title: Effects of Hormonal Replacement Therapy on Bone Metabolism in Young Adults with Beta-thalassemia Major

abstract: Abstract: The aim of our cross-sectional study was to evaluate the effects of hormonal replacement therapy (HRT) on a population of young thalassemics in order to understand better the role of hypogonadism in the balance of bone metabolism. Markers of bone turnover and bone mineral density (BMD) were measured in 40 young patients (mean age 19.8 ± 4.5 years) with beta-thalassemia major: 20 subjects were biochemically eugonadal, since they were undergoing HRT (group A, treated patients), and 20 were hypogonadic, having suspended HRT (group B, untreated patients). We also examined 20 healthy control subjects (group C) matched for age, anthropometric features and sex to the study groups. Our study shows that young thalassemic patients exhibit a significant loss of cortical and trabecular bone [aBMD L2–L4: 0.886 ± 0.052 g/cm2 (group A), 0.726 ± 0.040 g/cm2 (group B), 1.083 ± 0.090 g/cm2 (group C); aBMD femoral neck: 0.890 ± 0.071 g/cm2 (group A), 0.700 ± 0.065 g/cm2 (group B), 0.934 ± 0.076 g/cm2 (group C)]. Osteoporosis is only observed at the lumbar level in treated thalassemic patients, while in untreated patients it involves the femoral neck also. Bone turnover in thalassemic patients is higher in the resorptive phase, than in the neoformation phase and this is more marked in hypogonadicuntreated patients. In conclusion, our data demonstrate the important role played by hypogonadism in the development and deterioration of osteopenia/osteoporosis in thalassemia major. Consequently, sex hormone replacement therapy represents an appropriate tool in the prevention and treatment of osteoporosis in thalassemics, probably together with bisphosphonates in cases with severely increased bone resorption.


title: Characterization of nanocellulose- reinforced shape memory polyurethanes

abstract: BACKGROUND: Shape memory polymers are capable of fixing a transient shape and of recovering their original dimensions by the application of an external stimulus. Their major drawback is their low stiffness compared to smart materials based on metals and ceramics. To overcome this disadvantage, nanocellulose was utilized as reinforcement. ::: ::: ::: ::: RESULTS: Composites were prepared by casting stable nanocellulose/segmented polyurethane suspensions. The heat of melting of the polyurethane soft segment phase increased on cellulose addition. Composites showed higher tensile modulus and strength than unfilled films (53% modulus increase at 1 wt% nanocellulose), with higher elongation at break. Creep deformation decreased as cellulose concentration increased (36% decrease in 60-minute creep by addition of 1 wt% nanocellulose). The nanocomposites displayed shape memory properties equivalent to those of the neat polyurethane, with recoveries of the order of 95% (referred to second and further cycles). ::: ::: ::: ::: CONCLUSIONS: It is possible to markedly improve the rigidity of shape memory polymers by adding small amounts of well-dispersed nanocellulose. However, this improvement did not have substantial effects on the material shape fixity or recovery. Shape memory behavior seems to continue to be controlled by the polymer properties. Copyright © 2007 Society of Chemical Industry


title: Detecting performance anti-patterns for applications developed using object-relational mapping

abstract: Object-Relational Mapping (ORM) provides developers a conceptual abstraction for mapping the application code to the underlying databases. ORM is widely used in industry due to its convenience; permitting developers to focus on developing the business logic without worrying too much about the database access details. However, developers often write ORM code without considering the impact of such code on database performance, leading to cause transactions with timeouts or hangs in large-scale systems. Unfortunately, there is little support to help developers automatically detect suboptimal database accesses. In this paper, we propose an automated framework to detect ORM performance anti-patterns. Our framework automatically flags performance anti-patterns in the source code. Furthermore, as there could be hundreds or even thousands of instances of anti-patterns, our framework provides sup- port to prioritize performance bug fixes based on a statistically rigorous performance assessment. We have successfully evaluated our framework on two open source and one large-scale industrial systems. Our case studies show that our framework can detect new and known real-world performance bugs and that fixing the detected performance anti- patterns can improve the system response time by up to 98%.


title: Intuitive ontology authoring using controlled natural language

abstract: Ontologies have been proposed and studied in the last couple of decades as a way to capture and share people's knowledge about the world in a way that is processable by computer systems. Ontologies have the potential to serve as a bridge between the human conceptual understanding of the world and the data produced, processed and stored in computer systems. However, ontologies so far have failed to gather widespread adoption, failing to realise the original vision of the semantic web as a next generation of the world wide web: where everyone would be able to contribute and interlink their data and knowledge as easily as ::: they can contribute and interlink their websites. ::: ::: One of the main reasons for this lack of widespread adoption of ontologies is the steep learning curve for authoring them: most people find it too dfficult to ::: learn the syntax and formal semantics of ontology languages. Most research has tried to alleviate this problem by finding ways to help people to collaborate with ::: knowledge engineers when building ontologies; this approach however, requires the wide availability of knowledge engineers, who in practice are scarce. In the ::: context of the semantic web, recent research has started looking at ways to directly capture knowledge from domain experts as ontologies. One such approach ::: advocates the use of Controlled Natural Languages (CNL) as a promising way to alleviate the syntactical impediment to writing ontological constructs. However, not much is yet known about the capabilities and limitations of CNL-based ontology authoring by domain experts. It is also unknown what type of automatic tool support can and should be provided to novice ontology authors, although such ::: intelligent tool support is becoming possible due to advances in reasoning with existing ontologies and other related areas such as natural language processing. ::: ::: This PhD investigates how CNL-based ontology authoring systems can make ontology authoring more accessible to domain experts by providing intelligent tool support. In particular, this thesis iteratively investigates the impact of providing various types of intelligent tool support for authoring ontologies using the Web Ontology Language (OWL) and a controlled natural language called Rabbit. After each iteration of added tool support, we evaluate how it impacts the ontology authoring process and what are the main limitations of the resulting ontology authoring system. Based on the found limitations, we decide which further tool ::: support would be most beneficial to novice ontology authors. This methodology resulted in iteratively providing support for (i) understanding the syntactic capabilities and limitations of the chosen controlled natural language; (ii) following appropriate ontology engineering methodologies; (iii) fostering awareness about the logical consequences of adding new knowledge to an ontology and (iv) ::: interacting with the ontology authoring system via dialogues. ::: ::: The main contributions of this PhD are (i) showing that domain experts benefit from guidance about the ontology authoring process and understandable syntax error messages for finding the correct CNL syntax; (ii) the definition ::: of a framework to integrate the syntactical and semantic analyses of ontology authors' inputs; (iii) showing that intuitive feedback about the integration of ontology authors' inputs into an existing ontology benefits ontology authors as they become aware of potential ontology defects; (iv) the definition of a framework to analyse and describe ontology authoring in terms of dialogue moves and their ::: discourse structure.


title: Navigating Occluded Intersections with Autonomous Vehicles Using Deep Reinforcement Learning

abstract: Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.


title: Named Entity Recognition in Twitter using Images and Text

abstract: Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limitations with correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results. Thus, devising models to deal with such linguistically complex contexts is still challenging. In this paper, we propose a novel multi-level architecture that does not rely on any specific linguistic resource or encoded rule. Unlike traditional approaches, we use features extracted from images and text to classify named entities. Experimental tests against state-of-the-art NER for Twitter on the Ritter dataset present competitive results (0.59 F-measure), indicating that this approach may lead towards better NER models.


Robot Signaling its Intentions in Human-Robot Teaming


title: De-identification of patient notes with recurrent neural networks

abstract: Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) defines 18 types of protected health information (PHI) that needs to be removed to de-identify patient notes. Manual de-identification is impractical given the size of EHR databases, the limited number of researchers with access to the non-de-identified notes, and the frequent mistakes of human annotators. A reliable automated de-identification system would consequently be of high value. ::: Materials and Methods: We introduce the first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems. We compare the performance of the system with state-of-the-art systems on two datasets: the i2b2 2014 de-identification challenge dataset, which is the largest publicly available de-identification dataset, and the MIMIC de-identification dataset, which we assembled and is twice as large as the i2b2 2014 dataset. ::: Results: Our ANN model outperforms the state-of-the-art systems. It yields an F1-score of 97.85 on the i2b2 2014 dataset, with a recall 97.38 and a precision of 97.32, and an F1-score of 99.23 on the MIMIC de-identification dataset, with a recall 99.25 and a precision of 99.06. ::: Conclusion: Our findings support the use of ANNs for de-identification of patient notes, as they show better performance than previously published systems while requiring no feature engineering.


