title: Using Graphical Features To Improve Demographic Prediction From Smart Phone Data

abstract: Demographic information such as gender, age, ethnicity, level of education, disabilities, employment, and socio-economic status are important in the area of social science, survey and marketing. But it is difficult to obtain the demographic information from users due to reluctance of users to participate and low response rate. Through automated demographics prediction from smart phone sensor data, researchers can obtain this valuable information in a nonintrusive and cost-effective manner. We approach the problem of demographic prediction, namely, classification of gender, age group and job type, through the use of a graphical feature based framework. The framework represents information collected from sensor networks as graphs, extracts useful and relevant graphical features, and predicts demographic information. We evaluated our approach on the Nokia Mobile Phone dataset for the three classification tasks: gender, age-group and job-type. Our approach produced comparable results with most of the state of the art methods while having the additional advantage of general applicability to sensor networks without using sophisticated and application-specific feature generation techniques, background knowledge and special techniques to address class imbalance.


title: Shaking the status quo: Business accreditation and positional competition

abstract: Modern business schools exist in a complex world of rankings, ratings, and credentials. Some argue that in increasingly competitive global higher education markets, signaling status and quality has...


title: On Measuring Bias in Online Information

abstract: Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.


title: Cellular planning for next generation wireless mobile network using novel energy efficient CoMP

abstract: Cellular planning also called radio network planning is a crucial stage for the deployment of a wireless network. The enormous increase in data traffic requires augmentation of coverage, capacity, throughput, quality of service and optimized cost of the network. The main goal of cellular planning is to enhance the spectral efficiency and hence the throughput of a network. A novel CoMP algorithm has been discussed with two-tier heterogeneous network. Number of clusters has been obtained using V-R (variance ratio) Criterion. The centroid of a cluster obtained using K-means algorithm provides the deployment of BS position. Application of CoMP in this network using DPS approach with sleep mode of power saving, provides higher energy efficiency, SINR and throughput as compared to nominal CoMP. CoMP basically describes a scheme in which a group of base stations (BS) dynamically co-ordinate and co-operate among themselves to convert interference into a beneficial signal. Network planning using stochastic method and Voronoi Tessellation with two-tier network has been applied to a dense region of Surat city in Gujarat state of India. The results show clear improvement in signal-to-interference plus noise ratio (SINR) by 25% and energy efficiency of the network by 28% using the proposed CoMP transmission.


title: A dynamic wireless charging system for electric vehicles based on DC/AC converters with SiC MOSFET-IGBT switches and resonant gate-drive

abstract: This paper presents a high-performance dynamic Wireless Power Transfer (WPT) system for electric vehicles, which has high efficiency and the potential for large scale deployment. The detailed single stage 22kW DC/AC converter design of the transmitter side is presented. The converter is connected to a series-series WPT compensated system and has features like hybrid switches with fast 0-Ω gate-drive circuit for the SiC MOSFET and resonant gate-drive circuit for the IGBT, which allows operation without ZVS switching. Experimental results are presented to confirm the correct operation of the converter with two test cases: 1) a DC current imposed through an inductive load for converter efficiency measurements in hard-switching and 2) an AC output current imposed in a full scale static laboratory WPT system, similar to the dynamic one.


title: Formation of beauvericin by selected strains of Beauveria bassiana.

abstract: Various strains of Beauveria bassiana were cultivated in submerged cultures and examined for their abilities to produce beauvericin, cyclodepsipeptide antibiotic displaying antibacterial and insecticidal properties. It has been found that among twenty four strains of Beauveria bassiana only three produced beauvericin. Apparently, the striking differences among tested strains concerning several secondary product formations have been observed.


title: Endoscopic mini/less open sublay operation for treatment of primary and secondary ventral hernias of the abdominal wall

abstract: Background ::: Primary and secondary ventral hernias of the abdominal wall are very common conditions; however, the ideal operative treatment has not been found yet.


title: Kaizen: A Method of Process Improvement in the Emergency Department

abstract: Introduction: Recent position statements from health care organizations have placed a strong emphasis on continuous quality improvement (CQI). CQI finds many of its roots in kaizen, which emphasizes small, low-cost, low-risk improvements. Based on the successful Kaizen Programs at organizations such as Toyota, the authors thought the emergency department (ED) would be an ideal environment to benefit from such a program. ::: ::: Objectives: The authors sought to create a CQI program using a suggestion-based model that did not require a large time commitment, was easy to implement, and had the potential to empower all physicians in the department. It would not take the place of other improvement efforts, but instead augment them. The hypothesis was that such a program would foster sustainable engagement of emergency physicians in system improvement efforts and lead to a continuous stream of low-cost implementable system improvement interventions. ::: ::: Methods: A CQI program was created for the physician staff of the Department of Emergency Medicine at Vanderbilt University Medical Center, focusing on a suggestion-based model using kaizen philosophy. Lectures teaching kaizen philosophy were presented. Over the past 4 years, a methodology was developed utilizing a Web-based application, the Kaizen Tracker, which aids in the submission and implementation of suggestions that are called kaizen initiatives (KIs). The characteristics of the KIs submitted, details regarding resident and faculty participation, and the effectiveness of the Kaizen Tracker were retrospectively reviewed. ::: ::: Results: There were 169, 105, and 101 KIs placed in the postimplementation calendar years 2006, 2007, and 2008, respectively. Seventy-six percent of KIs submitted thus far have identified a “process problem.” Fifty-three percent of KIs submitted have led to operational changes within the ED. Ninety-three percent of the resident physicians entered at least one KI, and 73% of these residents submitted more than one KI. Sixty-nine percent of the attendings entered at least one KI, and 89% of these attendings submitted more than one KI. ::: ::: Conclusions: Over the past 4 years, the Kaizen Program at Vanderbilt has been widely and frequently used within the ED. It has resulted in over 400 changes in our adult ED system and has met the challenge of using CQI to drive ED improvements. There are limitations to this study, including the fact that its impact on patient outcomes remains unknown. However, this Kaizen Program may be an excellent tool for other departments to assist with quality improvement and should be studied with a multicenter prospective approach.


title: When nurses double as interpreters: a study of Spanish-speaking patients in a US primary care setting.

abstract: The United States is experiencing one of its largest migratory waves, so health providers are caring for many patients who do not speak English. Bilingual nurses who have not been trained as medical interpreters frequently translate for these patients. To examine the accuracy of medical interpretations provided by nurses untrained in medical interpreting, we conducted a qualitative, cross-sectional study at a multi-ethnic, university-affiliated primary care clinic in southern California. Medical encounters of 21 Spanish-speaking patients who required a nurse-interpreter to communicate with their physicians were videorecorded. Encounters were transcribed by blinded research assistants. Transcriptions were translated and analyzed for types of interpretive errors and processes that promoted the occurrence of errors. In successful interpretations where misunderstandings did not develop, nurse-interpreters translated the patient's comments as completely as could be remembered and allowed the physician to extract the clinically-relevant information. In such cases, the physician periodically summarized his/her perception of the problem for back-translation and verification or correction by the patient. On the other hand, approximately one-half of the encounters had serious miscommunication problems that affected either the physician's understanding of the symptoms or the credibility of the patient's concerns. Interpretations that contained errors that led to misunderstandings occurred in the presence of one or more of the following processes: (1) physicians resisted reconceptualizing the problem when contradictory information was mentioned; (2) nurses provided information congruent with clinical expectations but not congruent with patients' comments; (3) nurses slanted the interpretations, reflecting unfavorably on patients and undermining patients' credibility; and (4) patients explained the symptoms using a cultural metaphor that was not compatible with Western clinical nosology. We conclude that errors occur frequently in interpretations provided by untrained nurse-interpreters during cross-language encounters, so complaints of many non-English-speaking patients may be misunderstood by their physicians.


title: Learning feedback controller design of switching converters via MATLAB/SIMULINK

abstract: The application of MATLAB/SIMULINK is developed for simulation and learning feedback controller design of DC-DC switching converters. With the help of its intuitive graphical user interface and some basic circuit theories, the corresponding SIMULINK models of the switching converter circuits can be easily constructed. Students can then use these models to learn and evaluate the closed-loop behavior of the entire system in the MATLAB/SIMULINK environment, after feedback controllers are devised by any classical or modern control theory. The accuracy of this approach is also verified by comparing the simulation results with the responses obtained from a buck-type DC-DC switching converter circuit and existing experimental results.


Credit Card Fraud Detection using Neural Network


title: A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria

abstract: People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management.


