title: Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit

abstract: Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.


title: Through wall sensing of human breathing and heart beating by monochromatic radar

abstract: A radar for detecting and testing health condition of an individual, who is separated from the radar by a non-metal wall or in poor visibility, could be useful in many critical services such as medicine, rescue service, law and antiterrorists enforcements In this paper, we describe the theoretical approach and experiments utilizing a continuous-wave subsurface radar. New theoretical results, experimental records and their frequency spectrums for heartbeat, respiration and articulation of a man are presented.


title: Wall Teichoic Acids of Gram-Positive Bacteria

abstract: The peptidoglycan layers of many gram-positive bacteria are densely functionalized with anionic glycopolymers known as wall teichoic acids (WTAs). These polymers play crucial roles in cell shape determination, regulation of cell division, and other fundamental aspects of gram-positive bacterial physiology. Additionally, WTAs are important in pathogenesis and play key roles in antibiotic resistance. We provide an overview of WTA structure and biosynthesis, review recent studies on the biological roles of these polymers, and highlight remaining questions. We also discuss prospects for exploiting WTA biosynthesis as a target for new therapies to overcome resistant infections.


title: Using SiMPLE++ for improved modeling efficiencies and extending model life cycles

abstract: SiMPLE++ is an object-oriented simulation environment for modeling all types of manufacturing, logistics and service systems. AESOP’s SiMPLE++ is a fully object-oriented implementation and uses a unique simulation modeling paradigm that increases the value of modeling through reusability and connectivity while leveraging object-oriented concepts for modeling efficiencies. SiMPLE++ has been linked with a wide variety of other decision support software. In addition, a number of simulation application libraries are available. Together, these create a highly efficient, highly flexible tool for simulation and support of a number of simulation-based manufacturing, scheduling, logistics, staffing and maintenance support applications.


title: IoT Access Control Issues: A Capability Based Approach

abstract: Resource and information protection plays a relevant role in distributed systems. Most of the currently proposed authorization frameworks do not provide scalable, manageable, effective, and efficient mechanisms to support distributed systems with many interacting services. The advent of IoT will further increase the need for scalable and manageable solutions able to face the potentially unbound number of sensors, actuators and related resources, services and subjects. This is even more relevant if we take into account that IoT environments can envisage not only a greater number of resources to manage, but also a substantial increase of the interaction dynamics. This paper presents a capability based access control system that enterprises, or even individuals, can use to manage their own access control processes to services and information. The proposed mechanism supports rights delegation and a more sophisticated access control customization. The proposed approach is being developed within the European FP7 IoT@Work project to manage access control for some of the project's services deployed in the shop floor.


title: Quantitative analysis of literary styles

abstract: Writers are often viewed as having an inherent style that can serve as a literary fingerprint. By quantifying relevant features related to literary style, one may hope to classify written works and even attribute authorship to newly discovered texts. Beyond its intrinsic interest, the study of literary styles presents the opportunity to introduce and motivate many standard multivariate statistical techniques. Today the statistical analysis of literary styles is made much simpler by the wealth of real data readily available from the Internet. This article presents an overview and brief history of the analysis of literary styles. In addition we use canonical discriminant analyis and principal component analysis to identify structure in the data and distinguish authorship.


title: Learning haptic representation for manipulating deformable food objects

abstract: Manipulation of complex deformable semi-solids such as food objects is an important skill for personal robots to have. In this work, our goal is to model and learn the physical properties of such objects. We design actions involving use of tools such as forks and knives that obtain haptic data containing information about the physical properties of the object. We then design appropriate features and use supervised learning to map these features to certain physical properties (hardness, plasticity, elasticity, tensile strength, brittleness, adhesiveness). Additionally, we present a method to compactly represent the robot's beliefs about the object's properties using a generative model, which we use to plan appropriate manipulation actions. We extensively evaluate our approach on a dataset including haptic data from 12 categories of food (including categories not seen before by the robot) obtained in 941 experiments. Our robot prepared a salad during 60 sequential robotic experiments where it made a mistake in only 4 instances.


