title: Rickets vs. abuse: a national and international epidemic

abstract: In the May 2007 issue of Pediatric Radiology, the article “Can classic metaphyseal lesions follow uncomplicated caesarean section?” [1] suggested that enough trauma could occur under these circumstances to produce fractures previously described as “highly specific for child abuse” [2]. However, the question of whether themetaphyses were normal to begin with was not raised. Why should this be an issue? Vitamin D deficiency (DD), initially believed to primarily affect the elderly and dark-skinned populations in the US, is now being demonstrated in otherwise healthy young adults, children, and infants of all races. In a review article on vitamin D published in the New England Journal of Medicine last year [3], Holick reviewed some of the recent literature, showing deficiency and insufficiency rates of 52% among Hispanic and African-American adolescents in Boston, 48% among white preadolescent females in Maine, 42% among African American females between 15 and 49 years of age, and 32% among healthy white men and women 18 to 29 years of age in Boston. A recent study of healthy infants and toddlers aged 8 to 24 months in Boston found an insufficiency rate of 40% and a deficiency rate of 12.1% [4]. In September 2007, a number of articles about congenital rickets were published in the Archives of Diseases in Childhood including an international perspective of mother and newborn DD reported from around the world [5]. Concentrations of 25-hydroxyvitamin D [25(OH)D] less than 25 nmol/l (10 ng/ml) were found in 18%, 25%, 80%, 42% and 61% of pregnant women in the UK, UAE, Iran, northern India and New Zealand, respectively, and in 60 to 84% of non-western women in the Netherlands. Currently, most experts in the US define DD as a 25(OH)D level less than 50 nmol/l (20 ng/ml). Levels between 20 and 30 ng/ml are considered to indicate insufficiency, reflecting increasing parathyroid hormone (PTH) levels and decreasing calcium absorption [3]. With such high prevalence of DD in our healthy young women, congenital deficiency is inevitable, since neonatal 25(OH)D concentrations are approximately two-thirds the maternal level [6]. Bodnar et al. [7] at the University of Pittsburgh, in the largest US study of mother and newborn infant vitamin D levels, found deficient or insufficient levels in 83% of black women and 92% of their newborns, as well as in 47% of white women and 66% of their newborns. The deficiencies were worse in the winter than in the summer. Over 90% of these women were on prenatal vitamins. Research is currently underway to formulate more appropriate recommendations for vitamin D supplementation during pregnancy (http://clinicaltrials.gov, ID: R01 HD043921). The obvious question is, “Why has DD once again become so common?” Multiple events have led to the high rates of DD. In the past, many foods were fortified with Pediatr Radiol DOI 10.1007/s00247-008-1001-z


title: Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial Information

abstract: Sequence-to-sequence (seq2seq) neural models have been actively investigated for abstractive summarization. Nevertheless, existing neural abstractive systems frequently generate factually incorrect summaries and are vulnerable to adversarial information, suggesting a crucial lack of semantic understanding. In this paper, we propose a novel semantic-aware neural abstractive summarization model that learns to generate high quality summaries through semantic interpretation over salient content. A novel evaluation scheme with adversarial samples is introduced to measure how well a model identifies off-topic information, where our model yields significantly better performance than the popular pointer-generator summarizer. Human evaluation also confirms that our system summaries are uniformly more informative and faithful as well as less redundant than the seq2seq model.


title: Effects of heavy metals and of deficiency of zinc on mortality rates in mice infected with encephalomyocarditis virus.

abstract: Salts of cadmium, lead, mercury, and nickel given orally to mice increased encephalomyocarditis virus-induced mortality rates. Although lead was the least toxic of the 4 metals, it enhanced the mortality the most. Concentrations of mercuric chloride as low as 0.01 ppm intensified the mortality; a minimal concentration that resulted in no effect was not ascertained. Zinc deficiency caused pronounced retardation of growth in young mice, but it did not influence mortality due to encephalomyocarditis virus.


title: Broadband Phase Shifter Using Loaded Transmission Line

abstract: This letter presents a novel loaded transmission line, which has a constant phase shift over a wide bandwidth, referenced to a uniform transmission line. Compared with the conventional coupled line phase shifter, the configuration shows good performance with simplicity in both design and fabrication. The optimal design parameters for different phase shift values up to 120° are also presented to allow for a quick design process. To verify the configuration, a 90° phase shifter using a T shaped open stub loaded transmission line is designed, fabricated and measured. Excellent performance is achieved with small insertion loss and phase deviation over a bandwidth of 82%.


title: Factors predicting quality of work life among nurses in tertiary‐level hospitals, Bangladesh

abstract: AIM ::: This study examined the level of quality of work life and predictability of years of education, monthly income, years of experience, job stress, organizational commitment and work environment on quality of work life among nurses in tertiary-level hospitals in the People's Republic of Bangladesh. ::: ::: ::: BACKGROUND ::: There is an acute shortage of nurses worldwide including Bangladesh. Quality of work life is important for quality of patient care and nurse retention. Nurses in Bangladesh are fighting to provide quality care for emerging health problems for the achievement of sustainable development goals. ::: ::: ::: METHODS ::: We collected data from 288 randomly selected registered nurses, from six tertiary-level hospitals. All nurses were requested to fill questionnaire consisted of Demographic Data Sheet, Quality of Nursing Work Life Survey, Expanded Nursing Stress Scale, Questionnaire of Organizational Commitment and Practice Environment Scale of the Nursing Work Index. Data were analysed by descriptive statistics and multiple regression. ::: ::: ::: RESULTS ::: The quality of work life as perceived by nurses in Bangladesh was at moderate level. Monthly income was found as the best predictor followed by work environment, organizational commitment and job stress. ::: ::: ::: DISCUSSION ::: A higher monthly income helps nurses to fulfil their personal needs; positive work environment helps to provide quality care to the patients. ::: ::: ::: LIMITATION ::: Quality of work life and predictors measured by self-report only may not reflect the original picture of the quality of work life among nurses. ::: ::: ::: CONCLUSION AND POLICY IMPLICATIONS ::: Findings provide information for nursing and health policymakers to develop policies to improve quality of work life among nurses that can contribute to quality of nursing care. This includes the working environment, commitment to the organization and measures to reduce job stress.


title: A Q-band (40–45 GHz) 16-element phased-array transmitter in 0.18-μm SiGe BiCMOS technology

abstract: A 16-element phased-array transmitter based on 4-bit RF phase shifters is designed in 0.18-mum SiGe BiCMOS technology for Q-band applications. The phased-array shows 12.5plusmn1.5 dB of power gain per channel at 42.5 GHz for all phase states and the 3-dB gain bandwidth is 40-45.6 GHz. The input and output return loss is less than -10 dB at 37.5-50 GHz. The transmitter also results in les8.8deg of RMS phase error and les1.2 dB of RMS gain error for all phase states at 30-50 GHz. The maximum saturated output power is -2.5plusmn1.5 dBm per channel at 42.5 GHz. The RMS gain variation and RMS phase mismatch between all 16 channels is les0.5 dB and les4.5deg, respectively. The chip consumes 720 mA from a 5 V supply voltage and overall chip size is 2.6times3.2 mm2. To our knowledge, this is the first implementation of a 16-element phased array on a silicon chip with the RF phase shifting architecture at any frequency.


title: A Simple, Effective Lead-Acid Battery Modeling Process for Electrical System Component Selection

abstract: Electrical system capacity determination for conventional vehicles can be expensive due to repetitive empirical vehicle-level testing. Electrical system modeling and simulation have been proposed to reduce the amount of physical testing necessary for component selection [1, 2].


title: Multilingual Image Description with Neural Sequence Models

abstract: In this paper we present an approach to multi-language image description bringing together insights from neural machine translation and neural image description. To create a description of an image for a given target language, our sequence generation models condition on feature vectors from the image, the description from the source language, and/or a multimodal vector computed over the image and a description in the source language. In image description experiments on the IAPR-TC12 dataset of images aligned with English and German sentences, we find significant and substantial improvements in BLEU4 and Meteor scores for models trained over multiple languages, compared to a monolingual baseline.


title: Non-parametric context-based object classification in images

abstract: Segmentation and classification of objects in images is one of the most important and yet one of the most complex problems in computer vision. Many different things contribute to the complexity of this problem, but amongst those that are most significant are variations in object appearance due to various perspectives, changes in scene illumination and typically a large number of partially occluded objects in a scene. Humans have the ability to successfully deal with these complications and to recognize objects in scenes without seemingly much effort. However, to aid their recognition process, humans use different sources of information such as previous experiences, context of objects placed in a scene and rules learned in the past that suggest how the physical world should appear and behave. Computer vision is trying to replicate the same strategies used by human vision and apply them to computer systems with the aim of improving typical tasks of detection, localization and classification of image objects. In this work we propose a new model for natural image object classification using contextual information at the level of image segments. Context modeling is largely independent of appearance-based classification and proposed model enables simple upgrade of existing systems with information from global and/or local context. Context modeling is based on non-parametric use of appearance-based classification results which is a novel approach compared to previous systems that model context on a limited number of rules expressed with a fixed set of parameters. Model implementation resulted in a system that, in our simulations, showed stable improvement of the appearance-based object classification. DOI: http://dx.doi.org/10.5755/j01.itc.46.1.13610