Closed Shapes : New Perceptual Categories ?


title: Pharmacokinetics of the monoclonal antibody B72.3 and its fragments labeled with either 125I or 111In.

abstract: A comparison of the pharmacokinetics of intact B72.3 (a murine monoclonal antibody specific for human breast and colon carcinoma) with F(ab')2 and Fab fragments labeled with 111In and 125I was done in athymic mice bearing target (LS174T) and non-target (HCT-15) tumors. IgG B72.3 labeled with either isotype imaged LS174T. Biodistributions of both labels were similar in all organs except liver. F(ab')2 also imaged the LS174T tumor, while Fab bearing either isotype did not. The blood clearance was Fab greater than F(ab')2 greater than immunoglobulin G B72.3 for both isotopes. 111In-labeled fragments yielded large accumulations in the kidneys which persisted for 2 days. The different patterns of biodistribution for the various forms of B72.3 labeled with the two isotopes suggest that the most desirable combination of fragment and isotope will depend on the intended use.


title: Data Centroid Based Multi-Level Fuzzy Min-Max Neural Network

abstract: Recently, a multi-level fuzzy min max neural network (MLF) was proposed, which improves the classification accuracy by handling an overlapped region (area of confusion) with the help of a tree structure. In this brief, an extension of MLF is proposed which defines a new boundary region, where the previously proposed methods mark decisions with less confidence and hence misclassification is more frequent. A methodology to classify patterns more accurately is presented. Our work enhances the testing procedure by means of data centroids. We exhibit an illustrative example, clearly highlighting the advantage of our approach. Results on standard datasets are also presented to evidentially prove a consistent improvement in the classification rate.


title: Protocol design and throughput analysis for multi-user cognitive cooperative systems

abstract: This paper deals with protocol design for cognitive cooperative systems with many secondary users. In contrast with previous cognitive configurations, the channel model considered assumes a cluster of secondary users which perform both a sensing process for transmitting opportunities and can relay data for the primary user. Appropriate relaying improves the throughput of the primary users and can increase the transmission opportunities for the cognitive users. Based on different multi-access protocols, the schemes investigated enable relaying either between the primary user and a selected secondary user or between two selected secondary users. This collaboration can be a simple distributed multiple-input single-output transmission of the primary data or a simultaneous transmission of primary and secondary data using dirty-paper coding (DPC). The parametrization of DPC as well as its combination with opportunistic relay selection yields an interesting trade-off between the primary and the secondary performance which is investigated by theoretical and simulation results under the perspective of a desired primary throughput. The proposed protocols are studied from a networking point of view and the stable throughput for primary and secondary users is derived based on the principles of queueing theory.


title: Fundamental limits on adversarial robustness

abstract: The goal of this paper is to analyze an intriguing phenomenon recently discovered in deep networks, that is their instability to adversarial perturbations (Szegedy et al., 2014). We provide a theoretical framework for analyzing the robustness of classifiers to adversarial perturbations, and establish fundamental limits on the robustness of some classifiers in terms of a distinguishability measure between the classes. Our result implies that in tasks involving small distinguishability, no classifier in the considered set will be robust to adversarial perturbations, even if a good accuracy is achieved. Moreover, we show the existence of a clear distinction between the robustness of a classifier to random noise and its robustness to adversarial perturbations. Specifically, in high dimensions, the former is shown to be much larger than the latter for linear classifiers. This result gives a theoretical explanation for the discrepancy between the two robustness properties, which was empirically observed in (Szegedy et al., 2014) in the context of neural networks. Our theoretical framework shows that the adversarial instability is a phenomenon that goes beyond deep networks, and affects all classifiers. Unlike the initial belief that adversarial examples are caused by the high non-linearity of neural networks, our results suggest instead that this phenomenon is due to the low flexibility of classifiers, compared to the difficulty of the classification task, which is captured by the distinguishability measure. We believe these results ICML 2015 Workshop on Deep Learning, Lille, France. Copyright 2015 by the author(s). contribute to a better understanding of the phenomenon of adversarial instability to reach the goal of designing robust classifiers.


title: Compassion Fatigue and the Healthy Work Environment

abstract: Burnout is a concern for critical care nurses in high-intensity environments. Studies have highlighted the importance of a healthy work environment in promoting optimal nurse and patient outcomes, but research examining the relationship between a healthy work environment and burnout is limited.To examine how healthy work environment components relate to compassion fatigue (eg, burnout, secondary trauma) and compassion satisfaction.Nurses (n = 105) in 3 intensive care units at an academic medical center completed a survey including the Professional Quality of Life and the American Association of Critical-Care Nurses' Healthy Work Environment standards.Regression models using each Healthy Work Environment component to predict each outcome, adjusting for background variables, showed that the 5 Healthy Work Environment components predicted burnout and that meaningful recognition and authentic leadership predicted compassion satisfaction.Findings on associations between healthy work environment standards and burnout suggest the potential importance of implementing the American Association of Critical-Care Nurses' Healthy Work Environment standards as a mechanism for decreasing burnout.


title: Peer Influence on Children's Physical Activity: An Experience Sampling Study

abstract: on their activity intensity and whether the activity was solitary or with others for seven consecutive days. Results Children were more likely to report more intense physical activity when in the company of peers or close friends. Overweight children reported greater physical activity when in the presence of peers than did lean children; however, overweight children also reported more time spent alone. Conclusions Taken together, findings highlight the importance of considering peer relationships in studies of physical activity and childhood ‘‘obesity.’’


title: Acoustical Sound Database in Real Environments for Sound Scene Understanding and Hands-Free Speech Recognition

abstract: LREC2000: the 2nd International Conference on Language Resources and Evaluation, May 31 - June 2, 2000, Athens, Greece.


title: The contribution of peer and media influences to the development of body satisfaction and self-esteem in young girls: A prospective study.

abstract: This study aimed to prospectively examine the role of peer and media influences in the development of body satisfaction (incorporating the desire for thinness and satisfaction with appearance) in young girls, as well as the relationship between body satisfaction and self-esteem. A sample of 97 girls 5–8 years of age completed individual interviews at Time 1 and 1 year later at Time 2. Linear panel analyses found that Time 1 perception of peers’ desire for thinness was temporally antecedent to girls’ desire for thinness, appearance satisfaction, and self-esteem 1 year later. In addition, the watching of appearancefocused television programs was temporally antecedent to appearance satisfaction. Finally, girls’ desire for thinness was found to temporally precede low self-esteem. Thus, as early as school entry, girls appear to already live in a culture in which peers and the media transmit the thin ideal in a way that negatively influences the development of body image and self-esteem.


title: Volumetric Acoustic Imaging via Circular Multipass Aperture Synthesis

abstract: In this paper, volumetric imaging via multipass circular synthetic aperture sonar (CSAS) is demonstrated using an autonomous underwater vehicle (AUV). A multidimensional aperture is synthesized by performing a series of circular scans at varying grazing angles around targets and coherently combining the backscattering information from the set of scans to form high-resolution volumetric images. A data-driven technique for precision alignment of the individual scans comprising the multipass set enables synthesis of a multidimensional array. To beamform in the vertical dimension using the irregular and undersampled multipass aperture, a compressive-sensing-based approach is adopted which is similar to methods used in analogous synthetic aperture radar tomography applications but modified to accommodate for the wider fractional bandwidth of the synthetic aperture sonar (SAS) system. The modification exploits a joint sparsity assumption in the vertical scattering profile at different subbands and adapts a standard joint sparse solving algorithm to the relevant case in which the sparsity profile is common between solution vectors but the sensing matrices are different. Results are shown for a variety of targets, including proud and obliquely buried unexploded ordnance, a 2-1 solid aluminum cylinder, and a steel oil drum.


title: Anti-ferroelectric ZrO2, an enabler for low power non-volatile 1T-1C and 1T random access memories

abstract: Recently it was demonstrated that an asymmetric DRAM capacitor stack can introduce non-volatility and at the same time outperform ferroelectric HfO 2 based FeRAM in terms of cycle endurance. With the present work, we provide an in-depth study of the underlying mechanisms and perform a comprehensive retention study that characterizes ferroelectric memories. Piezoelectric force microscopy is applied to prove the ultimate scalability of the proposed concept beyond capacitor based application. Finally, switching density plots reveal a much lower device to device variability and high switching uniformity with respect to ferroelectric HfO2 based films.


title: Five years after the Deep Learning revolution of computer vision : State of the art methods for online image and video analysis

abstract: The purpose of this document is to reect on novel and upcoming methods for computer vision that might have relevance for application in robot vision and video analytics. The document covers many di ...


title: Learning word embeddings efficiently with noise-contrastive estimation

abstract: Continuous-valued word embeddings learned by neural language models have recently been shown to capture semantic and syntactic information about words very well, setting performance records on several word similarity tasks. The best results are obtained by learning high-dimensional embeddings from very large quantities of data, which makes scalability of the training method a critical factor. ::: ::: We propose a simple and scalable new approach to learning word embeddings based on training log-bilinear models with noise-contrastive estimation. Our approach is simpler, faster, and produces better results than the current state-of-the-art method. We achieve results comparable to the best ones reported, which were obtained on a cluster, using four times less data and more than an order of magnitude less computing time. We also investigate several model types and find that the embeddings learned by the simpler models perform at least as well as those learned by the more complex ones.


title: What's in an Electronic Business Model?

