title: Fast optimization of nano-CMOS voltage-controlled oscillator using polynomial regression and genetic algorithm

abstract: Fast optimization of CMOS circuits is needed to reduce design cycle time and chip cost and to enhance yield. Mature electronic design automation (EDA) tools and well-defined abstraction-levels for digital circuits have largely automated the digital design process. However, analog circuit design and optimization is still not automated. Custom design of analog circuits and slow analog in SPICE has always needed maximum efforts, skills and design cycle time. In this paper, two novel design flows are presented for fast multiobjective optimization of nano-CMOS circuits: actual-value optimization and normalized-value optimization. The design flows consider two characteristics for optimization i.e. power and frequency in a current-starved 50nm voltage-controlled oscillator (VCO). Accurate polynomial-regression based models have been developed for power (including leakage) and frequency of the VCO to speedup the design optimization. In the actual-value optimization flow, the power model is minimized using genetic algorithm, while treating frequency >=100MHz as a constraint. The actual-value optimization flow achieved 21.67% power savings, while maintaining a frequency >=100MHz. In the normalized-value optimization flow, the normalized form of these models are subjected to a weighted optimization using genetic algorithm. The normalized-value optimization flow achieved 16.67% power savings, with frequency >=100MHz. It is observed that while the actual-value optimization approach provides a better exploration of the design space, the normalized-value optimization approach provides a ~5x speedup in the computation time.


title: Achieving Centimeter-Accuracy Indoor Localization on WiFi Platforms: A Multi-Antenna Approach

abstract: Channel frequency response (CFR) is a fine-grained location-specific information in WiFi systems that can be utilized in indoor positioning systems (IPSs). However, CFR-based IPSs can hardly achieve an accuracy at the centimeter level due to the limited bandwidth in WiFi systems. To achieve such accuracy using WiFi devices, we propose an IPS that fully harnesses the spatial diversity in multiple-input-multiple-output WiFi systems, which leads to a much larger effective bandwidth than the bandwidth of a WiFi channel. The proposed IPS obtains CFRs associated with locations-of-interest on multiple antenna links during the training phase. In the positioning phase, the IPS captures instantaneous CFRs from a location to be estimated and compares it with the CFRs acquired in the training phase via the time-reversal resonating strength with residual synchronization errors compensated. Extensive experiment results in an office environment with a measurement resolution of 5 cm demonstrate that, with a single pair of WiFi devices and an effective bandwidth of 321 MHz, the proposed IPS achieves detection rates of 99.91% and 100% with false alarm rates of 1.81% and 1.65% under the line-of-sight (LOS) and non-LOS (NLOS) scenarios, respectively. Meanwhile, the proposed IPS is robust against environment dynamics. Moreover, experiment results with a measurement resolution of 0.5 cm demonstrate a localization accuracy of 1-2 cm in the NLOS scenario.


title: Does underestimated penile size impact erectile function in healthy men?

abstract: The aim of this study is to assess the impact of objective (stretched) and subjective penile size in the erectile function in a urological check-up program on a cross-sectional study including 689 men aged 35–70 years. IIEF-5 questionnaire, physical examination (penile length, prostate volume, blood pressure, body mass index-BMI), metabolic syndrome (MS), comorbidities, habits (sexual intercourse frequency, physical activity, alcohol, and tobacco use), level of education, serum glucose, total testosterone, estradiol, PSA, lipid profile, and self-perceptions (ejaculation time and subjective penile size) were examined in multivariate models using logistic and linear regressions. Penile objective mean length was 13.08 cm ± 2.32 and 67 (9.72%) patients referred small penis self-perception. Seventy-six (11.03%) participants had severe erectile dysfunction (ED), 75 (10.88%) had mild to moderate and moderate ED, 112 (16.25%) had mild ED and 426 (61.83%) had no ED. Risk factors for ED that held statistical significance were self-perceived small penis (OR = 2.23, 95% CI 1.35–3.69, p = .0017), sexual intercourse frequency (per week) (OR = 0.45, 95% CI 0.38–0.52, p < .0001), satisfactory ejaculation time (no vs. yes, OR = 2.06, 95% CI 1.46–2.92, p < .0001), comorbidity (yes vs. no, OR = 2.01, 95% CI 1.46–2.76, p < .0001), age >65 years (OR = 2.93, 95% CI 1.53–5.61, p < .0001), tobacco use (yes vs. no, OR = 1.41, 95% CI 1.02–1.96, p < .0375), regular physical activity (no vs. yes, OR = 1.59, 95% CI 1.13–2.23, p < .0083), serum total testosterone < 200 ng/dl (OR = 3.48, 95% CI 1.69–7.16, p = 0.0009), serum glucose > 100 mg/dl (OR = 1.69, 95% CI 1.18–2.43, p = 0.0044) and systolic blood pressure > 130 mmHg (OR = 1.60, 95% CI 1.16–2.19, p = 0.0037). Results suggest that in addition to previously reported risk factors, patient’s subjective impressions of penile size negatively impacts sexual life in about 10% of men considered healthy, while objective penile length does not play significant role in erectile function.


title: InfoCatVAE: Representation Learning with Categorical Variational Autoencoders

abstract: This paper describes InfoCatVAE, an extension of the variational autoencoder that enables unsupervised disentangled representation learning. InfoCatVAE uses multimodal distributions for the prior and the inference network and then maximizes the evidence lower bound objective (ELBO). We connect the new ELBO derived for our model with a natural soft clustering objective which explains the robustness of our approach. We then adapt the InfoGANs method to our setting in order to maximize the mutual information between the categorical code and the generated inputs and obtain an improved model.


title: An application of blockchain and smart contracts for machine-to-machine communications in cyber-physical production systems

abstract: Blockchain, which is a new technology of distributed data storage in the form of a chain of blocks, has become one of the most popular topics in the field of information technology for today. Although the term “blockchain” is often associated with operations in cryptocurrencies (Bitcoin, for example), its application is not limited to this area. The algorithm for automatic confirmation of transactions has already been successfully applied in accounting, postal services, and smart devices. Additionally, people can participate in the confirmation of transactions. This article considers the possibility of using the technology in creating distributed networks of industrial equipment, i.e. creation of a backbone network for cyber-physical production systems. The proposed approach is detailed and discussed with the support of relevant use cases. A number of architectures of support networks based on blockchain technology are proposed, the positive and negative aspects of this approach are considered.


title: Visual sensitivity-based low-bit-rate image compression algorithm

abstract: In this study, the authors present a visual sensitivity-based low-bit-rate image compression algorithm. The authors algorithm combines both visual sensitivity and compression techniques so that a higher compression rate, with satisfactory visual quality, can be achieved. In the coding process, the input image is divided into blocks, and each block is classified as an edge block (EB), a textural block (TB) or a flat block (FB). For EBs, which are most important to the subjective quality of decoded images, the standard Joint Photographic Experts Group (JPEG) coding scheme with a tolerant quantisation step is employed so as to restrict the blocking artefacts caused by the quantisation error to an acceptable level. For FBs, a skipping scheme is employed on blocks in the compression process so as to save the bits. The coding of the skip blocks, identified by the skipping scheme, will make reference to the reconstructed regions of the image in the encoding process. Owing to the masking effects of the human visual system on high-frequency textures, standard JPEG compression coding with a greater quantisation step is employed on the down-scaled version of non-skip blocks and TBs. Experimental results show the superior performance of our method in terms of both compression efficiency and visual quality.


title: Wireless optical data transfer in underwater systems

abstract: We present wireless optical communication system for data transfer in the underwater networks. We use the optical channel to facilitate the communication link in free space and under water. This work bypasses the limitations involved in the use of electromagnetic waves and acoustics for free space and underwater communication. The system shows that optical communication using light can be a viable solution for underwater data transmission applications that requires high data rate at the moderate distances. We have designed, implemented and tested our system in real time and provide the evaluations results.


title: Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models

abstract: Text mining the relations between chemicals and proteins is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical-protein relations in running text (PubMed abstracts). This manuscript describes our submission, which is an ensemble of three systems, including a Support Vector Machine, a Convolutional Neural Network, and a Recurrent Neural Network. Their output is combined using a decision based on majority voting or stacking. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an f-score of 0.6410, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature. Our submission achieved the highest performance in the task during the 2017 challenge.