title: Battery capacity measurement and analysis using lithium coin cell battery

abstract: In this paper, we look at different battery capacity models that have been introduced in the literatures. These models describe the battery capacity utilization based on how the battery is discharged by the circuits that consume power. In an attempt to validate these models, we characterize a commercially available lithium coin cell battery through careful measurements of the current and the voltage output of the battery under different load profile applied by a micro sensor node. In the result, we show how the capacity of the battery is affected by the different load profile and provide analysis on whether the conventional battery models are applicable in the real world. One of the most significant finding of our work will show that DC/DC converter plays a significant role in determining the battery capacity, and that the true capacity of the battery may only be found by careful measurements.


title: SVF: interprocedural static value-flow analysis in LLVM

abstract: This paper presents SVF, a tool that enables scalable and precise interprocedural Static Value-Flow analysis for C programs by leveraging recent advances in sparse analysis. SVF, which is fully implemented in LLVM, allows value-flow construction and pointer analysis to be performed in an iterative manner, thereby providing increasingly improved precision for both. SVF accepts points- to information generated by any pointer analysis (e.g., Andersen’s analysis) and constructs an interprocedural memory SSA form, in which the def-use chains of both top-level and address-taken variables are captured. Such value-flows can be subsequently exploited to support various forms of program analysis or enable more precise pointer analysis (e.g., flow-sensitive analysis) to be performed sparsely. By dividing a pointer analysis into three loosely coupled components: Graph, Rules and Solver, SVF provides an extensible interface for users to write their own solutions easily. SVF is publicly available at http://unsw-corg.github.io/SVF.


title: DAPASA: Detecting Android Piggybacked Apps Through Sensitive Subgraph Analysis

abstract: With the exponential growth of smartphone adoption, malware attacks on smartphones have resulted in serious threats to users, especially those on popular platforms, such as Android. Most Android malware is generated by piggybacking malicious payloads into benign applications (apps), which are called piggybacked apps. In this paper, we propose DAPASA, an approach to detect Android piggybacked apps through sensitive subgraph analysis. Two assumptions are established to reflect the different invocation patterns of sensitive APIs in the injected malicious payloads (rider) of a piggybacked app and in its host app (carrier). With these two assumptions, DAPASA generates a sensitive subgraph (SSG) to profile the most suspicious behavior of an app. Five features are constructed from SSG to depict the invocation patterns. The five features are fed into the machine learning algorithms to detect whether the app is piggybacked or benign. DAPASA is evaluated on a large real-world data set consisting of 2551 piggybacked apps and 44 921 popular benign apps. Extensive evaluation results demonstrate that the proposed approach exhibits an impressive detection performance compared with that of three baseline approaches even with only five numeric features. Furthermore, the proposed approach can complement permission-based approaches and API-based approaches with the combination of our five features from a new perspective of the invocation structure.


title: AC-DIMM: associative computing with STT-MRAM

abstract: With technology scaling, on-chip power dissipation and off-chip memory bandwidth have become significant performance bottlenecks in virtually all computer systems, from mobile devices to supercomputers. An effective way of improving performance in the face of bandwidth and power limitations is to rely on associative memory systems. Recent work on a PCM-based, associative TCAM accelerator shows that associative search capability can reduce both off-chip bandwidth demand and overall system energy. Unfortunately, previously proposed resistive TCAM accelerators have limited flexibility: only a restricted (albeit important) class of applications can benefit from a TCAM accelerator, and the implementation is confined to resistive memory technologies with a high dynamic range (RHigh/RLow), such as PCM. This work proposes AC-DIMM, a flexible, high-performance associative compute engine built on a DDR3-compatible memory module. AC-DIMM addresses the limited flexibility of previous resistive TCAM accelerators by combining two powerful capabilities---associative search and processing in memory. Generality is improved by augmenting a TCAM system with a set of integrated, user programmable microcontrollers that operate directly on search results, and by architecting the system such that key-value pairs can be co-located in the same TCAM row. A new, bit-serial TCAM array is proposed, which enables the system to be implemented using STT-MRAM. AC-DIMM achieves a 4.2X speedup and a 6.5X energy reduction over a conventional RAM-based system on a set of 13 evaluated applications.