title: Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments

abstract: In this work, it is shown that student access time series generated from Moodle log files contain information sufficient for successful prediction of student final results in blended learning courses. It is also shown that if time series is transformed into frequency domain, using discrete Fourier transforms DFT, the information contained in it will be preserved. Hence, resulting periodogram and its DFT coefficients can be used for generating student performance models with the algorithms commonly used for that purposes. The amount of data extracted from log files, especially for lengthy courses, can be huge. Nevertheless, by using DFT, drastic compression of data is possible. It is experimentally shown, by means of several commonly used modelling algorithms, that if in average all but 5-10% of most intensive and most frequently used DFT coefficients are removed from datasets, the modelling with the remained data will result with the increase of the model accuracy. Resulting accuracy of the calculated models is in accordance with results for student performance models calculated for different dataset types reported in literature. The advantage of this approach is its applicability because the data are automatically collected in Moodle logs.


title: Epigenetic mechanisms and endocannabinoid signalling.

abstract: The endocannabinoid system, composed of endogenous lipids, their target receptors and metabolic enzymes, has been implicated in multiple biological functions in health and disease, both in the central nervous system and in peripheral organs. Despite the exponential growth of experimental evidence on the key role of endocannabinoid signalling in basic cellular processes, and on its potential exploitation for therapeutic interventions, much remains to be clarified about the respective regulatory mechanisms. Epigenetics refers to a set of post-translational modifications that regulate gene expression without causing variation in DNA sequence, endowed with a major impact on signal transduction pathways. The epigenetic machinery includes DNA methylation, histone modifications, nucleosome positioning and non-coding RNAs. Due to the reversibility of epigenetic changes, an emerging field of interest is the possibility of an 'epigenetic therapy' that could possibly be applied also to endocannabinoids. Here, we review current knowledge of epigenetic regulation of endocannabinoid system components under both physiological and pathological conditions, as well as the epigenetic changes induced by endocannabinoid signalling.


Physiological receptors as physicochemical entities.


title: LMF-Based Control Algorithm for Single Stage Three-Phase Grid Integrated Solar PV System

abstract: This paper proposes the use of a least mean fourth (LMF)-based algorithm for single-stage three-phase grid-integrated solar photovoltaic (SPV) system. It consists of an SPV array, voltage source converter (VSC), three-phase grid, and linear/nonlinear loads. This system has an SPV array coupled with a VSC to provide three-phase active power and also acts as a static compensator for the reactive power compensation. It also conforms to an IEEE-519 standard on harmonics by improving the quality of power in the three-phase distribution network. Therefore, this system serves to provide harmonics alleviation, load balancing, power factor correction and regulating the terminal voltage at the point of common coupling. In order to increase the efficiency and maximum power to be extracted from the SPV array at varying environmental conditions, a single-stage system is used along with perturb and observe method of maximum power point tracking (MPPT) integrated with the LMF-based control technique. The proposed system is modeled and simulated using MATLAB/Simulink with available simpower system toolbox and the behaviour of the system under different loads and environmental conditions are verified experimentally on a developed system in the laboratory.


title: ‘HighChest’: An Augmented Freezer Designed for Smart Food Management and Promotion of Eco-Efficient Behaviour

abstract: This paper introduces HighChest, an innovative smart freezer designed to promote energy efficient behavior and the responsible use of food. Introducing a novel human-machine interface (HMI) design developed through assessment phases and a user involvement stage, HighChest is state of the art, featuring smart services that exploit embedded sensors and Internet of things functionalities, which enhance the local capabilities of the appliance. The industrial design thinking approach followed for the advanced HMI is intended to maximize the social impact of the food management service, enhancing both the user experience of the product and the user's willingness to adopt eco- and energy-friendly behaviors. The sensor equipment realizes automatic recognition of food by learning from the users, as well as automatic localization inside the deposit space. Moreover, it provides monitoring of the appliance's usage, avoiding temperature and humidity issues related to improper use. Experimental tests were conducted to evaluate the localization system, and the results showed 100% accuracy for weights greater or equal to 0.5 kg. Drifts due to the lid opening and prolonged usage time were also measured, to implement automatic reset corrections.


title: LWT-SVD secure image watermarking technique

abstract: In this paper we have proposed a new image watermarking technique which consists in applying the Singular Values Decomposition (SVD) in LWT domain in order to insert the watermark image in the cover image. This technique also use signature in the embedding and extraction procedure. The obtained results from the application of the proposed technique on a number of images show clearly that this technique is secure and both the watermarked and the watermark images are with good visible quality.


title: The string edit distance matching problem with moves

abstract: The edit distance between two strings S and R is defined to be the minimum number of character inserts, deletes and changes needed to convert R to S. Given a text string t of length n, and a pattern string p of length m, informally, the string edit distance matching problem is to compute the smallest edit distance between p and substrings of t. A well known dynamic programming algorithm takes time O(nm) to solve this problem, and it is an important open problem in Combinatorial Pattern Matching to significantly improve this bound.We relax the problem so that (a) we allow an additional operation, namely, substring moves, and (b) we approximate the string edit distance upto a factor of O(log n log*n). Our result is a near linear time deterministic algorithm for this version of the problem. This is the first known significantly subquadratic algorithm for a string edit distance problem in which the distance involves nontrivial alignments. Our results are obtained by embedding strings into L 1 vector space using a simplified parsing technique we call Edit Sensitive Parsing (ESP). This embedding is approximately distance preserving, and we show many applications of this embedding to string proximity problems including nearest neighbors, outliers, and streaming computations with strings.


title: Learning to ask the right questions to help a learner learn

abstract: Intelligent systems require substantial bodies of problem-solving knowledge. Machine learning techniques hold much appeal for acquiring such knowledge but typically require extensive amounts of user-supplied training data. Alternatively, informed question asking can supplement machine learning by directly eliciting critical knowledge from a user. Question asking can reduce the amount of training data required, and hence the burden on the user; furthermore, focused question asking holds significant promise for faster and more accurate acquisition of knowledge. In previous work, we developed static strategies for question asking that provide background knowledge for a base learner, enabling the learner to make useful generalizations even with few training examples. Here, we extend that work with a learning approach for automatically acquiring question-asking strategies that better accommodate the interdependent nature of questions. We present experiments validating the approach and showing its usefulness for acquiring efficient, context-dependent question-asking strategies.


title: Classification of hardware trojan detection techniques

abstract: The majority of techniques that have been developed to detect hardware trojans are based on only specific attributes. Further, the ad hoc approaches that have been employed to design methods for trojan detection are largely ineffective. These trojans have a number of attributes which can be used in a systematic way to develop detection techniques. Based on this concept, a detailed examination of current trojan detection techniques and the characteristics of existing hardware trojans is presented. This is used to develop a new approach to hardware trojan identification and classification.


title: A Survey of Denial-of-Service and Distributed Denial of Service Attacks and Defenses in Cloud Computing

abstract: Cloud Computing is a computing model that allows ubiquitous, convenient and on-demand access to a shared pool of highly configurable resources (e.g., networks, servers, storage, applications and services). Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are serious threats to the Cloud services’ availability due to numerous new vulnerabilities introduced by the nature of the Cloud, such as multi-tenancy and resource sharing. In this paper, new types of DoS and DDoS attacks in Cloud Computing are explored, especially the XML-DoS and HTTP-DoS attacks, and some possible detection and mitigation techniques are examined. This survey also provides an overview of the existing defense solutions and investigates the experiments and metrics that are usually designed and used to evaluate their performance, which is helpful for the future research in the domain.


title: Pairing Wi-Fi and Bluetooth MAC addresses through passive packet capture

abstract: The majority of smart devices used nowadays (e.g., smartphones, laptops, tablets) is capable of both Wi-Fi and Bluetooth wireless communications. Both network interfaces are identified by a unique 48-bits MAC address, assigned during the manufacturing process and unique worldwide. Such addresses, fundamental for link-layer communications and contained in every frame transmitted by the device, can be easily collected through packet sniffing and later used to perform higher level analysis tasks (user tracking, crowd density estimation, etc.). In this work we propose a system to pair the Wi-Fi and Bluetooth MAC addresses belonging to a physical unique device, starting from packets captured through a network of wireless sniffers. We propose several algorithms to perform such a pairing and we evaluate their performance through experiments in a controlled scenario. We show that the proposed algorithms can pair the MAC addresses with good accuracy. The findings of this paper may be useful to improve the precision of indoor localization and crowd density estimation systems and open some questions on the privacy issues of Wi-Fi and Bluetooth enabled devices.


title: Neural fitted Q iteration – first experiences with a data efficient neural reinforcement learning method

abstract: This paper introduces NFQ, an algorithm for efficient and effective training of a Q-value function represented by a multi-layer perceptron. Based on the principle of storing and reusing transition experiences, a model-free, neural network based Reinforcement Learning algorithm is proposed. The method is evaluated on three benchmark problems. It is shown empirically, that reasonably few interactions with the plant are needed to generate control policies of high quality.


title: A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots

abstract: Objective: To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair. Methods: In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in 5 experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from one hour to two months) to assess the system robustness over time. The pre-specified path was divided in 7 stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before. Results: In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials. Conclusions: The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks. Significance: These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and non-invasive BCI.