Demo: Finger and Hand Gesture Recognition using Smartwatch


title: Dynamics and control of a 4-dof wearable cable-driven upper arm exoskeleton

abstract: In this paper, we present the dynamics, control, and preliminary experiments on a wearable upper arm exoskeleton intended for human users with four degrees-of-freedom (dof), driven by six cables. The control of this cable-driven exoskeleton is complicated because the cables can transmit forces to the arm only under tension. The standard PD controllers or Computed torque controllers perform only moderately since the cables need to be in tension. Future efforts will seek to refine these control strategies and their implementations to improve functionality of a human user.


title: Usability testing for educational computer game using observation method

abstract: For most children the first contact with the computer is through some sort of educational game. Therefore, it is important that computer games for children are well-designed and usable for the children. In order to evaluate the usability of the computer game, children must be observed while exploring and playing the game and given the chance to express their opinions. Therefore, to involve children effectively, appropriate evaluation techniques are needed. This paper will discuss an observation method that has been adopted in a usability testing with the children. In this study, a computer game called Jelajah is chosen as the game to be tested for the usability evaluation with children. Jelajah is suitable for the experiment because it is an adventure game which is a very common game genre for children. Five preschool children aged between five and six years old are selected for this study. During the usability testing session, facilitator observes the child and fill in the observation checklist form in order to track the effectiveness and efficiency of the game. After the test session completed, the child is required to answer a simple interview-based questionnaire to know their level of satisfaction after playing the game. Data collected are both qualitative and quantitative. The collected data are analyzed to determine the levels of effectiveness, efficiency and satisfaction. The levels of effectiveness, efficiency and satisfaction are being measured by averaging them to a single metric that can represent the usability of Jelajah. The result obtained showed that the approach adopted to conduct a usability testing of a computer game with children using Jelajah can also be adopted for evaluating any educational computer game with children.


title: Humans' attachment to their mobile phones and its relationship with interpersonal attachment style

abstract: Humans have a biological predisposition to form attachment to social partners, and they seem to form attachment even toward non-human and inanimate targets. Attachment styles influence not only interpersonal relationships, but interspecies and object attachment as well.We hypothesized that young people form attachment toward their mobile phone, and that people with higher attachment anxiety use the mobile phone more likely as a compensatory attachment target. We constructed a scale to observe people's attachment to their mobile and we assessed their interpersonal attachment style.In this exploratory study we found that young people readily develop attachment toward their phone: they seek the proximity of it and experience distress on separation. People's higher attachment anxiety predicted higher tendency to show attachment-like features regarding their mobile. Specifically, while the proximity of the phone proved to be equally important for people with different attachment styles, the constant contact with others through the phone was more important for anxiously attached people.We conclude that attachment to recently emerged artificial objects, like the mobile may be the result of cultural co-option of the attachment system. People with anxious attachment style may face challenges as the constant contact and validation the computer-mediated communication offers may deepen their dependence on others. Young people develop attachment toward their phone, expressed by proximity-seeking and separation stress behaviour.People's attachment style influences their relationship with the mobile phone.People's higher attachment anxiety predicted higher tendency to show attachment-like features regarding their mobile.The proximity of the phone is equally important for people with different attachment styles.The constant contact with others through the phone is more important for people with higher attachment anxiety.


title: Design Principles for Energy-Efficient Legged Locomotion and Implementation on the MIT Cheetah Robot

abstract: This paper presents the design principles for highly efficient legged robots, the implementation of the principles in the design of the MIT Cheetah, and the analysis of the high-speed trotting experimental results. The design principles were derived by analyzing three major energy-loss mechanisms in locomotion: heat losses from the actuators, friction losses in transmission, and the interaction losses caused by the interface between the system and the environment. Four design principles that minimize these losses are discussed: employment of high torque-density motors, energy regenerative electronic system, low loss transmission, and a low leg inertia. These principles were implemented in the design of the MIT Cheetah; the major design features are large gap diameter motors, regenerative electric motor drivers, single-stage low gear transmission, dual coaxial motors with composite legs, and the differential actuated spine. The experimental results of fast trotting are presented; the 33-kg robot runs at 22 km/h (6 m/s). The total power consumption from the battery pack was 973 W and resulted in a total cost of transport of 0.5, which rivals running animals' at the same scale. 76% of the total energy consumption is attributed to heat loss from the motor, and the remaining 24% is used in mechanical work, which is dissipated as interaction loss as well as friction losses at the joint and transmission.


title: Gender differences in narcissism: a meta-analytic review.

abstract: Despite the widely held belief that men are more narcissistic than women, there has been no systematic review to establish the magnitude, variability across measures and settings, and stability over time of this gender difference. Drawing on the biosocial approach to social role theory, a meta-analysis performed for Study 1 found that men tended to be more narcissistic than women (d = .26; k = 355 studies; N = 470,846). This gender difference remained stable in U.S. college student cohorts over time (from 1990 to 2013) and across different age groups. Study 1 also investigated gender differences in three facets of the Narcissistic Personality Inventory (NPI) to reveal that the narcissism gender difference is driven by the Exploitative/Entitlement facet (d = .29; k = 44 studies; N = 44,108) and Leadership/Authority facet (d = .20; k = 40 studies; N = 44,739); whereas the gender difference in Grandiose/Exhibitionism (d = .04; k = 39 studies; N = 42,460) was much smaller. We further investigated a less-studied form of narcissism called vulnerable narcissism—which is marked by low self-esteem, neuroticism, and introversion—to find that (in contrast to the more commonly studied form of narcissism found in the DSM and the NPI) men and women did not differ on vulnerable narcissism (d = −.04; k = 42 studies; N = 46,735). Study 2 used item response theory to rule out the possibility that measurement bias accounts for observed gender differences in the three facets of the NPI (N = 19,001). Results revealed that observed gender differences were not explained by measurement bias and thus can be interpreted as true sex differences. Discussion focuses on the implications for the biosocial construction model of gender differences, for the etiology of narcissism, for clinical applications, and for the role of narcissism in helping to explain gender differences in leadership and aggressive behavior. Readers are warned against overapplying small effect sizes to perpetuate gender stereotypes.


title: Auto-Generation of Smart Contracts from Domain-Specific Ontologies and Semantic Rules

abstract: Smart contracts are essential in implementing and automating transactions in a blockchain network. The translation of existing business rules on transactions to a smart contract is challenging and time consuming. These types of constraints are often re-used among different contracts in a given application area. Automatic generation of smart contracts can reduce the level of expertise required for, along with the time and cost incurred in, specifying them. In this paper, we provide a novel framework for auto-generating smart contracts by enabling seamless translation of constraints encoded in a knowledge representation to blockchain requirements. Our framework uses ontologies and semantic rules to encode domain-specific knowledge and then leverages the structure of abstract syntax trees to incorporate the required constraints. We successfully demonstrate the functionality and effectiveness of our method in two different applications: translating eligibility criteria for clinical trials and car rentals.


title: Association of excessive smartphone use with psychological well-being among university students in Chiang Mai, Thailand

abstract: Background Despite the pervasive use of smartphones among university students, there is still a dearth of research examining the association between smartphone use and psychological well-being among this population. The current study addresses this research gap by investigating the relationship between smartphone use and psychological well-being among university students in Thailand. Methods This cross-sectional study was conducted from January to March 2018 among university students aged 18–24 years from the largest university in Chiang Mai, Thailand. The primary outcome was psychological well-being, and was assessed using the Flourishing Scale. Smartphone use, the primary independent variable, was measured by five items which had been adapted from the eight-item Young Diagnostic Questionnaire for Internet Addiction. All scores above the median value were defined as being indicative of excessive smartphone use. Results Out of the 800 respondents, 405 (50.6%) were women. In all, 366 (45.8%) students were categorized as being excessive users of smartphones. Students with excessive use of smartphones had lower scores the psychological well-being than those who did not use smartphone excessively (B = -1.60; P < 0.001). Female students had scores for psychological well-being that were, on average, 1.24 points higher than the scores of male students (P < 0.001). Conclusion This study provides some of the first insights into the negative association between excessive smartphone use and the psychological well-being of university students. Strategies designed to promote healthy smartphone use could positively impact the psychological well-being of students.


title: HCI for people with cognitive disabilities

abstract: The self-advocacy movement (Dybwad & Bersani, 1996) grew out of resistance to oppressive practices of institutionalization (and worse) for people with cognitive disabilities. Moving beyond the worst abuses, people with cognitive disabilities seek as full participation in society as possible.


title: Multi-view to Novel View: Synthesizing Novel Views With Self-learned Confidence

abstract: In this paper, we address the task of multi-view novel view synthesis, where we are interested in synthesizing a target image with an arbitrary camera pose from given source images. We propose an end-to-end trainable framework that learns to exploit multiple viewpoints to synthesize a novel view without any 3D supervision. Specifically, our model consists of a flow prediction module and a pixel generation module to directly leverage information presented in source views as well as hallucinate missing pixels from statistical priors. To merge the predictions produced by the two modules given multi-view source images, we introduce a self-learned confidence aggregation mechanism. We evaluate our model on images rendered from 3D object models as well as real and synthesized scenes. We demonstrate that our model is able to achieve state-of-the-art results as well as progressively improve its predictions when more source images are available.


title: Semi-supervised Deep Learning with Memory

abstract: We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data. To address this problem, existing semi-supervised deep learning methods often rely on the up-to-date “network-in-training” to formulate the semi-supervised learning objective. This ignores both the discriminative feature representation and the model inference uncertainty revealed by the network in the preceding learning iterations, referred to as the memory of model learning. In this work, we propose a novel Memory-Assisted Deep Neural Network (MA-DNN) capable of exploiting the memory of model learning to enable semi-supervised learning. Specifically, we introduce a memory mechanism into the network training process as an assimilation-accommodation interaction between the network and an external memory module. Experiments demonstrate the advantages of the proposed MA-DNN model over the state-of-the-art semi-supervised deep learning methods on three image classification benchmark datasets: SVHN, CIFAR10, and CIFAR100.


title: Using "The Machine Stops" for Teaching Ethics in Artificial Intelligence and Computer Science

abstract: A key front for ethical questions in artificial intelligence, and computer science more generally, is teaching students how to engage with the questions they will face in their professional careers based on the tools and technologies we teach them. In past work (and current teaching) we have advocated for the use of science fiction as an appropriate tool which enables AI researchers to engage students and the public on the current state and potential impacts of AI. We present teaching suggestions for E.M. Forster's 1909 story, "The Machine Stops," to teach topics in computer ethics. In particular, we use the story to examine ethical issues related to being constantly available for remote contact, physically isolated, and dependent on a machine --- all without mentioning computer games or other media to which students have strong emotional associations. We give a high-level view of common ethical theories and indicate how they inform the questions raised by the story and afford a structure for thinking about how to address them.