title: Real exhibitions in a virtual museum

abstract: When creating a virtual environment open to the public a number of challenges have to be addressed. The equipment has to be chosen carefully in order to be be able to withstand hard everyday usage, and the application has not only to be robust and easy to use, but has also to be appealing to the user, etc. The current paper presents findings gathered from the creation of a multi-thematic virtual museum environment to be offered to visitors of real world museums. A number of design and implementation aspects are described along with an experiment designed to evaluate alternative approaches for implementing the navigation in a virtual museum environment. The paper is concluded with insights gained from the development of the virtual museum and portrays future research plans.


title: Financial portfolio management through the goal programming model: Current state-of-the-art

abstract: Since Markowitz (1952) formulated the portfolio selection problem, many researchers have developed models aggregating simultaneously several conflicting attributes such as: the return on investment, risk and liquidity. The portfolio manager generally seeks the best combination of stocks/assets that meets his/her investment objectives. The Goal Programming (GP) model is widely applied to finance and portfolio management. The aim of this paper is to present the different variants of the GP model that have been applied to the financial portfolio selection problem from the 1970s to nowadays.


title: An Efficient, Electrically Small Antenna Designed for VHF and UHF Applications

abstract: An electrically small electric-based metamaterial-in- spired antenna that is designed for narrow bandwidth operations in the VHF and UHF bands is presented. It is demonstrated that the idealized lossless versions have overall efficiencies at 100% without any external matching circuits and quality factors approaching the Chu limit. When conductor losses are introduced, the overall efficiencies remain high.


Clustering for Mining in Large Spatial Databases


title: Morin Induces Heme Oxygenase-1 via ERK-Nrf2 Signaling Pathway

abstract: Background: Oxidative stress damages to cells or tissues, however, cellular defense systems including heme oxygenase-1 (HO-1) protects them against oxidative s tress. Flavonoid compounds can activate cellular defense mechanisms against oxidative stress and it can reduce cell damages. In the present study, the cytoprotective effects of morin (3,5,7,2’,4’-pentahydroxyflavone), in terms of HO-1 enzyme, against the oxidative stress and its involved mechanisms was elucidated. Methods: RT-PCR and western blot analysis were assessed to detect the mRNA and protein expression, respectively. Cell viability was measured by using MTT test. The immunocytochemistry was performed to define location of target protein. Electrophoretic mobility shift assay performed to measure transcription factor-promoter site binding activity. Results: Morin elevated mRNA and protein levels of HO-1 in human lens epithelial cells (HLE-B3). HO-1 inhibitor ZnPP attenuated the protective effect of morin against H2O2-induced cytotoxicity. Morin increased the protein level of transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2), which up-regulates HO-1 expression by binding to the antioxidant response element (ARE) within the HO-1 gene promoter. Moreover, morin induced the translocation of Nrf2 from the cytosol into the nucleus. Morin activated extracellular-regulated kinase (ERK), while ERK inhibitor attenuated morin-enhanced Nrf2 and HO-1 expression. Conclusions: Morin activates ERK-Nrf2 signaling cascades in HLE-B3 cells, leading to the up-regulation of HO-1 and cytoprotection against oxidative stress. (J Cancer Prev 2013;18:249-256)


title: Cement volume is the most important modifiable predictor for pain relief in BKP: results from SWISSspine, a nationwide registry

abstract: Purpose ::: The effectiveness of vertebral augmentation techniques is a currently highly debated issue. The biomechanical literature suggests that cement filling volumes may play an important role in the “dosage” of vertebral augmentation and its pain alleviating effect. Good clinical data about filling volumes are scarce and most patient series are small. Therefore, we investigated the predictors of pain alleviation after balloon kyphoplasty in the nationwide SWISSspine registry where cement volumes are also recorded.