abstract: An electronic business model is an important baseline for the development of e-commerce system applications. Essentially, it provides the design rationale for e-commerce systems from the business point of view. However, how an e-business model must be defined and specified is a largely open issue. Business decision makers tend to use the notion in a highly informal way, and usually there is a big gap between the business view and that of IT developers. Nevertheless, we show that conceptual modelling techniques from IT provide very useful tools for precisely pinning down what e-business models actually are, as well as for their structured specification. We therefore present a (lightweight) ontology of what should be in an e-business model. The key idea we propose and develop is that an e-business model ontology centers around the core concept of value, and expresses how value is created, interpreted and exchanged within a multi-party stakeholder network. Our e-business model ontology is part of a wider methodology for e-business modelling, called e3 -value, that is currently under development. It is based on a variety of industrial applications we are involved in, and it is illustrated by discussing a free Internet access service as an example.


title: Substrate integrated waveguide fed LTCC microstrip patch antenna for 94 GHz applications

abstract: This paper presents the design, electromagnetic simulation and experimental results of a 94 GHz microstrip patch antenna with four parasitic patches. The structure was designed for a six tape Low Temperature Co-Fired Ceramics process (LTCC). The antenna element is fed by a substrate integrated waveguide, taking advantage of the 3D vertical integration possibilities of the LTCC technology. The measured input matching bandwidth (|S11| < − 10 dB) is between 89.5 – 95.9 GHz. The measured half-power gain bandwidth is between 80 – 103 GHz.


Anatomical relationship between Kambin’s triangle and exiting nerve root


title: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models

abstract: This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow, through the inverse wavelet transform, reconstructing the future behavior of the price series and therefore to forecast prices. Results from the electricity market of mainland Spain in year 2002 are reported.


title: A Semantic Concordance

abstract: A semantic concordance is a textual corpus and a lexicon so combined that every substantive word in the text is linked to its appropriate sense in the lexicon. Thus it can be viewed either as a corpus in which words have been tagged syntactically and semantically, or as a lexicon in which example sentences can be found for many definitions. A semantic concordance is being constructed to use in studies of sense resolution in context (semantic disambiguation). The Brown Corpus is the text and WordNet is the lexicon. Semantic tags (pointers to WordNet synsets) are inserted in the text manually using an interface, ConText, that was designed to facilitate the task. Another interface supports searches of the tagged text. Some practical uses for semantic concordances am proposed.


title: Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts

abstract: Objective ::: Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media. ::: ::: ::: Materials and Methods ::: We developed a recurrent neural network (RNN) model that labels words in an input sequence with ADR membership tags. The only input features are word-embedding vectors, which can be formed through task-independent pretraining or during ADR detection training. ::: ::: ::: Results ::: Our best-performing RNN model used pretrained word embeddings created from a large, non-domain-specific Twitter dataset. It achieved an approximate match F-measure of 0.755 for ADR identification on the dataset, compared to 0.631 for a baseline lexicon system and 0.65 for the state-of-the-art conditional random field model. Feature analysis indicated that semantic information in pretrained word embeddings boosted sensitivity and, combined with contextual awareness captured in the RNN, precision. ::: ::: ::: Discussion ::: Our model required no task-specific feature engineering, suggesting generalizability to additional sequence-labeling tasks. Learning curve analysis showed that our model reached optimal performance with fewer training examples than the other models. ::: ::: ::: Conclusion ::: ADR detection performance in social media is significantly improved by using a contextually aware model and word embeddings formed from large, unlabeled datasets. The approach reduces manual data-labeling requirements and is scalable to large social media datasets.


title: Visual Span in Expert Chess Players: Evidence from Eye Movements

abstract: The reported research extends classic findings that after briefly viewing structured, but not random, chess positions, chess masters reproduce these positions much more accurately than less- skilled players. Using a combination of the gaze-contingent window paradigm and thechange blindness flicker paradigm, we documented dramatically larger visual spans for experts while processing struc- tured, but not random, chess positions. In addition, in a check- detection task, a minimized 3 ◊ 3 chessboard containing a King and potentially checking pieces was displayed. In this task, experts made fewer fixations per trial than less-skilled players, and had a greater proportion of fixations between individual pieces, rather than on pieces. Our results provide strong evidence for a perceptual encoding advantage for experts attributable to chess experience, rather than to a general perceptual or memory superiority. Simon and Chase (1973) proposed that much as Drosophila can be used as a model organism for the study of genetics, chess offers cognitive scientists an ideal task environment for the study of skilled performance. Since 1946, when de Groot (1978) conducted his pio- neering investigation showing that perception and memory are more important differentiators of expertise than is the ability to think ahead in the search for good moves, chess research has been instrumental in enhancing understanding of human expertise (Ericsson & Charness, 1994) and in contributing to the study of artificial intelligence (Char- ness, 1992). In a classic study, Chase and Simon (1973a, 1973b) replicated and extended de Groot's findings that after viewing chess positions for only a few seconds, chess masters were able to reproduce these positions much more accurately than less-skilled players. There was little difference in performance as a function of expertise when random board configurations were used, indicating that the superior immediate memory performance of the skilled players was not attrib- utable to a general superiority or unique structure of their memory systems or processes (e.g., photographic memory; see Binet, 1894). Rather, Chase and Simon postulated that experts use chess knowledge to create meaningful chunks consisting of several chess pieces, and are thus able to encode structured, but not random, chess configura- tions more quickly and accurately. More recently, a very small but reliable advantage in recall for random configurations has been shown for more expert players, though this is probably attributable to the occasional presence of familiar chunks in random positions (Gobet & Simon, 1996a). Further illustrating the critical importance of knowl- edge structures for performance, Chi's (1978) work comparing chil- dren who were skilled chess players with novice adults showed an advantage for children in chess recall, but an advantage for adults in digit recall. Chase and Simon (1973a, 1973b) hypothesized that much of the skilled chess player's advantage lies in the early perceptual organiza- tion and internal representation of the chess position. Specifically, they argued that the link between skilled perception and skilled prob- lem solving was to be found in the associations between perceptual chunks and generation of plausible moves. The size of an expert's vocabulary of chess-related configurations was initially estimated to be 50,000 to 100,000 chunks (Simon & Gilmartin, 1973), although small perceptual chunks are most likely supplemented by larger struc- tures termed templates (Gobet & Simon, 1996b).


title: Surveillance of psychosomatic disorders in internal medicine in Cipto Mangunkusumo Hospital, Jakarta, Indonesia.

abstract: AIM ::: to examine certain characteristics of patients who suffer from psychosomatic disorders. ::: ::: ::: METHODS ::: We called data through medical report outpatient clinic of the Psychosomatic Division, Department of internal medicine, Cipto Mangunkusumo Central General Hospital/Faculty of Medicine of the University of Indonesia (FKUI/RSUPN-CM), Jakarta, Indonesia, in 1996. The data was processed manually and by computer from which table and graphic were obtained. The descriptive analysis was performed to the objective the study. ::: ::: ::: RESULTS ::: the FPD patients consisted of those with vegetative imbalance (multiple psychosomatic syndrome) (30.2%), dyspepsia (20.8%), functional heart disease (11.3%) and others 1%-6%. All of SPD consisted of chronic disease, such as hypertension (38.3%), diabetes mellitus (29.8%), bronchial asthma (10.6%), coronary artery disease (6.4%), and others 2%-5%. According to DSM IV, among the psychosomatic patients, 52.7% met the criteria for anxiety, 29.3% for depression, 14.2% for mixed anxiety and depression, and 3.8% unclear. The psychosocial stressor groups were family problems (38%), physical conditions (16%), work-related problems (13.4%), marriage problems (8.4%) and others (1%-4%). ::: ::: ::: CONCLUSION ::: The most common physical symptoms of psychosomatic disorders were functional. Common functional psychosomatic disorders were multiple psychosomatic syndrome, dyspepsia and functional heart disease. Structural disorders found were chronic diseases. There was no difference in prevalence between males and females. The most frequent functional disorders were more commonly found among those under 40 years of age, while those with structural disorders were more common among patients 40 years of age or more. The psychological diagnoses were anxiety and depression. The most frequent psychological stressors were family problems, medical conditions, work-related problems and marriage problems.


title: Regulation of Growth and Pigmentation of Monascus Purpureus by Carbon and Nitrogen Concentrations

abstract: Growth and pigment production of Monascus purpureus were affected by the ratio of glucose to ammonium nitrate concentration in a synthetic medium. Mycelial growth increased with an increase of glucose and ammonium nitrate concentrations. Larger amounts of ammonium nitrate were required to give maximum growth in media containing higher concentrations of glucose. The level of ammonium nitrate for maximum pigment production was much lower than that for maximum growth, even at high glucose concentrations. Total pigmentation increased with increased glucose concentration. Greater amounts of red pigments were formed in media containing high levels of ammonium nitrate and low glucose. Mycelial growth and pigment production decreased above 50 g/1 ammonium nitrate.