title: Glacial lakes in the Indian Himalayas--from an area-wide glacial lake inventory to on-site and modeling based risk assessment of critical glacial lakes.

abstract: Glacial lake hazards and glacial lake distributions are investigated in many glaciated regions of the world, but comparably little attention has been given to these topics in the Indian Himalayas. In this study we present a first area-wide glacial lake inventory, including a qualitative classification at 251 glacial lakes >0.01 km(2). Lakes were detected in the five states spanning the Indian Himalayas, and lake distribution pattern and lake characteristics were found to differ significantly between regions. Three glacial lakes, from different geographic and climatic regions within the Indian Himalayas were then selected for a detailed risk assessment. Lake outburst probability, potential outburst magnitudes and associated damage were evaluated on the basis of high-resolution satellite imagery, field assessments and through the use of a dynamic model. The glacial lakes analyzed in the states of Jammu and Kashmir and Himachal Pradesh were found to present moderate risks to downstream villages, whereas the lake in Sikkim severely threatens downstream locations. At the study site in Sikkim, a dam breach could trigger drainage of ca. 16×10(6)m(3) water and generate maximum lake discharge of nearly 7000 m(3) s(-). The identification of critical glacial lakes in the Indian Himalayas and the detailed risk assessments at three specific sites allow prioritizing further investigations and help in the definition of risk reduction actions.


title: A Risk Factor Profile for Pre-diabetes: Biochemical, Behavioural, Psychosocial and Cultural Factors

abstract: In this study a profile is presented of a group of people diagnosed with pre-diabetes and who volunteered to be part of a lifestyle intervention to improve their health. Baseline (pre-intervention) data on biochemical, anthropometric, psychosocial, cultural and lifestyle factors were collected, providing a picture of the extent to which the behaviours of the participants were suboptimal for health. The sample enabled comparison of a Chinese-Australian group with an Anglo-Australian group, as well as male-female comparisons. Participants reported several barriers to healthy eating and exercise, with negative mood, particularly depression, significantly associated with more perceived barriers and weaker motivation to change. Men were less healthy eaters than women. The Anglo-Australian pre-diabetics were characterised by biochemical and anthropomorphic features reflecting the metabolic syndrome, showing risk factors not only for diabetes but also for cardio-vascular disease. The Chinese sample did not reflect metabolic syndrome risk factors to the same extent and, possibly as a consequence, were less motivated to change their behaviour in response to their diagnosis of pre-diabetes. The implications for lifestyle interventions were discussed.


title: A Review on the ESD Robustness of Drain-Extended MOS Devices

abstract: This paper reviews electrostatic discharge (ESD) investigations on laterally diffused MOS (LDMOS) and drain-extended MOS (DeMOS) devices. The limits of the safe operating area of LDMOS/DeMOS devices and device physics under ESD stress are discussed under various biasing conditions and layout schemes. Specifically, the root cause of early filament formation is highlighted. Differences in filamentary nature among various LDMOS/DeMOS devices are shown. Based on the physical understanding, device optimization guidelines are given. Finally, an outlook on technology scaling is presented.


title: Penile Enlargement Surgery: Is It Feasible?

abstract: Penile size is a major concern among men all over world. Men throughout history and still today, feel the need to enlarge their penis in order to improve their self-esteem and sexual performance. There are a variety of social, cultural, and psychological aspects regarding the size of men genitals, resulting such that, men often feel the need to enlarge their penis. “Bigger is better” is still a relevant belief in our days and based on the “phallic identity” – the tendency of males to seek their personality in their penis. This trend is supported by the numerous and still increasing number of penile enlargement procedures performed in the past years and today, generally in men with normal size penises. This condition is called “the locker room syndrome” – men concerned about their flaccid penile size even though in most cases their penile length and girth are normal. however, the surgical procedures available for changing penile appearance remains highly controversial mainly due to high complication rates and low satisfactory surgical outcomes.


title: TU Group at NTCIR9-RITE: Leveraging Diverse Lexical Resources for Recognizing Textual Entailment

abstract: This paper describes the TU system that participated in the Entrance Exam Subtask of NTCIR-9 RITE. The system consists of two phases: alignment and entailment relation recognition. In the alignment phase, the system aligns words in the two sentences by exploiting diverse lexical resources such as entailment information, hypernym-hyponym relations and synonyms. Based on the alignments and relations between them, the system recognizes semantic relations between two sentences. Our system achieved an accuracy of 0.672 on the development data, and an accuracy of 0.6493 on the formal run.


title: Attendance management system using hybrid face recognition techniques

abstract: Attendance recording of a student in an academic organization plays a vital role in judging students performance. As manual labor involved in this process is time consuming, an automated Attendance Management System (AMS) based on face detection and face recognition techniques is proposed in this paper. The system employs modified Viola-Jones algorithm for face detection, and alignment-free partial face recognition algorithm for face recognition. After successful recognition of a student, the system automatically updates the attendance in the excel sheet. The proposed system improves the performance of existing attendance management systems by eliminating manual calling, marking and entry of attendance in institutional websites.


title: Embedded Assistive Stick for Visually Impaired Persons

abstract: In this paper, a smart stick is intended and executed to aid blind persons so that they can walk independently without much difficulty. Firstly, pothole detection and avoidance system are implemented by setting the ultrasonic sensor at 30-degree angle on a suitable blind stick to sense if there is a hole or staircase in front of the blind at about 30 cm distance to avoid a person from falling and as a result may be producing many damages. Secondly, a moisture sensor is placed at the down of stick to measure the degree of water land soil moisture in forward-facing of the user and aware him as soon as that degree exceeds a measured level that may submerge the foot of him. Thirdly, knee above obstacle detection and avoidance system is implemented by using an additional ultrasonic sensor on the top of the stick to turn an alarm and vibration ON when there is a person, obstacle or wall at a distance of 50 cm in front to avoid an accident and thus helping the person to move independently. Fourthly, an ultrasonic sensor is placed down the stick at about 20 cm from the ground level to detect and avoid knee below obstacles and stairs at a distance of 70 cm in front of the user. Fifthly, a wireless remote consisting of RF modules (transmitter and receiver) is implemented, so if a person drops stick or forget it somewhere, he can press a switch of the remote consisting of transmitter part, and as a result alarm with vibrations will turn on, so the user can know the location of the stick. The stick is implemented practically using single wheel leg blinding cane, Arduino microcontroller three ultrasonic sensors RF modules. Also, two buzzers and two vibration motor are used on the stick to fit on when any difficulties occur.


title: Robust Microstrip-to-Waveguide Transitions for Millimeter-Wave Radar Sensor Applications

abstract: Two robust millimeter-wave microstrip-to-waveguide transitions are proposed in this paper. The central operating frequency is 77 GHz, for collision-avoidance radar applications. The microstrip line has been first transformed into an air-filled rectangular waveguide whose height is equal to the substrate thickness. Then, a conventional multisection lambda/4 transformer has been used in the first transition, and a patch antenna in the second transition, to transform to the feeding WR12 waveguide. Experimental results for the first transition show an insertion loss less than 2 dB and a return loss better than 15 dB in a bandwidth of 3 GHz for the back-to-back transition.


title: Self-cutting versus intentional overdose: Psychological risk factors.

abstract: Abstract Individuals who present to emergency departments with self-harm are at elevated risk of further self-harm and suicide, and these risks are yet higher among patients who self-cut. Repetitive self-injury has previously been explained using a behaviourist approach focussing on operant conditioning, but we propose that the increased risk of self-harm repetition among those who present with self-cutting is at least partly mediated by pre-existing psychological risk factors. Several studies show that those who present with self-cutting differ from intentional overdose patients on demographic, psychiatric and social factors, but, based on findings from community-based studies, we hypothesise that there may be additional psychological differences that may also be associated with increased repetition risk. We conducted a small-scale cohort study of 29 self-harm patients presenting to A&E and compared theoretically-derived psychological variables between 8 self-cutting and 21 overdose patients. Those presenting with self-cutting scored significantly higher on hopelessness and lower on non-reactivity to inner experience and generally had a more vulnerable profile than those presenting with overdose. These findings support our hypothesis that the association between self-cutting and prospective repetition is at least partly due to pre-existing psychological vulnerabilities that increase both the likelihood of engaging in self-cutting as a method of self-harm and the likelihood of subsequent repetition of self-harm. Existing evidence suggests that self-cutting is a risk factor for repetition of self-harm, and it is possible that reducing and preventing repetition among these patients can be achieved by implementing psychological interventions to reduce hopelessness and increase tolerance of emotional distress.


title: Mining and Classifying Image Posts on Social Media to Analyse Fires

abstract: We propose a methodology to study the occurrence of fires through image posts on Flickr; crowd-sourcing information from a noisy social media dataset can estimate the presence of fires. We collect several years worth of photos and associated metadata using fire-related search terms. We use an image classification model to detect geotagged photos that are further analysed to determine if a fire event did occur at a particular time and place. Furthermore, a case study investigates image features and spatio-temporal elements in the metadata, as well as location information contained in camera EXIF data.


