title: Heavy-Payload Omnidirectional Robot

abstract: This paper elaborates a Heavy-Payload Omnidirectional Robot (Hobot) that can efficiently carry heavy payloads on uneven indoor floors. It consists of four Steer-able Differential-Geared Dual-Wheels (SDD), composed a differential-geared dual-wheel, a passive tilt-adapting joint and a suspension washer. In order to combine heavy-payload and omnidirectionality, the mechanism is designed by means of evaluating its four qualitative criteria and two quantitative metrics. The criteria are ground contact, energy efficiency, type of omnidirectionality and internal stress distribution. The metrics are Support Force Isotropy (SFI) and Critically Loaded Mass (CLM). The feasibility of Hobot prototype was verified by omnidirectional motion experiments under heavy payloads. The results show that Hobot can efficiently carry a total mass of 2,130kg under speed of 0.3m/s using only an average power of 646W.


title: On the effectiveness of multi-variant program execution for vulnerability detection and prevention

abstract: Multi-variant program execution is an application of n-version programming, in which several slightly different instances of the same program are executed in lockstep on a multiprocessor. These variants are created in such a way that they behave identically under "normal" operation and diverge when "out of specification" events occur, which may be indicative of attacks. This paper assess the effectiveness of different code variation techniques to address different classes of vulnerabilities. In choosing a variant or combination of variants, security demands need to be balanced against runtime overhead. Our study indicates that a good combination of variations when running two variants is to choose one of instruction set randomization, system call number randomization, and register randomization, and use that together with library entry point randomization. Running more variants simultaneously makes it exponentially more difficult to take over the system.


title: Fringing Field Formulas and Winding Loss Due to an Air Gap

abstract: The paper describes a simple treatment for the fringing fields of an air gap in the core of a magnetic component such as an inductor or a transformer. It verifies the derived analytical formulas for the fields by using numerical (finite-element) calculations. It then applies these formulas to the calculation of high-frequency eddy-current losses for two types of winding arrangements, both of which employ thin rectangular conductors. The rectangular conductors are commonly used in flex circuit windings, printed circuit windings, and thin-film windings. The two types of winding configurations are flat and barrel wound. Each behaves in a different way as a function of the position of the conductor.


title: An overview of algae biofuel production and potential environmental impact.

abstract: Algae are among the most potentially significant sources of sustainable biofuels in the future of renewable energy. A feedstock with virtually unlimited applicability, algae can metabolize various waste streams (e.g., municipal wastewater, carbon dioxide from industrial flue gas) and produce products with a wide variety of compositions and uses. These products include lipids, which can be processed into biodiesel; carbohydrates, which can be processed into ethanol; and proteins, which can be used for human and animal consumption. Algae are commonly genetically engineered to allow for advantageous process modification or optimization. However, issues remain regarding human exposure to algae-derived toxins, allergens, and carcinogens from both existing and genetically modified organisms (GMOs), as well as the overall environmental impact of GMOs. A literature review was performed to highlight issues related to the growth and use of algal products for generating biofuels. Human exposure and environmental impact issues are identified and discussed, as well as current research and development activities of academic, commercial, and governmental groups. It is hoped that the ideas contained in this paper will increase environmental awareness of issues surrounding the production of algae and will help the algae industry develop to its full potential.


title: Disco: Discover Your Processes

abstract: Disco is a complete process mining toolkit from Fluxicon that makes process mining fast, easy, and simply fun.


Random Number Generation


title: Prediction Reweighting for Domain Adaptation

abstract: There are plenty of classification methods that perform well when training and testing data are drawn from the same distribution. However, in real applications, this condition may be violated, which causes degradation of classification accuracy. Domain adaptation is an effective approach to address this problem. In this paper, we propose a general domain adaptation framework from the perspective of prediction reweighting, from which a novel approach is derived. Different from the major domain adaptation methods, our idea is to reweight predictions of the training classifier on testing data according to their signed distance to the domain separator, which is a classifier that distinguishes training data (from source domain) and testing data (from target domain). We then propagate the labels of target instances with larger weights to ones with smaller weights by introducing a manifold regularization method. It can be proved that our reweighting scheme effectively brings the source and target domains closer to each other in an appropriate sense, such that classification in target domain becomes easier. The proposed method can be implemented efficiently by a simple two-stage algorithm, and the target classifier has a closed-form solution. The effectiveness of our approach is verified by the experiments on artificial datasets and two standard benchmarks, a visual object recognition task and a cross-domain sentiment analysis of text. Experimental results demonstrate that our method is competitive with the state-of-the-art domain adaptation algorithms.


Large-scale refinement of digital historic newspapers with named entity recognition


title: A novel control strategy of inverter in distributed generation with seamless transfer capability

abstract: In order to improve power reliability of critical load, distributed generation (DG) should be with intentional islanding operation when utility is outage. This paper proposes a novel control strategy for three phase inverter in DG to realize transferring between grid-tied and islanding modes, which is with an inner inductor current loop and outer capacitor voltage loop in the synchronous rotating framework (SRF). When islanding happens, the drift of amplitude and frequency in load voltage can be restricted in the given range allowed by the utility. Finally, the proposed control strategy is verified by simulation and experimental results.


title: Anatomic variations of the pelvic floor nerves adjacent to the sacrospinous ligament: a female cadaver study

abstract: Our objective was to document variations in the topography of pelvic floor nerves (PFN) and describe a nerve-free zone adjacent to the sacrospinous ligament (SSL). Pelvic floor dissections were performed on 15 female cadavers. The course of the PFN was described in relation to the ischial spine (IS) and the SSL. The pudendal nerve (PN) passed medial to the IS and posterior to the SSL at a mean distance of 0.6 cm (SD = +/-0.4) in 80% of cadavers. In 40% of cadavers, an inferior rectal nerve (IRN) variant pierced the SSL at a distance of 1.9 cm (SD = +/-0.7) medial to the IS. The levator ani nerve (LAN), coursed over the superior surface of the SSL-coccygeus muscle complex at a mean distance of 2.5 cm (SD = +/-0.7) medial to the IS. Anatomic variations were found which challenge the classic description of PFN. A nerve-free zone is situated in the medial third of the SSL.


title: The Research of Internet of Things' Supporting Technologies Which Face the Logistics Industry

abstract: This paper base on the application of the Internet of things in the logistics industry as the breakthrough point, to investigate the identification technology, network structure, middleware technology support and so on, which is used in the Internet of things, also to analyze the bottleneck of technology that the Internet of things could meet. At last, summarize the Internet of things’ application in the logistics industry with the intelligent port architecture.


title: Framework of wargame CGF system based on multi-agent

abstract: Compared with other large-scale warfare simulation systems, wargame, as a traditional type of warfare simulations, has the advantages of low cost, convenience, practicability, etc. In order to realize Human-Computer Confrontation, Computer Generated Forces (CGF) technology has been added to wargame, which would enhance wargame's training and decision support ability. Based on computerization of “Future: Korea War” which is a strategic-level wargame, this paper presents a framework of wargame CGF system based on multi-agent and defines its Atomic Action Library. The framework and the Atomic Action Library provide the basic platform for farther research on the intelligent behaviors of each agent in wargame CGF system.


title: Development of deep learning-based facial expression recognition system

abstract: Deep learning is considered to be a breakthrough in the field of computer vision, since most of the world records of the recognition tasks are being broken. In this paper, we try to apply such deep learning techniques to recognizing facial expressions that represent human emotions. The procedure of our facial expression recognition system is as follows: First, face is detected from input image using Haar-like features. Second, the deep network is used for recognizing facial expression using detected faces. In this step, two different deep networks can be used such as deep neural network and convolutional neural network. Consequently, we compared experimentally two types of deep networks, and the convolutional neural network had better performance than deep neural network.


title: A Model-Driven Engineering Approach for ROS using Ontological Semantics

abstract: This paper presents a novel ontology-driven software engineering approach for the development of industrial robotics control software. It introduces the ReApp architecture that synthesizes model-driven engineering with semantic technologies to facilitate the development and reuse of ROS-based components and applications. In ReApp, we show how different ontological classification systems for hardware, software, and capabilities help developers in discovering suitable software components for their tasks and in applying them correctly. The proposed model-driven tooling enables developers to work at higher abstraction levels and fosters automatic code generation. It is underpinned by ontologies to minimize discontinuities in the development workflow, with an integrated development environment presenting a seamless interface to the user. First results show the viability and synergy of the selected approach when searching for or developing software with reuse in mind.


title: Locally Affine Sparse-to-Dense Matching for Motion and Occlusion Estimation

abstract: Estimating a dense correspondence field between successive video frames, under large displacement, is important in many visual learning and recognition tasks. We propose a novel sparse-to-dense matching method for motion field estimation and occlusion detection. As an alternative to the current coarse-to-fine approaches from the optical flow literature, we start from the higher level of sparse matching with rich appearance and geometric constraints collected over extended neighborhoods, using an occlusion aware, locally affine model. Then, we move towards the simpler, but denser classic flow field model, with an interpolation procedure that offers a natural transition between the sparse and the dense correspondence fields. We experimentally demonstrate that our appearance features and our complex geometric constraints permit the correct motion estimation even in difficult cases of large displacements and significant appearance changes. We also propose a novel classification method for occlusion detection that works in conjunction with the sparse-to-dense matching model. We validate our approach on the newly released Sintel dataset and obtain state-of-the-art results.


