compiled for a few interactive theorem provers. We tested our definition against a standard test suite for Ethereum implementations. Using our definition, we proved some safety properties of Ethereum smart contracts in an interactive theorem prover Isabelle/HOL. To our knowledge, ours is the first formal EVM definition for smart contract verification that implements all instructions. Our definition can serve as a basis for further analysis and generation of Ethereum smart contracts.