title: Learning how to Prove: From the Coq Proof Assistant to Textbook Style

abstract: We have developed an alternative approach to teaching computer science students how to prove. First, students are taught how to prove theorems with the Coq proof assistant. In a second, more difficult, step students will transfer their acquired skills to the area of textbook proofs. In this article we present a realisation of the second step. ::: Proofs in Coq have a high degree of formality while textbook proofs have only a medium one. Therefore our key idea is to reduce the degree of formality from the level of Coq to textbook proofs in several small steps. For that purpose we introduce three proof styles between Coq and textbook proofs, called line by line comments, weakened line by line comments, and structure faithful proofs. ::: While this article is mostly conceptional we also report on experiences with putting our approach into practise.


title: The Mirage of Action-Dependent Baselines in Reinforcement Learning

abstract: Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. Several recent papers extend the baseline to depend on both the state and action and suggest that this significantly reduces variance and improves sample efficiency without introducing bias into the gradient estimates. To better understand this development, we decompose the variance of the policy gradient estimator and numerically show that learned state-action-dependent baselines do not in fact reduce variance over a state-dependent baseline in commonly tested benchmark domains. We confirm this unexpected result by reviewing the open-source code accompanying these prior papers, and show that subtle implementation decisions cause deviations from the methods presented in the papers and explain the source of the previously observed empirical gains. Furthermore, the variance decomposition highlights areas for improvement, which we demonstrate by illustrating a simple change to the typical value function parameterization that can significantly improve performance.


title: Natural Scene Image Segmentation Based on Multi-Layer Feature Extraction

abstract: This paper addresses the problem of natural image segmentation by extracting information from a multi-layer array which is constructed based on color, gradient, and statistical properties of the local neighborhoods in an image. A Gaussian Mixture Model (GMM) is used to improve the effectiveness of local spectral histogram features. Grouping these features leads to forming a rough initial over-segmented layer which contains coherent regions of pixels. The regions are merged by using two proposed functions for calculating the distance between two neighboring regions and making decisions about their merging. Extensive experiments are performed on the Berkeley Segmentation Dataset to evaluate the performance of our proposed method and compare the results with the recent state-of-the-art methods. The experimental results indicate that our method achieves higher level of accuracy for natural images compared to recent methods.


title: Big Data in Natural Disaster Management: A Review

abstract: Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural disasters. From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses. In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management. This paper aimed at making a systematic review of the literature in analyzing the role of big data in natural disaster management and highlighting the present status of the technology in providing meaningful and effective solutions in natural disaster management. The paper has presented the findings of several researchers on varied scientific and technological perspectives that have a bearing on the efficacy of big data in facilitating natural disaster management. In this context, this paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.


title: FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

abstract: Abstract. We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.


title: Factors Affecting Burnout and School Engagement among High School Students: Study Habits, Self- Efficacy Beliefs, and Academic Success.

abstract: This study examines high school students' levels of burnout and school engagement with respect to academic success, study habits, and self-efficacy beliefs. The data were gathered during the 2011-2012 school year from 633 students attending six high schools located in Ankara, Turkey. The analyses were conducted on responses from 605 students. The research methods included the Personal Information Form comprising items about students' demographic characteristics, the Maslach Burnout Inventory-Student Form, the Utrecht School Engagement Scale, the Study Habits Inventory, and the Scale for Self-Efficacy Expectations among Adolescents. The data were analyzed with multivariate analysis of variance. The results suggested that students with low self-efficacy beliefs had higher burnout levels. In addition, students with inadequate study skills and those with low self-efficacy beliefs were at higher risk of losing their beliefs. Another finding was that students with high academic success also had high self-efficacy. Unexpectedly, students with inadequate study skills and low self-efficacy beliefs were found to have high self-efficacy. Students with adequate study skills and high self- efficacy beliefs also had high school engagement levels. The study findings were discussed in relation to the literature and interpreted. Based on the interpretations, recommendations were made to school counselors and researchers.


Pathways for emotions and memory II . Afferent input to the anterior thalamic nuclei from prefrontal , temporal , hypothalamic areas and the basal ganglia in the rhesus monkey


title: HCI Education: Innovation, Creativity and Design Thinking

abstract: Human-Computer Interaction (HCI) education needs re-thinking. In this paper, we explore how and what creativity and design thinking could contribute with, if included as a part of the HCI curriculum. The findings from courses where design thinking was included, indicate that design thinking contributed to increased focus on innovation and creativity, as well as prevented too early fixation on a single solution in the initial phases of HCI design processes, fostering increased flexibility and adaptability in learning processes. The creativity and adaptability may be the best long-term foci that HCI education can add to its curriculums and offer to students when preparing them for future work


title: A DPWM-controlled three-level T-type inverter for photovoltaic generation considering unbalanced neutral-point voltage

abstract: The three-level T type inverter has been extensively used in photovoltaic (PV) generation. Various PV panels in cascaded connection normally require unequal dc voltage to accomplish separate maximum power point tracking (MPPT). This paper proposes a DPWM-controlled three-level T type inverter for two strings of PV panels in cascaded connection. The proposed modulation is developed based on the so-called circuit-level decoupling principle so that the inverter is able to generate balanced line voltage even in unequal dc voltage. The different dc voltages of PV strings are controlled by injection of zero-sequence voltage while total dc voltage is regulated to control the real power of the inverter. Thus both PV strings are able to operate at their respective MPPT while there is almost no low-order harmonic distortion at the inverter output. The proposed method was experimentally evaluated.


title: Hybrid approach for project portfolio selection based on the business strategy and the scoring model

abstract: From among numerous opportunities, organizations must select the projects they decide to pursue. The consequences of poor decisions can be extremely expensive. This is why many methods and models for project portfolio selection have been developed. In this paper, we propose a project portfolio selection approach that combines a business strategy method with a scoring model. The proposed hybrid approach also includes recommendations on the selection criteria provided. Two criteria are identified: the value and risk, the third is at the option of the organization for more flexibility.


Widespread dissemination of multidrug-resistant Acinetobacter baumannii producing OXA-23 carbapenemase and ArmA 16S ribosomal RNA methylase in a Bulgarian university hospital


title: A suboptimal scheme for uplink NOMA in 5G systems

abstract: Non-orthogonal multiple access (NOMA) scheme is a key technique towards the 5th generation (5G) of wireless communication networks due to its higher spectral efficiency comparing to its orthogonal multiple access (OMA) counterpart. However, the complexity of conventional NOMA scheme is extremely high in massive-access scenes. In this paper, we propose a user-pairing based suboptimal scheme for uplink multiple-access by combining NOMA and OMA to facilitate the massive-access scenes. Furthermore, a user-pairing algorithm with O(nlogn) complexity is also derived. According to the Monte Carlo simulation, the proposed scheme shows only a little throughput loss comparing to the optimal transmission scheme in information-theoretic perspective.


title: Complete mesocolic excision with D3 lymph node dissection in laparoscopic colectomy for stages II and III colon cancer: long-term oncologic outcomes in 168 patients

abstract: BACKGROUND ::: There is emerging evidence that complete mesocolic excision (CME) for colon cancer produces favorable oncologic outcomes. The applicability of CME technique in laparoscopic colectomy has not been fully explored. The aim of our retrospective study was to evaluate the feasibility of the CME technique with D3 lymphadenectomy in laparoscopic colectomy and its short- and long-term outcomes. ::: ::: ::: METHODS ::: Between September 2006 and December 2009, 168 laparoscopic colectomies were performed for stages II and III colon cancer. Prospectively, collected data on demographics, tumor characteristics, complications, and outcomes were analyzed retrospectively. ::: ::: ::: RESULTS ::: Eighty-seven patients (51.8 %) had stage II colon cancer, and 81 patients had stage III cancer. The mean operative time was 196.0 ± 61.2 min. The overall morbidity rate was 17.8 %, which included anastomotic leak in 10 patients (5.9 %). There was no operative mortality. The number of lymph nodes harvested was 27.8 ± 13.6. With a median follow-up of 57.3 months, locoregional recurrence and systemic metastasis developed in 6 (3.6 %) and 14 patients (8.3 %), respectively. Seven patients died of causes related to cancer, and all had stage III cancer. Disease-free survival at 5-years was 95.2 % for patients with stage II and 80.9 % for patients with stage III. ::: ::: ::: CONCLUSIONS ::: Standardization of laparoscopic CME and D3 lymphadenectomy is expedient. The technique is associated with acceptable morbidity and provides excellent oncologic outcomes for stage II and stage III colon cancer. A longer follow-up is needed to validate the enhancement of oncological outcome related to this surgical concept.