title: MODELING OF JITTER CHARACTERISTICS FOR THE SECOND ORDER BANG-BANG CDR

abstract: Bang-Bang clock and data recovery (BBCDR) circuits are hard nonlinear systems due to the nonlinearity introduced by the binary phase detector (BPD). The specification of the CDR frequency response is determined by jitter tolerance and jitter transfer. In this paper, jitter transfer and jitter tolerance of the second-order BBCDR are characterized by formulating the time domain waveforms. As a result, a new equation is presented to obtain corner frequency. Also, the jitter tolerance is expressed in closed form as a function of loop parameters. The proposed method is general enough to be used for designing BBCDR. The analysis is verified using behavioral simulations in MATLAB. Simulation results demonstrate the validity of the result obtained by analytical equations.


title: A Framework for Externalizing Implicit Error Using Visualization

abstract: This paper presents a framework for externalizing and analyzing expert knowledge about discrepancies in data through the use of visualization. Grounded in an 18-month design study with global health experts, the framework formalizes the notion of data discrepancies as implicit error , both in global health data and more broadly. We use the term implicit error to describe measurement error that is inherent to and pervasive throughout a dataset, but that isn't explicitly accounted for or defined. Instead, implicit error exists in the minds of experts, is mainly qualitative, and is accounted for subjectively during expert interpretation of the data. Externalizing knowledge surrounding implicit error can assist in synchronizing, validating, and enhancing interpretation, and can inform error analysis and mitigation. The framework consists of a description of implicit error components that are important for downstream analysis, along with a process model for externalizing and analyzing implicit error using visualization. As a second contribution, we provide a rich, reflective, and verifiable description of our research process as an exemplar summary toward the ongoing inquiry into ways of increasing the validity and transferability of design study research.


title: Coherent Path Tracing

abstract: Packet tracing is a popular and efficient method for accelerating ray tracing. However, packet traversal techniques become inefficient when they are applied to path tracing since the secondary rays are incoherent. In this paper, we present a simple technique for improving the coherency of secondary rays. This technique uses the same sequence of random numbers for generating secondary rays for all the pixels in each sample. This improves the efficiency of the packet tracing algorithm but creates structured noise patterns in the image. We propose an interleaved sampling scheme that reduces the correlation in the noise and makes it virtually imperceptible in the final image. Coherent path tracing is unbiased, simple to implement, and outperforms standard path tracing with packet tracing, while producing images with similar RMS error values.


title: A survey on area, production and productivity of Groundnut crop in India

abstract: This survey has been carried out to discuss the ground nut area, production and productivity in India, Andhra Pradesh State and Anantapuram district. It analyzed the area, production and productivity of groundnut crop at national level, state level and district level during 1996-2000 to 2001-2008. The present comparative analysis of groundnut production in A.P and in Anantapuram district during 1996-2000 to 2001- 2006. The groundnut crop area, production and productivity at national level, state level and Anantapuram district level of during 1996-2000 to 2001-2006 were collected and presented graphically. Based on the results collected some conclusions are made about the improving the production of groundnut crop.


title: Improvement of the on-body performance of a dual-band textile antenna using an EBG structure

abstract: A new dual-band wearable textile antenna designed for body-centric communications is introduced. The antenna is fully characterized in free space and on the body model, with and without an electromagnetic band gap (EBG) substrate. The band gap array consists of 3 × 3 elements and is used to reduce the interaction with human tissues. With the EBG back reflector, the radiation into the body is reduced by more than 15 dB. Increases of 5.2 dB and 3 dB gain are noticed at 2.45 GHz and 5.5 GHz, respectively. The results are presented for the return loss, radiation pattern, efficiency, and specific absorption rate (SAR).


title: Emerging topic detection using dictionary learning

abstract: Streaming user-generated content in the form of blogs, microblogs, forums, and multimedia sharing sites, provides a rich source of data from which invaluable information and insights maybe gleaned. Given the vast volume of such social media data being continually generated, one of the challenges is to automatically tease apart the emerging topics of discussion from the constant background chatter. Such emerging topics can be identified by the appearance of multiple posts on a unique subject matter, which is distinct from previous online discourse. We address the problem of identifying emerging topics through the use of dictionary learning. We propose a two stage approach respectively based on detection and clustering of novel user-generated content. We derive a scalable approach by using the alternating directions method to solve the resulting optimization problems. Empirical results show that our proposed approach is more effective than several baselines in detecting emerging topics in traditional news story and newsgroup data. We also demonstrate the practical application to social media analysis, based on a study on streaming data from Twitter.


title: Manual for Using Homomorphic Encryption for Bioinformatics

abstract: Biological data science is an emerging field facing multiple challenges for hosting, sharing, computing on, and interacting with large data sets. Privacy regulations and concerns about the risks of leaking sensitive personal health and genomic data add another layer of complexity to the problem. Recent advances in cryptography over the last five years have yielded a tool, homomorphic encryption, which can be used to encrypt data in such a way that storage can be outsourced to an untrusted cloud, and the data can be computed on in a meaningful way in encrypted form, without access to decryption keys. This paper introduces homomorphic encryption to the bioinformatics community, and presents an informal “manual” for using the Simple Encrypted Arithmetic Library (SEAL), which we have made publicly available for bioinformatic, genomic, and other research purposes.


title: QiLoc: A Qi wireless charging based system for robust user-initiated indoor location services

abstract: This paper presents the design and implementation of a novel user-initiated indoor localization system called QiLoc. QiLoc is a simple yet effective way to accurately locate and identify occupants of Qi-compatible devices inside buildings. The system is composed of QiLoc Stations and a QiLoc Server. A QiLoc Station is usually embedded inside or under a desk/table. By using the Qi wireless charging protocol, it can extract the unique ID of a charging device, thus locating the occupant. The QiLoc Server maintains the location information of all occupants, and provides a set of APIs via standard web services such as location, ad hoc group membership, and authentication. Using these primitives, QiLoc enables various smart home applications. We have deployed QiLoc in an office building and implemented a number of applications, including a smartphone app that identifies people in the same physical space, and a Windows-based notification system that provides live updates of colleagues' locations. We also demonstrate how QiLoc can enhance human interaction and productivity by integrating with industrial solutions like calendar, instant messaging, and email systems.


title: 3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold

abstract: Recognizing human actions in 3-D video sequences is an important open problem that is currently at the heart of many research domains including surveillance, natural interfaces and rehabilitation. However, the design and development of models for action recognition that are both accurate and efficient is a challenging task due to the variability of the human pose, clothing and appearance. In this paper, we propose a new framework to extract a compact representation of a human action captured through a depth sensor, and enable accurate action recognition. The proposed solution develops on fitting a human skeleton model to acquired data so as to represent the 3-D coordinates of the joints and their change over time as a trajectory in a suitable action space. Thanks to such a 3-D joint-based framework, the proposed solution is capable to capture both the shape and the dynamics of the human body, simultaneously. The action recognition problem is then formulated as the problem of computing the similarity between the shape of trajectories in a Riemannian manifold. Classification using k-nearest neighbors is finally performed on this manifold taking advantage of Riemannian geometry in the open curve shape space. Experiments are carried out on four representative benchmarks to demonstrate the potential of the proposed solution in terms of accuracy/latency for a low-latency action recognition. Comparative results with state-of-the-art methods are reported.


title: Analysis of the long jump technique in the transition from approach to takeoff based on time-continuous kinematic data

abstract: There are many studies on the biomechanics of the long jump, but few researchers have investigated how the athlete has to perform the last strides in order to prepare for takeoff. In this investigation, a pattern recognition approach was applied to analyze the movement structure during the last strides of the approach run and the jump. Time-continuous kinematic data of 57 trials (4.45-6.84 m) was analyzed. Cluster analysis identified at coarse level different movement patterns for each flight and support phase. Above these structural differences, individual movement patterns were diagnosed, especially for the jump. Further, the contribution of single variables on the differences of the complex movement patterns was determined by discriminant analysis. Based on the results, conclusions were drawn concerning the long jump and individuality in training. Overall, the applied pattern recognition method allows for the identification of structural changes of movement patterns as well as individual movement style...


title: A Randomized Algorithm for CCA

abstract: We present RandomizedCCA, a randomized algorithm for computing canonical analysis, suitable for large datasets stored either out of core or on a distributed file system. Accurate results can be obtained in as few as two data passes, which is relevant for distributed processing frameworks in which iteration is expensive (e.g., Hadoop). The strategy also provides an excellent initializer for standard iterative solutions.