title: Analysis of Digital Image Watermark Attacks

abstract: Digital watermarking is one of the most widely used techniques for protection of ownership rights of digital audio, images and video. Its commercial applications range from copyright protection to digital rights management. The success of a digital watermarking technology depends heavily on its robustness to withstand attacks that are aimed at removing or destroying the watermark from its host data. This paper provides analysis of a number of digital image watermark attacks and attempts to classify them into categories. A set of experimental results are also provided to show the effect of these attacks on watermarks produced using different watermarking techniques.


title: Who's Afraid of Itten: Using the Art Theory of Color Combination to Analyze Emotions in Abstract Paintings

abstract: Color plays an essential role in everyday life and is one of the most important visual cues in human perception. In abstract art, color is one of the essential means to convey the artist's intention and to affect the viewer emotionally. However, colors are rarely experienced in isolation, rather, they are usually presented together with other colors. In fact, the expressive properties of two-color combinations have been extensively studied by artists. It is intriguing to try to understand how color combinations in abstract paintings might affect the viewer emotionally, and to investigate if a computer algorithm can learn this mechanism. In this work, we propose a novel computational approach able to analyze the color combinations in abstract paintings and use this information to infer whether a painting will evoke positive or negative emotions in an observer. We exploit art theory concepts to design our features and the learning algorithm. To make use of the color-group information, we propose inferring the emotions elicited by paintings based on the sparse group lasso approach. Our results show that a relative improvement of between 6% and 8% can be achieved in this way. Finally, as an application, we employ our method to generate Mondrian-like paintings and do a prospective user study to evaluate the ability of our method as an automatic tool for generating abstract paintings able to elicit positive and negative emotional responses in people.


title: Toward a modular soft sensor-embedded glove for human hand motion and tactile pressure measurement

abstract: The ability to measure human hand motions and interaction forces is critical to improving our understanding of manual gesturing and grasp mechanics. This knowledge serves as a basis for developing better tools for human skill training and rehabilitation, exploring more effective methods of designing and controlling robotic hands, and creating more sophisticated human-computer interaction devices which use complex hand motions as control inputs. This paper presents work on the design, fabrication, and experimental validation of a soft sensor-embedded glove which measures both hand motion and contact pressures during human gesturing and manipulation tasks. We design an array of liquid-metal embedded elastomer sensors to measure up to hundreds of Newtons of interaction forces across the human palm during manipulation tasks and to measure skin strains across phalangeal and carpal joints for joint motion tracking. The elastomeric sensors provide the mechanical compliance necessary to accommodate anatomical variations and permit a normal range of hand motion. We explore methods of assembling this soft sensor glove from modular, individually fabricated pressure and strain sensors and develop design guidelines for their mechanical integration. Experimental validation of a soft finger glove prototype demonstrates the sensitivity range of the designed sensors and the mechanical robustness of the proposed assembly method, and provides a basis for the production of a complete soft sensor glove from inexpensive modular sensor components.


title: AllConcur: Leaderless Concurrent Atomic Broadcast

abstract: Many distributed systems require coordination between the components involved. With the steady growth of such systems, the probability of failures increases, which necessitates scalable fault-tolerant agreement protocols. The most common practical agreement protocol, for such scenarios, is leader-based atomic broadcast. In this work, we propose AllConcur, a distributed system that provides agreement through a leaderless concurrent atomic broadcast algorithm, thus, not suffering from the bottleneck of a central coordinator. In AllConcur, all components exchange messages concurrently through a logical overlay network that employs early termination to minimize the agreement latency. Our implementation of AllConcur supports standard sockets-based TCP as well as high-performance InfiniBand Verbs communications. AllConcur can handle up to 135 million requests per second and achieves 17x higher throughput than today's standard leader-based protocols, such as Libpaxos. Thus, AllConcur is highly competitive with regard to existing solutions and, due to its decentralized approach, enables hitherto unattainable system designs in a variety of fields.