title: I'm sad you're sad: emotional contagion in CMC

abstract: An enduring assumption about computer-mediated communication is that it undermines emotional understanding. The present study examined emotional communication in CMC by inducing negative affect in one condition and neutral affect in another. The results revealed that 1) participants experiencing negative affect produced fewer words, used more sad terms, and exchanged messages at a slower rate, 2) their partners were able to detect their partners emotional state, and 3) emotional contagion took place, in which partners interacting with participants in the negative affect condition had significantly less positive affect than partners in the control condition. These data support a relational view of CMC.


title: Learning to Protect Communications with Adversarial Neural Cryptography

abstract: We ask whether neural networks can learn to use secret keys to protect ::: information from other neural networks. Specifically, we focus on ::: ensuring confidentiality properties in a multiagent system, and we ::: specify those properties in terms of an adversary. Thus, a ::: system may consist of neural networks named Alice and Bob, and we aim ::: to limit what a third neural network named Eve learns from ::: eavesdropping on the communication between Alice and Bob. ::: We do not prescribe specific cryptographic algorithms to these neural networks; ::: instead, we train end-to-end, adversarially. ::: We demonstrate that the neural networks can learn ::: how to perform forms of encryption and decryption, and also ::: how to apply these operations selectively in order to meet ::: confidentiality goals.


title: Automatic Labelling of Topic Models Learned from Twitter by Summarisation

abstract: Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived from social media poses however new challenges since topics may characterise novel events happening in the real world. Existing automatic topic labelling approaches which depend on external knowledge sources become less applicable here since relevant articles/concepts of the extracted topics may not exist in external sources. In this paper we propose to address the problem of automatic labelling of latent topics learned from Twitter as a summarisation problem. We introduce a framework which apply summarisation algorithms to generate topic labels. These algorithms are independent of external sources and only rely on the identification of dominant terms in documents related to the latent topic. We compare the efficiency of existing state of the art summarisation algorithms. Our results suggest that summarisation algorithms generate better topic labels which capture event-related context compared to the top-n terms returned by LDA.


title: Partially labeled topic models for interpretable text mining

abstract: Abstract Much of the world's electronic text is annotated with human-interpretable labels, such as tags on web pages and subject codes on academic publications. Effective text mining in this setting requires models that can flexibly account for the textual patterns that underlie the observed labels while still discovering unlabeled topics. Neither supervised classification, with its focus on label prediction, nor purely unsupervised learning, which does not model the labels explicitly, is appropriate. In this paper, we present two new partially supervised generative models of labeled text, Partially Labeled Dirichlet Allocation (PLDA) and the Partially Labeled Dirichlet Process (PLDP). These models make use of the unsupervised learning machinery of topic models to discover the hidden topics within each label, as well as unlabeled, corpus-wide latent topics. We explore applications with qualitative case studies of tagged web pages from del.icio.us and PhD dissertation abstracts, demonstrating improved model interpretability over traditional topic models. We use the many tags present in our del.icio.us dataset to quantitatively demonstrate the new models' higher correlation with human relatedness scores over several strong baselines.


title: Ninf-G: A Reference Implementation of RPC-based Programming Middleware for Grid Computing

abstract: GridRPC, which is an RPC mechanism tailored for the Grid, is an attractive programming model for Grid computing. This paper reports on the design and implementation of a GridRPC programming system called Ninf-G. Ninf-G is a reference implementation of the GridRPC API which has been proposed for standardization at the Global Grid Forum. In this paper, we describe the design, implementations and typical usage of Ninf-G. A preliminary performance evaluation in both WAN and LAN environments is also reported. Implemented on top of the Globus Toolkit, Ninf-G provides a simple and easy programming interface based on standard Grid protocols and the API for Grid Computing. The overhead of remote procedure calls in Ninf-G is acceptable in both WAN and LAN environments.


title: The Interplay of Beauty, Goodness, and Usability in Interactive Products

abstract: Two studies considered the interplay between user-perceived usability (i.e., pragmatic attributes), hedonic attributes (e.g., stimulation, identification), goodness (i.e., satisfaction), and beauty of 4 different MP3-player skins. As long as beauty and goodness stress the subjective valuation of a product, both were related to each other. However, the nature of goodness and beauty was found to differ. Goodness depended on both perceived usability and hedonic attributes. Especially after using the skins, perceived usability became a strong determinant of goodness. In contrast, beauty largely depended on identification; a hedonic attribute group, which captures the product's ability to communicate important personal values to relevant others. Perceived usability as well as goodness was affected by experience (i.e., actual usability, usability problems), whereas hedonic attributes and beauty remained stable over time. All in all, the nature of beauty is rather self-oriented than goal-oriented, whereas goodness relates to both.


title: Light Stemming for Arabic Information Retrieval

abstract: Computational Morphology is an urgent problem for Arabic Natural Language Processing, because Arabic is a highly inflected language. We have found, however, that a full solution to this problem is not required for effective information retrieval. Light stemming allows remarkably good information retrieval without providing correct morphological analyses. We developed several light stemmers for Arabic, and assessed their effectiveness for information retrieval using standard TREC data. We have also compared light stemming with several stemmers based on morphological analysis. The light stemmer, light10, outperformed the other approaches. It has been included in the Lemur toolkit, and is becoming widely used Arabic information retrieval.


title: Trellis-Coded OAM-QAM Union Modulation with Single-Point Receiver

abstract: Union modulation scheme is proposed and analyzed based on trellis-coded modulation, orbital angular momentum (OAM), and quadrature amplitude modulation (QAM). Specifically, source data symbols are mapped to a 3-D constellation diagram consisting of OAM modes and QAM constellation. Moreover, the constellation point is jointly detected by an OAM single-point detector and a pruned Viterbi decoder at the receiver. Compared with standalone modulation schemes, larger Euclidean distance with lower bit error rate (BER) and lower complexity can be obtained. Furthermore, the simulation results show that the single-point detection is more robust than the phase-gradient detection at a large receiving distance.


title: EFFECTS OF ANIMATION-BASED CAMSTUDIO PHYSICS INSTRUCTION ON SECONDARY SCHOOL STUDENTS’ PERFORMANCE IN MINNA, NIGERIA

abstract: This study was carried out to determine the effect of Animationbased CamStudio Physics Instructional Package (ACPIP) on secondary school physics students’ performance in Minna, Nigeria. Two research hypotheses were formulated and tested at 0.05 level of significance. Purposive sampling technique was employed in selecting two co-educational secondary schools in Minna, metropolis from where eighty (80) senior secondary school two (SS II) Physics students were sampled for the study. Students in the experimental group ((20boys & 20girls)) were taught physics using ACPIP while their counterparts in the control group ((20boys & 20girls)) were taught using the conventional lecture method. Animationbased CamStudio Physics Instructional Package (ACPIP) which contains three secondary school Physics concepts in Waves and the Physics Achievement Test (PAT) were the two research instruments used for the study. The PAT comprises 30-item multiple-choice Physics questions. The Kudar-Richardson (KR=21) formula used to determine the reliability coefficient of PAT yielded 0.89, while mean and t-test statistics were used to test the two hypotheses in SPSS 16.0. Findings revealed that students taught Physics using Animation-based CamStudio Instructional Package performed significantly better than those taught using conventional lecture method (tcal= 6.043, df = 78, p = 0.000), but there was no significant difference between the mean achievement scores of male and female students taught Physics using ACPIP (tcal= 0.604, df = 38, p = 0.550). Based on the findings, it was recommended that Animation-based physics instruction should be used to supplement conventional classroom instruction and for revision purposes in order to improve students’ performance in physics.


title: A Simple but Tough-to-Beat Baseline for Sentence Embeddings

abstract: The success of neural network methods for computing word embeddings has motivated methods for generating semantic embeddings of longer pieces of text, such as sentences and paragraphs. Surprisingly, Wieting et al (ICLR'16) showed that such complicated methods are outperformed, especially in out-of-domain (transfer learning) settings, by simpler methods involving mild retraining of word embeddings and basic linear regression. The method of Wieting et al. requires retraining with a substantial labeled dataset such as Paraphrase Database (Ganitkevitch et al., 2013). ::: ::: The current paper goes further, showing that the following completely unsupervised sentence embedding is a formidable baseline: Use word embeddings computed using one of the popular methods on unlabeled corpus like Wikipedia, represent the sentence by a weighted average of the word vectors, and then modify them a bit using PCA/SVD. This weighting improves performance by about 10% to 30% in textual similarity tasks, and beats sophisticated supervised methods including RNN's and LSTM's. It even improves Wieting et al.'s embeddings. This simple method should be used as the baseline to beat in future, especially when labeled training data is scarce or nonexistent. ::: ::: The paper also gives a theoretical explanation of the success of the above unsupervised method using a latent variable generative model for sentences, which is a simple extension of the model in Arora et al. (TACL'16) with new "smoothing" terms that allow for ::: words occurring out of context, as well as high probabilities for words like and, not in all contexts.