title: Towards Improving Human Arithmetic Learning using Machine Learning

abstract: Basic arithmetic is an essential skill that is used in almost all career paths in some way. Ensuring that young children have a solid foundation in simple mathematical concepts is a worldwide goal and new methods to improve arithmetic learning are constantly being developed. Our aim is to utilise machine learning to assist learners with developing their basic mathematics skills by identifying the types of problems a user struggles with and presenting them with targeted questions to improve in these areas. In this paper we focus only on the prediction component: given a set of arithmetic questions and corresponding answers, can we predict which future questions a user will answer incorrectly? The accuracy and suitability of four machine learning models are evaluated using data from computer-generated agents as well as human users. On simulated agents, our models achieve accuracies of around 79% to 96% with decision trees performing the best. On human data, our models achieve accuracies in the range of 63% to 69%, with the decision tree once again outperforming other approaches. We hope that these error predictions models could be incorporated into future E-learning systems targeted at human arithmetic learning.


title: Forecasting the Saudi Arabia stock prices based on artificial neural networks model

abstract: In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous days to be used in predicting the next day closing price data has been choosing based on experimental simulation carried out to determine the best possible value. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a very low RMSE down to 1.8174, very low MAD down to 18.2835, very low MAPE of down to 1.6476 and very high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively on the analysis and prediction of Saudi stock market in general.


title: ERMIA: Fast Memory-Optimized Database System for Heterogeneous Workloads

abstract: Large main memories and massively parallel processors have triggered not only a resurgence of high-performance transaction processing systems optimized for large main-memory and massively parallel processors, but also an increasing demand for processing heterogeneous workloads that include read-mostly transactions. Many modern transaction processing systems adopt a lightweight optimistic concurrency control (OCC) scheme to leverage its low overhead in low contention workloads. However, we observe that the lightweight OCC is not suitable for heterogeneous workloads, causing significant starvation of read-mostly transactions and overall performance degradation. In this paper, we present ERMIA, a memory-optimized database system built from scratch to cater the need of handling heterogeneous workloads. ERMIA adopts snapshot isolation concurrency control to coordinate heterogeneous transactions and provides serializability when desired. Its physical layer supports the concurrency control schemes in a scalable way. Experimental results show that ERMIA delivers comparable or superior performance and near-linear scalability in a variety of workloads, compared to a recent lightweight OCC-based system. At the same time, ERMIA maintains high throughput on read-mostly transactions when the performance of the OCC-based system drops by orders of magnitude.


title: Collinear facilitation and suppression at the periphery

abstract: Abstract Collinear facilitation is a common phenomenon in the fovea, but it has been recently challenged at the human periphery. Since physiological studies show that facilitation is found at the periphery but only from outside the receptive field, our hypothesis was that facilitation at the periphery exists but from larger target–flanker separations than the fovea. Here, we applied a recent paradigm ( Polat & Sagi, 2007 ) to probe facilitation at the periphery. We used a Yes/No detection task by measuring the false-positive reports (false-alarm, pfa) and hit-rate (phit) for a low-contrast Gabor target (between two flankers) that appeared randomly at the fovea or at the periphery (2° or 4°) to the right or left side. We used different target–flanker separations and orientations at the fovea and at the periphery. Importantly, we found that phit is affected by the target–flanker separations and orientations. Short distances show a suppression effect, but the range of suppression increases with increasing eccentricity. A facilitation effect was found for collinear configuration outside of the suppression range. A similar effect was found for the decisional criterion (Cr), which was correlated with suppression (positive) and facilitation (negative). All together, our results indicate that facilitation exists at the periphery when the target–flanker distance is properly scaled. Thus, our results indicate that collinear facilitation is a common phenomenon that exists in both the periphery and fovea. The suppression range indicates that the perceptual receptive field increases with increasing eccentricity. Our results provide a working hypothesis that explains the functional differences found between the fovea and the periphery. This supports the basic phenomena underlying visual perception, such as collinear facilitation, visual crowding, and backward masking.


End User Security and Privacy Concerns with Smart Homes


title: Social media mining for identification and exploration of health-related information from pregnant women

abstract: Widespread use of social media has led to the generation of substantial amounts of information about individuals, including health-related information. Social media provides the opportunity to study health-related information about selected population groups who may be of interest for a particular study. In this paper, we explore the possibility of utilizing social media to perform targeted data collection and analysis from a particular population group -- pregnant women. We hypothesize that we can use social media to identify cohorts of pregnant women and follow them over time to analyze crucial health-related information. To identify potentially pregnant women, we employ simple rule-based searches that attempt to detect pregnancy announcements with moderate precision. To further filter out false positives and noise, we employ a supervised classifier using a small number of hand-annotated data. We then collect their posts over time to create longitudinal health timelines and attempt to divide the timelines into different pregnancy trimesters. Finally, we assess the usefulness of the timelines by performing a preliminary analysis to estimate drug intake patterns of our cohort at different trimesters. Our rule-based cohort identification technique collected 53,820 users over thirty months from Twitter. Our pregnancy announcement classification technique achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user timelines. Analysis of the timelines revealed that pertinent health-related information, such as drug-intake and adverse reactions can be mined from the data. Our approach to using user timelines in this fashion has produced very encouraging results and can be employed for other important tasks where cohorts, for which health-related information may not be available from other sources, are required to be followed over time to derive population-based estimates.


title: Stuxnet: What Has Changed?

abstract: This paper considers the impact of Stuxnet on cyber-attacks and cyber-defense. It first reviews trends in cyber-weapons and how Stuxnet fits into these trends. Because Stuxnet targeted an industrial control system in order to wreak physical damage, the focus is on weapons that target systems of that type and produce physical effects. The paper then examines the impact of Stuxnet on various domains of action where cyber-attacks play a role, including state-level conflict, terrorism, activism, crime, and pranks. For each domain, it considers the potential for new types of cyber-attacks, especially attacks against industrial control systems, and whether such attacks would be consistent with other trends in the domain. Finally, the paper considers the impact of Stuxnet on cyber-defense.


title: Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens

abstract: Importance A breast pathology diagnosis provides the basis for clinical treatment and management decisions; however, its accuracy is inadequately understood. Objectives To quantify the magnitude of diagnostic disagreement among pathologists compared with a consensus panel reference diagnosis and to evaluate associated patient and pathologist characteristics. Design, Setting, and Participants Study of pathologists who interpret breast biopsies in clinical practices in 8 US states. Exposures Participants independently interpreted slides between November 2011 and May 2014 from test sets of 60 breast biopsies (240 total cases, 1 slide per case), including 23 cases of invasive breast cancer, 73 ductal carcinoma in situ (DCIS), 72 with atypical hyperplasia (atypia), and 72 benign cases without atypia. Participants were blinded to the interpretations of other study pathologists and consensus panel members. Among the 3 consensus panel members, unanimous agreement of their independent diagnoses was 75%, and concordance with the consensus-derived reference diagnoses was 90.3%. Main Outcomes and Measures The proportions of diagnoses overinterpreted and underinterpreted relative to the consensus-derived reference diagnoses were assessed. Results Sixty-five percent of invited, responding pathologists were eligible and consented to participate. Of these, 91% (N = 115) completed the study, providing 6900 individual case diagnoses. Compared with the consensus-derived reference diagnosis, the overall concordance rate of diagnostic interpretations of participating pathologists was 75.3% (95% CI, 73.4%-77.0%; 5194 of 6900 interpretations). Among invasive carcinoma cases (663 interpretations), 96% (95% CI, 94%-97%) were concordant, and 4% (95% CI, 3%-6%) were underinterpreted; among DCIS cases (2097 interpretations), 84% (95% CI, 82%-86%) were concordant, 3% (95% CI, 2%-4%) were overinterpreted, and 13% (95% CI, 12%-15%) were underinterpreted; among atypia cases (2070 interpretations), 48% (95% CI, 44%-52%) were concordant, 17% (95% CI, 15%-21%) were overinterpreted, and 35% (95% CI, 31%-39%) were underinterpreted; and among benign cases without atypia (2070 interpretations), 87% (95% CI, 85%-89%) were concordant and 13% (95% CI, 11%-15%) were overinterpreted. Disagreement with the reference diagnosis was statistically significantly higher among biopsies from women with higher (n = 122) vs lower (n = 118) breast density on prior mammograms (overall concordance rate, 73% [95% CI, 71%-75%] for higher vs 77% [95% CI, 75%-80%] for lower,P Conclusions and Relevance In this study of pathologists, in which diagnostic interpretation was based on a single breast biopsy slide, overall agreement between the individual pathologists’ interpretations and the expert consensus–derived reference diagnoses was 75.3%, with the highest level of concordance for invasive carcinoma and lower levels of concordance for DCIS and atypia. Further research is needed to understand the relationship of these findings with patient management.