title: Glutamate and GABA synthesis, release, transport and metabolism as targets for seizure control

abstract: Abstract The synthesis, release, reuptake, and metabolism of the excitatory and inhibitory neurotransmitters glutamate and GABA, respectively, are tightly controlled. Given the role that these two neurotransmitters play in normal and abnormal neurotransmission, it is important to consider the processes whereby they are regulated. This brief review is focused entirely on the metabolic aspects of glutamate and GABA synthesis and neurotransmission. It describes in limited detail the synthesis, release, reuptake, metabolism, cellular compartmentation and pharmacology of the glutamatergic and GABAergic synapse. This review also provides a summary and brief description of the pathologic and phenotypic features of the various genetic animal models that have been developed in an effort to provide a greater understanding of the role that each of the aforementioned metabolic processes plays in controlling excitatory and inhibitory neurotransmission and how their use will hopefully facilitate the development of safer and more efficacious therapies for the treatment of epilepsy and other neurological disorders.


title: Hardware Trojan detection using path delay fingerprint

abstract: Trusted IC design is a recently emerged topic since fabrication factories are moving worldwide in order to reduce cost. In order to get a low-cost but effective hardware trojan detection method to complement traditional testing methods, a new behavior-oriented category method is proposed to divide trojans into two categories: explicit payload trojan and implicit payload trojan. This categorization method makes it possible to construct trojan models and then lower the cost of testing. Path delays of nominal chips are collected to construct a series of fingerprints, each one representing one aspect of the total characteristics of a genuine design. Chips are validated by comparing their delay parameters to the fingerprints. The comparison of path delays makes small trojan circuits significant from a delay point of view. The experimentpsilas results show that the detection rate on explicit payload trojans is 100%, while this method should be developed further if used to detect implicit payload trojans.


title: Optimization of sentiment analysis using machine learning classifiers

abstract: Words and phrases bespeak the perspectives of people about products, services, governments and events on social media. Extricating positive or negative polarities from social media text denominates task of sentiment analysis in the field of natural language processing. The exponential growth of demands for business organizations and governments, impel researchers to accomplish their research in sentiment analysis. This paper leverages four state-of-the-art machine learning classifiers viz. Naive Bayes, J48, BFTree and OneR for optimization of sentiment analysis. The experiments are performed using three manually compiled datasets; two of them are captured from Amazon and one dataset is assembled from IMDB movie reviews. The efficacies of these four classification techniques are examined and compared. The Naive Bayes found to be quite fast in learning whereas OneR seems more promising in generating the accuracy of 91.3% in precision, 97% in F-measure and 92.34% in correctly classified instances.


title: Building Enterprise Systems Infrastructure Flexibility as Enabler of Organisational Agility: Empirical Evidence

abstract: Enterprise systems (ES) that capture the most advanced developments of information technology are becoming common fixtures in most organisations. However, how ES affect organizational agility (OA) has been less researched and the existing research remains equivocal. From the perspective that ES can positively contribute to OA, this research via theory-based model development and rigorous empirical investigation of the proposed model, has bridged significant research gaps and provided empirical evidence for, and insights into, the effect of ES on OA. The empirical results based on data collected from 179 large organizations in Australia and New Zealand which have implemented and used ES for at least one year show that organisations can achieve agility out of their ES in two ways: by developing ES technical competences to build ES-enabled capabilities that digitise their key sensing and responding processes; and when ES-enabled sensing and responding capabilities are aligned in relatively turbulent environment.


title: SDN-based distributed mobility management for 5G networks

abstract: Software-Defined Networking (SDN) is transforming the networking ecosystem. SDN allows network operators to easily and quickly introduce new services and flexibly adapt to their requirements, while simplifying the network management to reduce the cost of operation, maintenance and deployment. On the other hand, mobility is a key aspect for the future mobile networks. In this context, Distributed Mobility Management (DMM) has been recently introduced as a new trend to overcome the limitations of the today's mobility management protocols which are highly centralized and hierarchical. Driven from the fact that DMM and SDN share the same principle in which the data and control plane are decoupled, we propose a DMM solution based on SDN architecture called S-DMM. This solution offers a lot of advantages including no need to deploy any mobility-related component at the access router, independence of the underlying technologies, and per-flow mobility support. On one hand, the numerical results prove that S-DMM is more scalable than the legacy DMM. On the other hand, the experiment results from a real implementation show that S-DMM comes at no performance penalty (in terms of handover latency and end-to-end delay) compared to legacy DMM, yet at a slightly better management cost, which makes S-DMM a promising candidate for a mobility management solution in the context of 5G networks.


title: A Novel Key-Frame Extraction Approach for Both Video Summary and Video Index

abstract: Existing key-frame extraction methods are basically video summary oriented; yet the index task of key-frames is ignored. This paper presents a novel key-frame extraction approach which can be available for both video summary and video index. First a dynamic distance separability algorithm is advanced to divide a shot into subshots based on semantic structure, and then appropriate key-frames are extracted in each subshot by SVD decomposition. Finally, three evaluation indicators are proposed to evaluate the performance of the new approach. Experimental results show that the proposed approach achieves good semantic structure for semantics-based video index and meanwhile produces video summary consistent with human perception.


title: AN UPWIND FINITE-DIFFERENCE METHOD FOR TOTAL VARIATION–BASED IMAGE SMOOTHING

abstract: In this paper we study finite-difference approximations to the variational problem using the bounded variation (BV) smoothness penalty that was introduced in an image smoothing context by Rudin, Osher, and Fatemi. We give a dual formulation for an upwind finite-difference approximation for the BV seminorm; this formulation is in the same spirit as one popularized by the first author for a simpler, less isotropic, finite-difference approximation to the (isotropic) BV seminorm. We introduce a multiscale method for speeding up the approximation of both Chambolle's original method and of the new formulation of the upwind scheme. We demonstrate numerically that the multiscale method is effective, and we provide numerical examples that illustrate both the qualitative and quantitative behavior of the solutions of the numerical formulations.


title: A relative similarity based method for interactive patient risk prediction

abstract: This paper investigates the patient risk prediction problem in the context of active learning with relative similarities. Active learning has been extensively studied and successfully applied to solve real problems. The typical setting of active learning methods is to query absolute questions. In a medical application where the goal is to predict the risk of patients on certain disease using Electronic Health Records (EHR), the absolute questions take the form of "Will this patient suffer from Alzheimer's later in his/her life?", or "Are these two patients similar or not?". Due to the excessive requirements of domain knowledge, such absolute questions are usually difficult to answer, even for experienced medical experts. In addition, the performance of absolute question focused active learning methods is less stable, since incorrect answers often occur which can be detrimental to the risk prediction model. In this paper, alternatively, we focus on designing relative questions that can be easily answered by domain experts. The proposed relative queries take the form of "Is patient A or patient B more similar to patient C?", which can be answered by medical experts with more confidence. These questions poll relative information as opposed to absolute information, and even can be answered by non-experts in some cases. In this paper we propose an interactive patient risk prediction method, which actively queries medical experts with the relative similarity of patients. We explore our method on both benchmark and real clinic datasets, and make several interesting discoveries including that querying relative similarities is effective in patient risk prediction, and sometimes can even yield better prediction accuracy than asking for absolute questions.


title: Identity and Deception in the Virtual Community

abstract: Identity plays a key role in virtual communities. In communication, which is the primary activity, knowing the identity of those with whom you communicate is essential for understanding and evaluating an interaction. Yet in the disembodied world of the virtual community, identity is also ambiguous. Many of the basic cues about personality and social role we are accustomed to in the physical world are absent. The goal of this paper is to understand how identity is established in an online community and to examine the effects of identity deception and the conditions that give rise to it.