title: Novel coplanar-waveguide bandpass filters using loaded air-bridge enhanced capacitors and broadside-coupled transition structures for wideband spurious suppression

abstract: Novel inline coplanar-waveguide (CPW) bandpass filters composed of quarter-wavelength stepped-impedance resonators are proposed, using loaded air-bridge enhanced capacitors and broadside-coupled microstrip-to-CPW transition structures for both wideband spurious suppression and size miniaturization. First, by suitably designing the loaded capacitor implemented by enhancing the air bridges printed over the CPW structure and the resonator parameters, the lower order spurious passbands of the proposed filter may effectively be suppressed. Next, by adopting the broadside-coupled microstrip-to-CPW transitions as the fed structures to provide required input and output coupling capacitances and high attenuation level in the upper stopband, the filter with suppressed higher order spurious responses may be achieved. In this study, two second- and fourth-order inline bandpass filters with wide rejection band are implemented and thoughtfully examined. Specifically, the proposed second-order filter has its stopband extended up to 13.3f 0, where f0 stands for the passband center frequency, and the fourth-order filter even possesses better stopband up to 19.04f0 with a satisfactory rejection greater than 30 dB


title: OPTIMIZATION OF STEEL CATENARY RISERS USING BIOINSPIRED ALGORITHMS

abstract: Abstract. The design of a riser is very time consuming, since a large number of parameters (e.g.: thickness, top angle, and material properties) are involved and tight safety requirements must be met. This leads to the study of tools, such as optimization algorithms, that can speed up the process of elaborating a feasible riser project for certain conditions. Considering that some of the parameters in the design of a riser can assume a discrete set of values, the utilization of mathematical programming algorithms becomes unfeasible. It is then necessary to use metaheuristic algorithms, such as Genetic Algorithm and Particle Swarm Optimization.In this context, this paper presents a study on the application of bio-inspired algorithms,including GA and PSO, to the design optimization of steel catenary risers. The problem consists of finding the riser material and wall thickness that minimize the cost to fabricate a viable riser, in conformance with the requirements of technical standards. The main hypotheses that were adopted are presented, along with the description of the methodology employed. The results show that a significant reduction in riser cost is achieved when the riser is divided in multiple segments with different thickness and material. The efficiency of the utilized algorithms in finding an optimum riser design for the specified conditions is onfirmed by the obtained numerical results. Keywords: Bio-inspired algorithms, Optimization, Risers


title: Valuation of Restricted Shares by Conflicting Shareholders in the Split Share Structure Reform

abstract: The recent Split Share Structure Reform launched by the government in the Chinese stock market terminates trading constraints on restricted shares. In exchange for the consent of freely traded shareholders, restricted shareholders offer them consideration mainly in the form of restricted shares. We estimate the implied discount of restricted shares to be 38.22% on average, which is in line with the empirical and theoretical findings in the literature, suggesting that the consideration is not systematically underpaid and the reform is fair at the market level. At the firm level, however, freely traded shareholders receive less consideration when their bargaining power is weaker. The impact of state shareholders on the size of consideration has been found to be non-monotonic. Consistent with the literature that state shareholders exaggerate the agency problem, they tend to exploit freely traded shareholders by offering less consideration when the latter's bargaining power is weaker. Meanwhile, state shareholders are under political pressure to carry out the reform as quickly as possible and to set a good example for other firms. They therefore refrain from offering underpaid consideration when their freely traded counterparts have strong bargaining power and are more capable of rejecting unfair schemes and substantially delaying the progress of the reform.


Investigation of certain varieties of carbopol in ketorolac tromethamine hydrophilic matrix tablet formulations and evaluation of the kinetics of its in vitm release


title: Distance-based Confidence Score for Neural Network Classifiers

abstract: The reliable measurement of confidence in classifiers' predictions is very important for many applications and is, therefore, an important part of classifier design. Yet, although deep learning has received tremendous attention in recent years, not much progress has been made in quantifying the prediction confidence of neural network classifiers. Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with prohibitive computational costs. In this paper we propose a simple, scalable method to achieve a reliable confidence score, based on the data embedding derived from the penultimate layer of the network. We investigate two ways to achieve desirable embeddings, by using either a distance-based loss or Adversarial Training. We then test the benefits of our method when used for classification error prediction, weighting an ensemble of classifiers, and novelty detection. In all tasks we show significant improvement over traditional, commonly used confidence scores.


title: CodeBlue: An ad hoc sensor network infrastructure for emergency medical care

abstract: Sensor devices integrating embedded processors, low-power, lowbandwidth radios, and a modest amount of storage have the potential to enhance emergency medical care. Wearable vital sign sensors can track patient status and location, while simultaneously operating as active tags. We introduce CodeBlue, a wireless infrastructure intended for deployment in emergency medical care, integrating low-power, wireless vital sign sensors, PDAs, and PC-class systems. CodeBlue will enhance first responders’ ability to assess patients on scene, ensure seamless transfer of data among caregivers, and facilitate efficient allocation of hospital resources. Intended to scale to very dense networks with thousands of devices and extremely volatile network conditions, this infrastructure will support reliable, ad hoc data delivery, a flexible naming and discovery scheme, and a decentralized security model. This paper introduces our architecture and highlights research challenges being addressed by the CodeBlue


title: Feature extraction techniques for ultrasonic signal classification

abstract: In this paper, we present two feature extraction techniques for the classification of ultrasonic NDE signals acquired from weld inspection regions of boiling water reactor piping of nuclear power plants. The classification system consists of a pre-processing block that extracts features from the incoming patterns, and of an artificial neural network that assigns the computed features to a particular class of defect present on the inspected pipe. The two techniques are respectively based on the discrete Gabor transform (DGT) and on the discrete wavelet transform (DWT); a third feature extraction technique, based on the clustering of the wavelet coefficients, is also presented. The results carried out by artificial neural networks trained and tested using the described feature extraction techniques, demonstrate the usefulness of the clustered DWT method with respect to the well known techniques of DGT and DWT.


title: Torturing databases for fun and profit

abstract: Programmers use databases when they want a high level of reliability. Specifically, they want the sophisticated ACID (atomicity, consistency, isolation, and durability) protection modern databases provide. However, the ACID properties are far from trivial to provide, particularly when high performance must be achieved. This leads to complex and error-prone code--even at a low defect rate of one bug per thousand lines, the millions of lines of code in a commercial OLTP database can harbor thousands of bugs. ::: ::: Here we propose a method to expose and diagnose violations of the ACID properties. We focus on an ostensibly easy case: power faults. Our framework includes workloads to exercise the ACID guarantees, a record/replay subsystem to allow the controlled injection of simulated power faults, a ranking algorithm to prioritize where to fault based on our experience, and a multi-layer tracer to diagnose root causes. Using our framework, we study 8 widely-used databases, ranging from open-source key-value stores to high-end commercial OLTP servers. Surprisingly, all 8 databases exhibit erroneous behavior. For the open-source databases, we are able to diagnose the root causes using our tracer, and for the proprietary commercial databases we can reproducibly induce data loss.


title: Image Enhancement by Entropy Maximization and Quantization Resolution Upconversion

abstract: This article introduces a new contrast enhancement algorithm of tone-preserving entropy maximization. Its design objective is to present the maximal amount of information content in the enhanced image, or being optimal in an information theoretical sense, while preventing the loss of tone continuity. The resulting optimization problem can be graph-theoretically modeled as the construction of K-edge maximum-weight path, and it can be solved efficiently by dynamic programming. Moreover, the proposed algorithm is made more effective by being combined with a preprocess of image restoration that aims to correct quantization errors caused by the analog-to-digital conversion of image signals. Empirical evidences are provided to demonstrate the superior visual quality obtained by the new image enhancement algorithm.


title: Disjunctive Boolean Kernels for Collaborative Filtering in Top-N Recommendation

abstract: In many personalized recommendation problems available data consists only of positive interactions (implicit feedback) between users and items. This problem is also known as One-Class Collaborative Filtering (OC-CF). Linear models usually achieves state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to improve the recommendations but with no much success. Recent analysis shows that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings. In this paper we propose a boolean kernel, called Disjunctive Kernel, which is less expressive than the linear one but it is able to alleviate the sparsity issue in CF contexts. The embedding of this kernel is composed by all the combinations of a certain degree $d$ of the input variables, and these combined features are semantically interpreted as disjunctions of the input variables. Experiments on several CF datasets show the effectiveness and the efficiency of the proposed kernel.


title: Female Genital Tract Cysts

abstract: Cystic diseases in the female pelvis are common. Cysts of the female genital tract comprise a large number of physiologic and pathologic cysts. The majority of cystic pelvic masses originate in the ovary, and they can range from simple, functional cysts to malignant ovarian tumors. Non-ovarian cysts of female genital system are appeared at least as often as ovarian cysts. In this review, we aimed to discuss the most common cystic lesions the female genital system.