title: Grasping the Intentions of Others: The Perceived Intentionality of an Action Influences Activity in the Superior Temporal Sulcus during Social Perception

abstract: An explication of the neural substrates for social perception is an important component in the emerging field of social cognitive neuroscience and is relevant to the field of cognitive neuroscience as a whole. Prior studies from our laboratory have demonstrated that passive viewing of biological motion (Pelphrey, Mitchell, et al., 2003; Puce et al., 1998) activates the posterior superior temporal sulcus (STS ) region. Furthermore, recent evidence has shown that the perceived context of observed gaze shifts (Pelphrey, Singerman, et al., 2003; Pelphrey et al., 2004) modulates STS activity. Here, using event-related functional magnetic resonance imaging at 4 T, we investigated brain activity in response to passive viewing of goal- and nongoal-directed reaching-to-grasp movements. Participants viewed an animated character making reaching-to-grasp movements either toward (correct) or away (incorrect) from a blinking dial. Both conditions evoked significant posterior STS activity that was strongly right lateralized. By examining the time course of the blood oxygenation level-dependent response from areas of activation, we observed a functional dissociation. Incorrect trials evoked significantly greater activity in the STS than did correct trials, while an area posterior and inferior to the STS (likely corresponding to the MT/ V5 complex) responded equally to correct and incorrect movements. Parietal cortical regions, including the superior parietal lobule and the anterior intraparietal sulcus, also responded equally to correct and incorrect movements, but showed evidence for differential responding based on the hand and arm (left or right) of the animated character used to make the reaching-to-grasp movement. The results of this study further suggest that a region of the right posterior STS is involved in analyzing the intentions of other people's actions and that activity in this region is sensitive to the context of observed biological motions.


title: Yoga for Adult Women with Chronic PTSD: A Long-Term Follow-Up Study.

abstract: INTRODUCTION ::: Yoga-the integrative practice of physical postures and movement, breath exercises, and mindfulness-may serve as a useful adjunctive component of trauma-focused treatment to build skills in tolerating and modulating physiologic and affective states that have become dysregulated by trauma exposure. A previous randomized controlled study was carried out among 60 women with chronic, treatment-resistant post-traumatic stress disorder (PTSD) and associated mental health problems stemming from prolonged or multiple trauma exposures. After 10 sessions of yoga, participants exhibited statistically significant decreases in PTSD symptom severity and greater likelihood of loss of PTSD diagnosis, significant decreases in engagement in negative tension reduction activities (e.g., self-injury), and greater reductions in dissociative and depressive symptoms when compared with the control (a seminar in women's health). The current study is a long-term follow-up assessment of participants who completed this randomized controlled trial. ::: ::: ::: METHODS ::: Participants from the randomized controlled trial were invited to participate in long-term follow-up assessments approximately 1.5 years after study completion to assess whether the initial intervention and/or yoga practice after treatment was associated with additional changes. Forty-nine women completed the long-term follow-up interviews. Hierarchical regression analysis was used to examine whether treatment group status in the original study and frequency of yoga practice after the study predicted greater changes in symptoms and PTSD diagnosis. ::: ::: ::: RESULTS ::: Group assignment in the original randomized study was not a significant predictor of longer-term outcomes. However, frequency of continuing yoga practice significantly predicted greater decreases in PTSD symptom severity and depression symptom severity, as well as a greater likelihood of a loss of PTSD diagnosis. ::: ::: ::: CONCLUSIONS ::: Yoga appears to be a useful treatment modality; the greatest long-term benefits are derived from more frequent yoga practice.


title: Sign language recognition using Microsoft Kinect

abstract: In last decade lot of efforts had been made by research community to create sign language recognition system which provide a medium of communication for differently-abled people and their machine translations help others having trouble in understanding such sign languages. Computer vision and machine learning can be collectively applied to create such systems. In this paper, we present a sign language recognition system which makes use of depth images that were captured using a Microsoft Kinect® camera. Using computer vision algorithms, we develop a characteristic depth and motion profile for each sign language gesture. The feature matrix thus generated was trained using a multi-class SVM classifier and the final results were compared with existing techniques. The dataset used is of sign language gestures for the digits 0-9.


title: UMLDiff: an algorithm for object-oriented design differencing

abstract: This paper presents UMLDiff, an algorithm for automatically detecting structural changes between the designs of subsequent versions of object-oriented software. It takes as input two class models of a Java software system, reverse engineered from two corresponding code versions. It produces as output a change tree, i.e., a tree of structural changes, that reports the differences between the two design versions in terms of (a) additions, removals, moves, renamings of packages, classes, interfaces, fields and methods, (b) changes to their attributes, and (c) changes of the dependencies among these entities. UMLDiff produces an accurate report of the design evolution of the software system, and enables subsequent design-evolution analyses from multiple perspectives in support of various evolution activities. UMLDiff and the analyses it enables can assist software engineers in their tasks of understanding the rationale of design evolution of the software system and planning future development and maintenance activities. We evaluate UMLDiff's correctness and robustness through a real-world case stud.


title: Globally Normalized Transition-Based Neural Networks

abstract: We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of-speech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models. We discuss the importance of global as opposed to local normalization: a key insight is that the label bias problem implies that globally normalized models can be strictly more expressive than locally normalized models.


eview loud manufacturing : Strategic vision and state-ofthe-art


title: PortoLivingLab: An IoT-Based Sensing Platform for Smart Cities

abstract: Smart cities aim to improve the citizens’ quality of life by leveraging information about urban scale processes extracted from heterogeneous data sources collected on city-wide deployments. The Internet-of-Things (IoT) is, thus, the enabler of smart city technologies at urban scale. In this paper, we present PortoLivingLab, a multisource sensing infrastructure that leverages IoT technology to achieve city-scale sensing of four phenomena: weather, environment, public transport, and people flows. To sense these processes on a city scale, we deployed a vehicular network with over 600 vehicles and 19 static environmental sensors. We also developed an easily reconfigurable crowdsensing platform and carried out several crowdsensing campaigns with more than 600 participants. The data is collected in a common backend and stored using similar spatio-temporal data models to simplify sharing and joint analysis for the characterization of urban dynamics. We describe the architecture and composing elements of PortoLivingLab, highlighting the IoT technologies, and challenges faced. We present several proof-of-concept use cases (e.g., passenger flows from WiFi connections) that provide new insights into different components of an evolving and moving city. Finally, we lay out the future lines of work that will strive for finding hidden phenomena by leveraging data from the three complementary platforms.


title: Pre-Exercise Nutrition: The Role of Macronutrients, Modified Starches and Supplements on Metabolism and Endurance Performance

abstract: Endurance athletes rarely compete in the fasted state, as this may compromise fuel stores. Thus, the timing and composition of the pre-exercise meal is a significant consideration for optimizing metabolism and subsequent endurance performance. Carbohydrate feedings prior to endurance exercise are common and have generally been shown to enhance performance, despite increasing insulin levels and reducing fat oxidation. These metabolic effects may be attenuated by consuming low glycemic index carbohydrates and/or modified starches before exercise. High fat meals seem to have beneficial metabolic effects (e.g., increasing fat oxidation and possibly sparing muscle glycogen). However, these effects do not necessarily translate into enhanced performance. Relatively little research has examined the effects of a pre-exercise high protein meal on subsequent performance, but there is some evidence to suggest enhanced pre-exercise glycogen synthesis and benefits to metabolism during exercise. Finally, various supplements (i.e., caffeine and beetroot juice) also warrant possible inclusion into pre-race nutrition for endurance athletes. Ultimately, further research is needed to optimize pre-exercise nutritional strategies for endurance performance.


title: Mean Box Pooling: A Rich Image Representation and Output Embedding for the Visual Madlibs Task

abstract: We present Mean Box Pooling, a novel visual representation that pools over CNN representations of a large number, highly overlapping object proposals. We show that such representation together with nCCA, a successful multimodal embedding technique, achieves state-of-the-art performance on the Visual Madlibs task. Moreover, inspired by the nCCA's objective function, we extend classical CNN+LSTM approach to train the network by directly maximizing the similarity between the internal representation of the deep learning architecture and candidate answers. Again, such approach achieves a significant improvement over the prior work that also uses CNN+LSTM approach on Visual Madlibs.


title: Discovery is never by chance: designing for (un)serendipity

abstract: Serendipity has a long tradition in the history of science as having played a key role in many significant discoveries. Computer scientists, valuing the role of serendipity in discovery, have attempted to design systems that encourage serendipity. However, that research has focused primarily on only one aspect of serendipity: that of chance encounters. In reality, for serendipity to be valuable chance encounters must be synthesized into insight. In this paper we show, through a formal consideration of serendipity and analysis of how various systems have seized on attributes of interpreting serendipity, that there is a richer space for design to support serendipitous creativity, innovation and discovery than has been tapped to date. We discuss how ideas might be encoded to be shared or discovered by 'association-hunting' agents. We propose considering not only the inventor's role in perceiving serendipity, but also how that inventor's perception may be enhanced to increase the opportunity for serendipity. We explore the role of environment and how we can better enable serendipitous discoveries to find a home more readily and immediately.


title: Network Topology Modulation for Energy and Data Transmission in Internet of Magneto-Inductive Things

abstract: Internet-of-things (IoT) architectures connecting a massive number of heterogeneous devices need energy efficient, low hardware complexity, low cost, simple and secure mechanisms to realize communication among devices. One of the emerging schemes is to realize simultaneous wireless information and power transfer (SWIPT) in an energy harvesting network. Radio frequency (RF) solutions require special hardware and modulation methods for RF to direct current (DC) conversion and optimized operation to achieve SWIPT which are currently in an immature phase. On the other hand, magneto-inductive (MI) communication transceivers are intrinsically energy harvesting with potential for SWIPT in an efficient manner. In this article, novel modulation and demodulation mechanisms are presented in a combined framework with multiple-access channel (MAC) communication and wireless power transmission. The network topology of power transmitting active coils in a transceiver composed of a grid of coils is changed as a novel method to transmit information. Practical demodulation schemes are formulated and numerically simulated for two-user MAC topology of small size coils. The transceivers are suitable to attach to everyday objects to realize reliable local area network (LAN) communication performances with tens of meters communication ranges. The designed scheme is promising for future IoT applications requiring SWIPT with energy efficient, low cost, low power and low hardware complexity solutions.


title: Dual averaging methods for regularized stochastic learning and online optimization

abstract: We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is the loss function of the learning task, and the other is a simple regularization term such as l1-norm for promoting sparsity. We develop a new online algorithm, the regularized dual averaging (RDA) method, that can explicitly exploit the regularization structure in an online setting. In particular, at each iteration, the learning variables are adjusted by solving a simple optimization problem that involves the running average of all past subgradients of the loss functions and the whole regularization term, not just its subgradient. Computational experiments show that the RDA method can be very effective for sparse online learning with l1-regularization.