title: A serious game model for cultural heritage

abstract: Serious games present a promising opportunity for learning, but the genre still lacks methodologies and tools for efficient and low-cost production, particularly for teacher and domain experts. This article gives an authoring framework that aims to provide structured support, from content design to final implementation. In particular, we have abstracted a conceptual model—the SandBox Serious Game - which relies on a generalization of task-based learning theory. The model invites players to perform cognitive tasks contextually while exploring information-rich virtual environments. We consider it particularly suited for cultural heritage entertainment applications. The model defines games that are set in realistic virtual worlds enriched with embedded educational tasks, which we have implemented as minigames. This approach simplifies the authoring work, which can easily be supported by visual authoring tools for ontology-based urban 3D modeling and implementation tasks, thus allowing an approach similar to the mind-maps concept. We propose a top-down methodology for content preparation, starting from a city-level analysis down to the single points of interest and associated tasks, which are instances of simple predefined minigame/quiz typologies. We provide examples and discuss criteria for selecting task typologies according to the authors' cognitive targets. Finally, we discuss the results of a user test, which took place in a lab, aimed at verifying the acquisition of cultural heritage knowledge in a pleasant and engaging way. Games appear particularly suited for supporting the study of images, especially of iconography. Compared to reading text, a game forces the player to focus more strongly on problems, which favors knowledge acquisition and retention. Learning complex concepts requires an investigative attitude, which can be spurred by well-designed games. Good design involves usability, graphic appeal, appropriate content, and the presence of connections which a player must discover in the content. Players should be asked to pay attention to and reason about their whole game activity - including the relationships between the game content, the brief introduction, and concluding texts. More comprehensive tests are needed to better investigate the educational effectiveness—however, the first results are promising, especially in terms of user motivation and creation of new opportunities for learning about CH.


title: Organizational characteristics as prescriptive factors of knowledge management initiatives

abstract: Purpose – The use of knowledge in organizations is largely a discretionary behavior that can be encouraged but not demanded. As such, the firm can only attempt to provide the right conditions for employees to endorse the role of knowledge workers. The purpose of this paper is to examine how the organization of the firm affects knowledge management.Design/methodology/approach – This research proposes a new framework showing the prescriptive role of organizational characteristics onto knowledge management (KM) initiatives. Based on this framework, data were generated from nine semi‐structured interviews conducted in the American, British and Japanese offices of a major Japanese pharmaceutical company, using a Boolean approach and qualitative content analysis.Findings – Organizational characteristics, specifically – structure, membership, relationship, and strategy affect KM, namely – knowledge acquisition, storage, diffusion, and application respectively.Research limitations/implications – Even though the d...


title: Continuous Probability Distribution Prediction of Image Emotions via Multitask Shared Sparse Regression

abstract: Previous works on image emotion analysis mainly focused on predicting the dominant emotion category or the average dimension values of an image for affective image classification and regression. However, this is often insufficient in various real-world applications, as the emotions that are evoked in viewers by an image are highly subjective and different. In this paper, we propose to predict the continuous probability distribution of image emotions which are represented in dimensional valence-arousal space. We carried out large-scale statistical analysis on the constructed Image-Emotion-Social-Net dataset, on which we observed that the emotion distribution can be well-modeled by a Gaussian mixture model. This model is estimated by an expectation-maximization algorithm with specified initializations. Then, we extract commonly used emotion features at different levels for each image. Finally, we formalize the emotion distribution prediction task as a shared sparse regression (SSR) problem and extend it to multitask settings, named multitask shared sparse regression (MTSSR), to explore the latent information between different prediction tasks. SSR and MTSSR are optimized by iteratively reweighted least squares. Experiments are conducted on the Image-Emotion-Social-Net dataset with comparisons to three alternative baselines. The quantitative results demonstrate the superiority of the proposed method.


title: AN EFFICIENT POWER CONTROL GAME MODEL FOR WIRELESS SENSOR NETWORKS

abstract: Efficient energy usage is a major design challenge in wireless sensor networks. In this paper, an efficient power control scheme that mitigates interference and reduces the energy usage of the sensor nodes in a wireless sensor network is presented using the game theory. A non-cooperative game was formulated among the sensor nodes in the modeled network by setting a transmission power limit at the receiving nodes which ensured that the transmitting nodes transmits at the optimal power level. The utility of the sensor nodes and the interference proportion within the network was evaluated at the optimal and discrete transmit powers. The Nash equilibrium of the proposed game was studied and it corresponds to a stability point where the network performance was optimized. Simulation results showed that the proposed scheme is effective for optimization of network resource utilization, reduction in the energy consumption of the nodes, increasing the transmission sum rate, reduction of interference within the network, and improving the network capacity. Keywords : Power control, Wireless sensor network, Non-cooperative game, Interference, Energy


title: Universal Matrix Completion

abstract: The problem of low-rank matrix completion has recently generated a lot of interest leading to several results that offer exact solutions to the problem. However, in order to do so, these methods make assumptions that can be quite restrictive in practice. More specifically, the methods assume that: a) the observed indices are sampled uniformly at random, and b) for every new matrix, the observed indices are sampled afresh. In this work, we address these issues by providing a universal recovery guarantee for matrix completion that works for a variety of sampling schemes. In particular, we show that if the set of sampled indices come from the edges of a bipartite graph with large spectral gap (i.e. gap between the first and the second singular value), then the nuclear norm minimization based method exactly recovers all low-rank matrices that satisfy certain incoherence properties. Moreover, we also show that under certain stricter incoherence conditions, $O(nr^2)$ uniformly sampled entries are enough to recover any rank-$r$ $n\times n$ matrix, in contrast to the $O(nr\log n)$ sample complexity required by other matrix completion algorithms as well as existing analyses of the nuclear norm method.


title: Detecting Android Malware Based on Extreme Learning Machine

abstract: To relieve increasingly prominent security issues of Android applications, static malware-detection techniques have become essential, due to their rapid and convenient detection processes which do not require running the detected applications. Most of current commercial anti-malware tools utilize signatures of known malicious Android codes for static detection, but are unable to find out unknown, especially newly created, malware. Many existing malware-detection researches rely on traditional machine learning techniques to analyze some static features of Android applications such as permissions and API calls, but the detection approaches still have room for improvement with respect to simplicity, effectiveness or efficiency. To overcome the limitations of the above detection techniques, we propose a novel static approach to detect malicious Android applications by proposing a set of Android program features, consisting of sensitive permissions and sensitive API calls, and by utilizing Extreme Learning Machine. We implemented our approach with an automated testing tool called WaffleDetector. Controlled experiments have been conducted to compare our approach and the existing ones on detecting malicious Android applications, and the results show that our approach excels the existing ones with minimal human intervention, better detection effectiveness and less detection time.


title: Development and evaluation of interactive humanoid robots

abstract: We report the development and evaluation of a new interactive humanoid robot that communicates with humans and is designed to participate in human society as a partner. A human-like body will provide an abundance of nonverbal information and enable us to smoothly communicate with the robot. To achieve this, we developed a humanoid robot that autonomously interacts with humans by speaking and gesturing. Interaction achieved through a large number of interactive behaviors, which are developed by using a visualizing tool for understanding the developed complex system. Each interactive behavior is designed by using knowledge obtained through cognitive experiments and implemented by using situated recognition. The robot is used as a testbed for studying embodied communication. Our strategy is to analyze human-robot interaction in terms of body movements using a motion-capturing system that allows us to measure the body movements in detail. We performed experiments to compare the body movements with subjective evaluation based on a psychological method. The results reveal the importance of well-coordinated behaviors as well as the performance of the developed interactive behaviors and suggest a new analytical approach to human-robot interaction.


title: Polarization Reconfigurable Eccentric Annular Ring Slot Antenna Design

abstract: A microstrip-fed reconfigurable eccentric annular ring slot (EARS) antenna with switchable polarization is proposed. To generate orthogonal circular polarization (CP) radiations, two identical perturbation slots (PSs) are loaded below the eccentric ring-shaped slot. By incorporating a PIN diode switch across each PS, the proposed antenna can switch between left-hand CP (LHCP), right-hand CP (RHCP), and linear polarization (LP). All three polarizations have shown good impedance bandwidth and broad CP bandwidth that can cover the WLAN 2.4 GHz operating band (2400–2480 MHz). Furthermore, desirable gains of 2.49–2.82 dBi and 2.94–3.24 dBic are also exhibited at LP and LHCP/RHCP, respectively.


title: Reflector Antenna Developments: A Perspective on the Past, Present and Future

abstract: i»?Reflector antennas confine most of the electromagnetic energy captured over their apertures into a focal plane or redirect the radiated field of the feed into far field. This paper presents a concise history of reflector antenna developments over an extended time span. Representative examples are provided for different periods that impacted various developments of reflector antennas covering past, present, and future. Due to page limitations, not all worldwide aspects of reflector antenna developments are touched upon in this paper, and the authors have confined themselves to the areas that have influenced their research activities.


title: Industrial Wireless Monitoring with Energy-Harvesting Devices

abstract: Vibration monitoring and analysis techniques are used increasingly for predictive maintenance. While traditional vibration monitoring relies on wired sensor networks, recent industrial technologies such as WirelessHART, ISA100.11a, and IEEE 802.15.4e have brought a paradigm shift in the automation sector by integrating the flexibility of wireless technologies with the versatility of Internet tools. However, these wireless technologies aren't designed to support strict resource constraint devices, and thus are still unable to fulfill the needs of many modern industrial applications. This article describes a new industrial wireless monitoring system capable of supporting energy-harvested devices through intelligent sensing and network management.