title: Speed control and electrical braking of axial flux BLDC motor

abstract: Axial flux brushless direct current motors (AFBLDC) are becoming popular in many applications including electrical vehicles because of their ability to meet the demand of high power density, high efficiency, wide speed range, robustness, low cost and less maintenance. In this paper, AFBLDC motor drive with single sided configuration having 24 stator poles and 32 permanent magnets on the rotor is proposed. It is driven by six pulse inverter that is fed from a single phase AC supply through controlled AC to DC converter. The speed control and braking methods are also proposed based on pulse width modulation technique. The overall scheme is simulated in MATLAB environment and tested under different operating conditions. A prototype of proposed AFBLDC motor drive is designed and fabricated. The control methods are implemented using DSC dsPIC33EP256MC202 digital signal controller. Tests are performed on this prototype to validate its performance at different speeds with and without braking mode. It is observed that the proposed scheme works effectively and can be used as wheel direct driven motor for electrical vehicle.


title: Exchange constants and spin waves of the orbital ordered, non-collinear spinel MnV$_2$O$_4$

abstract: We study the exchange constants of MnV${}_{2}$O${}_{4}$ using magnetic force theorem and local spin density approximation of density functional theory supplemented with a correction due to on-site Hubbard interaction $U$. We obtain the exchanges for three different orbital orderings of the vanadium atoms of the spinel, two sizes of trigonal distortion, and several values of Coulomb parameter $U$. We then map the exchange constants to a Heisenberg model with single-ion anisotropy and solve for the spin wave excitations in the noncollinear, low-temperature phase of the spinel. The single-ion anisotropy parameters are obtained from an atomic multiplet exact-diagonalization program, taking into effect the crystal-field splitting and the spin-orbit coupling. We find good agreement between the spin waves of one of our orbital ordered setups with previously reported experimental spin waves as determined by neutron scattering. We can therefore determine the correct orbital order from various proposals that exist in the literature.


title: Systems & Design Thinking: A Conceptual Framework for Their Integration

abstract: This paper explores the relationship between Systems and Design Thinking. It specifically looks into the role of Design in Systems Thinking and how looking at the world through a systems lens influences Design. Our intention is to show the critical concepts developed in the Systems and Design Thinking fields, their underlying assumptions, and the ways in which they can be integrated as a cohesive conceptual framework. While there are many important distinctions that must be considered to understand the similarities and differences of these concepts, gaining a complete understanding of these factors is more than can be covered in this paper. Nevertheless, the most critical classifying variable used to distinguish these concepts will be discussed in order to make their integration possible. This variable, the recognition of purposeful behavior, will be used to develop a conceptual vision for how a combined approach can be used to research, plan, design and manage social systems.Systems in which people play the principle role.


title: Argentine tango in Parkinson disease – a systematic review and meta-analysis

abstract: BACKGROUND ::: Parkinson's Disease (PD) is a neurodegenerative disease with increasing motor and non-motor symptoms in advanced stages. In addition to conventional exercise therapy and drug treatment, Argentine Tango (AT) is discussed as an appropriate intervention for patients to improve physical functioning and health-related quality of life. This review aimed to summarize the current research results on the effectiveness of AT for individuals with PD. ::: ::: ::: METHODS ::: The global literature search with the search terms "(Parkinson OR Parkinson's disease) AND tango" was conducted in PubMED, AMED, CAMbase, and Google Scholar for publications in English and German. There were no limitations on the study design, year of publication, stage of disease, considered outcome or the age of participants. ::: ::: ::: RESULTS ::: Thirteen studies met the inclusion criteria. These included 9 randomized-controlled trials, one non-randomized trial, two case studies and one uncontrolled pre-post study. Our meta-analysis revealed significant overall effects in favor of tango for motor severity measured with the Unified Parkinson's Disease Rating Scale 3 (ES = -0.62, 95 % CI [-.1.04, -0.21]), balance as measured with the Mini-BESTest (ES = 0.96 [0.60, 1.31]) or Berg Balance Scale (ES = 0.45 [0.01, 0.90]), and gait with the Timed Up and Go Test (ES = -.46 [-0.72, -0.20]). However, gait as measured with a 6-Minute Walk Test did not demonstrate statistical significance (ES = 0.36 [-0.06, 0.77]). For freezing of gait, no significant effects were observed in favor of AT (ES = 0.16 [-.62, 0.31]). Further, our systematic review revealed a tendency for positive effects on fatigue, activity participation and Parkinson-associated quality of life. A limitation of the studies is the small number of participants in each study (maximum 75). Moreover, most studies are from the same research groups, and only a few are from other researchers. ::: ::: ::: CONCLUSIONS ::: Future studies should enroll more individuals and should also focus on long-term effects. In addition, future research should address more closely the effects of AT on personal relationships, the individual social network as well as on aspects of quality of life.