title: Automatic trimap generation for image matting

abstract: Image matting is a longstanding problem in computational photography. Although, it has been studied for more than two decades, yet there is a challenge of developing an automatic matting algorithm which does not require any human efforts. Most of the state-of-the-art matting algorithms require human intervention in the form of trimap or scribbles to generate the alpha matte form the input image. In this paper, we present a simple and efficient approach to automatically generate the trimap from the input image and make the whole matting process free from human-in-the-loop. We use learning based matting method to generate the matte from the automatically generated trimap. Experimental results demonstrate that our method produces good quality trimap which results into accurate matte estimation. We validate our results by replacing the automatically generated trimap by manually created trimap while using the same image matting algorithm.


title: Patterns of intelligent and mobile agents

abstract: Almost all agent development to date has been “homegrown” [4] and done from scratch, independently, byeach development team. This has led to the followingproblems:• Lack of an agreed definition: Agents built bydifferent teams have different capabilities.• Duplication of effort: There has been little reuse ofagent architectures, designs, or components.• Inability to satisfy industrial strengthrequirements: Agents must integrate with existingsoftware and computer infrastructure. They must alsoaddress security and scaling concerns.Agents are complex and ambitious software systems thatwill be entrusted with critical applications. As such,agent based systems must be engineered with validsoftware engineering principles and not constructed in anad hoc fashion.Agent systems must have a strong foundation based onmasterful software patterns. Software patterns arose outof Alexander’s [2] work in architecture and urbanplanning. Many urban plans and architectures aregrandiose and ill-fated. Overly ambitious agent basedsystems built in an ad hoc fashion risk the same fate.They may never be built, or, due to their fragile nature,they may be built and either never used or used once andthen abandoned. A software pattern is a recurringproblem and solution; it may address conceptual,architectural or design problems.A pattern is described in a set format to ease itsdissemination. The format states the problem addressedby the pattern and the forces acting on it. There is also acontext that must be present for the pattern to be valid, astatement of the solution, and any known uses. Thefollowing sections summarize some key patterns of agentbased systems; for brevity, many of the patterns arepresented in an abbreviated “patlet” form. When kn ownuses are not listed for an individual pattern, it means thatthe pattern has arisen from the JAFIMA activity. Thepatterns presented in this paper represent progress towarda pattern language or living methodology for intelligentand mobile agents.


title: Planar Internal LTE/WWAN Monopole Antenna for Tablet Computer Application

abstract: By using a T-shaped parasitic shorted strip, this work presents a planar coupled-fed U-shaped monopole antenna with eight-band LTE/WWAN operation in tablet computers. The obtained impedance bandwidths across dual operating bands approach 281 MHz and 1088 MHz at the LTE and WWAN bands, respectively. The proposed uniplanar antenna reduces the antenna size by at least 20% since the overall antenna size is only 50×15×0.8 mm3. The measured peak gains and antenna efficiencies are approximately 2.9/5.4 dBi and 72/80% for the LTE/WWAN bands, respectively. Moreover, with a compact structure, the proposed planar antenna can be deposited inside the tablet computer and complies with the body specific absorption rate (SAR) requirement (1.6 W/kg for 1-g body tissue) for practical applications.


title: Low-power implementation of the Bluetooth subband audio codec

abstract: This paper describes a low-power implementation of the Bluetooth subband codec (SBC) for high-fidelity wireless audio. The design uses a configurable weighted overlap-add (WOLA) filterbank coprocessor to implement the analysis and synthesis filterbanks. A new method to convert the two-times over-sampled, complex WOLA subband signals to equivalent critically sampled, real-valued SBC subband signals is presented. The WOLA coprocessor allows for an efficient parallel implementation of the filterbank and quantization portions of the SBC algorithm. Details of the overall system design are also presented, including measurements of power consumption and resource requirements. The final real-time, fixed-point implementation is compared to an off-line floating-point reference and found to produce no audible difference in decoded signal quality.


title: DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images

abstract: Understanding fashion images has been advanced by benchmarks with rich annotations such as DeepFashion, whose labels include clothing categories, landmarks, and consumer-commercial image pairs. However, DeepFashion has nonnegligible issues such as single clothing-item per image, sparse landmarks (4∼8 only), and no per-pixel masks, making it had significant gap from real-world scenarios. We fill in the gap by presenting DeepFashion2 to address these issues. It is a versatile benchmark of four tasks including clothes detection, pose estimation, segmentation, and retrieval. It has 801K clothing items where each item has rich annotations such as style, scale, view- point, occlusion, bounding box, dense landmarks (e.g. 39 for ‘long sleeve outwear’ and 15 for ‘vest’), and masks. There are also 873K Commercial-Consumer clothes pairs. The annotations of DeepFashion2 are much larger than its counterparts such as 8× of FashionAI Global Challenge. A strong baseline is proposed, called Match R- CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner. Extensive evaluations are conducted with different criterions in Deep- Fashion2. DeepFashion2 Dataset will be released at : https://github.com/switchablenorms/DeepFashion2


title: Robust hand detection

abstract: Vision-based hand gesture interfaces require fast and extremely robust hand detection. Here, we study view-specific hand posture detection with an object recognition method proposed by Viola and Jones. Training with this method is computationally very expensive, prohibiting the evaluation of many hand appearances for their suitability to detection. In this paper, we present a frequency analysis-based method for instantaneous estimation of class separability, without the need for any training. We built detectors for the most promising candidates, their receiver operating characteristics confirming the estimates. Next, we found that classification accuracy increases with a more expressive feature type. Lastly, we show that further optimization of training parameters yields additional detection rate improvements. In summary, we present a systematic approach to building an extremely robust hand appearance detector, providing an important step towards easily deployable and reliable vision-based hand gesture interfaces.


title: A random-effects regression model for meta-analysis.

abstract: Many meta-analyses use a random-effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random-effects regression approach for the synthesis of 2 x 2 tables allows the inclusion of covariates that may explain heterogeneity. A simulation study found that the random-effects regression method performs well in the context of a meta-analysis of the efficacy of a vaccine for the prevention of tuberculosis, where certain factors are thought to modify vaccine efficacy. A smoothed estimator of the within-study variances produced less bias in the estimated regression coefficients. The method provided very good power for detecting a non-zero intercept term (representing overall treatment efficacy) but low power for detecting a weak covariate in a meta-analysis of 10 studies. We illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy. The model also applies to the meta-analysis of continuous outcomes when covariates are present.


title: Investigating Cybersecurity Issues In Active Traffic Management Systems

abstract: Active Traffic Management (ATM) systems have been introduced by transportation agencies to manage recurrent and non-recurrent congestion. ATM systems rely on the interconnectivity of components made possible by wired and/or wireless networks. Unfortunately, this connectivity that supports ATM systems also provides potential system access points that results in vulnerability to cyberattacks. This is becoming more pronounced as ATM systems begin to integrate internet of things (IoT) devices. Hence, there is a need to rigorously evaluate ATM systems for cyberattack vulnerabilities, and explore design concepts that provide stability and graceful degradation in the face of cyberattacks. In this research, a prototype ATM system along with a real-time cyberattack monitoring system were developed for a 1.5-mile section of I-66 in Northern Virginia. The monitoring system detects deviation from expected operation of an ATM system by comparing lane control states generated by the ATM system with lane control states deemed most likely by the monitoring system. In case of any deviation between two sets of states, the monitoring system displays the lane control states generated by the back-up data source. In a simulation experiment, the prototype ATM system and cyberattack monitoring system were subject to emulated cyberattacks. The evaluation results showed that when subject to cyberattack, the mean speed reduced by 15% compared to the case with the ATM system and was similar to the baseline case. This illustrates that the effectiveness of the ATM system was negated by cyberattacks. The monitoring system however, allowed the ATM system to revert to an expected safe state and reduced the negative impact of cyberattacks. These results illustrate the need to revisit ATM system design concepts as a means to protect against cyberattacks in addition to traditional system intrusion prevention approaches.


title: Evaluating co-occurrence order for automatic thesaurus construction

abstract: Identifying the best semantically similar terms in an automatic thesaurus construction task is still an open problem in natural language processing. Many methods have been proposed to solve this problem. In this work we present a comparison between three corpus based methods for automatically build thesaurus. These methods look for related terms using the relations between terms and they differ among themselves in the co-occurrences order of these relations. The evaluation process was carried out by domain specialists who evaluated the related terms generated by each method in an experiment applied to the data privacy domain.


title: Deep Convolutional Neural Networks for efficient vision based tunnel inspection

abstract: The inspection, assessment, maintenance and safe operation of the existing civil infrastructure consists one of the major challenges facing engineers today. Such work requires either manual approaches, which are slow and yield subjective results, or automated approaches, which depend upon complex handcrafted features. Yet, for the latter case, it is rarely known in advance which features are important for the problem at hand. In this paper, we propose a fully automated tunnel assessment approach; using the raw input from a single monocular camera we hierarchically construct complex features, exploiting the advantages of deep learning architectures. Obtained features are used to train an appropriate defect detector. In particular, we exploit a Convolutional Neural Network to construct high-level features and as a detector we choose to use a Multi-Layer Perceptron due to its global function approximation properties. Such an approach achieves very fast predictions due to the feedforward nature of Convolutional Neural Networks and Multi-Layer Perceptrons.