Non-intrusive Load Monitoring Using Imaging Time Series and Convolutional Neural Networks


abstract: With the ubiquity of new information technology and media, more effective and friendly methods for human computer interaction (HCI) are being developed which do not rely on traditional devices such as keyboards, mice and displays. The first step for any intelligent HCI system is face detection, and one of most friendly HCI systems is hand gesture. Face Detection and Gesture Recognition for Human-Computer Interaction introduces the frontiers of vision-based interfaces for intelligent human computer interaction with focus on two main issues: face detection and gesture recognition. The first part of the book reviews and discusses existing face detection methods, followed by a discussion on future research. Performance evaluation issues on the face detection methods are also addressed. The second part discusses an interesting hand gesture recognition method based on a generic motion segmentation algorithm. The system has been tested with gestures from American Sign Language with promising results. We conclude this book with comments on future work in face detection and hand gesture recognition. Face Detection and Gesture Recognition for Human-Computer Interaction will interest those working in vision-based interfaces for intelligent human computer interaction. It also contains a comprehensive survey on existing face detection methods, which will serve as the entry point for new researchers embarking on such topics. Furthermore, this book also covers in-depth discussion on motion segmentation algorithms and applications, which will benefit more seasoned graduate students or researchers interested in motion pattern recognition.


title: Review of Cold Cathode Research at the Air Force Research Laboratory

abstract: Over the last decade, the Air Force Research Laboratory, Directed Energy Directorate (AFRL/DE) has engaged in a high current density field emission cathode research program. This program explored the aspects of cathode materials as well as the details of cathode geometries and emission physics. This paper summarizes the results of this ongoing research effort to date. We review the history and motivation for the program, which provide insight into the physics issues of concern for various vacuum electronic sources. One important aspect of the program consists of the investigation of new cathode materials. For many high power microwave (HPM) sources, neutral out-gassing, which ties critically with cathode materials, plays a key role in the effective operation of the source. These material properties influence plasma formation, which in turn dictates the operation of an HPM device. For a cathode material, AFRL chose to focus on cesium-iodide-coated carbon fiber cathodes, which we discuss in detail here. A second important aspect of the program consists of understanding emission physics and the optimum geometries for a cathode. This aspect couples closely with electron beam quality, which in turns effects the electron beam interaction with microwaves in the HPM structure. This paper concludes with a discussion of the implementation of the cathode material on both a Magnetically Insulated transmission Line Oscillator and a relativistic magnetron.


title: Towards reliable interactive data cleaning: a user survey and recommendations

abstract: Data cleaning is frequently an iterative process tailored to the requirements of a specific analysis task. The design and implementation of iterative data cleaning tools presents novel challenges, both technical and organizational, to the community. In this paper, we present results from a user survey (N = 29) of data analysts and infrastructure engineers from industry and academia. We highlight three important themes: (1) the iterative nature of data cleaning, (2) the lack of rigor in evaluating the correctness of data cleaning, and (3) the disconnect between the analysts who query the data and the infrastructure engineers who design the cleaning pipelines. We conclude by presenting a number of recommendations for future work in which we envision an interactive data cleaning system that accounts for the observed challenges.


title: Parasitic-Aware Common-Centroid FinFET Placement and Routing for Current-Ratio Matching

abstract: The FinFET technology is regarded as a better alternative for modern high-performance and low-power integrated-circuit design due to more effective channel control and lower power consumption. However, the gate-misalignment problem resulting from process variation and the parasitic resistance resulting from interconnecting wires based on the FinFET technology becomes even more severe compared with the conventional planar CMOS technology. Such gate misalignment and unwanted parasitic resistance may increase the threshold voltage and decrease the drain current of transistors. When applying the FinFET technology to analog circuit design, the variation of drain currents can destroy current-ratio matching among transistors and degrade circuit performance. In this article, we present the first FinFET placement and routing algorithms for layout generation of a common-centroid FinFET array to precisely match the current ratios among transistors. Experimental results show that the proposed matching-driven FinFET placement and routing algorithms can obtain the best current-ratio matching compared with the state-of-the-art common-centroid placer.


title: Articulate: a conversational interface for visual analytics

abstract: While many visualization tools exist that offer sophisticated functions for charting complex data, they still expect users to possess a high degree of expertise in wielding the tools to create an effective visualization. This poster presents Articulate, an attempt at a semi-automated visual analytic model that is guided by a conversational user interface. The goal is to relieve the user of the physical burden of having to directly craft a visualization through the manipulation of a complex user-interface, by instead being able to verbally articulate what the user wants to see, and then using natural language processing and heuristics to semi-automatically create a suitable visualization.


Parallel Hierarchical Attention Networks with Shared Memory Reader for Multi-Stream Conversational Document Classification


title: Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images Combined With DSM

abstract: Recently, approaches based on fully convolutional networks (FCN) have achieved state-of-the-art performance in the semantic segmentation of very high resolution (VHR) remotely sensed images. One central issue in this method is the loss of detailed information due to downsampling operations in FCN. To solve this problem, we introduce the maximum fusion strategy that effectively combines semantic information from deep layers and detailed information from shallow layers. Furthermore, this letter develops a powerful backend to enhance the result of FCN by leveraging the digital surface model, which provides height information for VHR images. The proposed semantic segmentation scheme has achieved an overall accuracy of 90.6% on the ISPRS Vaihingen benchmark.


title: Determination of Rare Earth Elements in Garnet Minerals, Geological Materials by Inductively Coupled Plasma–Atomic Emission Spectral and Mass Spectral Analysis

abstract: Abstract The performance of inductively coupled plasma–atomic emission spectrometry (ICP‐AES) for the determination of 14 lanthanides and Yttrium was evaluated by comparison with inductively coupled plasma–mass spectral (ICP‐MS) analysis. The geochemical reference samples (GRS), DNC‐1(diabase), AGV‐1(andesite), Sy‐2(syenite), MRG‐1(gabbro), AN‐G(anorthosite), AC‐E(granite), and MAG‐1(marine mud) were chosen as test materials and analyzed for checking the precision and reproducibility of the methods. The mineral garnet is separated from the black sands of the southwest coast of India, and the combined cation exchange–ICP method of AES analysis and MS analysis were carried out for the determination of rare earth elements. Both techniques are within the requirements needed for garnet minerals. The determination of rare‐earth elements in these minerals, which contain other elements as major contribution and trace distribution of rare‐earth elements, shows that ICP applied under the proper working condition li...


title: A Scalable SIEM Correlation Engine and Its Application to the Olympic Games IT Infrastructure

abstract: The security event correlation scalability has become a major concern for security analysts and IT administrators when considering complex IT infrastructures that need to handle gargantuan amounts of events or wide correlation window spans. The current correlation capabilities of Security Information and Event Management (SIEM), based on a single node in centralized servers, have proved to be insufficient to process large event streams. This paper introduces a step forward in the current state of the art to address the aforementioned problems. The proposed model takes into account the two main aspects of this field: distributed correlation and query parallelization. We present a case study of a multiple-step attack on the Olympic Games IT infrastructure to illustrate the applicability of our approach.


title: An Efficient Training Algorithm for Kernel Survival Support Vector Machines

abstract: Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support. In order to leverage large amounts of patient data, efficient optimisation routines are paramount. We propose an efficient training algorithm for the kernel survival support vector machine (SSVM). We directly optimise the primal objective function and employ truncated Newton optimisation and order statistic trees to significantly lower computational costs compared to previous training algorithms, which require $O(n^4)$ space and $O(p n^6)$ time for datasets with $n$ samples and $p$ features. Our results demonstrate that our proposed optimisation scheme allows analysing data of a much larger scale with no loss in prediction performance. Experiments on synthetic and 5 real-world datasets show that our technique outperforms existing kernel SSVM formulations if the amount of right censoring is high ($\geq85\%$), and performs comparably otherwise.


title: Automated statistics collection in DB2 UDB

abstract: The use of inaccurate or outdated database statistics by the query optimizer in a relational DBMS often results in a poor choice of query execution plans and hence unacceptably long query processing times. Configuration and maintenance of these statistics has traditionally been a time-consuming manual operation, requiring that the database administrator (DBA) continually monitor query performance and data changes in order to determine when to refresh the statistics values and when and how to adjust the set of statistics that the DBMS maintains. In this paper we describe the new Automated Statistics Collection (ASC) component of IBM® DB2® Universal DatabaseTM (DB2 UDB). This autonomic technology frees the DBA from the tedious task of manually supervising the collection and maintenance of database statistics. ASC monitors both the update-delete-insert (UDI) activities on the data as well as query feedback (QF), i.e., the results of the queries that are executed on the data. ASC uses these two sources of information to automatically decide which statistics to collect and when to collect them. This combination of UDI-driven and QF-driven autonomic processes ensures that the system can handle unforeseen queries while also ensuring good performance for frequent and important queries. We present the basic concepts, architecture, and key implementation details of ASC in DB2 UDB, and present a case study showing how the use of ASC can speed up a query workload by orders of magnitude without requiring any DBA intervention.