LIPEDEMA OF THE LEGS: A SYNDROME CHARACTERIZED BY FAT LEGS AND EDEMA


title: Towards Exploring Literals to Enrich Data Linking in Knowledge Graphs

abstract: Knowledge graph completion is still a challenging solution that uses techniques from distinct areas to solve many different tasks. Most recent works, which are based on embedding models, were conceived to improve an existing knowledge graph using the link prediction task. However, even considering the ability of these solutions to solve other tasks, they did not present results for data linking, for example. Furthermore, most of these works focuses only on structural information, i.e., the relations between entities. In this paper, we present an approach for data linking that enrich entity embeddings in a model with their literal information and that do not rely on external information of these entities. The key aspect of this proposal is that we use a blocking scheme to improve the effectiveness of the solution in relation to the use of literals. Thus, in addition to the literals from object elements in a triple, we use other literals from subjects and predicates. By merging entity embeddings with their literal information it is possible to extend many popular embedding models. Preliminary experiments were performed on real-world datasets and our solution showed competitive results to the performance of the task of data linking.


title: Tomato Leaf Disease Detection Using Convolutional Neural Networks

abstract: The tomato crop is an important staple in the Indian market with high commercial value and is produced in large quantities. Diseases are detrimental to the plant's health which in turn affects its growth. To ensure minimal losses to the cultivated crop, it is crucial to supervise its growth. There are numerous types of tomato diseases that target the crop's leaf at an alarming rate. This paper adopts a slight variation of the convolutional neural network model called LeNet to detect and identify diseases in tomato leaves. The main aim of the proposed work is to find a solution to the problem of tomato leaf disease detection using the simplest approach while making use of minimal computing resources to achieve results comparable to state of the art techniques. Neural network models employ automatic feature extraction to aid in the classification of the input image into respective disease classes. This proposed system has achieved an average accuracy of 94–95 % indicating the feasibility of the neural network approach even under unfavourable conditions.


title: VPSearch: Achieving Verifiability for Privacy-Preserving Multi-Keyword Search over Encrypted Cloud Data

abstract: Although cloud computing offers elastic computation and storage resources, it poses challenges on verifiability of computations and data privacy. In this work we investigate verifiability for privacy-preserving multi-keyword search over outsourced documents. As the cloud server may return incorrect results due to system faults or incentive to reduce computation cost, it is critical to offer verifiability of search results and privacy protection for outsourced data at the same time. To fulfill these requirements, we design a <underline>V</underline>erifiable <underline>P </underline>rivacy-preserving keyword <underline>S</underline>earch scheme, called VPSearch, by integrating an adapted homomorphic MAC technique with a privacy-preserving multi-keyword search scheme. The proposed scheme enables the client to verify search results efficiently without storing a local copy of the outsourced data. We also propose a random challenge technique with ordering for verifying top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math> <alternatives><inline-graphic xlink:href="wan-ieq1-2635128.gif"/></alternatives></inline-formula> search results, which can detect incorrect top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="wan-ieq2-2635128.gif"/></alternatives></inline-formula> results with probability close to 1. We provide detailed analysis on security, verifiability, privacy, and efficiency of the proposed scheme. Finally, we implement VPSearch using Matlab and evaluate its performance over three UCI bag-of-words data sets. Experiment results show that authentication tag generation incurs about 3 percent overhead only and a search query over 300,000 documents takes about 0.98 seconds on a laptop. To verify 300,000 similarity scores for one query, VPSearch costs only 0.29 seconds.


title: RFI suppression for ultra wideband radar

abstract: An estimate-and-subtract algorithm is presented for the real-time digital suppression of radio frequency interference (RFI) in ultrawideband (UWB) synthetic aperture radar (SAR) systems used for foliage- and ground-penetrating imaging. The algorithm separately processes fixed- and variable-frequency interferers. Excision of estimated targets greatly reduces bias in RFI estimates, thereby reducing target energy loss and sidelobe levels in SAR imagery. Performance is demonstrated on data collected with the Army Research Laboratory's UWB rail SAR.


title: Artificial intelligence meets natural stupidity

abstract: As a field, artificial intelligence has always been on the border of respectability, and therefore on the border of crackpottery. Many critics , have urged that we are over the border. We have been very defensive toward this charge, drawing ourselves up with dignity when it is made and folding the cloak of Science about us. On the other hand, in private, we have been justifiably proud of our willingness to explore weird ideas, because pursuing them is the only way to make progress.


title: Teacher Evaluations of Executive Functioning in Schoolchildren Aged 9–12 and the Influence of Age, Sex, Level of Parental Education

abstract: Executive functions (EFs) develop over the period of early childhood and adolescence up until young adulthood. Individual children differ substantially in the pace at which their EFs develop, and characteristics such as sex and the level of parental education (LPE) are thought to contribute to these differences. In the present study, we assessed age-related changes in EFs as perceived and evaluated by teachers and parents as well as the influence of sex and LPE on their evaluations. We used a newly developed observer-report questionnaire, the Amsterdam Executive Function Inventory (AEFI). The AEFI assesses three important components of the executive aspects of daily life behavior in 13 questions: Attention; Self-control & Self-monitoring; and Planning & Initiative taking. Teachers and parents evaluated these aspects of executive functioning in 186 schoolchildren in grades 3-6 (age: 9-12 years). Age effects within grades and differences in social economic status (SES) between the four participating schools were controlled. Results showed a significant increase in teacher-perceived EFs from third to fourth grades and from fifth to sixth grades. This development was influenced both by the sex of the child and by the LPE. As perceived by teachers, the component self-control & self-monitoring was higher for girls than for boys, and planning abilities were higher for children from families with a higher LPE. Additional analyses showed that there is a systematic and statistically significant difference between the evaluations of the teachers and that of parents. Parents reported higher scores for planning, whereas teachers reported higher scores for self-control & self-monitoring. Evaluations by parents and teachers were different for girls, but not for boys. These findings are important because they imply that the development of EFs as perceived by parents and teachers is influenced by child-related factors. Second, there are clear differences in evaluations between teachers and parents. The AEFI appears to be a tool that is easily used by parents and teachers and shows potential for monitoring the development of EFs as perceived by significant others during young adolescence.


title: Recent Evidence for Large Rebound: Elucidating the Drivers and their Implications for Climate Change Models

abstract: New research using data spanning centuries reveals the presence of very large energy efficiency rebound magnitudes, calling into question the energy use forecasts relied on by international bodies investigating climate change mitigation policy. This article uses those recent results to highlight and explain the key drivers that future energy modelers need to incorporate.


title: New Wars on the Balkans - Business as Usual

abstract: The monopoly over the exercise of violence is the main defining element of states according to the Weberian ideal-type taxonomy. (Weber 1991, in Jung, 78) However, the ability to wield effective control over a fixed territory and to use physical force, extract taxes and operate a system of arbitration has been claimed and successfully attained by other actors. The military conflict in Former Yugoslavia, which raged throughout the 1990s and has serious implications to this day, has often been assessed in humanitarian and traditional International Relations terms. In this paper, I discuss the Balkan conflicts as 'New Wars' which unlike traditional statist military exercise pose a lot more questions and confront one's perceptions of right and wrong.


title: Benefits of Digital Phased Array Radars

abstract: In this paper, an overview is given of the radar benefits of digital arrays in comparison with conventional phased arrays. Considered are passive and active phased arrays as well as subarray- and element-level digital arrays; their key differences are highlighted. A discussion of several radar attributes and performance measures follows to show the advantages and promise of element-level digital arrays as the newest generation architecture for radar and other electronic systems. Radar attributes considered are antenna patterns and beam control (including adaptive interference cancellation), dynamic range, in-band linearity, system phase noise, and angle measurement accuracy.


title: Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning

abstract: We present a framework combining hierarchical and multi-agent deep reinforcement learning approaches to solve coordination problems among a multitude of agents using a semi-decentralized model. The framework extends the multi-agent learning setup by introducing a meta-controller that guides the communication between agent pairs, enabling agents to focus on communicating with only one other agent at any step. This hierarchical decomposition of the task allows for efficient exploration to learn policies that identify globally optimal solutions even as the number of collaborating agents increases. We show promising initial experimental results on a simulated distributed scheduling problem.


title: Relationships of Coaching Behaviors to Student-Athlete Well-Being

abstract: Research on the association between coaching behaviors and student-athlete well-being has revealed significant relationships among coaching behaviors and a range of outcomes including anxiety, burnout, self-confidence, college choice satisfaction, and willingness to cheat to win. Findings from multiple studies suggested the need for improvements in coaching education. Overall, this review of extant literature suggested the need for additional research and empirically supported practices for coach and athlete development that support well-being.


title: Random Forests for Multiclass Classification: Random

abstract: Several supervised learning algorithms are suited to classify instances into a multiclass value space. MultiNomial Logit (MNL) is recognized as a robust classifier and is commonly applied within the CRM (Customer Relationship Management) domain. Unfortunately, to date, it is unable to handle huge feature spaces typical of CRM applications. Hence, the analyst is forced to immerse himself into feature selection. Surprisingly, in sharp contrast with binary logit, current software packages lack any feature-selection algorithm for MultiNomial Logit. Conversely, Random Forests, another algorithm learning multiclass problems, is just like MNL robust but unlike MNL it easily handles high-dimensional feature spaces. This paper investigates the potential of applying the Random Forests principles to the MNL framework. We propose the Random MultiNomial Logit (RMNL), i.e. a random forest of MNLs, and compare its predictive performance to that of (a) MNL with expert feature selection, (b) Random Forests of classification trees. We illustrate the Random MultiNomial Logit on a cross-sell CRM problem within the home-appliances industry. The results indicate a substantial increase in model accuracy of the RMNL model to that of the MNL model with expert feature selection.


title: Beyond the 'digital natives' debate: towards a more nuanced understanding of students' technology experiences

abstract: The idea of the ‘digital natives’, a generation of tech-savvy young people immersed in digital technologies for which current education systems cannot cater, has gained widespread popularity on the basis of claims rather than evidence. Recent research has shown flaws in the argument that there is an identifiable generation or even a single type of highly adept technology user. For educators, the diversity revealed by these studies provides valuable insights into students’ experiences of technology inside and outside formal education. While this body of work provides a preliminary understanding, it also highlights subtleties and complexities that require further investigation. It suggests, for example, that we must go beyond simple dichotomies evident in the digital natives debate to develop a more sophisticated understanding of our students’ experiences of technology. Using a review of recent research findings as a starting point, this paper identifies some key issues for educational researchers, offers new ways of conceptualizing key ideas using theoretical constructs from Castells, Bourdieu and Bernstein, and makes a case for how we need to develop the debate in order to advance our understanding.