title: Wideband crossed-guide waveguide directional couplers

abstract: Crossed-guide X-band waveguide couplers with bandwidths of up to 40% and coupling factors of better than 5 dB are presented. The tight coupling and wide bandwidth are achieved by using reduced height waveguide. Design graphs and measured data are presented.


title: Spatial Competition in Retail Markets: Movie Theaters

abstract: Retail markets are extremely important, but economists have few practical tools for analyzing the way dispersed buyers and sellers affect the properties of markets. I develop an econometric model of retail demand in which products are location specific and consumers have preferences over both geographic proximity and other store and product characteristics. The model uses data on the observed geographic distribution of consumers within a market to (1) help explain observed variation in market shares and (2) affect predicted substitution patterns between stores. Using data from the U.S. cinema industry, I use the estimated model to evaluate the form of consumer transport costs, the effect of a theater's price and quality choices on rivals, the effects of geographic differentiation, and the nature and extent of market power.


title: Blews: Using blogs to provide context for news articles

abstract: An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article’s social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harnessing implicit and explicit contextual information from the blogosphere. Specifically, we track thousands of blogs and the news articles they cite, collapsing news articles that have highly overlapping content. We then tag each article with the number of blogs citing it, the political orientation of those blogs, and the level of emotional charge expressed in the blog posts that link to the news article. We summarize and present the results to the user via a novel visualization which displays this contextual information; the user can then find the most popular articles, the articles most cited by liberals, the articles most emotionally discussed in the political blogosphere, etc.


Graph-Based Natural Language Processing and Information Retrieval: Language Networks


title: Complex water management in modern agriculture: Trends in the water-energy-food nexus over the High Plains Aquifer

abstract: In modern agriculture, the interplay between complex physical, agricultural, and socioeconomic water use drivers must be fully understood to successfully manage water supplies on extended timescales. This is particularly evident across large portions of the High Plains Aquifer where groundwater levels have declined at unsustainable rates despite improvements in both the efficiency of water use and water productivity in agricultural practices. Improved technology and land use practices have not mitigated groundwater level declines, thus water management strategies must adapt accordingly or risk further resource loss. In this study, we analyze the water-energy-food nexus over the High Plains Aquifer as a framework to isolate the major drivers that have shaped the history, and will direct the future, of water use in modern agriculture. Based on this analysis, we conclude that future water management strategies can benefit from: (1) prioritizing farmer profit to encourage decision-making that aligns with strategic objectives, (2) management of water as both an input into the water-energy-food nexus and a key incentive for farmers, (3) adaptive frameworks that allow for short-term objectives within long-term goals, (4) innovative strategies that fit within restrictive political frameworks, (5) reduced production risks to aid farmer decision-making, and (6) increasing the political desire to conserve valuable water resources. This research sets the foundation to address water management as a function of complex decision-making trends linked to the water-energy-food nexus. Water management strategy recommendations are made based on the objective of balancing farmer profit and conserving water resources to ensure future agricultural production.


title: Nonparametric Canonical Correlation Analysis

abstract: Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections among families of functions, which the user specifies (by choosing a kernel or neural network structure), and are computationally demanding. Interestingly, the theory of nonlinear CCA, without functional restrictions, had been studied in the population setting by Lancaster already in the 1950s, but these results have not inspired practical algorithms. We revisit Lancaster's theory to devise a practical algorithm for nonparametric CCA (NCCA). Specifically, we show that the solution can be expressed in terms of the singular value decomposition of a certain operator associated with the joint density of the views. Thus, by estimating the population density from data, NCCA reduces to solving an eigenvalue system, superficially like kernel CCA but, importantly, without requiring the inversion of any kernel matrix. We also derive a partially linear CCA (PLCCA) variant in which one of the views undergoes a linear projection while the other is nonparametric. Using a kernel density estimate based on a small number of nearest neighbors, our NCCA and PLCCA algorithms are memory-efficient, often run much faster, and perform better than kernel CCA and comparable to deep CCA.


title: SDPA: Enhancing Stateful Forwarding for Software-Defined Networking

abstract: As the prevailing technique of Software-Defined Networking (SDN), OpenFlow introduces significant programmability, granularity and flexibility for many network applications to effectively manage and process network flows. However, OpenFlow only provides a simple "match-action" paradigm and lacks the function of stateful forwarding for SDN data plane, which limits it to support advanced network applications. Heavily relying on SDN controllers for all state maintenance incurs both scalability and performance issues. In this paper, we propose a novel Stateful Data Plane Architecture (SDPA) for SDN data plane. A co-processing unit, Forwarding Processor (FP), is designed for SDN switches to manage state information through new instructions and state tables. We design and implement an extended OpenFlow protocol to implement the communication between the controller and FP. To demonstrate the practicality and feasibility of our approach, we implement both software and hardware prototypes of SDPA switches, and develop a sample network function chain with stateful firewall, DNS reflection attack defense and NAT applications in one SDPA-based switch. Experimental results show that the SDPA architecture can effectively improve the forwarding efficiency with manageable processing overhead for those applications that need stateful forwarding in SDN-based networks.


title: Posterosuperior and anterosuperior impingement of the shoulder in overhead athletes—evolving concepts

abstract: During throwing motion the athlete puts enormous stress on both the dynamic and the static stabilisers of the shoulder. Repetitive forces cause adaptive soft tissue and bone changes that initially improve performance but ultimately may lead to shoulder pathologies. Although a broad range of theories have been suggested for the pathophysiology of internal impingement, the reasons are obviously multifactorial. This review aims to critically analyse the current literature and to summarise clinically important information. The cardinal lesions of internal impingement, articular-sided rotator cuff tears and posterosuperior labral lesions, have been shown to occur in association with a number of other findings, most importantly glenohumeral internal rotation deficit and SICK scapula syndrome, but also with posterior humeral head lesions, posterior glenoid bony injury and, rarely, with Bankart and inferior glenohumeral ligament lesions. Extensive biomechanical and clinical research is necessary before a complete understanding and reconciliation of the varying theories of the pathomechanisms of injury can be developed.


title: Reproducible network experiments using container-based emulation

abstract: In an ideal world, all research papers would be runnable: simply click to replicate all results, using the same setup as the authors. One approach to enable runnable network systems papers is Container-Based Emulation (CBE), where an environment of virtual hosts, switches, and links runs on a modern multicore server, using real application and kernel code with software-emulated network elements. CBE combines many of the best features of software simulators and hardware testbeds, but its performance fidelity is unproven. In this paper, we put CBE to the test, using our prototype, Mininet-HiFi, to reproduce key results from published network experiments such as DCTCP, Hedera, and router buffer sizing. We report lessons learned from a graduate networking class at Stanford, where 37 students used our platform to replicate 18 published results of their own choosing. Our experiences suggest that CBE makes research results easier to reproduce and build upon.


title: A Bayesian Mixed Effects Model of Literary Character

abstract: We consider the problem of automatically inferring latent character types in a collection of 15,099 English novels published between 1700 and 1899. Unlike prior work in which character types are assumed responsible for probabilistically generating all text associated with a character, we introduce a model that employs multiple effects to account for the influence of extra-linguistic information (such as author). In an empirical evaluation, we find that this method leads to improved agreement with the preregistered judgments of a literary scholar, complementing the results of alternative models.


title: An efficient local Chan-Vese model for image segmentation

abstract: In this paper, a new local Chan-Vese (LCV) model is proposed for image segmentation, which is built based on the techniques of curve evolution, local statistical function and level set method. The energy functional for the proposed model consists of three terms, i.e., global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. In addition, the time-consuming re-initialization step widely adopted in traditional level set methods can be avoided by introducing a new penalizing energy. To avoid the long iteration process for level set evolution, an efficient termination criterion is presented which is based on the length change of evolving curve. Particularly, we proposed constructing an extended structure tensor (EST) by adding the intensity information into the classical structure tensor for texture image segmentation. It can be found that by combining the EST with our LCV model, the texture image can be efficiently segmented no matter whether it presents intensity inhomogeneity or not. Finally, experiments on some synthetic and real images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with the well-known Chan-Vese (CV) model and recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times and be less sensitive to the location of initial contour and the selection of governing parameters.