title: Swiss rectifier — A novel three-phase buck-type PFC topology for Electric Vehicle battery charging

abstract: This paper introduces a novel three-phase buck-type unity power factor rectifier appropriate for high power Electric Vehicle battery charging mains interfaces. The characteristics of the converter, named the Swiss Rectifier, including the principle of operation, modulation strategy, suitable control structure, and dimensioning equations are described in detail. Additionally, the proposed rectifier is compared to a conventional 6-switch buck-type ac-dc power conversion. According to the results, the Swiss Rectifier is the topology of choice for a buck-type PFC. Finally, the feasibility of the Swiss Rectifier concept for buck-type rectifier applications is demonstrated by means of a hardware prototype.


title: A Walk with SGD

abstract: We present novel empirical observations regarding how stochastic gradient descent (SGD) navigates the loss landscape of over-parametrized deep neural networks (DNNs). These observations expose the qualitatively different roles of learning rate and batch-size in DNN optimization and generalization. Specifically we study the DNN loss surface along the trajectory of SGD by interpolating the loss surface between parameters from consecutive \textit{iterations} and tracking various metrics during training. We find that the loss interpolation between parameters before and after each training iteration's update is roughly convex with a minimum (\textit{valley floor}) in between for most of the training. Based on this and other metrics, we deduce that for most of the training update steps, SGD moves in valley like regions of the loss surface by jumping from one valley wall to another at a height above the valley floor. This 'bouncing between walls at a height' mechanism helps SGD traverse larger distance for small batch sizes and large learning rates which we find play qualitatively different roles in the dynamics. While a large learning rate maintains a large height from the valley floor, a small batch size injects noise facilitating exploration. We find this mechanism is crucial for generalization because the valley floor has barriers and this exploration above the valley floor allows SGD to quickly travel far away from the initialization point (without being affected by barriers) and find flatter regions, corresponding to better generalization.


title: Male genital self-mutilation: a systematic review of psychiatric disorders and psychosocial factors.

abstract: OBJECTIVE ::: To identify psychiatric diagnoses and psychosocial factors associated with intentional male genital self-mutilation (GSM) of specific injury subtypes. ::: ::: ::: METHODS ::: A search of MEDLINE, EMBASE, PsycINFO, PubMed, Web of Science and CINAHL for cases of GSM was conducted until December 2015, based on GSM and related terms. Cases were examined for injury subtype, psychiatric diagnosis and psychosocial factors. Chi-square analyses were employed to determine differences in frequency of such factors across injury subtypes. ::: ::: ::: RESULTS ::: Data were obtained from 173 cases: genital mutilation (n=21), penile amputation (n=62), castration (n=56) and combined amputation/castration (n=34). Common psychiatric disorders included schizophrenia spectrum (49%), substance use (18.5%), personality (15.9%) and gender dysphoric disorders (15.3%). Chi-square analyses revealed that schizophrenia spectrum disorders occurred significantly more often among auto-amputates as compared with self-castrators or mutilators. Gender dysphoria occurred significantly more often among self-castrators than auto-amputates. No significant differences emerged regarding psychosocial factors across GSM subtypes. However, associations were observed between psychosocial factors and psychiatric diagnoses. Although altogether not commonly reported, experiential factors were reported in 82% of psychotic individuals. Treatment inaccessibility was noted among 71% of gender dysphorics engaging in auto-castration. ::: ::: ::: CONCLUSION ::: Clinicians must consider the diverse range of psychiatric disorders and psychosocial factors underlying GSM.


title: Federated Multi-Task Learning

abstract: Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA, that is robust to practical systems issues. Our method and theory for the first time consider issues of high communication cost, stragglers, and fault tolerance for distributed multi-task learning. The resulting method achieves significant speedups compared to alternatives in the federated setting, as we demonstrate through simulations on real-world federated datasets.


title: Elevated Free Thyroxine Levels Are Associated with Poorer Overall Survival in Patients with Gastroesophageal Cancer: A Retrospective Single Center Analysis

abstract: As endocrinological parameters such as thyroid hormones modulate proliferative, metabolic, and angiogenic pathways, it is surmised that their levels can be associated with cancer development and progression. Most patients with gastroesophageal cancer are diagnosed very late and have a poor prognosis, yet the association with endocrinological parameters has not been addressed so far. The aim of this study was to correlate hormones with the outcome, so new prognostic and potentially therapeutic markers can be defined. We analyzed clinical and endocrinological parameters including history of thyroid disorders and laboratory analyses of thyroid hormones and correlated these with the overall survival in a large European cohort of patients with inoperable locally advanced or metastatic gastroesophageal cancer treated between 2002 and 2018 at the Vienna General Hospital, Austria. In total, the survival outcome of 258 patients was evaluated. Higher levels of fT4 (p = 0.041, HR = 2.202) and lower levels of T3 (p = 0,003, HR = 0,141) were associated with significantly shorter survival. However, the overall survival of patients with known thyroid disorders did not differ significantly from euthyroid patients (euthyroid, 283 days; hyperthyroid, 354 days; hypothyroid, 284 days; p = 0.472). Elevated fT4 levels are associated with poorer overall survival of patients with gastroesophageal cancer in advanced stages. Since data on the correlation of endocrinological parameters and gastroesophageal cancer are scarce, this analysis is an important impulse for further studies concerning the impact of thyroxine on patients with cancer of the upper GI tract.


title: A review of natural language processing techniques for opinion mining systems

abstract: Abstract As the prevalence of social media on the Internet, opinion mining has become an essential approach to analyzing so many data. Various applications appear in a wide range of industrial domains. Meanwhile, opinions have diverse expressions which bring along research challenges. Both of the practical demands and research challenges make opinion mining an active research area in recent years. In this paper, we present a review of Natural Language Processing (NLP) techniques for opinion mining. First, we introduce general NLP techniques which are required for text preprocessing. Second, we investigate the approaches of opinion mining for different levels and situations. Then we introduce comparative opinion mining and deep learning approaches for opinion mining. Opinion summarization and advanced topics are introduced later. Finally, we discuss some challenges and open problems related to opinion mining.


title: Comparative performance study of several pitch detection algorithms

abstract: A comparative performance study of five pitch detection algorithms was conducted. A speech data base, consisting of eight utterances spoken by three males, three females, and one child was constructed. Both telephone and wideband recordings were made of each of the utterances. For each of the utterances in the data base a “standard” pitch contour was semiautomatically measured using a highly sophistocated interactive pitch detection program. The “standard” pitch contour was then compared with the pitch contour that was obtained from each of the five programmed pitch detectors. The algorithms used in this study were (1) a center clipping, infinite‐peak clipping, modified autocorrelation method; (2) the cepstral method; (3) the SIFT method; (4) the parallel processing time domain method; and (5) the data reduction method. A set of measurements were made on the pitch contours to quantify the various types of errors which occur in each of the above methods. Included among the error measurements were the avera...


Secondary Batteries : Lead Acid Battery Thermal Runaway Chapter Heading for Encyclopedia of Electrochemical Power Sources


DIRT - Discovery of Inference Rules from Text


title: On the Differential Privacy of Bayesian Inference

abstract: We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on probabilistic graphical models. These include two mechanisms for adding noise to the Bayesian updates, either directly to the posterior parameters, or to their Fourier transform so as to preserve update consistency. We also utilise a recently introduced posterior sampling mechanism, for which we prove bounds for the specific but general case of discrete Bayesian networks; and we introduce a maximum-a-posteriori private mechanism. Our analysis includes utility and privacy bounds, with a novel focus on the influence of graph structure on privacy. Worked examples and experiments with Bayesian naive Bayes and Bayesian linear regression illustrate the application of our mechanisms.