Common Consensus: a web-based game for collecting commonsense goals


A software product line of feature modeling notations and cross-tree constraint languages


title: Ultra Wideband positioning technology in tracking moving objects in indoor spaces – a pilot study

abstract: The article presents a pilot study in which we have undertaken the task of evaluating the performance of Ultra Wideband system in tracking objects moving in indoor spaces. First trials were conducted for different path shapes. For the moving object, average positioning errors of 13.5 cm were noted with the maximum error not exceeding 35 cm. Such positioning error values are sufficient, e.g. to navigate a blind person in unfamiliar indoor spaces.


title: Interval Electrical Betweenness Method for Power Grid Vulnerability Assessment Considering Wind Power

abstract: Wind energy has been integrated into power grid on a large scale. Due to its volatility and intermittency, the integration has brought new challenges to the security and vulnerability assessment of power grid. This paper proposes an interval electrical betweenness method to identify the vulnerable lines and nodes of power grid with wind power. Firstly, the wind generation is modelled as interval numbers, and the interval power flow is employed to obtain the interval power in transmission lines. Then, according to the topology and operation of the power grid lines and nodes, an interval electrical betweenness function is presented to represent the vulnerability. Furthermore, the interval number ranking method is used to rank the obtained interval electrical betweenness so as to identify vulnerable lines and nodes. The simulation results of IEEE 39-bus system verify the comprehensiveness and effectiveness of the proposed method.


title: An Overview of PSO- Based Approaches in Image Segmentation

abstract: Particle swarm optimization (PSO) is recent approach that can be employed in a wide range of applications. It is an evolutionary computing method based on colony aptitude which is a better parallel searching algorithm. Image segmentation is a low level vision task which is applicable in various applications such as object recognition, medical imaging, document analysis, just to name a few. PSO itself is a very powerful technique and when combined with other computational intelligence technique results in a truly affected approach. In this paper we have reviewed how PSO can be combined with various other methodologies such as neural networks, rough sets, clustering, thresholding, genetic algorithm, wavelets and fuzzy systems.


title: Consensus in the Age of Blockchains

abstract: The blockchain initially gained traction in 2008 as the technology underlying bitcoin, but now has been employed in a diverse range of applications and created a global market worth over $150B as of 2017. What distinguishes blockchains from traditional distributed databases is the ability to operate in a decentralized setting without relying on a trusted third party. As such their core technical component is consensus: how to reach agreement among a group of nodes. This has been extensively studied already in the distributed systems community for closed systems, but its application to open blockchains has revitalized the field and led to a plethora of new designs. ::: The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematic and comprehensive study of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: first protocols based on proof-of-work (PoW), second proof-of-X (PoX) protocols that replace PoW with more energy-efficient alternatives, and third hybrid protocols that are compositions or variations of classical consensus protocols. We develop a framework to evaluate their performance, security and design properties, and use it to systematize key themes in the protocol categories described above. This evaluation leads us to identify research gaps and challenges for the community to consider in future research endeavours.


title: A Twelve-Beam Steering Low-Profile Patch Antenna With Shorting Vias for Vehicular Applications

abstract: A low-profile twelve-beam printed patch antenna is presented for pattern reconfigurable applications. The patch is fed at the four sides using coaxial feeds. Diagonal lines of vias are inserted on the patch surface to restrict currents to the edges. While majority of patch antennas have only axial beam pattern, the proposed antenna produces twelve different beams. When one of its four feeds on the sides is excited and the remaining feeds are open circuited, the antenna generates a linearly polarized tilted beam (6.1 dBi, $\theta _{\mathrm {max}}= 30^{\circ }$ ). This beam is directed away from feeding patch corner. Therefore, the antenna can steer its tilted beam in four different space quadrants in front of the antenna by exciting one feed at a time. The antenna is also capable of generating eight other beams using multiple feed excitations. They are four additional titled beams (6.1 dBi, $\theta _{\mathrm {{max}}}= 30^{\circ }$ ), two tilted-twin beam (5.8 dBi, $\theta _{\mathrm {{max}}}= \pm 36^{\circ }$ ), one semi-doughnut beam (5.8 dBi), and one axial beam (8.2 dBi). The antenna is designed to operate at the test frequency of 2.4 GHz and has a height = 1.5 mm ( $\lambda _{\mathrm {{0}}}$ /83). The impedance matching to $50~\Omega $ is achieved using right-angle slots etched on the patch antenna.


title: A printed LPDA with UWB capability

abstract: This work deals with the design of a wideband microstrip log periodic array operating between 4 and 18 GHz (thus working in C,X and Ku bands). A few studies, since now, have been proposed but they are significantly less performing and usually quite complicated. Our solution is remarkably simple and shows both SWR and gain better than likely structures proposed in the literature. The same antenna can also be used as an UWB antenna. The design has been developed using CST MICROWAVE STUDIO 2009, a general purpose and specialist tool for the 3D electromagnetic simulation of microwave high frequency components.


title: A high-linearity, LC-Tuned, 24-GHz T/R switch in 90-nm CMOS

abstract: This paper presents an LC-tuned, 24-GHz single-pole double-throw (SPDT) transmit/receive (T/R) switch implemented in 90-nm CMOS. The design focuses on the techniques to increase the power handling capability in the transmit (Tx) mode under 1.2-V operation. The switch achieves a measured P-1dB of 28.7 dBm, which represents the highest linearity, reported to date, for CMOS millimeter-wave T/R switches. The transmit and receive (Rx) branches employ different switch topologies to minimize the power leakage into the Rx path during Tx mode, and hence improve the linearity. To accommodate large signal swing, AC floating bias is applied using large bias resistors to all terminals of the switch devices. Triple-well devices are utilized to effectively float the substrate terminals. The switch uses a single 1.2-V digital control signal for T/R mode selection and for source/drain bias. The measured insertion loss is 3.5 dB and return loss is better than -10 dB at 24 GHz.


title: Visual cues can modulate integration and segregation of objects in auditory scene analysis

abstract: The task of assigning concurrent sounds to different auditory objects is known to depend on temporal and spectral cues. When tones of high and low frequencies are presented in alternation, they can be perceived as a single, integrated melody, or as two parallel, segregated melodic lines, according to the presentation rate and frequency distance between the sounds. At an intermediate distance, in the ‘ambiguous’ range, both percepts are possible. We conducted an electrophysiological experiment to determine whether an ambiguous sound organization could be modulated toward an integrated or segregated percept by the synchronous presentation of visual cues. Two sets of sounds (one high frequency and one low frequency) were interleaved. To promote integration or segregation, visual stimuli were synchronized to either the within-set frequency pattern or to the across-set intensity pattern. Elicitation of the mismatch negativity (MMN) component of event-related brain potentials was used to index the segregated organization, when no task was performed with the sounds. MMN was elicited only when the visual pattern promoted the segregation of the sounds. The results demonstrate cross-modal effects on auditory object perception in that sound ambiguity was resolved by synchronous presentation of visual stimuli, which promoted either an integrated or segregated perception of the sounds.


title: Turnover of new graduate nurses in their first job using survival analysis.

abstract: PURPOSE ::: To examine factors related to turnover of new graduate nurses in their first job. ::: ::: ::: DESIGN ::: Data were obtained from a 3-year panel survey (2006-2008) of the Graduates Occupational Mobility Survey that followed-up college graduates in South Korea. The sample consisted of 351 new graduates whose first job was as a full-time registered nurse in a hospital. ::: ::: ::: METHODS ::: Survival analysis was conducted to estimate survival curves and related factors, including individual and family, nursing education, hospital, and job dissatisfaction (overall and 10 specific job aspects). ::: ::: ::: FINDINGS ::: The estimated probabilities of staying in their first job for 1, 2, and 3 years were 0.823, 0.666, and 0.537, respectively. Nurses reporting overall job dissatisfaction had significantly lower survival probabilities than those who reported themselves to be either neutral or satisfied. Nurses were more likely to leave if they were married or worked in small (vs. large), nonmetropolitan, and nonunionized hospitals. Dissatisfaction with interpersonal relationships, work content, and physical work environment was associated with a significant increase in the hazards of leaving the first job. ::: ::: ::: CONCLUSIONS ::: Hospital characteristics as well as job satisfaction were significantly associated with new graduates' turnover. ::: ::: ::: CLINICAL RELEVANCE ::: The high turnover of new graduates could be reduced by improving their job satisfaction, especially with interpersonal relationships, work content, and the physical work environment.


title: Incremental Mining of System Log Format

abstract: In modern computer system, system logs are important for problem determination in troubleshooting. Especially in the troubleshooting of systems, system administrators need to understand overview of the problems and identify the root causes quickly, and system logs can help the system administrators. However large numbers of unfamiliar system logs when are generated problems occur, and it's difficult to understand and use them. Most of the existing methods for interpreting system logs don't work immediately and are not useful for troubleshooting situations. We have devised a new method for mining log formats and retrieving log types and parameters in incremental log messages. By creating a structured tree using the nodes generated from log messages, we created a method for mining and refining log format continuously in realtime. Our experiments shows that our method can identify the formats of real system logs without prior knowledge.