title: An Approximately Efficient TDOA Localization Algorithm in Closed-Form for Locating Multiple Disjoint Sources With Erroneous Sensor Positions

abstract: This paper considers the problem of time difference-of-arrival (TDOA) source localization when the TDOA measurements from multiple disjoint sources are subject to the same sensor position displacements from the available sensor positions. This is a challenging problem and closed-form solution with good localization accuracy has yet to be found. This paper proposes an estimator that can achieve this purpose. The proposed algorithm jointly estimates the unknown source and sensor positions to take the advantage that the TDOAs from different sources have the same sensor position displacements. The joint estimation is a highly nonlinear problem due to the coupling of source and sensor positions in the measurement equations. We introduce the novel idea of hypothesized source locations in the algorithm development to enable the formulation of psuedolinear equations, thereby leading to the establishment of closed-form solution for source location estimates. Besides the advantage of closed-form, the newly developed algorithm is shown analytically, under the condition that the TDOA measurement noise and the sensor position errors are sufficiently small, to reach the CRLB accuracy. For clarity, the localization of two disjoint sources is used in the algorithm development. The developed algorithm is then examined under the special case of a single source and extended to the more general case of more than two unknown sources. The theoretical developments are supported by simulations.


title: Learning content–social influential features for influence analysis

abstract: We address how to measure the information propagation probability between users given certain contents. In sharp contrast to existing works that oversimplify the propagation model as predefined distributions, our approach fundamentally attempts to answer why users are influenced (e.g., by content or relations) and whether the corresponding influential features (e.g., hidden factors) can be inferred from the propagation in the entire network. In particular, we propose a novel method to deeply learn the unified feature representations for both user pair and content, where the homogeneous feature similarity can be used to estimate the propagation probability between users with given content. The features are dubbed content–social influential feature since we consider not only the content of the propagation information but also how it propagates over the social network. We design a fast asynchronous parallel algorithm for the feature learning. Through extensive experiments on a real-world social network with 53 million users and 838 million tweets, we show significantly improved performance as compared to other state-of-the-art methods on various social influence analysis tasks.


title: Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

abstract: We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable. Our approach—Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say ‘dog’ in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (e.g.VGG), (2) CNNs used for structured outputs (e.g.captioning), (3) CNNs used in tasks with multi-modal inputs (e.g.visual question answering) or reinforcement learning, all without architectural changes or re-training. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are robust to adversarial perturbations, (d) are more faithful to the underlying model, and (e) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show that even non-attention based models learn to localize discriminative regions of input image. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names (Bau et al. in Computer vision and pattern recognition, 2017) to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a ‘stronger’ deep network from a ‘weaker’ one even when both make identical predictions. Our code is available at https://github.com/ramprs/grad-cam/, along with a demo on CloudCV (Agrawal et al., in: Mobile cloud visual media computing, pp 265–290. Springer, 2015) (http://gradcam.cloudcv.org) and a video at http://youtu.be/COjUB9Izk6E.


title: Low-cost, real-time obstacle avoidance for mobile robots

abstract: The goal of this project1 is to advance the field of automation and robotics by utilizing recently-released, low-cost sensors and microprocessors to develop a mechanism that provides depth-perception and autonomous obstacle avoidance in a plug-and-play fashion. We describe the essential hardware components that can enable such a low-cost solution and an algorithm to avoid static obstacles present in the environment. The mechanism utilizes a novel single-point LIDAR module that affords more robustness and invariance than popular approaches, such as Neural Networks and Stereo. When this hardware is coupled with the proposed efficient obstacle avoidance algorithm, this mechanism is able to accurately represent environments through point clouds and construct obstacle-free paths to a destination, in a small timeframe. A prototype mechanism has been installed on a quadcopter for visualization on how actual implementation may take place2. We describe experimental results based on this prototype.


title: Distributed Bayesian Matrix Factorization with Minimal Communication

abstract: Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals. Scaling up the posterior inference for massive-scale matrices is challenging and requires distributing both data and computation over many workers, making communication the main computational bottleneck. Embarrassingly parallel inference would remove the communication needed, by using completely independent computations on different data subsets, but it suffers from the inherent unidentifiability of BMF solutions. We introduce a hierarchical decomposition of the joint posterior distribution, which couples the subset inferences, allowing for embarrassingly parallel computations in a sequence of at most three stages. Using an efficient approximate implementation, we show improvements empirically on both real and simulated data. Our distributed approach is able to achieve a speed-up of almost an order of magnitude over the full posterior, with a negligible effect on predictive accuracy. Our method outperforms state-of-the-art embarrassingly parallel MCMC methods in accuracy, and achieves results competitive to other available distributed and parallel implementations of BMF.


title: Tricolor R/G/B Laser Diode Based Eye-Safe White Lighting Communication Beyond 8 Gbit/s

abstract: White light generation by mixing red, green, and blue laser diodes (RGB LDs) was demonstrated with Commission International de l’Eclairage coordinates of (0.2928, 0.2981), a correlated color temperature of 8382 K, and a color rendering index of 54.4 to provide a maximal illuminance of 7540 lux. All the white lights generated using RGB LDs were set within the risk group-1 criterion to avoid the blue-light hazard to human eyes. In addition, the RGB-LD mixed white light was diffused using a frosted glass to avoid optical aberration and to improve the performance of the lighting source. In addition, visible light communication (VLC) by using RGB-LD mixed white-light carriers and a point-to-point scheme over 1 m was performed in the directly modulated 16-QAM OFDM data format. In back-to-back transmission, the maximal allowable data rate at 10.8, 10.4, and 8 Gbps was determined for R, G, and B LDs, respectively. Moreover, the RGB-LD mixed white light-based indoor wavelength-division multiplexing (WDM)-VLC system yielded a total allowable transmission data rate of 8.8 Gbps over 0.5 m in free space. Such a high-speed RGB-LD mixed WDM-VLC system without any channel interference can be used to simultaneously provide data transmission and white lighting in an indoor environment.


title: Link prediction in graph streams

abstract: Link prediction is a fundamental problem that aims to estimate the likelihood of the existence of edges (links) based on the current observed structure of a graph, and has found numerous applications in social networks, bioinformatics, E-commerce, and the Web. In many real-world scenarios, however, graphs are massive in size and dynamically evolving in a fast rate, which, without loss of generality, are often modeled and interpreted as graph streams. Existing link prediction methods fail to generalize in the graph stream setting because graph snapshots where link prediction is performed are no longer readily available in memory, or even on disks, for effective graph computation and analysis. It is therefore highly desirable, albeit challenging, to support link prediction online and in a dynamic way, which, in this paper, is referred to as the streaming link prediction problem in graph streams. In this paper, we consider three fundamental, neighborhood-based link prediction target measures, Jaccard coefficient, common neighbor, and Adamic-Adar, and provide accurate estimation to them in order to address the streaming link prediction problem in graph streams. Our main idea is to design cost-effective graph sketches (constant space per vertex) based on MinHash and vertex-biased sampling techniques, and to propose efficient sketch based algorithms (constant time per edge) with both theoretical accuracy guarantee and robust estimation results. We carry out experimental studies in a series of real-world graph streams. The results demonstrate that our graph sketch based methods are accurate, efficient, cost-effective, and thus can be practically employed for link prediction in real-world graph streams.


title: Automating Game Design In Three Dimensions

abstract: We describe ANGELINA-5, a new iteration of the AN- GELINA framework for investigating and building software which automates the process of videogame design. ANGELINA-5 is the first automated game design tool that produces 3D games. We outline here the system’s structure, the challenges inherent in building an auto- mated game designer in a modern game engine, and we discuss the future research directions for the project.


title: Excel as a Qualitative Data Analysis Tool

abstract: Qualitative research seeks to examine the interconnections in rich, complex data sources. The statistical tools of quantitative methods separate out pieces of data in a manner that defeats the purpose. But, like quantitative researchers, qualitative researchers often still find themselves overwhelmed by the amount of data and equally in need of tools to extend their human senses. This has led the development of a number of software packages designed for this purpose. An often overlooked option, however, is Microsoft Excel. Excel is generally considered a number cruncher. However, its structure and data manipulation and display features can be utilized for qualitative analysis. In this article, the authors discuss data preparation, analysis, and presentation, including discussion of lesser known features of Excel.