Cross-lingual Name Tagging and Linking for 282 Languages


title: Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

abstract: Forecasting the flow of crowds is of great importance to traffic management and public safety, yet a very challenging task affected by many complex factors, such as inter-region traffic, events and weather. In this paper, we propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the in-flow and out-flow of crowds in each and every region through a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the framework of the residual neural networks to model the temporal closeness, period, and trend properties of the crowd traffic, respectively. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of the crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We evaluate ST-ResNet based on two types of crowd flows in Beijing and NYC, finding that its performance exceeds six well-know methods.


title: Deeply-Learned Part-Aligned Representations for Person Re-identification

abstract: In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras. We propose a simple yet effective human part-aligned representation for handling the body part misalignment problem. Our approach decomposes the human body into regions (parts) which are discriminative for person matching, accordingly computes the representations over the regions, and aggregates the similarities computed between the corresponding regions of a pair of probe and gallery images as the overall matching score. Our formulation, inspired by attention models, is a deep neural network modeling the three steps together, which is learnt through minimizing the triplet loss function without requiring body part labeling information. Unlike most existing deep learning algorithms that learn a global or spatial partition-based local representation, our approach performs human body partition, and thus is more robust to pose changes and various human spatial distributions in the person bounding box. Our approach shows state-of-the-art results over standard datasets, Market-$1501$, CUHK$03$, CUHK$01$ and VIPeR.


title: Quantification of liver iron overload by T2 quantitative magnetic resonance imaging in thalassemia: impact of chronic hepatitis C on measurements.

abstract: PURPOSE ::: Measurement of liver T2 values seems to be an accurate and sensitive magnetic resonance imaging (MRI) method for the quantification of liver hemosiderosis in multiple transfused patients with thalassemia. Because many of these patients have coexistent chronic hepatitis C virus (HCV) infection, the effect of inflammatory changes on liver T2 values was assessed. ::: ::: ::: MATERIALS AND METHODS ::: Liver MRI studies of 35 HCV+ and 17 HCV- patients with beta-thalassemia, 9 HCV+ patients without thalassemia, and 10 healthy controls of the same age range (13 to 32 years) were reviewed. Iron status was assessed by serum ferritin in all patients, and determination of liver iron concentration (LIC) was available in 16 HCV+ patients with thalassemia. Histologic activity index (HAI) and grades of siderosis were evaluated in all HCV+ patients with thalassemia. ::: ::: ::: RESULTS ::: Patients with thalassemia had significantly lower T2 values (P < 0.0001) than subjects without thalassemia, whereas no difference existed between HCV+ patients without thalassemia and healthy controls. In HCV+ patients, LIC correlated more nearly with T2 values (r = 0.93) than with serum ferritin (r = 0.73). T2 values were not influenced by HAI score or fibrosis. ::: ::: ::: CONCLUSION ::: Liver T2 values were found to be more accurate than serum ferritin in predicting liver iron overload and were not influenced by the presence of chronic hepatitis C. Therefore, MRI could serve as a noninvasive alternative to liver biopsy for the quantification of hemosiderosis in HCV+ patients with thalassemia.