title: Memory access scheduling

abstract: The bandwidth and latency of a memory system are strongly dependent on the manner in which accesses interact with the “3-D” structure of banks, rows, and columns characteristic of contemporary DRAM chips. There is nearly an order of magnitude difference in bandwidth between successive references to different columns within a row and different rows within a bank. This paper introduces memory access scheduling, a technique that improves the performance of a memory system by reordering memory references to exploit locality within the 3-D memory structure. Conservative reordering, in which the first ready reference in a sequence is performed, improves bandwidth by 40% for traces from five media benchmarks. Aggressive reordering, in which operations are scheduled to optimize memory bandwidth, improves bandwidth by 93% for the same set of applications. Memory access scheduling is particularly important for media processors where it enables the processor to make the most efficient use of scarce memory bandwidth.


title: Storage and Executive Processes in the Frontal Lobes

abstract: The human frontal cortex helps mediate working memory, a system that is used for temporary storage and manipulation of information and that is involved in many higher cognitive functions. Working memory includes two components: short-term storage (on the order of seconds) and executive processes that operate on the contents of storage. Recently, these two components have been investigated in functional neuroimaging studies. Studies of storage indicate that different frontal regions are activated for different kinds of information: storage for verbal materials activates Broca's area and left-hemisphere supplementary and premotor areas; storage of spatial information activates the right-hemisphere premotor cortex; and storage of object information activates other areas of the prefrontal cortex. Two of the fundamental executive processes are selective attention and task management. Both processes activate the anterior cingulate and dorsolateral prefrontal cortex.


title: Video gameplay, personality and academic performance

abstract: The relationship between video gameplay, video game genre preference, personality, and GPA was investigated in an online correlational study with university students. In addition to administering self-report measures of GPA and personality, we asked three different questions regarding styles of video gameplay. The first asked the average time spent playing video games per week (habitual players), the second asked the total time spent playing favorite video games (selective players), and the third asked the number of different video games played in a year (diverse players). Students who were medium in selective player style (spent 11-50h) had significantly higher GPAs than students low on selective player style (spent 0-10h). Students high on habitual playing style (7 or more hours a week) showed significantly lower levels of Conscientiousness compared to students low on habitual playing style (0-1h a week). Students who were high on the diverse style (i.e., 7 or more games played a year) showed significantly higher Openness scores than students low on the diverse style (0-3 games a year). Finally, several notable relations were found between video game genre preference, GPA, and personality. Results are discussed in terms of the positive implications of video gameplay on academic performance.


title: GP-GAN: Towards Realistic High-Resolution Image Blending

abstract: It is common but challenging to address high-resolution image blending in the automatic photo editing application. In this paper, we would like to focus on solving the problem of high-resolution image blending, where the composite images are provided. We propose a framework called Gaussian-Poisson Generative Adversarial Network (GP-GAN) to leverage the strengths of the classical gradient-based approach and Generative Adversarial Networks. To the best of our knowledge, it's the first work that explores the capability of GANs in high-resolution image blending task. Concretely, we propose Gaussian-Poisson Equation to formulate the high-resolution image blending problem, which is a joint optimization constrained by the gradient and color information. Inspired by the prior works, we obtain gradient information via applying gradient filters. To generate the color information, we propose a Blending GAN to learn the mapping between the composite images and the well-blended ones. Compared to the alternative methods, our approach can deliver high-resolution, realistic images with fewer bleedings and unpleasant artifacts. Experiments confirm that our approach achieves the state-of-the-art performance on Transient Attributes dataset. A user study on Amazon Mechanical Turk finds that the majority of workers are in favor of the proposed method.


title: Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects

abstract: This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images. A novel structured prediction approach is considered, where 3D poses are represented as skeletal models that naturally operate on manifolds. Given an input depth image, the problem of predicting the most proper articulation of underlying skeletal model is thus formulated as sequentially searching for the optimal skeletal configuration. This is subsequently addressed by convolutional neural nets trained end-to-end to render sequential prediction of the joint locations as regressing a set of tangent vectors of the underlying manifolds. Our approach is examined on various articulated objects including human hand, mouse, and fish benchmark datasets. Empirically it is shown to deliver highly competitive performance with respect to the state-of-the-arts, while operating in real-time (over 30 FPS).


title: Calcite: Completing Code Completion for Constructors Using Crowds

abstract: Calcite is a new Eclipse plugin that helps address the difficulty of understanding and correctly using an API. Calcite finds the most popular ways to instantiate a given class or interface by using code examples. To allow the users to easily add these object instantiations to their code, Calcite adds items to the popup completion menu that will insert the appropriate code into the user’s program. Calcite also uses crowd sourcing to add to the menu instructions in the form of comments that help the user perform functions that people have identified as missing from the API. In a user study, Calcite improved users’ success rate by 40%.


title: Few-shot Object Detection

abstract: In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-shot object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.


title: Sparse probabilistic principal component analysis

abstract: Compositions including at least one organic ligand including a first hetero atom and a second hetero atom directly bonded to the first hetero atom or located one carbon atom away from the first hetero atom, with at least one of the hetero atoms being nitrogen, and a transition metal moiety partially complexes by the organic ligand are provided. Such complexes have a controlled adaptable proton transfer ability and/or a hydrogen bonding ability which makes such compositions particularly useful as chemical reaction facilitators. Examples of chemical reactions facilitated by such compositions include hydrolysis reactions and carbon dioxide conversion reactions.


title: Assessing the News Landscape: A Multi-Module Toolkit for Evaluating the Credibility of News

abstract: Today, journalist, information analyst, and everyday news consumers are tasked with discerning and fact-checking the news. This task has became complex due to the ever-growing number of news sources and the mixed tactics of maliciously false sources. To mitigate these problems, we introduce the The News Landscape (NELA) Toolkit: an open source toolkit for the systematic exploration of the news landscape. NELA allows users to explore the credibility of news articles using well-studied content-based markers of reliability and bias, as well as, filter and sort through article predictions based on the user's own needs. In addition, NELA allows users to visualize the media landscape at different time slices using a variety of features computed at the source level. NELA is built with a modular, pipeline design, to allow researchers to add new tools to the toolkit with ease. Our demo is an early transition of automated news credibility research to assist human fact-checking efforts and increase the understanding of the news ecosystem as a whole.


title: A comparative review of plausible hole filling strategies in the context of scene depth image completion

abstract: Abstract Despite significant research focus on 3D scene capture systems, numerous unresolved challenges remain in relation to achieving full coverage scene depth estimation which is the key part of any modern 3D sensing system. This has created an area of research where the goal is to complete the missing 3D information post capture via a secondary depth filling process. In many downstream applications, an incomplete depth scene is of limited value, requiring many special cases for subsequent utilization, and thus techniques are required to “fill the holes” that exist in terms of both missing depth and color scene information. An analogous problem exists within the scope of scene filling post object removal in the same context. Although considerable research has resulted in notable progress in the synthetic expansion or reconstruction of missing color scene information in both statistical (texture synthesis) and structural (image completion) forms, work on the plausible completion of missing scene depth is contrastingly limited. This survey aims to provide a state of the art overview within this growing field of depth synthesis work whilst noting related solutions in the space of traditional texture synthesis and color image completion for hole filling. To these ends, we concentrate on the plausible completion of both underlying depth structure and relief texture to provide both greater understanding and future development in the area. Our analyses are in part supported by illustrative experimental examples of the comparative use of a subset of representative approaches over common depth completion examples.


title: Teacher use of ICT: Challenges and opportunities

abstract: This paper presents the summary of findings from large-scale evaluations of ICT-related teacher professional development programs run by the Cyprus Ministry of Education and Culture, the Cyprus Pedagogical Institute, and the Centre for the Advancement of Research and Development in Educational Technology (CARDET). More than eleven thousand teachers participated in professional development programs, which focused among others on computer literacy skills, pedagogical knowledge needed for technology integration, infusing technology-based activities in the curriculum, and online learning. In addition to a large-scale survey conducted, twenty-four case studies were developed in order to examine in depth the effectiveness of teacher professional development and the challenges faced by practicing teachers with respect to the integration of Information and Communication Technologies (ICT) in the curricula. This paper discusses findings from the large scale survey and enriches the findings with qualitative data from the ethnographic case studies to illustrate insights on effective practices of ICT integration in Cyprus education. The findings presented in this paper shed light to the complexities of integrating ICT in teaching and learning. The research reported is the first of its kind to be conducted in the Cyprus context. Findings reveal that teachers are willing to integrate technology into their teaching practices. However, even though they realize the benefits of ICT integration, a lot of teachers today seem resistant to integrating technologies and using online learning environments. Data collected and analyzed, illustrate that this is due to several factors, such as lack of time, the ill-structured design of the school curriculum, the lack of infrastructure and tools to better support teachers and learners. In order to aid the endeavors of teachers in integrating technologies, more robust professional development programs as well as appropriate technologies, need to be developed that would continuously provide support in order for teachers to be able to overcome the aforementioned problems and challenges faced when attempting to integrate technology. Moreover, better coordination and organization of the professional development programs is necessary, as well as improvements in the school curriculum, infrastructure upgrades, and availability of software programs and supporting learning materials. During the presentation we will discuss examples from concrete projects (E.g. eLAT, OnlinePD, EUPT3) to illustrate the findings and provide recommendations for successful ICT integration and teacher professional development.