Almost Global Convergence to Global Minima for Gradient Descent in Deep Linear Networks


title: CRISPR/Cas9-mediated gene editing in human tripronuclear zygotes

abstract: ABSTRACTGenome editing tools such as the clustered regularly interspaced short palindromic repeat (CRISPR)-associated system (Cas) have been widely used to modify genes in model systems including animal zygotes and human cells, and hold tremendous promise for both basic research and clinical applications. To date, a serious knowledge gap remains in our understanding of DNA repair mechanisms in human early embryos, and in the efficiency and potential off-target effects of using technologies such as CRISPR/Cas9 in human pre-implantation embryos. In this report, we used tripronuclear (3PN) zygotes to further investigate CRISPR/Cas9-mediated gene editing in human cells. We found that CRISPR/Cas9 could effectively cleave the endogenous β-globin gene (HBB). However, the efficiency of homologous recombination directed repair (HDR) of HBB was low and the edited embryos were mosaic. Off-target cleavage was also apparent in these 3PN zygotes as revealed by the T7E1 assay and whole-exome sequencing. Furthermore, the endogenous delta-globin gene (HBD), which is homologous to HBB, competed with exogenous donor oligos to act as the repair template, leading to untoward mutations. Our data also indicated that repair of the HBB locus in these embryos occurred preferentially through the non-crossover HDR pathway. Taken together, our work highlights the pressing need to further improve the fidelity and specificity of the CRISPR/Cas9 platform, a prerequisite for any clinical applications of CRSIPR/Cas9-mediated editing.


title: Detecting Topics and Locations on Instagram Photos

abstract: Instagram has been the fastest growing social network in the last three years. It allows users sharing their status by uploading pictures with a description text, a location and some hashtags which not necessarily describe the content of the pictures, so this work presents a methodology to detect relevant content topics of pictures associated to a particular hashtag through text mining techniques and computer vision tools. For this purpose 7382 pictures associated with the hashtag #allyouneedisecuador were collected. Results show he most relevant topics and that the similarity between both descriptions is low.


title: Use of reprocessed external fixators in orthopaedic surgery: a survey of 243 orthopaedic trauma surgeons

abstract: Background ::: The increasing financial burden of orthopaedic implants on our health care system has prompted cost-control measures, such as implant reprocessing. The purpose of this study was to describe the current usage by orthopaedic trauma surgeons of reprocessed external fixators (EFs) for treatment of complex fractures. ::: ::: ::: Methods ::: A 16-question survey about use and perceptions of reprocessed EFs was distributed to 894 Orthopaedic Trauma Association members between August 2016 and June 2017 using a web-based survey system. ::: ::: ::: Results ::: The authors received 243 responses (27%). Thirty-seven percent of respondents reported using reprocessed EFs. Nonprofit hospitals used reprocessed EFs more commonly than did for-profit hospitals (41% vs 15%, P = .0004). Eighty-seven percent of respondents believed reprocessing could be cost-effective. The most common reason (32%) for not using reprocessed EFs was coordination/logistics of reprocessing. Concern about litigation was also reported as a main reason for not using (20%) or having recently stopped using (21%) reprocessed EFs. ::: ::: ::: Conclusions ::: Many orthopaedic traumatologists are interested in the reprocessing of EF components but few have reprocessing systems in place at their institutions. A major barrier to implementation is concern about litigation, which is likely unwarranted on the basis of Food and Drug Administration approval and a lack of previous litigation. Reprocessing by the original device manufacturers has yielded substantial savings at our institution and is an example of the cost savings that can be expected when implementing an EF reprocessing system.


title: Multi-Hop Real-Time Communications Over Bluetooth Low Energy Industrial Wireless Mesh Networks

abstract: Industrial wireless sensor networks (IWSNs) are used to acquire sensor data that need real-time processing, therefore they require predictable behavior and real-time guarantees. To be cost effective, IWSNs are also expected to be low cost and low power. In this context, Bluetooth low energy (BLE) is a promising technology, as it allows implementing low-cost industrial networks. As BLE is a short-range technology, a multihop mesh network is needed to cover a large area. Nevertheless, the recently published Bluetooth mesh networking specifications do not provide support for real-time communications over multihop mesh networks. To overcome this limitation, this paper proposes the multihop real-time BLE (MRT-BLE) protocol, a real-time protocol developed on top of BLE, that allows for bounded packet delays over mesh networks. MRT-BLE also provides priority support. This paper describes in detail the MRT-BLE protocol and how to implement it on commercial-off-the-shelf devices. Two kinds of performance evaluation for the MRT-BLE protocol are provided. The first one is a worst case end-to-end delay analysis, while the second one is based on the experimental results obtained through measurements on a real testbed.


title: Enhancing OAuth services security by an authentication service with face recognition

abstract: Controlling secure access to web Application Programming Interfaces (APIs) and web services has become more vital with advancement and use of the web technologies. The security of web services APIs is encountering critical issues in managing authenticated and authorized identities of users. Open Authorization (OAuth) is a secure protocol that allows the resource owner to grant permission to a third-party application in order to access the resource owner's protected resource on their behalf, without releasing their credentials. Most web APIs are still using the traditional authentication which is vulnerable to many attacks such as man-in-the middle attack. To reduce such vulnerability, we enhance the security of OAuth through the implementation of a biometric service. We introduce a face verification system based on Local Binary Patterns as an authentication service handled by the authorization server. The entire authentication process consists of three services: Image registration service, verification service, and access token service. The developed system is most useful in securing those services where a human identification is required.


title: Lean Sensing: Exploiting Contextual Information for Most Energy-Efficient Sensing

abstract: Cyber-physical technologies enable event-driven applications, which monitor in real-time the occurrence of certain inherently stochastic incidents. Those technologies are being widely deployed in cities around the world and one of their critical aspects is energy consumption, as they are mostly battery powered. The most representative examples of such applications today is smart parking. Since parking sensors are devoted to detect parking events in almost-real time, strategies like data aggregation are not well suited to optimize energy consumption. Furthermore, data compression is pointless, as events are essentially binary entities. Therefore, this paper introduces the concept of Lean Sensing , which enables the relaxation of sensing accuracy at the benefit of improved operational costs. To this end, this paper departs from the concept of instantaneous randomness and it explores the correlation structure that emerges from it in complex systems. Then, it examines the use of this system-wide aggregated contextual information to optimize power consumption, thus going in the opposite way; from the system-level representation to individual device power consumption. The discussed techniques include customizing the data acquisition to temporal correlations (i.e, to adapt sensor behavior to the expected activity) and inferring the system-state from incomplete information based on spatial correlations. These techniques are applied to real-world smart-parking application deployments, aiming to evaluate the impact that a number of system-level optimization strategies have on devices power consumption.


title: Identification of radar signals using neural network classifier with low-discrepancy optimisation

abstract: A hybrid low-discrepancy sequences optimisation approach is investigated and used for training neural network classifiers for recognition of radar signal emitters. Two sample case studies are developed in order to demonstrate and evaluate the presented approach. In the first one, generic intercepted radar signals are classified in two broad classes - with civil or military application, based on patterns in their pulse trains, whereas in the second one the classifier is trained to distinguish between several more specific radar functions. Very competitive results of about 84% accuracy are achieved on the testing data sets.


title: Tongue and tonsil carcinoma: increasing trends in the U.S. population ages 20-44 years.

abstract: BACKGROUND ::: An increasing incidence of oral carcinoma among young adults has been reported in the U.S. and Europe. Although the association between human papillomavirus infection and tonsillar carcinoma is now well established, to the authors' knowledge little is known about incidence trends in tonsillar carcinoma among younger adults. The objective of the current study was to explore the trends in both oral cavity and pharyngeal squamous cell carcinoma (SCC) in younger U.S. populations, in particular tongue and tonsillar SCC. ::: ::: ::: METHODS ::: Using the 1973-2001 Surveillance, Epidemiology and End Results (SEER) database, we computed age, race, and site-specific trends of oral and pharyngeal (excluding nasopharynx) carcinoma incidence rates. The percent change (PC) and annual percent change (APC) were computed to explore trends in incidence rates over time. ::: ::: ::: RESULTS ::: There were 2262 SCC of the oral cavity and 1251 SCC of the pharynx reported to the SEER program from 1973 to 2001 in adults aged 20-44 years. There was a statistically significant increase in the incidence of oral tongue SCC (APC = +2.1; P < 0.001), base of tongue SCC (APC = +1.7; P = 0.04), and palatine tonsil SCC (APC = +3.9; P < 0.001) among younger white individuals, whereas the incidence of SCC in all other oral and pharyngeal sites decreased or remained constant. ::: ::: ::: CONCLUSIONS ::: The increase in tonsil SCC incidence from 1973 to 2001 paralleled the increase in tongue SCC, whereas SCC in all other oral and pharyngeal sites remained constant or decreased. This may suggest similar etiologic factors for SCC affecting the palatine tonsils and tongue in younger populations.