Resting state networks and consciousness Alterations of multiple resting state network connectivity in physiological , pharmacological , and pathological consciousness states


title: Prevalence, morphology, and molecular analysis of Serrasentis sagittifer (Acanthocephala: Palaeacanthocephala: Rhadinorhynchidae), a parasite of the gilthead Sea bream Sparus aurata (Sparidae)

abstract: Seventy specimens of the gilthead sea bream Sparus aurata of the Red Sea were collected during the period from March to November 2013; they were dissected and examined for parasitic acanthocephalans. Only 40 (57.14%) specimens were found to be naturally infected with Serrasentis sagittifer belonging to family Rhadinorhynchidae. The infection was recorded in the intestine, pyloric ceca, and the external surfaces of some internal organs of the infected fish. Seasonally, the prevalence of infection was increased to 77.14% during summer season and decreased to 37.14% during winter. Light and scanning electron microscopic investigation revealed that the adult worm was elongated (with broad anterior and narrow posterior ends) and measured 6.9-8.6 (7.6 ± 0.2) × 0.57-0.73 (0.63 ± 0.02) mm for male and 10.2-12.1 (11.5 ± 0.2) × 0.71-0.82 (0.76 ± 0.02) mm for female. Proboscis was long and cylindrical with a length of 0.97-1.6 mm (1.2 ± 0.2) for male and 1.12-1.17 mm (1.14 ± 0.02) for female. It was covered with numerous uniform spines arranged longitudinally as 9-11 rows each equipped by 15-18 spines. Spines were triangular, arrow-shaped, strong, and covered with cuticular theca; they decreased in size from the apex to the base of the proboscis. The proboscis is followed by a short spineless neck region followed by the body proper which is supported by multiple combs of spines (16-20) on its ventral surface. Molecular analysis of 18S rDNA sequence for the parasite demonstrated a close identity (>83%) between the present acanthocephalan and other previously described species within class Palaeacanthocephala with 98% identity with the previously recorded S. sagittifer (acc. no. JX014227) which is supported by the morphological data and the presence of trunk spines arranged within rows (comb-like) and the presence of four cement glands in the males. So, according to the records of morphological and molecular analyses, the present parasite is classified as S. sagittifer belonging to class Palaeacanthocephala and family Rhadinorhynchidae with a new host record from the gilthead sea bream S. aurata of the Red Sea.


title: Learning to Generate Posters of Scientific Papers

abstract: Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.


title: HMD-enabled Virtual Screens as Alternatives to Large Physical Displays

abstract: Large displays are one of the core components of visualizing large-scale data sets, allowing users to render a larger amount of information and present it in the corresponding context. However, these displays still have many disadvantages, like the high costs that increase exponentially with display size and the immobility of the display setups. In this paper, we introduce a virtual large screen that is designed as an alternative to physical large displays. It is implemented with the help of the Oculus Rift head mounted display (HMD). Users wearing the virtual glasses are able to see and explore a virtual screen and make use of its size advantages without the cost and location restrictions of the physical counterpart. A preliminary evaluation of our system suggests that the users' perception and cognitive abilities are similar for both working on large physical displays and our large virtual screens.


title: Low Power Wide Area Network Analysis: Can LoRa Scale?

abstract: Low power wide area (LPWA) networks are making spectacular progress from design, standardization, to commercialization. At this time of fast-paced adoption, it is of utmost importance to analyze how well these technologies will scale as the number of devices connected to the Internet of Things inevitably grows. In this letter, we provide a stochastic geometry framework for modeling the performance of a single gateway LoRa network, a leading LPWA technology. Our analysis formulates the unique peculiarities of LoRa, including its chirp spread-spectrum modulation technique, regulatory limitations on radio duty cycle, and use of ALOHA protocol on top, all of which are not as common in today’s commercial cellular networks. We show that the coverage probability drops exponentially as the number of end-devices grows due to interfering signals using the same spreading sequence. We conclude that this fundamental limiting factor is perhaps more significant toward LoRa scalability than for instance spectrum restrictions. Our derivations for co-spreading factor interference found in LoRa networks enables rigorous scalability analysis of such networks.


title: Clustering and Spherical Visualization of Web Services

abstract: Web service clustering is one of a very efficient approach to discover Web services efficiently. Current clustering approaches use traditional clustering algorithms such as agglomerative as the clustering algorithm. The algorithms have not provided visualization of service clusters that gives inspiration for a specific domain from visual feedback and failed to achieve higher noise isolation. Furthermore iterative steps of algorithms consider about the similarity of limited number of services such as similarity of cluster centers. This leads to reduce the cluster performance. In this paper we apply a spatial clustering technique called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic similarity values between services as the affinity values. Most of the current clustering approaches use similarity distance measurement such as keyword, ontology and information-retrieval-based methods. These approaches have problem of short of high quality ontology and loss of semantic information. In this paper, we calculate the service similarity by using hybrid term similarity method which uses ontology learning and information retrieval. Experimental results show our clustering approach is able to plot similar services into same area and aid to search Web services by visualization of the service data on a spherical surface.


title: The Sharing Economy: Friend or Foe?

abstract: The sharing economy is spreading rapidly worldwide in a number of industries and markets. The disruptive nature of this phenomenon has drawn mixed responses ranging from active conflict to adoption and assimilation. Yet, in spite of the growing attention to the sharing economy, we still do not know much about it. With the abundant enthusiasm about the benefits that the sharing economy can unleash and the weekly reminders about its dark side, further examination is required to determine the potential of the sharing economy while mitigating its undesirable side effects. The panel will join the ongoing debate about the sharing economy and contribute to the discourse with insights about how digital technologies are critical in shaping this turbulent ecosystem. Furthermore, we will define an agenda for future research on the sharing economy as it becomes part of the mainstream society as well as part of the IS research


title: Functional Neuroanatomy of Emotion: A Meta-Analysis of Emotion Activation Studies in PET and fMRI

abstract: Neuroimagingstudies with positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have begun to describe the functional neuroanatomy of emotion. Taken separately, specific studies vary in task dimensions and in type(s) of emotion studied and are limited by statistical power and sensitivity. By examining findings across studies, we sought to determine if common or segregated patterns of activations exist across various emotional tasks. We reviewed 55 PET and fMRI activation studies (yielding 761 individual peaks) which investigated emotion in healthy subjects. Peak activation coordinates were transformed into a standard space and plotted onto canonical 3-D brain renderings. We divided the brain into 20 nonoverlapping regions, and characterized each region by its responsiveness across individual emotions (positive, negative, happiness, fear, anger, sadness, disgust), to different induction methods (visual, auditory, recall/imagery), and in emotional tasks with and without cognitive demand. Our review yielded the following summary observations: (1) The medial prefrontal cortex had a general role in emotional processing; (2) fear specifically engaged the amygdala; (3) sadness was associated with activity in the subcallosal cingulate; (4) emotional induction by visual stimuli activated the occipital cortex and the amygdala; (5) induction by emotional recall/imagery recruited the anterior cingulate and insula; (6) emotional tasks with cognitive demand also involved the anterior cingulate and insula. This review provides a critical comparison of findings across individual studies and suggests that separate brain regions are involved in different aspects of emotion.


title: Nonlocal Operators with Applications to Image Processing

abstract: We propose the use of nonlocal operators to define new types of flows and functionals for image processing and elsewhere. A main advantage over classical PDE-based algorithms is the ability to handle better textures and repetitive structures. This topic can be viewed as an extension of spectral graph theory and the diffusion geometry framework to functional analysis and PDE-like evolutions. Some possible applications and numerical examples are given, as is a general framework for approximating Hamilton–Jacobi equations on arbitrary grids in high demensions, e.g., for control theory.


title: An Obfuscation-Based Approach for Protecting Location Privacy

abstract: The pervasive diffusion of mobile communication devices and the technical improvements of location techniques are fostering the development of new applications that use the physical position of users to offer location-based services for business, social, or informational purposes. In such a context, privacy concerns are increasing and call for sophisticated solutions able to guarantee different levels of location privacy to the users. In this paper, we address this problem and present a solution based on different obfuscation operators that, when used individually or in combination, protect the privacy of the location information of users. We also introduce an adversary model and provide an analysis of the proposed obfuscation operators to evaluate their robustness against adversaries aiming to reverse the obfuscation effects to retrieve a location that better approximates the location of the users. Finally, we present some experimental results that validate our solution.


title: Penetration Testing with Improved Input Vector Identification

abstract: Penetration testing is widely used to help ensure the security of web applications. It discovers vulnerabilities by simulating attacks from malicious users on a target application. Identifying the input vectors of a web application and checking the results of an attack are important parts of penetration testing, as they indicate where an attack could be introduced and whether an attempted attack was successful. Current techniques for identifying input vectors and checking attack results are typically ad-hoc and incomplete, which can cause parts of an application to be untested and leave vulnerabilities undiscovered. In this paper, we propose a new approach to penetration testing that addresses these limitations by leveraging two recently-developed analysis techniques. The first is used to identify a web application's possible input vectors, and the second is used to automatically check whether an attack resulted in an injection. To empirically evaluate our approach, we compare it against a state-of-the-art, alternative technique. Our results show that our approach performs a more thorough penetration testing and leads to the discovery of more vulnerabilities.


title: Artificial intelligence, computational thinking, and mathematics education

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to examine the intersection of artificial intelligence (AI), computational thinking (CT), and mathematics education (ME) for young students (K-8). Specifically, it focuses on three key elements that are common to AI, CT and ME: agency, modeling of phenomena and abstracting concepts beyond specific instances. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The theoretical framework of this paper adopts a sociocultural perspective where knowledge is constructed in interactions with others (Vygotsky, 1978). Others also refers to the multiplicity of technologies that surround us, including both the digital artefacts of our new media world, and the human methods and specialized processes acting in the world. Technology is not simply a tool for human intention. It is an actor in the cognitive ecology of immersive humans-with-technology environments (Levy, 1993, 1998) that supports but also disrupts and reorganizes human thinking (Borba and Villarreal, 2005). ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: There is fruitful overlap between AI, CT and ME that is of value to consider in mathematics education. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Seeing ME through the lenses of other disciplines and recognizing that there is a significant overlap of key elements reinforces the importance of agency, modeling and abstraction in ME and provides new contexts and tools for incorporating them in classroom practice.