title: Context-Aware Attention Mechanism for Speech Emotion Recognition

abstract: In this work, we study the use of attention mechanisms to enhance the performance of the state-of-the-art deep learning model in Speech Emotion Recognition (SER). We introduce a new Long Short-Term Memory (LSTM)-based neural network attention model which is able to take into account the temporal information in speech during the computation of the attention vector. The proposed LSTM-based model is evaluated on the IEMOCAP dataset using a 5-fold cross-validation scheme and achieved 68.8% weighted accuracy on 4 classes, which outperforms the state-of-the-art models.


title: MRI in the diagnosis of Mayer-Rokitansky-Kuster-Hauser syndrome.

abstract: PURPOSE ::: The aim of this study was to establish the role of magnetic resonance imaging (MRI) in patients with Mayer-Rokitansky-Kuster-Hauser syndrome (MRKHS). ::: ::: ::: MATERIALS AND METHODS ::: Sixteen female MRKHS patients (mean age, 19.4 years; range, 11-39 years) were studied using MRI. Two experienced radiologists evaluated all the images in consensus to assess the presence or absence of the ovaries, uterus, and vagina. Additional urogenital or vertebral pathologies were also noted. ::: ::: ::: RESULTS ::: Of the 16 patients, complete aplasia of uterus was seen in five patients (31.3%). Uterine hypoplasia or remnant uterus was detected in 11 patients (68.8%). Ovaries were clearly seen in 10 patients (62.5%), and in two of the 10 patients, no descent of ovaries was detected. In five patients, ovaries could not be detected on MRI. In one patient, agenesis of right ovary was seen, and the left ovary was in normal shape. Of the 16 cases, 11 (68.8%) had no other extragenital abnormalities. Additional abnormalities were detected in six patients (37.5%). Two of the six had renal agenesis, and one patient had horseshoe kidney; renal ectopy was detected in two patients, and one patient had urachal remnant. Vertebral abnormalities were detected in two patients; one had L5 posterior fusion defect, bilateral hemisacralization, and rotoscoliosis, and the other had coccygeal vertebral fusion. ::: ::: ::: CONCLUSION ::: MRI is a useful and noninvasive imaging method in the diagnosis and evaluation of patients with MRKHS.


title: Ontology-based sentiment analysis of twitter posts

abstract: The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.


title: Knowledge discovery, analysis and prediction in healthcare using data mining and analytics

abstract: Taking care and maintenance of a healthy population is the Strategy of each country. Information and communication technologies in the health care system have led to many changes in order to improve the quality of health care services to patients, rational spending time and reduce costs. In the booming field of IT research, the reach of drug delivery, information on grouping of similar drugs has been lacking. The wealth distribution and drug affordability at a certain demographic has been interlinked and proposed in this paper. Looking at the demographic we analyze and group the drugs based on target action and link this to the wealth and the people to medicine ratio, which can be accomplished via data mining and web mining. The data thus mined will be analysed and made available to public and commercial purpose for their further knowledge and benefit.


title: Comparison of Global Algorithms in Word Sense Disambiguation

abstract: This article compares four probabilistic algorithms (global algorithms) for Word Sense Disambiguation (WSD) in terms of the number of scorer calls (local algo- rithm) and the F1 score as determined by a gold-standard scorer. Two algorithms come from the state of the art, a Simulated Annealing Algorithm (SAA) and a Genetic Algorithm (GA) as well as two algorithms that we first adapt from WSD that are state of the art probabilistic search algorithms, namely a Cuckoo search algorithm (CSA) and a Bat Search algorithm (BS). As WSD requires to evaluate exponentially many word sense combinations (with branching factors of up to 6 or more), probabilistic algorithms allow to find approximate solution in a tractable time by sampling the search space. We find that CSA, GA and SA all eventually converge to similar results (0.98 F1 score), but CSA gets there faster (in fewer scorer calls) and reaches up to 0.95 F1 before SA in fewer scorer calls. In BA a strict convergence criterion prevents it from reaching above 0.89 F1.


title: Self-sensing comparison of fractional slot pitch winding vs. distributed winding for FW- and FI-IPMSMs based on carrier signal injection at very low speed

abstract: This paper analyzes the effects of fractional pitch and distributed winding stators on self-sensing performance of PM machines. Three rotor configurations, an FW-IPMSM and two FI-IPMSMs, and two stator designs, distributed and concentrated windings, are presented to analyze the cross-saturation, secondary saliencies, saturation-induced saliencies and saliency ratio depending on the possible rotor and stator configurations. Finite element modeling (FEM) has been used to obtain a detailed understanding of the self-sensing performance of these machines.


License Plate Recognition Using Image Processing


title: The volcanormal density for radar-based extended target tracking

abstract: This paper presents a novel shape model for radar-based vehicle tracking. This model is a special probability density function (pdf), designed to approximate the radar measurement spread of a vehicle more adequately than widely used models. While this pdf is used to determine the likelihood of the spatial measurements, additionally a recent Doppler measurement model is integrated in a maximum likelihood estimator. This formulation allows the estimation of a vehicle's pose, motion and axis lengths based on the radar's spatial and Doppler dimension. On this basis, an efficient computation method is derived. An evaluation of actual radar and ground truth data demonstrates the precise estimation of a vehicle's state.


title: Deep structured features for semantic segmentation

abstract: We propose a highly structured neural network architecture for semantic segmentation of images that combines i) a Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random layer realizing a radial basis function kernel approximation, and iii) a linear classifier. While stages i) and ii) are completely pre-specified, only the linear classifier is learned from data. Thanks to its high degree of structure, our architecture has a very small memory footprint and thus fits onto low-power embedded and mobile platforms. We apply the proposed architecture to outdoor scene and aerial image semantic segmentation and show that the accuracy of our architecture is competitive with conventional pixel classification CNNs. Furthermore, we demonstrate that the proposed architecture is data efficient in the sense of matching the accuracy of pixel classification CNNs when trained on a much smaller data set.


title: Modeling and calibration of a structured light scanner for 3-D robot vision

abstract: In this report we have used projectivity theory to model the process of structured light scanning for 3D robot vision. The projectivity formalism is used to derive a 4 × 3 transformation matrix that converts points in the image plane into their corresponding 3D world coordinates. Calibration of the scanner consists of computing the coefficient of this matrix by showing to the system a set of lines generated by suitable object edges. We end this paper by showing how the matrix can be used to convert image pixel locations into the world coordinates of the corresponding object points using two different scanning strategies.


title: Perceived Helpfulness of Online Consumer Reviews: The Role of Message Content and Style

abstract: The rise of online reviews written by consumers makes possible an examination of how the content and style of these word-of-mouth messages contribute to their helpfulness. In this study, consumers are asked to judge the value of real online consumer reviews to their simulated shopping activities. The results suggest the benefits of moderate review length and of positive, but not negative, product evaluative statements. Non-evaluative product information and information about the reviewer were also found to be associated with review helpfulness. Stylistic elements that may impair clarity (such as spelling and grammatical errors) were associated with less valuable reviews, and elements that may make a review more entertaining (such as expressive slang and humor) were associated with more valuable reviews. These findings point to factors beyond product information that may affect the perceived helpfulness of an online consumer review. Copyright © 2012 John Wiley & Sons, Ltd.


title: Workshop on novelty and diversity in recommender systems - DiveRS 2011

abstract: Novelty and diversity have been identified as key dimensions of recommendation utility in real scenarios, and a fundamental research direction to keep making progress in the field. Yet recommendation novelty and diversity remain a largely open area for research. The DiveRS workshop gathered researchers and practitioners interested in the role of these dimensions in recommender systems. The workshop seeks to advance towards a better understanding of what novelty and diversity are, how they can improve the effectiveness of recommendation methods and the utility of their outputs. The workshop pursued the identification of open problems, relevant research directions, and opportunities for innovation in the recommendation business.