title: Recommendation by mining multiple user behaviors with group sparsity

abstract: Recently, some recommendation methods try to improve the prediction results by integrating information from user's multiple types of behaviors. How to model the dependence and independence between different behaviors is critical for them. In this paper, we propose a novel recommendation model, the Group-Sparse Matrix Factorization (GSMF), which factorizes the rating matrices for multiple behaviors into the user and item latent factor space with group sparsity regularization. It can (1) select out the different subsets of latent factors for different behaviors, addressing that users' decisions on different behaviors are determined by different sets of factors; (2) model the dependence and independence between behaviors by learning the shared and private factors for multiple behaviors automatically; (3) allow the shared factors between different behaviors to be different, instead of all the behaviors sharing the same set of factors. Experiments on the real-world dataset demonstrate that our model can integrate users' multiple types of behaviors into recommendation better, compared with other state-of-the-arts.


title: ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

abstract: Currently, the neural network architecture design is mostly guided by the \emph{indirect} metric of computation complexity, i.e., FLOPs. However, the \emph{direct} metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical \emph{guidelines} for efficient network design. Accordingly, a new architecture is presented, called \emph{ShuffleNet V2}. Comprehensive ablation experiments verify that our model is the state-of-the-art in terms of speed and accuracy tradeoff.


title: Human activity recognition in egocentric video using PNN, SVM, kNN and SVM+kNN classifiers

abstract: In recent years, the recognition of activity is a daring task which helps elderly people, disabled patients and so on. The aim of this paper is to design a system for recognizing the human activity in egocentric video. In this research work, the various textural features like gray level co-occurrence matrix and local binary pattern and point feature speeded up robust features are retrieved from activity videos which is a proposed work and classifiers like probabilistic neural network, support vector machine (SVM), k nearest neighbor (kNN) and proposed SVM+kNN classifiers are used to classify the activity. Here, multimodal egocentric activity dataset is chosen as input. The performance results showed that the SVM+kNN classifier outperformed other classifiers.


title: On the design of a dual-fed aperture-coupled circularly polarized microstrip patch antenna

abstract: This work addresses the design of a dual-fed aperture-coupled circularly polarized microstrip patch antenna, operating at its fundamental mode. A numerical parametric assessment was carried out, from which some general practical guidelines that may aid the design of such antennas were derived. Validation was achieved by a good match between measured and simulated results obtained for a specific antenna set assembled, chosen from the ensemble of the numerical analysis.


title: A Thai license plate localization using SVM

abstract: This paper proposes a method for localizing a Thai license plate from an image. The proposed method contains three main processes of: 1) a pre-processing; 2) a sub-image analysis; and 3) a license plate classification. In the pre-processing, a canny edge detection is applied to convert a given image into a corresponding edge image. This process helps to reduce image's noise caused by a cluttered background of the image and a cluttered background of the license plate itself. In the sub-image analysis, a sliding window technique is used to create a region of interest (ROI) which moves in pixels along both vertical and horizontal directions of the image. Then, in the license plate classification, a support vector machine (SVM) is employed as a classification tool which is used to distinguish a license plate from other objects. The trained SVM model is applied on ROIs in order to identify the license plate. In the experiment, a dataset of Thai license plates under some difficulties e.g. view variations and cluttered backgrounds is used to validate the promising performance of the proposed method.


title: Drawing plane graphs nicely

abstract: This paper presents two efficient algorithms for drawing plane graphs nicely. Both draw all edges of a graph as straight line segments without crossing lines. The first draws a plane graph “convex” if possible, that is, in a way that every inner face and the complement of the outer face are convex polygons. The second, using the first, produces a pleasing drawing of a given plane graph that satisfies the following property as far as possible: the complements of 3-connected components, together with inner faces and the complement of the outer face, are convex polygons. The running time and storage space of both algorithms are linear in the number of vertices of the graph.


Cooperative Information Systems: A Manifesto


title: Algorithms Unlocked

abstract: Have you ever wondered how your GPS can find the fastest way to your destination, selecting one route from seemingly countless possibilities in mere seconds? How your credit card account number is protected when you make a purchase over the Internet? The answer is algorithms. And how do these mathematical formulations translate themselves into your GPS, your laptop, or your smart phone? This book offers an engagingly written guide to the basics of computer algorithms. In Algorithms Unlocked, Thomas Cormen -- coauthor of the leading college textbook on the subject -- provides a general explanation, with limited mathematics, of how algorithms enable computers to solve problems. Readers will learn what computer algorithms are, how to describe them, and how to evaluate them. They will discover simple ways to search for information in a computer; methods for rearranging information in a computer into a prescribed order ("sorting"); how to solve basic problems that can be modeled in a computer with a mathematical structure called a "graph" (useful for modeling road networks, dependencies among tasks, and financial relationships); how to solve problems that ask questions about strings of characters such as DNA structures; the basic principles behind cryptography; fundamentals of data compression; and even that there are some problems that no one has figured out how to solve on a computer in a reasonable amount of time.


title: The impacts of information technology on total factor productivity: A look at externalities and innovations

abstract: The impacts of information technology (IT) on total factor productivity (TFP) are assessed through an integrative framework of IT-induced externalities and IT-leveraged innovations. Based on network externalities and endogenous growth theory, our study aims to reconcile the seeming discrepancy between the recent observed evidence and the prediction by neoclassical growth theory. We argue that computerization has reshaped the competitive landscape into a network economy with IT-induced externalities that benefit not only IT purchasers but also other stakeholders. Moreover, IT is a platform technology that can leverage innovations to enhance the technological level of production process. Consequently, these two factors of IT-induced externalities and IT-leveraged innovations exert positive impacts on TFP, suggesting IT plays a more pivotal role than input consumption and accumulation that neoclassical growth theory assumes for IT. We use panel data from 30 Organization of Economic Cooperation and Development (OECD) countries over the period of 2000–2009 to empirically test hypotheses on this IT-TFP link. Implications are drawn from our findings for future research to measure IT׳s contributions at the macro level more accurately, and policymakers are urged to cultivate IT׳s positive impacts on TFP to help sustain long-term economic growth.


title: Online supervised spam filter evaluation

abstract: Eleven variants of six widely used open-source spam filters are tested on a chronological sequence of 49086 e-mail messages received by an individual from August 2003 through March 2004. Our approach differs from those previously reported in that the test set is large, comprises uncensored raw messages, and is presented to each filter sequentially with incremental feedback. Misclassification rates and Receiver Operating Characteristic Curve measurements are reported, with statistical confidence intervals. Quantitative results indicate that content-based filters can eliminate 98p of spam while incurring 0.1p legitimate email loss. Qualitative results indicate that the risk of loss depends on the nature of the message, and that messages likely to be lost may be those that are less critical. More generally, our methodology has been encapsulated in a free software toolkit, which may used to conduct similar experiments.


title: Poisson shape interpolation

abstract: In this paper, we propose a novel shape interpolation approach based on Poisson equation. We formulate the trajectory problem of shape interpolation as solving Poisson equations defined on a domain mesh. A non-linear gradient field interpolation method is proposed to take both vertex coordinates and surface orientation into account. With proper boundary conditions, the in-between shapes are reconstructed implicitly from the interpolated gradient fields, while traditional methods usually manipulate vertex coordinates directly. Besides of global shape interpolation, our method is also applicable to local shape interpolation, and can be further enhanced by incorporating with deformation. Our approach can generate visual pleasing and physical plausible morphing sequences with stable area and volume changes. Experimental results demonstrate that our technique can avoid the shrinkage problem appeared in linear shape interpolation.


title: From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning

abstract: We consider the problem of constructing abstract representations for planning in high-dimensional, continuous environments. We assume an agent equipped with a collection of high-level actions, and construct representations provably capable of evaluating plans composed of sequences of those actions. ::: ::: We first consider the deterministic planning case, and show that the relevant computation involves set operations performed over sets of states. We define the specific collection of sets that is necessary and sufficient for planning, and use them to construct a grounded abstract symbolic representation that is provably suitable for deterministic planning. The resulting representation can be expressed in PDDL, a canonical high-level planning domain language; we construct such a representation for the Playroom domain and solve it in milliseconds using an off-the-shelf planner. ::: ::: We then consider probabilistic planning, which we show requires generalizing from sets of states to distributions over states. We identify the specific distributions required for planning, and use them to construct a grounded abstract symbolic representation that correctly estimates the expected reward and probability of success of any plan. In addition, we show that learning the relevant probability distributions corresponds to specific instances of probabilistic density estimation and probabilistic classification. We construct an agent that autonomously learns the correct abstract representation of a computer game domain, and rapidly solves it. ::: ::: Finally, we apply these techniques to create a physical robot system that autonomously learns its own symbolic representation of a mobile manipulation task directly from sensorimotor data---point clouds, map locations, and joint angles---and then plans using that representation. Together, these results establish a principled link between high-level actions and abstract representations, a concrete theoretical foundation for constructing abstract representations with provable properties, and a practical mechanism for autonomously learning abstract high-level representations.