title: A compact two-stage 120WGaN high power amplifier for SweepSAR radar systems

abstract: This work presents the design and measured results of a fully integrated switched power two-stage GaN HEMT high-power amplifier (HPA) achieving 60% power-added efficiency at over 120Woutput power. This high-efficiency GaN HEMT HPA is an enabling technology for L-band SweepSAR interferometric instruments that enable frequent repeat intervals and high-resolution imagery. The L-band HPA was designed using space-qualified state-of-the-art GaN HEMT technology. The amplifier exhibits over 34 dB of power gain at 51 dBm of output power across an 80 MHz bandwidth. The HPA is divided into two stages, an 8 W driver stage and 120 W output stage. The amplifier is designed for pulsed operation, with a high-speed DC drain switch operating at the pulsed-repetition interval and settles within 200 ns. In addition to the electrical design, a thermally optimized package was designed, that allows for direct thermal radiation to maintain low-junction temperatures for the GaN parts maximizing long-term reliability. Lastly, real radar waveforms are characterized and analysis of amplitude and phase stability over temperature demonstrate ultra-stable operation over temperature using integrated bias compensation circuitry allowing less than 0.2 dB amplitude variation and 2° phase variation over a 70°C range.


title: Gradient Metasurface With Both Polarization-Controlled Directional Surface Wave Coupling and Anomalous Reflection

abstract: A gradient metasurface is proposed to obtain both surface wave coupling and anomalous reflection. The unit cell of the gradient metasurface is composed by six electric field coupled resonators in different rotation angle, which provides a phase gradient. The simulation and measured results show that this gradient metasurface can work at 7.8 GHz as a surface wave coupler and at 10.288 GHz providing a phenomenon of anomalous reflection. At the two frequencies, the direction of the reflection is polarization-controlled, and the efficiencies are polarization-invariant. At left-handed polarization incidence, the direction of the coupled surface wave and the anomalous reflection are opposite to the situation at right-handed polarization incidence.


title: Automated Detection of Threat Objects Using Adapted Implicit Shape Model

abstract: Baggage inspection using X-ray screening is a priority task that reduces the risk of crime and terrorist attacks. Manual detection of threat items is tedious because very few bags actually contain threat items and the process requires a high degree of concentration. An automated solution would be a welcome development in this field. We propose a methodology for automatic detection of threat objects using single X-ray images. Our approach is an adaptation of a methodology originally created for recognizing objects in photographs based on implicit shape models. Our detection method uses a visual vocabulary and an occurrence structure generated from a training dataset that contains representative X-ray images of the threat object to be detected. Our method can be applied to single views of grayscale X-ray images obtained using a single energy acquisition system. We tested the effectiveness of our method for the detection of three different threat objects: 1) razor blades; 2) shuriken (ninja stars); and 3) handguns. The testing dataset for each threat object consisted of 200 X-ray images of bags. The true positive and false positive rates (TPR and FPR) are: (0.99 and 0.02) for razor blades, (0.97 and 0.06) for shuriken, and (0.89 and 0.18) for handguns. If other representative training datasets were utilized, we believe that our methodology could aid in the detection of other kinds of threat objects.


title: “I aspire to look and feel healthy like the posts convey”: engagement with fitness inspiration on social media and perceptions of its influence on health and wellbeing

abstract: Fitspiration is a popular social media trend containing images, quotes and advice related to exercise and healthy eating. This study aimed to 1) describe the types of fitspiration content that users access and how they engage with content, 2) investigate the disordered eating and exercise behaviours and psychological distress of individuals who access fitspiration, and 3) understand the perceived influence of fitspiration on health and wellbeing. Participants who access fitspiration content were recruited via social media to complete a cross-sectional online survey. Participants’ psychological distress was measured using the Kessler 10 Psychological Distress Scale (K10); disordered eating behaviours using the Eating Attitudes Test-26 (EAT-26); and compulsive exercise behaviours using the Exercise Addiction Inventory (EAI). Participants also answered a series of open-ended questions about their experiences with fitspiration. A descriptive statistical analysis was conducted for quantitative data. Responses to open-ended questions were analysed for key themes using an iterative process of open, axial and thematic coding. Participants (N = 180, 151 female, median age 23.0 years (IQR 19.0, 28.5)) most commonly accessed content posted by personal trainers and athletes (59.4%), posts tagged with the ‘fitspiration’ hashtag (53.9%) and posted by ‘everyday’ people (53.3%). Overall, 17.7% of participants were classified as high risk for an eating disorder, 17.4% reported very high levels of psychological distress, and 10.3% were at risk of addictive exercise behaviours. Participants described both positive and negative influences of engaging with fitspiration content. The influence on their health beliefs and behaviours was explained through four key themes: 1) Setting the ‘healthy ideal’, 2) Failure to achieve the ‘ideal’, 3) Being part of a community, and 4) Access to reliable health information. Many participants reported benefits of fitspiration content including increased social support and access to health information. However, participants also reported that fitspiration content could negatively influence their wellbeing and perception of healthy goals. Content posted by relatable individuals or qualified experts was perceived as most trustworthy. Future research is needed to determine the individual and content-related factors associated with negative and positive fitspiration experiences.


USING CHAOTIC SEQUENCE IN DIRECT SEQUENCE SPREAD SPECTRUM BASED ON CODE DIVISION MULTIPLE ACCESS ( DS-CDMA )


title: Transhiatal esophagectomy for carcinoma of the esophagus

abstract: Abstract One hundred thirty-one patients (107 men and 24 women) underwent transhiatal esophagectomy for carcinoma of the esophagus. Median age was 65.3 years (range, 30 to 89 years). Signs and symptoms were present in 130 patients, which included dysphagia in 96 (73.3%) and weight loss (median, 7.7 kg) in 52 (39.7%). The cancer involved the gastroesophageal junction in 94 patients, the lower half of the intrathoracic esophagus in 25, the upper half in 10, and multiple sites in 2. An adenocarcinoma was present in 101 patients (77.1%), squamous cell carcinoma in 29 (22.1%), and adenosquamous cell in 1 (0.8%). The cancer was classified as stage 0 in 4 patients, stage I in 16, stage IIA in 26, stage IIB in 18, stage III in 65, and stage IV in 1. The stomach was used to replace the esophagus in all patients. Operative mortality was 2.3%. Anastomotic leak developed in 32 patients; 6 leaks were not clinically significant, 12 healed with drainage alone, and 14 required further surgical intervention. Follow-up ranged from 1 month to 6.7 years (median, 1.4 years). Currently, 42 patients are alive, 34 without evidence of recurrence. Overall 5-year survival was 20.8% and varied according to stage. Five-year survival was 47.5% for patients with stage I disease compared with 37.7% for patients in stage II and only 5.8% 4-year survival for patients in stage III. Cell type also influenced survival. Five-year survival for patients with adenocarcinoma was 27.1% compared with zero for patients with squamous cell carcinoma ( p


title: 1 User Models for Adaptive Hypermedia and Adaptive Educational Systems

abstract: One distinctive feature of any adaptive system is the user model that represents essential information about each user. This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems. The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained. After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling.


title: Dengue and the risk of urban yellow fever reintroduction in São Paulo State, Brazil

abstract: Objective To propose a mathematical method for the estimation of the Basic Reproduction Number, R 0 , of urban yellow fever in a dengue-infested area. Methods The method is based on the assumption that, as the same vector (Aedes aegypti) causes both infections, all the quantities related to the mosquito, estimated from the initial phase of dengue epidemic, could be applied to yellow fever dynamics. It is demonstrated that R 0 for yellow fever is, on average, 43% lower than that for dengue. This difference is due to the longer dengue viremia and its shorter extrinsic incubation period. Results In this study the analysis was expanded to the epidemiological situation of dengue in Sao Paulo in the year 2001. The total number of dengue cases increased from 3,582 in 2000 to 51,348 in 2001. It was then calculated R 0 for yellow fever for every city which have shown R0 of dengue greater than 1. It was also estimated the total number of unprotected people living in highly risky areas for urban yellow fever. Conclusions Currently there is a great number of non-vaccinated people living in Aedes aegypti infested area in the state of Sao Paulo.


title: Solving the Rubik's Cube Optimally is NP-complete

abstract: In this paper, we prove that optimally solving an n x n x n Rubik's Cube is NP-complete by reducing from the Hamiltonian Cycle problem in square grid graphs. This improves the previous result that optimally solving an n x n x n Rubik's Cube with missing stickers is NP-complete. We prove this result first for the simpler case of the Rubik's Square--an n x n x 1 generalization of the Rubik's Cube--and then proceed with a similar but more complicated proof for the Rubik's Cube case. Our results hold both when the goal is make the sides monochromatic and when the goal is to put each sticker into a specific location.


title: Using PSpice in teaching impulse Voltage testing of power transformers to senior undergraduate students

abstract: This paper describes an efficient method of teaching impulse voltage testing of power transformers to undergraduate students of power system groups in electrical engineering departments, as a part of a high-voltage course for senior undergraduate students. The paper shows how to simulate the power transformer and impulse generator to teach students the basics of impulse voltage testing of power transformers and to practice analyzing the test results. In the first part of the paper, the effect of wave shaping of the voltage waveform is simulated to teach the behavior of the impulse generator, and in the second part, impulse voltage testing of a transformer is simulated. Evaluation of the simulation over several semesters with more than 80 students is very positive in terms of their developing confidence in an understanding of this test.