How SGD Selects the Global Minima in Over-parameterized Learning : A Dynamical Stability Perspective


title: Hand/Arm Robot Teleoperation by Inertial Motion Capture

abstract: The multi-fingered robot hand has much attention in various fields. Many robot hands have been proposed so far and we have developed a hand/arm robot with universal robot hand II. A teleoperation system allows intuitive manipulation of the hand/arm robot. Here, a motion capture system of measuring human motion is used for operating the robot remotely. Various types of the human motion capture have been developed so far. This paper deals with a motion capture system with inertial measurement units (IMUs) and a hand/arm teleoperation with the inertial motion capture.


title: Hardware obfuscation using PUF-based logic

abstract: There is a great need to develop universal and robust techniques for intellectual property protection of integrated circuits. In this paper, we introduce techniques for the obfuscation of an arbitrary circuit by using physical unclonable functions (PUFs) and programmable logic. Specifically, we introduce the notion of PUF-based logic which can be configured to be functionally equivalent to any arbitrary design, as well as a new architecture for wire merging that obfuscates signal paths exponentially. We systematically apply our techniques in such a way so as to maximize obfuscation while minimizing area and delay overhead. We analyze our techniques on popular benchmark circuits and show them to be resilient against very powerful reverse engineering attacks in which the adversary has knowledge of the complete netlist along with the ability to read and write to any flip-flop in the circuit.


title: Micro operators accelerating 5G deployment

abstract: 5G networks will address a variety of location specific deployment scenarios with stringent requirements for end-to-end service quality. This paper reviews the recent market and technology related trends in 5G deployments. To accelerate 5G deployment serving location specific needs arising in specific high-demand areas, the concept of 5G micro operators was recently proposed to open the mobile market for new entry. The micro operator concept allows different stakeholders to take a local operator role to deploy and operate small cell networks in specific premises and offer context related services and content. The local 5G micro operators can operate a closed network to serve its own customers, an open network for mobile network operators' (MNO) customers or a mix of both. This paper expands the recent micro operator concept by identifying and addressing key regulatory elements related its introduction to the mobile communication market. These elements include the role of operator with rights and obligations, spectrum authorization for obtaining quality guaranteed spectrum, access rights to infrastructure, and building of indoor networks.


title: Monitoring the railway infrastructure: Detection of surface defects using wavelets

abstract: For monitoring the conditions of railway infrastructures, axle box acceleration (ABA) measurements on board of trains is used. In this paper, the focus is on the early detection of short surface defects called squats. Different classes of squats are classified based on the response in the frequency domain of the ABA signal, using the wavelet power spectrum. For the investigated Dutch tracks, the power spectrum in the frequencies between 1060-1160Hz and around 300Hz indicate existence of a squat and also provide information of whether a squat is light, moderate or severe. The detection procedure is then validated relying on real-life measurements of ABA signals from measuring trains, and data of severity and location of squats obtained via a visual inspection of the tracks. Based on the real-life tests in the Netherlands, the hit rate of the system for light squats is higher than 78%, with a false alarm rate of 15%. In the case of severe squats the hit rate was 100% and zero false alarms.


title: Pure alexia as a disconnection syndrome: New diffusion imaging evidence for an old concept

abstract: Functional neuroimaging and studies of brain-damaged patients made it possible to delineate the main components of the cerebral system for word reading. However, the anatomical connections subtending the flow of information within this network are still poorly defined. Here we study the connectivity of the Visual Word Form Area (VWFA), a pivotal component of the reading network achieving the invariant identification of letter strings, and reproducibly located in the left lateral occipitotemporal sulcus. Diffusion images and functional imaging data were gathered in a patient who developed pure alexia following a small surgical lesion in the vicinity of his VWFA. We had a unique opportunity to compare images obtained before, early after, and late after surgery. Analysis of diffusion images with white matter tractography and voxel-based morphometry showed that the VWFA was mainly linked to the occipital cortex through the inferior longitudinal fasciculus (ILF), and to perisylvian language areas (supramarginal gyrus) through the arcuate fasciculus. After surgery, we observed the progressive and selective degeneration of the ILF, while the VWFA was anatomically intact. This allowed us to establish the critical causal role of this fiber tract in normal reading, and to show that its disruption is one pathophysiological mechanism of pure alexia, thus clarifying a long-standing debate on the role of disconnection in neurocognitive disorders.


title: Stable High-Capacity One-Hop Distributed Hash Tables

abstract: Most research on Distributed Hash Tables (DHTs) assumes ephemeral, lightly loaded deployments. Each node has a lifetime of a few hours and initiates a lookup once every few seconds or minutes. However, in giant internet data centers, each node has a lifetime of weeks or months and initiates hundreds or thousands of lookups every second. In such an environment, one-hop DHTs are superior to multi-hop DHTs. They use lookup bandwidth more efficiently. We qualify conflicting research to show that a single onehop DHT can indeed scale to at least a few hundred thousand nodes in stable, high-capacity enterprise networks. Two new designs are presented: One Hop Sites (1HS), a high-capacity DHT tailored for site redundancy; and the One Hop Federation (1HF), a global, hierarchic DHT that resolves an open latency problem. For both, the analysis a) confirms linear scalability to at least a few hundred thousand nodes and b) identifies the most sensitive design parameters.


title: Depth Map Reconstruction for Underwater Kinect Camera Using Inpainting and Local Image Mode Filtering

abstract: Underwater optical cameras are widely used for security monitoring in ocean, such as earthquake prediction and tsunami alarming. Optical cameras recognize objects for autonomous underwater vehicles and provide security protection for sea-floor networks. However, there are many issues for underwater optical imaging, such as forward and backward scattering, light absorption, and sea snow. Many underwater image processing techniques have been proposed to overcome these issues. Among these techniques, the depth map gives important information for many applications of the post-processing. In this paper, we propose a Kinect-based underwater depth map estimation method that uses a captured coarse depth map by Kinect with the loss of depth information. To overcome the drawbacks of low accuracy of coarse depth maps, we propose a corresponding reconstruction architecture that uses the underwater dual channels prior dehazing model, weighted enhanced image mode filtering, and inpainting. Our proposed method considers the influence of mud sediments in water and performs better than the traditional methods. The experimental results demonstrated that, after inpainting, dehazing, and interpolation, our proposed method can create high-accuracy depth maps.


title: Schema-agnostic vs Schema-based Configurations for Blocking Methods on Homogeneous Data

abstract: Entity Resolution constitutes a core task for data integration that, due to its quadratic complexity, typically scales to large datasets through blocking methods. These can be configured in two ways. The schema-based configuration relies on schema information in order to select signatures of high distinctiveness and low noise, while the schema-agnostic one treats every token from all attribute values as a signature. The latter approach has significant potential, as it requires no fine-tuning by human experts and it applies to heterogeneous data. Yet, there is no systematic study on its relative performance with respect to the schema-based configuration. This work covers this gap by comparing analytically the two configurations in terms of effectiveness, time efficiency and scalability. We apply them to 9 established blocking methods and to 11 benchmarks of structured data. We provide valuable insights into the internal functionality of the blocking methods with the help of a novel taxonomy. Our studies reveal that the schema-agnostic configuration offers unsupervised and robust definition of blocking keys under versatile settings, trading a higher computational cost for a consistently higher recall than the schema-based one. It also enables the use of state-of-the-art blocking methods without schema knowledge.


title: The enemy within: A behavioural intention model and an information security awareness process

abstract: Most employees in small and medium enterprise (SME) engineering firms now have access to their own personal workstations which have become part of their daily functions. This has led to an increased need for information security management to safeguard against loss/alteration or theft of the firm's important information. SMEs tend to be concerned with vulnerabilities from external threats, although industry research suggests that a substantial proportion of security incidents originate from insiders within the firm. Hence, physical preventative measures such as antivirus software and firewalls are proving to solve only part of the problem as the employees controlling them do not have adequate information security knowledge. This tends to expose the firm to costly mistakes that can be made by naive/uninformed employees. This paper presents an information security awareness process that seeks to cultivate positive security behaviours using the behavioural intentions models i.e. the Theory of Reasoned Action and the Protection Motivation Theory. The process presented has been tested at an SME engineering firm, and findings are also presented and discussed in this paper.


title: Cognitive therapy for command hallucinations: randomised controlled trial

abstract: Background Command hallucinations are a distressing and high-risk group of symptoms that have long been recognised but little understood, with few effective treatments. In line with our recent research, we propose that the development of an effective cognitive therapy for command hallucinations (CTCH) would be enhanced by applying insights from social rank theory. ::: ::: Aims We tested the efficacy of CTCH in reducing beliefs about the power of voices and thereby compliance, in a single-blind, randomised controlled trial. ::: ::: Method Atotal of 38 patients with command hallucinations, with which they had recently complied with serious consequences, were allocated randomly to CTCH or treatment as usual and followed up at 6 months and12 months. ::: ::: Results Large and significant reductions in compliance behaviour were obtained favouring the cognitive therapy group (effect size 1.1). Improvements were also observed in the CTCH but not the control group in degree of conviction in the power and superiority of the voices and the need to comply, and in levels of distress and depression. No change in voice topography (frequency, loudness, content) was observed. The differences were maintained at 12 months’ follow-up. ::: ::: Conclusions The results support the efficacy of cognitive therapy for CTCH.


title: Optimization of a Parallel Shoulder Mechanism to Achieve a High-Force, Low-Mass, Robotic-Arm Exoskeleton

abstract: This paper describes a robotic-arm exoskeleton that uses a parallel mechanism inspired by the human forearm to allow naturalistic shoulder movements. The mechanism can produce large forces through a substantial portion of the range of motion (RoM) of the human arm while remaining lightweight. This paper describes the optimization of the exoskeleton's torque capabilities by the modification of the key geometric design parameters.