title: Unit commitment with primary frequency regulation constraints

abstract: The unit commitment problem with tertiary reserve requirements has been broadly studied. Such reserves, when needed, are centrally deployed with relatively slow time constants of the order of minutes. In contrast, the scheduling of units offering primary frequency regulation reserve deployable in a decentralized manner within seconds of a contingency has received relatively little attention. In this paper, we formulate and solve a multiperiod unit commitment that simultaneously accounts for both primary and tertiary reserve constraints. What makes this scheduling problem particularly challenging is the characteristic that primary frequency regulation reserves have a common single degree of freedom, namely, the system frequency deviation.


title: Fast arbiters for on-chip network switches

abstract: The need for efficient implementation of simple crossbar schedulers has increased in the recent years due to the advent of on-chip interconnection networks that require low latency message delivery. The core function of any crossbar scheduler is arbitration that resolves conflicting requests for the same output. Since, the delay of the arbiters directly determine the operation speed of the scheduler, the design of faster arbiters is of paramount importance. In this paper, we present a new bit-level algorithm and new circuit techniques for the design of programmable priority arbiters that offer significantly more efficient implementations compared to already-known solutions. From the experimental results it is derived that the proposed circuits are more than 15% faster than the most efficient previous implementations, which under equal delay comparisons, translates to 40% less energy.


title: Scheduling Wireless Virtual Networks Functions

abstract: Network function virtualization (NFV) sits firmly on the networking evolutionary path. By migrating network functions from dedicated devices to general purpose computing platforms, NFV can help reduce the cost to deploy and operate large IT infrastructures. In particular, NFV is expected to play a pivotal role in mobile networks where significant cost reductions can be obtained by dynamically deploying and scaling virtual network functions (VNFs) in the core network. However, in order to achieve its full potential, NFV needs to extend its reach also to the radio access segment. Here, mobile virtual network operators shall be allowed to request radio access VNFs with custom resource allocation solutions. Such a requirement raises several challenges in terms of performance isolation and resource provisioning. In this work, we formalize the wireless VNF placement problem in the radio access network as an integer linear programming problem and we propose a VNF placement heuristic, named wireless network embedding (WiNE), to solve the problem. Moreover, we present a proof-of-concept implementation of an NFV management and orchestration framework for enterprise WLANs. The proposed architecture builds on a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing capable nodes.


Software security and privacy risks in mobile e-commerce


title: Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection

abstract: Classification of intrusion attacks and normal network traffic is a challenging and critical problem in pattern recognition and network security. In this paper, we present a novel intrusion detection approach to extract both accurate and interpretable fuzzy IF-THEN rules from network traffic data for classification. The proposed fuzzy rule-based system is evolved from an agent-based evolutionary framework and multi-objective optimization. In addition, the proposed system can also act as a genetic feature selection wrapper to search for an optimal feature subset for dimensionality reduction. To evaluate the classification and feature selection performance of our approach, it is compared with some well-known classifiers as well as feature selection filters and wrappers. The extensive experimental results on the KDD-Cup99 intrusion detection benchmark data set demonstrate that the proposed approach produces interpretable fuzzy systems, and outperforms other classifiers and wrappers by providing the highest detection accuracy for intrusion attacks and low false alarm rate for normal network traffic with minimized number of features.


title: Plastic waste inputs from land into the ocean

abstract: Plastic debris in the marine environment is widely documented, but the quantity of plastic entering the ocean from waste generated on land is unknown. By linking worldwide data on solid waste, population density, and economic status, we estimated the mass of land-based plastic waste entering the ocean. We calculate that 275 million metric tons (MT) of plastic waste was generated in 192 coastal countries in 2010, with 4.8 to 12.7 million MT entering the ocean. Population size and the quality of waste management systems largely determine which countries contribute the greatest mass of uncaptured waste available to become plastic marine debris. Without waste management infrastructure improvements, the cumulative quantity of plastic waste available to enter the ocean from land is predicted to increase by an order of magnitude by 2025.


title: Off-Road Obstacle Avoidance through End-to-End Learning

abstract: We describe a vision-based obstacle avoidance system for off-road mobile robots. The system is trained from end to end to map raw input images to steering angles. It is trained in supervised mode to predict the steering angles provided by a human driver during training runs collected in a wide variety of terrains, weather conditions, lighting conditions, and obstacle types. The robot is a 50cm off-road truck, with two forward-pointing wireless color cameras. A remote computer processes the video and controls the robot via radio. The learning system is a large 6-layer convolutional network whose input is a single left/right pair of unprocessed low-resolution images. The robot exhibits an excellent ability to detect obstacles and navigate around them in real time at speeds of 2 m/s.


title: Random Forests and Decision Trees

abstract: In this paper, we have compared the classification results of two models i.e. Random Forest and the J48 for classifying twenty versatile datasets. We took 20 data sets available from UCI repository [1] containing instances varying from 148 to 20000. We compared the classification results obtained from methods i.e. Random Forest and Decision Tree (J48). The classification parameters consist of correctly classified instances, incorrectly classified instances, F-Measure, Precision, Accuracy and Recall. We discussed the pros and cons of using these models for large and small data sets. The classification results show that Random Forest gives better results for the same number of attributes and large data sets i.e. with greater number of instances, while J48 is handy with small data sets (less number of instances). The results from breast cancer data set depicts that when the number of instances increased from 286 to 699, the percentage of correctly classified instances increased from 69.23% to 96.13% for Random Forest i.e. for dataset with same number of attributes but having more instances, the Random Forest accuracy increased.


title: Presence of Headache and Migraine in Asthma Patients

abstract: OBJECTIVES ::: Migraine is a diseases characterized with severe headaches, with neurological and systemic findings. The purpose of this study is to investigate the prevalence of migraine and to examine whether there is a relationship between atopic disorders, parental history and migraine in asthma patients. ::: ::: ::: MATERIAL AND METHODS ::: A total of 288 asthma outpatients, who had the diagnosis by an early or late test of reversibility showing a reversible airway obstruction according to hospital database were included. The presence of headache, atopic symptoms and parental history about asthma, atopic disorders and migraine were asked. The patients with headache were consultated by neurology department and investigated about the presence of migraine. The diagnosis of migraine headache was made if patients fulfilled the International Headache Society (IHS) criteria. ::: ::: ::: RESULTS ::: 60.4% of patients described a headache. There were 94 patients (32.6%) with headaches meeting the IHS criteria for migraine. Only 12 patients had migraine with aura. There were atopic symptoms in 86.8% of patients. According to parental history, there were asthma in 47.9%, atopic symptoms in 39.6% and migraine in 22.2% of parents. Patients with atopic symptoms were found to have significantly high rate of headaches (65.3%) "p=0.007". The prevalence of migraine was significantly high in patients with parental atopic symptoms (54%) "p=0.002". Multiple logistic regression analysis identified that gender, parental history of asthma, allergia and migraine, and smoking were independent risk factors for presence of migraine in asthmatics. ::: ::: ::: CONCLUSION ::: There is a high prevalence of migraine headaches in patients with asthma. The coexistence of asthma and headaches may be related with a similar pathophysiological mechanism; parental history, common genetic compounds and smoking may play role in this mechanism. The headaches in asthma patients, atopic symptoms and family history should be questioned, and clinicians should be careful about the presence of migraine.


title: ZombieCoin: Powering Next-Generation Botnets with Bitcoin

abstract: Botnets are the preeminent source of online crime and arguably the greatest threat to the Internet infrastructure. In this paper, we present ZombieCoin, a botnet command-and-control (C&C) mechanism that runs on the Bitcoin network. ZombieCoin offers considerable advantages over existing C&C techniques, most notably the fact that Bitcoin is designed to resist the very regulatory processes currently used to combat botnets. We believe this is a desirable avenue botmasters may explore in the near future and our work is intended as a first step towards devising effective countermeasures.


title: A 2.4-GHz ZigBee Transmitter Using a Function-Reuse Class-F DCO-PA and an ADPLL Achieving 22.6% (14.5%) System Efficiency at 6-dBm (0-dBm) $P_{\mathrm {out}}$

abstract: This paper describes a sub-1-V 2.4-GHz ZigBee transmitter (TX) with scalable output power ( $P_{\mathrm {out}}$ ) and system efficiency. It features a function-reuse class-F topology unifying the digital-controlled oscillator (DCO) and power amplifier (PA), designated as DCO-PA. Unlike the existing current-reuse topologies that rely on transistor stacking, here the power consumption of the DCO and PA-driver is absorbed into the DCO-PA without losing the voltage headroom, while allowing a low supply voltage. The DCO-PA also benefits from a six-port transformer with customized coupling coefficients and turn ratios to jointly perform the functions of resonant tank and output matching network, saving the chip area. A fractional-N all-digital phase-locked loop (ADPLL) realizes a two-point data modulation. Its phase-interpolated time-to-digital converter prevents time-consuming calibration. The entire TX fabricated in 65-nm CMOS occupies a 0.39-mm $^{\mathrm { {2}}}$ active area. The standalone DCO-PA shows a peak efficiency of 26.2% at a 6-dBm $P_{\mathrm {out}}$ , and a back-off efficiency of 17.7% at a −4.3 dBm $P_{\mathrm {out}}$ under a scalable supply voltage (0.3–0.7 V). The system efficiency, including the ADPLL, is 22.6% (14.5%) at 6-dBm (0-dBm) $P_{\mathrm {out}}$ . The HS-OQPSK modulated output complies with the ZigBee spectral mask with an adequate margin and the error vector magnitude is 2.29%.