title: Evolutionary foundations for cancer biology

abstract: New applications of evolutionary biology are transforming our understanding of cancer. The articles in this special issue provide many specific examples, such as microorganisms inducing cancers, the significance of within-tumor heterogeneity, and the possibility that lower dose chemotherapy may sometimes promote longer survival. Underlying these specific advances is a large-scale transformation, as cancer research incorporates evolutionary methods into its toolkit, and asks new evolutionary questions about why we are vulnerable to cancer. Evolution explains why cancer exists at all, how neoplasms grow, why cancer is remarkably rare, and why it occurs despite powerful cancer suppression mechanisms. Cancer exists because of somatic selection; mutations in somatic cells result in some dividing faster than others, in some cases generating neoplasms. Neoplasms grow, or do not, in complex cellular ecosystems. Cancer is relatively rare because of natural selection; our genomes were derived disproportionally from individuals with effective mechanisms for suppressing cancer. Cancer occurs nonetheless for the same six evolutionary reasons that explain why we remain vulnerable to other diseases. These four principles—cancers evolve by somatic selection, neoplasms grow in complex ecosystems, natural selection has shaped powerful cancer defenses, and the limitations of those defenses have evolutionary explanations—provide a foundation for understanding, preventing, and treating cancer.


title: Discriminative transfer learning for single-sample face recognition

abstract: Discriminant analysis is an important technique for face recognition because it can extract discriminative features to classify different persons. However, most existing discriminant analysis methods fail to work for single-sample face recognition (SSFR) because there is only a single training sample per person such that the within-class variation of this person cannot be estimated in such scenario. In this paper, we present a new discriminative transfer learning (DTL) approach for SSFR, where discriminant analysis is performed on a multiple-sample generic training set and then transferred into the single-sample gallery set. Specifically, our DTL learns a feature projection to minimize the intra-class variation and maximize the inter-class variation of samples in the training set, and minimize the difference between the generic training set and the gallery set, simultaneously. Experimental results on three face datasets including the FERET, CAS-PEAL-R1, and LFW datasets are presented to show the efficacy of our method.


title: Relationships among grit, academic performance, perceived academic failure, and stress in associate degree students

abstract: Abstract The present study examined the relationships among grit, academic performance, perceived academic failure, and stress levels of Hong Kong associate degree students using path analysis. Three hundred and forty-five students from a community college in Hong Kong voluntarily participated in the study. They completed a questionnaire that measured their grit (operationalized as interest and perseverance) and stress levels. The students also provided their actual academic performance and evaluated their perception of their academic performance as a success or a failure. The results of the path analysis showed that interest and perseverance were negatively associated with stress, and only perceived academic failure was positively associated with stress. These findings suggest that psychological appraisal and resources are more important antecedents of stress than objective negative events. Therefore, fostering students' psychological resilience may alleviate the stress experienced by associate degree students or college students in general.


title: Surface Reconstruction with Data-driven Exemplar Priors

abstract: In this paper, we propose a framework to reconstruct 3D models from raw scanned points by learning the prior knowledge of a specific class of objects. Unlike previous work that heuristically specifies particular regularities and defines parametric models, our shape priors are learned directly from existing 3D models under a framework based on affinity propagation. Given a database of 3D models within the same class of objects, we build a comprehensive library of 3D local shape priors. We then formulate the problem to select as-few-as-possible priors from the library, referred to as exemplar priors. These priors are sufficient to represent the 3D shapes of the whole class of objects from where they are generated. By manipulating these priors, we are able to reconstruct geometrically faithful models with the same class of objects from raw point clouds. Our framework can be easily generalized to reconstruct various categories of 3D objects that have more geometrically or topologically complex structures. Comprehensive experiments exhibit the power of our exemplar priors for gracefully solving several problems in 3D shape reconstruction such as preserving sharp features, recovering fine details and so on.


title: Facial skin beautification via sparse representation over learned layer dictionary

abstract: In this paper, we propose a facial skin beautification framework to remove facial spots based on layer dictionary learning and sparse representation. More precisely, we first decompose the face image into three layers: lighting layer, detail layer and color layer. The corresponding detail layer dictionary are learned by using 60 thousands beauty images collected from the Internet. Thereafter, the detail layer of the image is reconstructed by using sparse representation. Moreover, a binary mask obtained from the learned layer is used to transform detail information from original detail layer to the learned one. The experiment results demonstrate that the proposed method is more effective in eliminating moles, flaws and wrinkles in face image compared with representative commercial systems like PicTreat, Portrait+, Portraitrue and MeituPic.


title: Design of Dual Band Notch UWB Monopole Antenna Using Dual-Arm Spiral Resonator

abstract: An ultra wideband (UWB) monopole antenna with dual band-notch characteristic is proposed. Dual arm spiral resonators (DASR) are placed on both sides of the feed line to create band notches at 5.25 and 5.8GHz for the upper and lower WLAN bands respectively. First the antenna is optimized individually for both upper and lower WLAN band-notch behaviors then embedded together for dual band-notch characteristic. The measured results ensure that two band notches are achieved, and the antenna can be used for UWB applications without any interference from WLAN band.


title: Functional maps: a flexible representation of maps between shapes

abstract: We present a novel representation of maps between pairs of shapes that allows for efficient inference and manipulation. Key to our approach is a generalization of the notion of map that puts in correspondence real-valued functions rather than points on the shapes. By choosing a multi-scale basis for the function space on each shape, such as the eigenfunctions of its Laplace-Beltrami operator, we obtain a representation of a map that is very compact, yet fully suitable for global inference. Perhaps more remarkably, most natural constraints on a map, such as descriptor preservation, landmark correspondences, part preservation and operator commutativity become linear in this formulation. Moreover, the representation naturally supports certain algebraic operations such as map sum, difference and composition, and enables a number of applications, such as function or annotation transfer without establishing point-to-point correspondences. We exploit these properties to devise an efficient shape matching method, at the core of which is a single linear solve. The new method achieves state-of-the-art results on an isometric shape matching benchmark. We also show how this representation can be used to improve the quality of maps produced by existing shape matching methods, and illustrate its usefulness in segmentation transfer and joint analysis of shape collections.


title: Partition and compose: parallel complex event processing

abstract: Complex event processing uses patterns to detect composite events in streams of simple events. Typically, the events are logically partitioned by some key. For instance, the key can be the stock symbol in stock quotes, the author in tweets, the vehicle in transportation, or the patient in health-care. Composite event patterns often become meaningful only after partitioning. For instance, a pattern over stock quotes is typically meaningful over quotes for the same stock symbol. This paper proposes a pattern syntax and translation scheme organized around the notion of partitions. Besides making patterns meaningful, partitioning also benefits performance, since different keys can be processed in parallel. We have implemented partitioned parallel complex event processing as an extension to IBM's System S high-performance streaming platform. Our experiments with several benchmarks from finance and social media demonstrate processing speeds of up to 830,000 events per second, and substantial speedups for expensive patterns parallelized on multi-core machines as well as multi-machine clusters. Partitioning the event stream before detecting composite events makes event processing both more intuitive and parallel.


Factors Influencing Solid-Waste Management in the Developing World


title: How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning

abstract: Today, data availability has gone from scarce to superabundant. Technologies like IoT, trends in social media and the capabilities of smart-phones are producing and digitizing lots of data that was previously unavailable. This massive increase of data creates opportunities to gain new business models, but also demands new techniques and methods of data quality in knowledge discovery, especially when the data comes from different sources (e.g., sensors, social networks, cameras, etc.). The data quality process of the data set proposes conclusions about the information they contain. This is increasingly done with the aid of data cleaning approaches. Therefore, guaranteeing a high data quality is considered as the primary goal of the data scientist. In this paper, we propose a process for data cleaning in regression models (DC-RM). The proposed data cleaning process is evaluated through a real datasets coming from the UCI Repository of Machine Learning Databases. With the aim of assessing the data cleaning process, the dataset that is cleaned by DC-RM was used to train the same regression models proposed by the authors of UCI datasets. The results achieved by the trained models with the dataset produced by DC-RM are better than or equal to that presented by the datasets’ authors.


title: Challenges for Ontology Repositories and Applications to Biomedicine & Agronomy

abstract: The explosion of the number of ontologies and vocabularies available in the Semantic Web makes ontology libraries and repositories mandatory to find and use them. Their functionalities span from simple on-tology listing with more or less of metada-ta description to portals with advanced on-tology-based services: browse, search, vis-ualization, metrics, annotation, etc. Ontol-ogy libraries and repositories are usually developed to address certain needs and communities. BioPortal, the ontology repository built by the US National Center for Biomedical Ontologies BioPortal relies on a domain independent technology already reused in several projects from bio-medicine to agronomy and earth sciences. In this position paper, we describe six high level challenges for ontology repositories: metadata & selection, multilingualism, alignment, new generic ontology-based services, annotations & linked data, and interoperability & scalability. Then, we present some propositions to address these challenges and point to our previously published work and results obtained within applications –reusing NCBO technology– to biomedicine and agronomy in the context of the NCBO, SIFR and AgroPortal projects.