title: From bias to opinion: a transfer-learning approach to real-time sentiment analysis

abstract: Real-time interaction, which enables live discussions, has become a key feature of most Web applications. In such an environment, the ability to automatically analyze user opinions and sentiments as discussions develop is a powerful resource known as real time sentiment analysis. However, this task comes with several challenges, including the need to deal with highly dynamic textual content that is characterized by changes in vocabulary and its subjective meaning and the lack of labeled data needed to support supervised classifiers. In this paper, we propose a transfer learning strategy to perform real time sentiment analysis. We identify a task - opinion holder bias prediction - which is strongly related to the sentiment analysis task; however, in constrast to sentiment analysis, it builds accurate models since the underlying relational data follows a stationary distribution. Instead of learning textual models to predict content polarity (i.e., the traditional sentiment analysis approach), we first measure the bias of social media users toward a topic, by solving a relational learning task over a network of users connected by endorsements (e.g., retweets in Twitter). We then analyze sentiments by transferring user biases to textual features. This approach works because while new terms may arise and old terms may change their meaning, user bias tends to be more consistent over time as a basic property of human behavior. Thus, we adopted user bias as the basis for building accurate classification models. We applied our model to posts collected from Twitter on two topics: the 2010 Brazilian Presidential Elections and the 2010 season of Brazilian Soccer League. Our results show that knowing the bias of only 10% of users generates an F1 accuracy level ranging from 80% to 90% in predicting user sentiment in tweets.


title: Vibration Based Condition Monitoring: A Review

abstract: Vibration based condition monitoring refers to the use of in situ non-destructive sensing and analysis of system characteristics –in the time, frequency or modal domains –for the purpose of detecting changes, which may indicate damage or degradation. In the field of civil engineering, monitoring systems have the potential to facilitate the more economical management and maintenance of modern infrastructure. This paper reviews the state of the art in vibration based condition monitoring with particular emphasis on structural engineering applications.


title: High-Speed Dual-Gate a-IGZO TFT-Based Circuits With Top-Gate Offset Structure

abstract: Owing to bulk-accumulation, dual-gate (DG) amorphous-indium-gallium-zinc-oxide (a-IGZO) thin-film transistors (TFTs) with top- and bottom-gates electrically tied together (DG-driving) exhibit 2.53 times higher ON-current and subthreshold voltage swing of ~ 180 mV/decade, which is 50% lower than that of single-gate (SG)-driven a-IGZO TFTs. Here, through simulation and experimental results, we demonstrate that the use of DG-driven back-channel-etched a-IGZO TFTs with a top-gate offset structure enhances the switching speed of a-IGZO TFT-based circuits. In particular, fabricated SG-driven and DG-driven 11-stage ring oscillators exhibited respective oscillating frequencies of 334 and 781 kHz.


title: Multimedia answering: enriching text QA with media information

abstract: Existing community question-answering forums usually provide only textual answers. However, for many questions, pure texts cannot provide intuitive information, while image or video contents are more appropriate. In this paper, we introduce a scheme that is able to enrich text answers with image and video information. Our scheme investigates a rich set of techniques including question/answer classification, query generation, image and video search reranking, etc. Given a question and the community-contributed answer, our approach is able to determine which type of media information should be added, and then automatically collects data from Internet to enrich the textual answer. Different from some efforts that attempt to directly answer questions with image and video data, our approach is built based on the community-contributed textual answers and thus it is more feasible and able to deal with more complex questions. We have conducted empirical study on more than 3,000 QA pairs and the results demonstrate the effectiveness of our approach.


Representing Polysemy and Diachronic Lexico-Semantic Data on the Semantic Web ?


title: Detecting the overlapping and hierarchical community structure of complex networks

abstract: Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling to investigate different hierarchical levels of organization. Tests on real and artificial networks give excellent results.


title: Robust biomarker identification for cancer diagnosis with ensemble feature selection methods

abstract: Motivation: Biomarker discovery is an important topic in biomedical applications of computational biology, including applications such as gene and SNP selection from high-dimensional data. Surprisingly, the stability with respect to sampling variation or robustness of such selection processes has received attention only recently. However, robustness of biomarkers is an important issue, as it may greatly influence subsequent biological validations. In addition, a more robust set of markers may strengthen the confidence of an expert in the results of a selection method. ::: ::: Results: Our first contribution is a general framework for the analysis of the robustness of a biomarker selection algorithm. Secondly, we conducted a large-scale analysis of the recently introduced concept of ensemble feature selection, where multiple feature selections are combined in order to increase the robustness of the final set of selected features. We focus on selection methods that are embedded in the estimation of support vector machines (SVMs). SVMs are powerful classification models that have shown state-of-the-art performance on several diagnosis and prognosis tasks on biological data. Their feature selection extensions also offered good results for gene selection tasks. We show that the robustness of SVMs for biomarker discovery can be substantially increased by using ensemble feature selection techniques, while at the same time improving upon classification performances. The proposed methodology is evaluated on four microarray datasets showing increases of up to almost 30% in robustness of the selected biomarkers, along with an improvement of ~15% in classification performance. The stability improvement with ensemble methods is particularly noticeable for small signature sizes (a few tens of genes), which is most relevant for the design of a diagnosis or prognosis model from a gene signature. ::: ::: Contact: yvan.saeys@psb.ugent.be ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: Event study methodologies in information systems research

abstract: Event studies are based on the theoretical framework of efficient capital markets and the notion that security prices include all information available to the market. As a result, announcements made by firms provide to market participants information that can be impounded into the market price. This paper investigates the use of event studies in information systems and accounting information systems research using a three-pronged approach. First, this paper provides a comprehensive survey of research that uses event study methodologies, where the events are announcements made by firms about issues related to information systems, e.g., announcements of the adoption of enterprise resource planning systems and of the effect of security breaches in firms' information systems. Second, this paper summarizes event study methodologies used in prior research, along with some of the key parameters and concerns associated with their implementation. Third, this paper provides remarks on key event study modeling issues, and it offers recommendations to researchers.


title: NLP Techniques for Term Extraction and Ontology Population

abstract: This chapter investigates NLP techniques for ontology population, using a combination of rule-based approaches and machine learning. We describe a method for term recognition using linguistic and statistical techniques, making use of contextual information to bootstrap learning. We then investigate how term recognition techniques can be useful for the wider task of information extraction, making use of similarity metrics and contextual information. We describe two tools we have developed which make use of contextual information to help the development of rules for named entity recognition. Finally, we evaluate our ontology-based information extraction results using a novel technique we have developed which makes use of similarity-based metrics first developed for term recognition.


title: What Is Design Thinking and Why Is It Important?

abstract: Design thinking is generally defined as an analytic and creative process that engages a person in opportunities to experiment, create and prototype models, gather feedback, and redesign. Several characteristics (e.g., visualization, creativity) that a good design thinker should possess have been identified from the literature. The primary purpose of this article is to summarize and synthesize the research on design thinking to (a) better understand its characteristics and processes, as well as the differences between novice and expert design thinkers, and (b) apply the findings from the literature regarding the application of design thinking to our educational system. The authors’ overarching goal is to identify the features and characteristics of design thinking and discuss its importance in promoting students’ problem-solving skills in the 21st century.


Task-specific changes in anterior cingulate-amygdala network functional connectivity are correlated with individual differences in affective empathy


title: Time series data classification using recurrent neural network with ensemble learning

abstract: In statistics and signal processing, a time series is a sequence of data points, measured typically at successive times, spaced apart at uniform time intervals. Time series prediction is the use of a model to predict future events based on known past events; to predict future data points before they are measured. Solutions in such cases can be provided by non-parametric regression methods, of which each neural network based predictor is a class. As a learning method of time series data with neural network, Elman type Recurrent Neural Network has been known. In this paper, we propose the multi RNN. In order to verify the effectiveness of our proposed method, we experimented by the simple artificial data and the heart pulse wave data.


title: Parental influence on children's early eating environments and obesity risk: implications for prevention

abstract: Most childhood obesity prevention efforts have focused on school-age children and adolescents and have had limited success. We argue that the first years of life, including the prenatal period, the postnatal suckling period and the transition to the modified adult diet, may provide opportunities for preventive interventions. These early periods are characterized by high plasticity and rapid transitions, and parents have a high degree of control over children's environments and experiences. Observational and experimental evidence reveal persistent effects of early environments on eating behavior and obesity risk, suggesting that interventions should be tested during these early periods. The central task parents have in early development points to their potential as key targets and agents of change in early preventive interventions. In this paper, we review evidence of early environmental effects on children's eating and obesity risk, highlighting ways that parental feeding practices and parents' own behaviors impact these outcomes and calling for further experimental research to elucidate whether these factors are indeed promising targets for childhood obesity preventive interventions.


title: High-precision lane-level road map building for vehicle navigation

abstract: Lane relative vehicle navigation and control requires accurate lane-relative positioning of the vehicle. This relative position can be computed by comparing the vehicle absolute position with analytic roadway maps, which requires both high-accuracy positioning of the vehicle and high-accuracy lane maps. Carrier Phase Differential GPS (CPDGPS) aided INS or CPDGPS aided encoders is capable of estimating vehicle absolute position (relative to earth center) with centimeter level accuracy; however, to the best of the author's knowledge, the accuracy of lane level maps is currently not sufficient. In this paper, we first consider the structure of lane level maps that are compatible with standard practices of GIS road modeling. Then, various analytic lane definition are discussed. We also present a method of building lane level maps from high-accuracy positioning data along the lane center. The data is segmented according to road intersections. Shape points (vertices) as a function of arclength are located based on changes in estimated curvature. For each segment, the parameters are estimated by least-square criteria and can be refined as new datasets become available. This process is shown by an example.