title: An IoT Platform for Epilepsy Monitoring and Supervising

abstract: Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these approaches lack in ergonomic issues and in the suitable integration with the health system. This research makes an in-depth analysis of the main factors that an epileptic detection and monitoring tool should accomplish. Furthermore, we introduce the architecture for a specific epilepsy detection and monitoring platform, fulfilling these factors. Special attention has been given to the part of the system the patient should wear, providing details of this part of the platform. Finally, a partial implementation has been deployed and several tests have been proposed and carried out in order to make some design decisions.


title: Unsupervised, information-theoretic, adaptive image filtering for image restoration

abstract: Image restoration is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Hence, these methods lack the generality to be easily applied to new applications or diverse image collections. This paper describes a novel unsupervised, information-theoretic, adaptive filter (UINTA) that improves the predictability of pixel intensities from their neighborhoods by decreasing their joint entropy. In this way, UINTA automatically discovers the statistical properties of the signal and can thereby restore a wide spectrum of images. The paper describes the formulation to minimize the joint entropy measure and presents several important practical considerations in estimating neighborhood statistics. It presents a series of results on both real and synthetic data along with comparisons with current state-of-the-art techniques, including novel applications to medical image processing.


title: A computational model for solving problems from the Raven's Progressive Matrices intelligence test using iconic visual representations

abstract: We describe a computational model for solving problems from Raven's Progressive Matrices (RPM), a family of standardized intelligence tests. Existing computational models for solving RPM problems generally reason over amodal propositional representations of test inputs. However, there is considerable evidence that humans can also apply imagery-based reasoning strategies to RPM problems, in which processes rooted in perception operate over modal representations of test inputs. In this paper, we present the ''affine model,'' a computational model that simulates modal reasoning by using iconic visual representations together with affine and set transformations over these representations to solve a given RPM problem. Various configurations of the affine model successfully solve between 33 and 38 of the 60 problems on the Standard Progressive Matrices, which matches levels of performance for typically developing 9- to 11-year-old children. This suggests that, for at least a sizeable subset of RPM problems, it is not always necessary to extract amodal symbols in order to arrive at the correct answer, and iconic visual representations constitute a sufficient form of representation to successfully solve these problems. We intend for the affine model to serve as a complementary computational account to existing propositional models, which together may provide an integrated, dual-process account of human problem solving on the RPM.


Machine Learning from Imbalanced Data Sets 101 (Extended Abstract)


title: DoubleFusion: Real-Time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor

abstract: We propose DoubleFusion, a new real-time system that combines volumetric dynamic reconstruction with data-driven template fitting to simultaneously reconstruct detailed geometry, non-rigid motion and the inner human body shape from a single depth camera. One of the key contributions of this method is a double layer representation consisting of a complete parametric body shape inside, and a gradually fused outer surface layer. A pre-defined node graph on the body surface parameterizes the non-rigid deformations near the body, and a free-form dynamically changing graph parameterizes the outer surface layer far from the body, which allows more general reconstruction. We further propose a joint motion tracking method based on the double layer representation to enable robust and fast motion tracking performance. Moreover, the inner body shape is optimized online and forced to fit inside the outer surface layer. Overall, our method enables increasingly denoised, detailed and complete surface reconstructions, fast motion tracking performance and plausible inner body shape reconstruction in real-time. In particular, experiments show improved fast motion tracking and loop closure performance on more challenging scenarios.


title: Weight Perturbation: An Optimal Architecture and Learning Technique for Analog VLSI Feedforward and Recurrent Multi-Layer Networks

abstract: Previous work on analog VLSI implementation of multilayer perceptrons with on-chip learning has mainly targeted the implementation of algorithms such as back-propagation. Although back-propagation is efficient, its implementation in analog VLSI requires excessive computational hardware. It is shown that using gradient descent with direct approximation of the gradient instead of back-propagation is more economical for parallel analog implementations. It is shown that this technique (which is called 'weight perturbation') is suitable for multilayer recurrent networks as well. A discrete level analog implementation showing the training of an XOR network as an example is presented. >


title: Two Paths of Bourgeois Agrarian Evolution in European Russia: An Essay in Multivariate Analysis

abstract: Academician Koval'chenko is the doyen of Soviet quantitative historians and the mentor of many of them.* While the sheer volume of Koval'chenko's publications is due to his erudition and energy, some credit for the increasing sophistication of his work should go to his collaborators and first of all to L. I. Borodkin, who was initially trained in information science. The article published here is the culmination of a series, each more sophisticated than the last, devoted to the agrarian typology of prerevolutionary Russian provinces. The categorization itself holds no great surprises. It corresponds to the authors' earlier findings and, in general, to the common usage of social scientists. The importance of the article lies in showing the extent to which a province matches a particular type and, more particularly, the social and economic determinants of the agrarian development of provinces and types of provinces. Like Selunskaia, Koval'chenko and Borodkin emphasize the sway of the peasantry in prerevolutionary agriculture; indeed, what they now call the "manorial-peasant type" they once called simply the "manorial type." Yet they also bring out the viability, in some regions, of the development on a manorial or "Prussian" basis. In analysis of the kind they undertake, gains in sophistication mean losses in transparency, as more and more operations are performed behind the scenes, usually in the bowels of a computer.t One turns alrmost with relief to the wearying rows of numbers in their appendix, which represent the absolute values for each province that were the point of departure for their analysis; one-third of the rural population in Iaroslavl' Province was literate in 1897-there is a fact to sink your teeth into. Some years ago, Lawrence Stone wondered whether "the sophistication of...mathematical and algebraic formulae are [sic] not ultimately self-defeating, since they baffle most historians." He went on to complain of "the virtual impossibility of checking up on the reliability of final results, since [one] must depend not on published footnotes but on privately owned computer tapes..." t There is no reason to share Stone's disdain for what he cannot bother to understand. We can, however, share his nostalgia for an era, extending from the time of Gibbon until very recently, when all historians could adhere to a common standard of verification. The humble footnote was the basis of the comity of scholars. Koval'chenko, Borodkin and their colleagues explain what they are doing with admirable patience, they share their data with rare generosity, but they are increasingly obliged to characterize their data and the operations they perform rather than identify them, as historians used to do. We can directly verify their findings only by retracing step by step the arduous path of data collection and analysis that they travelled. Perhaps the lesson for historians is that humility and caution become ever more important as our methods become more complex.


title: Digital evidence challenges in the Internet of Things

abstract: The implementation of the Internet of Things will result in the connection of tens of billions of wireless devices to the Internet. These devices will form an intelligent substrate pervading all aspects of life. From intelligent home control to advanced city management systems, devices will sense their environment as well as interconnect and communicate with each other to form intelligent smart spaces. Individually and collectively, these devices produce and consume large amounts of personally sensitive data. This new environment provides a rich set of data sources; when used in conjunction with one another, they can greatly inform a historical situation that may have occurred with little or no reliable human witness evidence. However, this deeply pervasive environment will provide challenges to the various agencies that will need to interact with this new technology.


title: KinNet: Fine-to-Coarse Deep Metric Learning for Kinship Verification

abstract: Automatic kinship verification has attracted increasing attentions as it holds promise to an abundance of applications. However, existing kinship verification methods suffer from the lack of large scale real-world data. Without enough training data, it is difficult to learn proper features that are discriminant for blood-related peoples. In this work, we propose KinNet, a fine-to-coarse deep metric learning framework for kinship verification. In the framework, we transfer knowledge from the large-scale-data-driven face recognition task, which is a fine-grained version of kinship recognition, by pre-training the network with massive data for face recognition. Then, the network is fine-tuned to find a metric space where kin-related peoples are discriminant. The metric space is learned by minimizing a soft triplet loss on the augmented kinship dataset. An augmented strategy is proposed to balance the amount of images per family member. Finally, we ensemble four networks to further boost the performance. The experimental results on the 1st Large-Scale Kinship Recognition Data Challenge (Track 1) demonstrate that our KinNet achieves the state-of-the-art performance in kinship verification.


title: Social media for software engineering

abstract: Social media has changed the way that people collaborate and share information. In this paper, we highlight its impact for enabling new ways for software teams to form and work together. Individuals will self-organize within and across organizational boundaries. Grassroots software development communities will emerge centered around new technologies, common processes and attractive target markets. Companies consisting of lone individuals will able to leverage social media to conceive of, design, develop, and deploy successful and profitable product lines. A challenge for researchers who are interested in studying, influencing, and supporting this shift in software teaming is to make sure that their research methods protect the privacy and reputation of their stakeholders.


title: Quality expectations of machine translation

abstract: Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single ‘gold standard’ measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected ‘shelf-life’ of the translation itself.


title: A 60-GHz Millimeter-Wave CPW-Fed Yagi Antenna Fabricated by Using 0.18- $\mu\hbox{m}$ CMOS Technology

abstract: This letter presents a 60-GHz millimeter-wave on-chip Yagi antenna fabricated with a 0.18-mum CMOS process. A feeding network is designed in a coplanar waveguide (CPW) technology. The 0.18-mum six-metal-layer CMOS process allows the on-chip antenna to utilize a simple CPW-to-coplanar-stripline feed transition and the first metal layer to implement a reflector strip. The CMOS antenna chip size is 1.1 x 0.95 mm2 . A FEM-based 3D full-wave electromagnetic solver, the HFSS, is used for design simulation. The measured antenna input VSWR is less than two from 55 to 65 GHz. The front-to-back ratio of the Yagi antenna is about 9 dB. The measured maximum antenna power gain is about -10 dBi. The simulated antenna radiation efficiency is about 10%.