title: Calfuzzer: An extensible active testing framework for concurrent programs

abstract: Active testing has recently been introduced to effectively test concurrent programs. Active testing works in two phases. It first uses predictive off-the-shelf static or dynamic program analyses to identify potential concurrency bugs, such as data races, deadlocks, and atomicity violations. In the second phase, active testing uses the reports from these predictive analyses to explicitly control the underlying scheduler of the concurrent program to accurately and quickly discover real concurrency bugs, if any, with very high probability and little overhead. In this paper, we present an extensible framework for active testing of Java programs. The framework currently implements three active testers based on data races, atomic blocks, and deadlocks.


title: Near-Optimal Equalizer and Timing Adaptation for I/O Links Using a BER-Based Metric

abstract: A new adaptation strategy of I/O link equalizers is presented based on minimizing the bit error rate (BER) as the objective function to maximize the receiver voltage margin. The adaptation strategy is verified in a 90-nm test chip on both the transmitter finite-impulse response filter (Tx-FIR) and the receiver decision-feedback equalizer (Rx-DFE). The performance is compared with the commonly used sign-sign least mean square (SS-LMS) adaptation and demonstrates significant improvements especially in the case of the Tx-FIR. This paper also demonstrates that in a highly attenuating system that contains both a Tx-FIR and Rx-DFE, using a Tx-FIR subject to peak output power constraint to compensate pre-cursor ISI is worse than solely using an Rx-DFE. The adaptation strategy is further applied to adapt the sampling phase of the clock-and-data recovery loop (CDR). The technique enables near-optimal BER performance by substantially reducing the pre-cursor ISI and requires almost no additional hardware compared to SS-LMS adaptation.


title: Video benchmarks of human action datasets: a review

abstract: Vision-based Human activity recognition is becoming a trendy area of research due to its wide application such as security and surveillance, human–computer interactions, patients monitoring system, and robotics. In the past two decades, there are several publically available human action, and activity datasets are reported based on modalities, view, actors, actions, and applications. The objective of this survey paper is to outline the different types of video datasets and highlights their merits and demerits under practical considerations. Based on the available information inside the dataset we can categorise these datasets into RGB (Red, Green, and Blue) and RGB-D(depth). The most prominent challenges involved in these datasets are occlusions, illumination variation, view variation, annotation, and fusion of modalities. The key specification of these datasets is discussed such as resolutions, frame rate, actions/actors, background, and application domain. We have also presented the state-of-the-art algorithms in a tabular form that give the best performance on such datasets. In comparison with earlier surveys, our works give a better presentation of datasets on the well-organised comparison, challenges, and latest evaluation technique on existing datasets.


THERMOMETER ENCODING: ONE HOT WAY TO RESIST


title: Soliciting the Patient's Agenda

abstract: ContextPrevious research indicates physicians frequently ::: choose a patient problem to explore before determining the patient's ::: full spectrum of concerns.ObjectiveTo examine the extent to which experienced family ::: physicians in various practice settings elicit the agenda of concerns ::: patients bring to the office.DesignA cross-sectional survey using linguistic analysis of a ::: convenience sample of 264 patient-physician interviews.Setting and ParticipantsPrimary care offices of 29 ::: board-certified family physicians practicing in rural Washington ::: (n=1; 3%), semirural Colorado (n=20; ::: 69%), and urban settings in the United States and Canada ::: (n=8; 27%). Nine participants had fellowship training ::: in communication skills and family counseling.Main Outcome MeasuresPatient-physician verbal interactions, ::: including physician solicitations of patient concerns, rate of ::: completion of patient responses, length of time for patient responses, ::: and frequency of late-arising patient concerns.ResultsPhysicians solicited patient concerns in 199 interviews ::: (75.4%). Patients' initial statements of concerns were completed in ::: 74 interviews (28.0%). Physicians redirected the patient's opening ::: statement after a mean of 23.1 seconds. Patients allowed to complete ::: their statement of concerns used only 6 seconds more on average than ::: those who were redirected before completion of concerns. Late-arising ::: concerns were more common when physicians did not solicit patient ::: concerns during the interview (34.9% vs 14.9%). Fellowship-trained ::: physicians were more likely to solicit patient concerns and allow ::: patients to complete their initial statement of concerns (44% vs ::: 22%).ConclusionsPhysicians often redirect patients' initial ::: descriptions of their concerns. Once redirected, the descriptions are ::: rarely completed. Consequences of incomplete initial descriptions ::: include late-arising concerns and missed opportunities to gather ::: potentially important patient data. Soliciting the patient's agenda ::: takes little time and can improve interview efficiency and yield ::: increased data.


title: The OO-Method approach for information systems modeling: from object-oriented conceptual modeling to automated programming

abstract: Abstract Current and future (conventional) notations used in Conceptual Modeling Techniques should have a precise (formal) semantics to provide a well-defined software development process, in order to go from specification to implementation in an automated way. To achieve this objective, the OO-method approach to Information Systems Modeling presented in this paper attempts to overcome the conventional (informal)/formal dichotomy by selecting the best ideas from both approaches. The OO-method makes a clear distinction between the problem space (centered on what the system is) and the solution space (centered on how it is implemented as a software product). It provides a precise, conventional graphical notation to obtain a system description at the problem space level, however this notation is strictly based on a formal OO specification language that determines the conceptual modeling constructs needed to obtain the system specification. An abstract execution model determines how to obtain the software representations corresponding to these conceptual modeling constructs. In this way, the final software product can be obtained in an automated way.


title: IONavi: An Indoor-Outdoor Navigation Service via Mobile Crowdsensing

abstract: The proliferation of mobile computing has prompted navigation to be one of the most attractive and promising applications. Conventional designs of navigation systems mainly focus on either indoor or outdoor navigation. However, people have a strong need for navigation from a large open indoor environment to an outdoor destination in real life. This article presents IONavi, a joint navigation solution, which can enable passengers to easily deploy indoor-outdoor navigation service for subway transportation systems in a crowdsourcing way. Any self-motivated passenger records and shares individual walking traces from a location inside a subway station to an uncertain outdoor destination within a given range, such as one kilometer. IONavi further extracts navigation traces from shared individual traces, each of which is not necessary to be accurate. A subsequent following user achieves indoor-outdoor navigation services by tracking a recommended navigation trace. Extensive experiments are conducted on a subway transportation system. The experimental results indicate that IONavi exhibits outstanding navigation performance from an uncertain location inside a subway station to an outdoor destination. Although IONavi is to enable indoor-outdoor navigation for subway transportation systems, the basic idea can naturally be extended to joint navigation from other open indoor environments to outdoor environments.


title: Automatic Extraction of Synonyms for German Particle Verbs from Parallel Data with Distributional Similarity as a Re-Ranking Feature

abstract: We present a method for the extraction of synonyms for German particle verbs based on a word-aligned German-English parallel corpus: by translating the particle verb to a pivot, which is then translated back, a set of synonym candidates can be extracted and ranked according to the respective translation probabilities. In order to deal with separated particle verbs, we apply re-ordering rules to the German part of the data. In our evaluation against a gold standard, we compare different pre-processing strategies (lemmatized vs. inflected forms) and introduce language model scores of synonym candidates in the context of the input particle verb as well as distributional similarity as additional re-ranking criteria. Our evaluation shows that distributional similarity as a re-ranking feature is more robust than language model scores and leads to an improved ranking of the synonym candidates. In addition to evaluating against a gold standard, we also present a small-scale manual evaluation.


title: Heapviz: interactive heap visualization for program understanding and debugging

abstract: Understanding the data structures in a program is crucial to understanding how the program works, or why it doesn't work. Inspecting the code that implements the data structures, however, is an arduous task and often fails to yield insights into the global organization of a program's data. Inspecting the actual contents of the heap solves these problems but presents a significant challenge of its own: finding an effective way to present the enormous number of objects it contains. In this paper we present Heapviz, a tool for visualizing and exploring snapshots of the heap obtained from a running Java program. Unlike existing tools, such as traditional debuggers, Heapviz presents a global view of the program state as a graph, together with powerful interactive capabilities for navigating it. Our tool employs several key techniques that help manage the scale of the data. First, we reduce the size and complexity of the graph by using algorithms inspired by static shape analysis to aggregate the nodes that make up a data structure. Second, we introduce a dominator-based layout scheme that emphasizes hierarchical containment and ownership relations. Finally, the interactive interface allows the user to expand and contract regions of the heap to modulate data structure detail, inspect individual objects and field values, and search for objects based on type or connectivity. By applying Heapviz to both constructed and real-world examples, we show that Heapviz provides programmers with a powerful and intuitive tool for exploring program behavior.


A Framework for Detection of Video Spam on YouTube


title: A Procedure for Placement of Standard-Cell VLSI Circuits

abstract: This paper describes a method of automatic placement for standard cells (polycells) that yields areas within 10-20 percent of careful hand placements. The method is based on graph partitioning to identify groups of modules that ought to be close to each other, and a technique for properly accounting for external connections at each level of partitioning. The placement procedure is in production use as part of an automated design system; it has been used in the design of more than 40 chips, in CMOS, NMOS, and bipolar technologies.