title: Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models

abstract: Abnormal event detection is one of the important objectives in research and practical applications of video surveillance. However, there are still three challenging problems for most anomaly detection systems in practical setting: limited labeled data, ambiguous definition of"abnormal"and expensive feature engineering steps. This paper introduces a unified detection framework to handle these challenges using energy-based models, which are powerful tools for unsupervised representation learning. Our proposed models are firstly trained on unlabeled raw pixels of image frames from an input video rather than hand-crafted visual features; and then identify the locations of abnormal objects based on the errors between the input video and its reconstruction produced by the models. To handle video stream, we develop an online version of our framework, wherein the model parameters are updated incrementally with the image frames arriving on the fly. Our experiments show that our detectors, using Restricted Boltzmann Machines (RBMs) and Deep Boltzmann Machines (DBMs) as core modules, achieve superior anomaly detection performance to unsupervised baselines and obtain accuracy comparable with the state-of-the-art approaches when evaluating at the pixel-level. More importantly, we discover that our system trained with DBMs is able to simultaneously perform scene clustering and scene reconstruction. This capacity not only distinguishes our method from other existing detectors but also offers a unique tool to investigate and understand how the model works.


title: Non-invasive measurement of renal perfusion and oxygen metabolism to predict postoperative acute kidney injury in neonates and infants after cardiopulmonary bypass surgery

abstract: Abstract Background The pathophysiology of acute kidney injury (AKI) after cardiopulmonary bypass surgery for congenital heart disease is not completely understood. The aim of this study was to carry out a prospective analysis of the diagnostic value of non-invasive monitoring of renal oxygenation and microcirculation by combining laser Doppler flowmetry and tissue spectrometry. Methods In 50 neonates and infants who underwent repair ( n = 31) or neonatal palliation ( n = 19) of congenital heart disease with cardiopulmonary bypass, renal oxygenation, and microcirculatory flow, the approximate renal metabolic rate of oxygen and Doppler-based renal resistive index were determined after surgery. Correlations between these parameters and the occurrence of AKI according to the Pediatric Risk, Injury, Failure, Loss, End Stage Renal Disease criteria were investigated. Results Acute kidney injury occurred in 45% of patients after repair and in 32% after palliation. Renal oxygenation was significantly lower and the approximate renal metabolic rate of oxygen significantly higher in patients with AKI ( P P P Conclusions Monitoring of renal oxygen metabolism allows early prediction of AKI in infants after cardiac surgery. In contrast, renal resistive index does not allow prediction of AKI after neonatal palliation with aortopulmonary shunt establishment.


MODIFIED BREGMAN ITERATION FOR PORTFOLIO OPTIMIZATION


title: Efficient Spatial Keyword Search in Trajectory Databases

abstract: An increasing amount of trajectory data is being annotated with text descriptions to better capture the semantics associated with locations. The fusion of spatial locations and text descriptions in trajectories engenders a new type of top-$k$ queries that take into account both aspects. Each trajectory in consideration consists of a sequence of geo-spatial locations associated with text descriptions. Given a user location $\lambda$ and a keyword set $\psi$, a top-$k$ query returns $k$ trajectories whose text descriptions cover the keywords $\psi$ and that have the shortest match distance. To the best of our knowledge, previous research on querying trajectory databases has focused on trajectory data without any text description, and no existing work has studied such kind of top-$k$ queries on trajectories. This paper proposes one novel method for efficiently computing top-$k$ trajectories. The method is developed based on a new hybrid index, cell-keyword conscious B$^+$-tree, denoted by \cellbtree, which enables us to exploit both text relevance and location proximity to facilitate efficient and effective query processing. The results of our extensive empirical studies with an implementation of the proposed algorithms on BerkeleyDB demonstrate that our proposed methods are capable of achieving excellent performance and good scalability.


title: Radiofrequency treatment relieves chronic knee osteoarthritis pain: A double-blind randomized controlled trial

abstract: Chronic osteoarthritis (OA) pain of the knee is often not effectively managed with current non-pharmacological or pharmacological treatments. Radiofrequency (RF) neurotomy is a therapeutic alternative for chronic pain. We investigated whether RF neurotomy applied to articular nerve branches (genicular nerves) was effective in relieving chronic OA knee joint pain. The study involved 38 elderly patients with (a) severe knee OA pain lasting more than 3 months, (b) positive response to a diagnostic genicular nerve block and (c) no response to conservative treatments. Patients were randomly assigned to receive percutaneous RF genicular neurotomy under fluoroscopic guidance (RF group; n=19) or the same procedure without effective neurotomy (control group; n=19). Visual analogue scale (VAS), Oxford knee scores, and global perceived effect on a 7-point scale were measured at baseline and at 1, 4, and 12weeks post-procedure. VAS scores showed that the RF group had less knee joint pain at 4 (p<0.001) and 12 (p<0.001) weeks compared with the control group. Oxford knee scores showed similar findings (p<0.001). In the RF group, 10/17 (59%), 11/17 (65%) and 10/17 (59%) achieved at least 50% knee pain relief at 1, 4, and 12 weeks, respectively. No patient reported a post-procedure adverse event during the follow-up period. RF neurotomy of genicular nerves leads to significant pain reduction and functional improvement in a subset of elderly chronic knee OA pain, and thus may be an effective treatment in such cases. Further trials with larger sample size and longer follow-up are warranted.


title: The functional anatomy of attention to visual motion - A functional MRI study

abstract: Attention can enhance or modulate neural responses to stimuli at early and late stages of sensory processing. We were interested in the modulatory effect of attention to visual motion on cortical responses as measured by functional MRI. Subjects were scanned during repeated presentations of identical stimuli (visual motion) while only the attentional component of the task was varied. Enhanced haemodynamic responses during attentive conditions defined an occipitoparietofrontal system, including sensory and association areas, as well as the medial thalamus and superior colliculus. Attentional modulation was not restricted to extrastriate areas (including V3a and the V5 complex) but was also evident, to a lesser degree, in early visual areas close to the calcarine fissure (V1/V2 border). Attention-related enhancement of cortical responsiveness is discussed in terms of data that implicate modulatory short-term changes in synaptic efficacy and reciprocal connections between striate, extrastriate, parietal and frontal areas. Given the similarity of our attentional network to that controlling eye movements, the results of this study are in accord with theories linking oculomotor control and attention.


title: Front Tracking for Petroleum Reservoir Simulation

abstract: The front tracking method gives high quality solutions for problems with important discontinuities. The method has been applied to several model problems in reservoir simulation. Results are given for quarter 5-spot studies in an areal reservoir and the water coning problem in a vertical cylindrical section surrounding a production well. Both problems were treated as incompressible flow in the absence of capillary pressure. We have studied miscible and immiscible displacement for frontal mobility ratios of 1 and 5. Validation studies include mesh refinement, grid orientation and orthogonal coordinate changes.


title: Learning to Automatically Solve Algebra Word Problems

abstract: We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a system of linear equations, while simultaneously recovering an alignment of the variables and numbers in these equations to the problem text. The learning algorithm uses varied supervision, including either full equations or just the final answers. We evaluate performance on a newly gathered corpus of algebra word problems, demonstrating that the system can correctly answer almost 70% of the questions in the dataset. This is, to our knowledge, the first learning result for this task.


title: Novel zero-voltage and zero-current-switching (ZVZCS) full bridge PWM converter using transformer auxiliary winding

abstract: A novel zero voltage and zero current switching (ZVZCS) full bridge (FB) PWM converter is proposed to improve the demerits of the previously presented ZVZCS-FB-PWM converters such as use of lossy components or additional active switches. A simple auxiliary circuit which includes neither lossy components nor active switches provides ZVZCS conditions to primary switches, ZVS for leading-leg switches and ZCS for lagging-leg switches. Many advantages including simple circuit topology, high efficiency, and low cost make the new converter attractive for high power (>1 kW) applications. The operation, analysis, features and design considerations are illustrated and verified on a 2.5 kW, 100 kHz IGBT based experimental circuit.


title: EXECUTION ANALYSIS OF LOAD BALANCING ALGORITHMS IN CLOUD C OMPUTING ENVIRONMENT

abstract: The concept oft Cloud computing has significantly changed the field of parallel and distributed computing systems today. Cloud computing enables a wide range of users to access distributed, scalable, virtualized hardware and/or software infrastructure over the Internet. Load balancing is a methodology to distribute workload across multiple computers, or other resources over the network links to achieve optimal resource utilization, maximize throughput, minimum response time, and avoid overload. With recent advent of technology, resource control or load balancing in cloud computing is main challenging issue. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper presents a review of a few load balancing algorithms or technique in cloud computing. The objective of this paper is to identify qualitative components for simulation in cloud environment and then based on these components, execution analysis of load balancing algorithms are also presented.


title: Trust-aware recommender systems

abstract: Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.