"Parameter estimation and goodness-of-fit in log binomial regression".


title: IOS Press Midlife Coffee and Tea Drinking and the Risk of Late-Life Dementia: A Population-Based CAIDE Study

abstract: Caffeine stimulates central nervous system on a short term. However, the long-term impact of caffeine on cognition remains unclear. We aimed to study the association between coffee and/or tea consumption at midlife and dementia/Alzheimer's disease (AD) risk in late-life. Participants of the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) study were randomly selected from the survivors of a population-based cohorts previously surveyed within the North Karelia Project and the FINMONICA study in 1972, 1977, 1982 or 1987 (midlife visit). After an average follow-up of 21 years, 1409 individuals (71%) aged 65 to 79 completed the re-examination in 1998. A total of 61 cases were identified as demented (48 with AD). Coffee drinkers at midlife had lower risk of dementia and AD later in life compared with those drinking no or only little coffee adjusted for demographic, lifestyle and vascular factors, apolipoprotein E e4 allele and depressive symptoms. The lowest risk (65% decreased) was found in people who drank 3-5 cups per day. Tea drinking was relatively uncommon and was not associated with dementia/AD. Coffee drinking at midlife is associated with a decreased risk of dementia/AD later in life. This finding might open possibilities for prevention of dementia/AD.


title: Improved SIFT matching algorithm for 3D reconstruction from endoscopic images

abstract: SIFT (Scale Invariant Feature Transform) is wildly used in image matching but suffered from low matching pairs when employed in endoscope image. This paper presented an improved algorithm based on SIFT, the core contribution of which is Zone Matching approach. By collecting all feature points in a neighbor patch around the coordinate of an objective feature in the key image and finding the closest one to the feature in current image, the Zone Matching can obtain more matching pairs in shorter time. The experiment result shows a good improvement on the matching results both in matching number and computing time.


title: A Privacy-Preserving Bipartite Graph Matching Framework for Multimedia Analysis and Retrieval

abstract: The emergence of cloud computing provides an unlimited computation/storage for users, and yields new opportunities for multimedia analysis and retrieval research. However, privacy of users, e.g., search intention, may be leaked to the server and maliciously utilized by companies or individuals with animus. This paper presents a privacy-preserving multimedia analysis framework based on a widely-adopted structure, i.e., bipartite graph, so that multimedia analysis and retrieval in the encrypted domain is enabled. This work aims to keep the server unaware of what the user wants to retrieve, and at the same time take advantage of the server's computation power. Homomorphic encryption schemes and communication protocols in the encrypted domain are integrated to facilitate bipartite graph construction and implement the Hungarian algorithm to find the best matching. Two applications, video tag suggestion and video copy detection, are developed on top of the privacy-preserving framework, and the evaluation results demonstrate that performance obtained in the encrypted domain is comparable with that obtained in the plain text domain.


title: Translation-based Recommendation

abstract: Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems. One classical setting is predicting users' personalized sequential behavior (or 'next-item' recommendation), where the challenges mainly lie in modeling 'third-order' interactions between a user, her previously visited item(s), and the next item to consume. Existing methods typically decompose these higher-order interactions into a combination of pairwise relationships, by way of which user preferences (user-item interactions) and sequential patterns (item-item interactions) are captured by separate components. In this paper, we propose a unified method, TransRec, to model such third-order relationships for large-scale sequential prediction. Methodologically, we embed items into a 'transition space' where users are modeled as translation vectors operating on item sequences. Empirically, this approach outperforms the state-of-the-art on a wide spectrum of real-world datasets. Data and code are available at https://sites.google.com/a/eng.ucsd.edu/ruining-he/.


title: Annotating High-Level Structures of Short Stories and Personal Anecdotes

abstract: Stories are a vital form of communication in human culture; they are employed daily to persuade, to elicit sympathy, or to convey a message. Computational understanding of human narratives, especially high-level narrative structures, however, remain limited to date. Multiple literary theories for narrative structures exist, but operationalization of the theories has remained a challenge. We developed an annotation scheme by consolidating and extending existing narratological theories, including Labov and Waletsky's (1967) functional categorization scheme and Freytag's (1863) pyramid of dramatic tension, present 360 annotated short stories collected from online sources. In the future, this research will support an approach that enables systems to intelligently sustain complex communications with humans.


title: Leveraging multiple coflow attributes for information-agnostic coflow scheduling

abstract: Recently, designing information-agnostic coflow scheduling mechanisms attracts much attention since by leveraging priority queues, they could reduce coflow completion time in data-parallel clusters without a priori knowledge, such as flow size, coflow size. However, existing information-agnostic mechanisms generally schedule coflows only according to the sent data size of different coflows and ignore other useful coflow-level attributes like width, length and communication patterns. In this paper, we investigate that the coflow completion time could be further decreased by jointly leveraging multiple coflow-level attributes. Based on this investigation, we present a Multiple-attributes-based Coflow Scheduling (MCS) mechanism to reduce the coflow completion time. In MCS, a Shortest and Narrowest Coflow First (SNCF) algorithm is designed to separate coflows based on their widths and estimated lengths at the start of a coflow. During the transmission of coflows, one type of demotion thresholds employed in previous coflow scheduling mechanisms is too crude for various coflows. Therefore, we proposed a double-threshold scheme to adjust the priorities of narrow (small coflow width) and wide (large coflow width) coflows according to different thresholds. Trace-driven simulations with production workloads show that MCS outperforms the previous information-agnostic scheduler Aalo, and reduces the coflow completion time of small coflows.


title: RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

abstract: To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the water, RippleNet stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.


title: Augmented reality application for smart tourism: GökovAR

abstract: Usage of augmented reality technology in daily life is becoming more widespread. This technology is also being applied in the field of tourism. In this study, a prototype of mobile application for smart tourism has been developed by using augmented reality technology. The requirements, the competences and incompleteness of the methods is described. The prototype application is developed in the pilot region of Gokova Mugla. This application aims to introduce important centers, touristic places, restaurants, hotels and sightseeing places to domestic and foreign tourists. The intensity, ratings, comments, current social media data and price information about these areas will be provided simultaneously on the mobile application. Image processing techniques and location data will be used for implementing augmented reality technology.


Staying connected while on the move: Cell phone use and social connectedness


Detecting malicious domain names using deep learning approaches at scale


Temporal Summaries of News Topics


title: Syndromes of the First and Second Branchial Arches, Part 2: Syndromes

abstract: A variety of congenital syndromes affecting the face occur due to defects involving the first and second BAs. Radiographic evaluation of craniofacial deformities is necessary to define aberrant anatomy, plan surgical procedures, and evaluate the effects of craniofacial growth and surgical reconstructions. High-resolution CT has proved vital in determining the nature and extent of these syndromes. The radiologic evaluation of syndromes of the first and second BA should begin first by studying a series of isolated defects (cleft lip with or without CP, micrognathia, and EAC atresia) that compose the major features of these syndromes and allow a more specific diagnosis. After discussion of these defects and the associated embryology, we discuss PRS, HFM, ACS, TCS, Stickler syndrome, and VCFS.


HOLMES : Holistic Mice-Elephants Stochastic Scheduling in Data Center Networks


title: Introduction to a LargeScale General Purpose Ground Truth Database: Methodology, Annotation Tool and Benchmarks” EMMCVPR07, (to appear

abstract: This paper presents a large scale general purpose image database with human annotated ground truth. Firstly, an all-in-all labeling framework is proposed to group visual knowledge of three levels: scene level (global geometric description), object level (segmentation, sketch representation, hierarchical decomposition), and low-mid level (2.1D layered representation, object boundary attributes, curve completion, etc.). Much of this data has not appeared in previous databases. In addition, And-Or Graph is used to organize visual elements to facilitate top-down labeling. An annotation tool is developed to realize and integrate all tasks. With this tool, we've been able to create a database consisting of more than 636,748 annotated images and video frames. Lastly, the data is organized into 13 common subsets to serve as benchmarks for diverse evaluation endeavors.