title: An actuator for the tactile vest - a torso-based haptic device

abstract: Our goal is to design a robust, versatile actuator for a Tactile Vest, a wearable device that provides haptic feedback to the torso. The technology we propose to use as the actuator for this device is Nitinol, a shape memory alloy (SMA). Nitinol is capable of generating large stresses and strains, but is small and flexible and able to fit in the thin, dynamic workspace of a vest. In experimental tests conducted on the SMA, a pulse 1 A in amplitude and 1 s in duration produced an average displacement of 3.7 mm and peak pressures about 20 times the touch threshold for the torso. These results indicate promise in using Nitinol to create a torso-based haptic device that can generate a wide range of stimuli, yet is comfortable to the point of being invisible to the user until a stimulus occurs.


title: Document Layout Analysis Using Multigaussian Fitting

abstract: This paper proposes a novel technique for layout analysis of documents with complex Manhattan layouts. The technique is designed for Indic script newspapers and works on many types of documents not necessarily with Indic scripts with Manhattan layout. The main idea behind the algorithm is to categorise the physical elements of a document into noise, text, titles and graphics based on their heights. A histogram of heights is computed from the bounding boxes of connected components and a multigaussian fit is used to discover optimal split points between the categories. The gaussian with the highest peak is assumed to correspond to running text. Running text regions are grouped into blocks using nearest neighbour analysis. These initial regions are further refined using a second-level classification of the other elements into graphics, light-coloured text on a dark background, and graphical separators. The resulting layouts show accuracies comparable to some of the best and most popular algorithms such as MHS (winner of ICDAR-RDCL2015 competition) and PRImA's Aletheia (tool developed by PRImA Research Lab). Results of testing on many Indic script newspapers and other documents, and comparison with Aletheia and MHS on ICDAR dataset show its performance. Our initial results on an Indic document dataset show high performance in identifying running text (> 98%) with an accuracy of 82% on identifying the other elements. Ground truth data for the Indic script newspaper documents is being generated for a more extensive quantitative testing. The strength of our algorithm is that it requires only one parameter - the number of gaussians to fit the height histogram data and is therefore easy to automate and adapt to many documents.


title: Internet Media and the Public Sphere: The 2007 Australian E-Electioneering Experience

abstract: Internet media have come under increasing examination since the early 1990s within a number of theoretical frameworks, including their use and potential influence in the public sphere of political discourse. Increasing use of internet media was identified in the 2000 and 2004 US presidential elections, with the latter being described as 'a critical turning point'. However, the development of what some call 'new media' or 'social media' based on Web 2.0 internet technology has overtaken many findings of previous research. Some of the most popular Web 2.0-based media currently in use were developed after 2004. These technological changes, coinciding with declining television viewing and newspaper readership, suggest that new forms of internet media need ongoing critical review. This paper analyses findings from a study of internet media use in the 2007 Australian federal election and explores their implications in relation to the public sphere.


title: Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof

abstract: Where is the energy spent inside my app? Despite the immense popularity of smartphones and the fact that energy is the most crucial aspect in smartphone programming, the answer to the above question remains elusive. This paper first presents eprof, the first fine-grained energy profiler for smartphone apps. Compared to profiling the runtime of applications running on conventional computers, profiling energy consumption of applications running on smartphones faces a unique challenge, asynchronous power behavior, where the effect on a component's power state due to a program entity lasts beyond the end of that program entity. We present the design, implementation and evaluation of eprof on two mobile OSes, Android and Windows Mobile. We then present an in-depth case study, the first of its kind, of six popular smartphones apps (including Angry-Birds, Facebook and Browser). Eprof sheds lights on internal energy dissipation of these apps and exposes surprising findings like 65%-75% of energy in free apps is spent in third-party advertisement modules. Eprof also reveals several "wakelock bugs", a family of "energy bugs" in smartphone apps, and effectively pinpoints their location in the source code. The case study highlights the fact that most of the energy in smartphone apps is spent in I/O, and I/O events are clustered, often due to a few routines. Thismotivates us to propose bundles, a new accounting presentation of app I/O energy, which helps the developer to quickly understand and optimize the energy drain of her app. Using the bundle presentation, we reduced the energy consumption of four apps by 20% to 65%.


title: A Methodology for Building Regression Models using Extreme Learning Machine: OP-ELM

abstract: This paper proposes a methodology named OP-ELM, based on a recent development –the Extreme Learning Machine– decreasing drastically the training speed of networks. Variable selection is beforehand performed on the original dataset for proper results by OP-ELM: the network is first created using Extreme Learning Process, selection of the most relevant nodes is performed using Least Angle Regression (LARS) ranking of the nodes and a Leave-One-Out estimation of the performances. Results are globally equivalent to LSSVM ones with reduced computational time.


title: Severe asthma: anti-IgE or anti-IL-5?

abstract: Severe asthma is a discrete clinical entity characterised by recurrent exacerbations, reduced quality of life and poor asthma control as ordinary treatment regimens remain inadequate. Difficulty in managing severe asthma derives partly from the multiple existing phenotypes and our inability to recognise them. Though the exact pathogenetic pathway of severe allergic asthma remains unclear, it is known that numerous inflammatory cells and cytokines are involved, and eosinophils represent a key inflammatory cell mediator. Anti-IgE (omalizumab) and anti-IL-5 (mepolizumab) antibodies are biological agents that interfere in different steps of the Th2 inflammatory cascade and are licensed in severe asthma. Both exhibit a favourable clinical outcome as they reduce exacerbation rate and improve asthma control and quality of life, while mepolizumab also induces an oral steroid sparing effect. Nevertheless, it is still questionable which agent is more suitable in the management of severe allergic asthma since no comparable studies have been conducted. Omalizumab's established effectiveness in clinical practice over a long period is complemented by a beneficial effect on airway remodelling process mediated mainly through its impact on eosinophils and other parameters strongly related to eosinophilic inflammation. However, it is possible that mepolizumab through nearly depleting eosinophils could have a similar effect on airway remodelling. Moreover, to date, markers indicative of the patient population responding to each treatment are unavailable although baseline eosinophils and exacerbation rate in the previous year demonstrate a predictive value regarding anti-IL-5 therapy effectiveness. On the other hand, a better therapeutic response for omalizumab has been observed when low forced expiratory volume in 1 sec, high-dose inhaled corticosteroids and increased IgE concentrations are present. Consequently, conclusions are not yet safe to be drawn based on existing knowledge, and additional research is necessary to unravel the remaining issues for the severe asthmatic population.


title: Sibyl: a practical Internet route oracle

abstract: Network operators measure Internet routes to troubleshoot problems, and researchers measure routes to characterize the Internet. However, they still rely on decades-old tools like traceroute, BGP route collectors, and Looking Glasses, all of which permit only a single query about Internet routes--what is the path from here to there? This limited interface complicates answering queries about routes such as "find routes traversing the Level3/AT&T peering in Atlanta," to understand the scope of a reported problem there. ::: ::: This paper presents Sibyl, a system that takes rich queries that researchers and operators express as regular expressions, then issues and returns traceroutes that match even if it has never measured a matching path in the past. Sibyl achieves this goal in three steps. First, to maximize its coverage of Internet routing, Sibyl integrates together diverse sets of traceroute vantage points that provide complementary views, measuring from thousands of networks in total. Second, because users may not knowwhich measurements will traverse paths of interest, and because vantage point resource constraints keep Sibyl from tracing to all destinations from all sources, Sibyl uses historical measurements to predict which new ones are likely to match a query. Finally, based on these predictions, Sibyl optimizes across concurrent queries to decide which measurements to issue given resource constraints. We show that Sibyl provides researchers and operators with the routing information they need--in fact, it matches 76% of the queries that it could match if an oracle told it which measurements to issue.


title: Foodie fooderson a conversational agent for the smart kitchen

abstract: Conversational agents aim to offer an alternative to traditional methods for humans to engage with technology. This can mean reducing the effort to complete a task using reasoning capabilities and by exploiting context, or allow voice interaction when traditional methods are not available or inconvenient. This paper introduces Foodie Fooderson, a conversational kitchen assistant built using IBM Watson technology. Foodie minimizes food wastage by optimizing the use of groceries and assist families in improving their eating habits through recipe recommendations taking into account personal context, such as allergies and dietary goals, while helping reduce food waste and managing grocery budgets. This paper discusses Foodie's architecture, use and benefits. Foodie uses services from CAPRecipes---our context-aware personalized recipe recommender system, SmarterContext---our personal context management system, and selected publicly available nutrition databases. Foodie reasons using IBM Watson's conversational services to recognize users' intents and understand events related to the users and their context. We also discuss our experiences in building conversational agents with Watson, including desired features that may improve the development experience with Watson for creating rich conversations in this exciting era of cognitive computing.


title: Digital forensic science approach by file recovery research

abstract: Although many studies have been conducted on digital forensics, their viewpoints keep changing, and new studies are continuously required on fields that have not been researched yet. This paper proposes a privacy protection measure by applying permanent deletion to the Internet-of-Things platform: a digital forensic technique regarding privacy protection that has been regarded as a cautious subject. In addition, we propose a file recovery method by analyzing a file allocation area in a UNIX file system, which has not been addressed, and identifying the clue for journal area using a magic number.