title: Mobile robot navigation system in outdoor pedestrian environment using vision-based road recognition

abstract: We present a mobile robot navigation system guided by a novel vision-based road recognition approach. The system represents the road as a set of lines extrapolated from the detected image contour segments. These lines enable the robot to maintain its heading by centering the vanishing point in its field of view, and to correct the long term drift from its original lateral position. We integrate odometry and our visual road recognition system into a grid-based local map that estimates the robot pose as well as its surroundings to generate a movement path. Our road recognition system is able to estimate the road center on a standard dataset with 25,076 images to within 11.42 cm (with respect to roads at least 3 m wide). It outperforms three other state-of-the-art systems. In addition, we extensively test our navigation system in four busy college campus environments using a wheeled robot. Our tests cover more than 5 km of autonomous driving without failure. This demonstrates robustness of the proposed approach against challenges that include occlusion by pedestrians, non-standard complex road markings and shapes, shadows, and miscellaneous obstacle objects.


title: A framework for ontology-based question answering with application to parasite immunology

abstract: BackgroundLarge quantities of biomedical data are being produced at a rapid pace for a variety of organisms. With ontologies proliferating, data is increasingly being stored using the RDF data model and queried using RDF based querying languages. While existing systems facilitate the querying in various ways, the scientist must map the question in his or her mind to the interface used by the systems. The field of natural language processing has long investigated the challenges of designing natural language based retrieval systems. Recent efforts seek to bring the ability to pose natural language questions to RDF data querying systems while leveraging the associated ontologies. These analyze the input question and extract triples (subject, relationship, object), if possible, mapping them to RDF triples in the data. However, in the biomedical context, relationships between entities are not always explicit in the question and these are often complex involving many intermediate concepts.ResultsWe present a new framework, OntoNLQA, for querying RDF data annotated using ontologies which allows posing questions in natural language. OntoNLQA offers five steps in order to answer natural language questions. In comparison to previous systems, OntoNLQA differs in how some of the methods are realized. In particular, it introduces a novel approach for discovering the sophisticated semantic associations that may exist between the key terms of a natural language question, in order to build an intuitive query and retrieve precise answers. We apply this framework to the context of parasite immunology data, leading to a system called AskCuebee that allows parasitologists to pose genomic, proteomic and pathway questions in natural language related to the parasite, Trypanosoma cruzi. We separately evaluate the accuracy of each component of OntoNLQA as implemented in AskCuebee and the accuracy of the whole system. AskCuebee answers 68 % of the questions in a corpus of 125 questions, and 60 % of the questions in a new previously unseen corpus. If we allow simple corrections by the scientists, this proportion increases to 92 %.ConclusionsWe introduce a novel framework for question answering and apply it to parasite immunology data. Evaluations of translating the questions to RDF triple queries by combining machine learning, lexical similarity matching with ontology classes, properties and instances for specificity, and discovering associations between them demonstrate that the approach performs well and improves on previous systems. Subsequently, OntoNLQA offers a viable framework for building question answering systems in other biomedical domains.


title: Counting in Anonymous Dynamic Networks: An Experimental Perspective

abstract: Counting is a fundamental problem of every distributed system as it represents a basic building block to implement high level abstractions. In anonymous dynamic networks, counting is far from being trivial as nodes have no identity and the knowledge about the network is limited to the local perception of the process itself. Moreover, nodes have to cope with continuous changes of the topology imposed by an external adversary. A relevant example of such kind of networks is represented by wireless sensor networks characterized by the dynamicity of the communication links due to possible collisions or to the presence of duty-cycles aimed at battery preservation. In a companion paper [14], a leader-based algorithms to count the number of processes in an anonymous dynamic network, namely \(\mathcal {A}_{NoK}\), has been proposed. Such algorithm employs a technique that mimics an energy transfer from the anonymous nodes to the leader to converge to an exact count of the number of nodes having no knowledge on the dynamic network. Unfortunately \(\mathcal {A}_{NoK}\) is an unconscious counting algorithm, i.e., the algorithm eventually converges to the exact count but there is no node in the network that is able to detect when this happens. In this paper, we define a new algorithm, called \(\mathcal {A}^*_{NoK}\), by augmenting \(\mathcal {A}_{NoK}\) with a termination heuristic that allows the leader to decide when it should output the current count and we provide an experimental evaluation, for both \(\mathcal {A}_{NoK}\) and \(\mathcal {A}^*_{NoK}\), considering different types of dynamic graphs.


title: Coupled Discriminative Feature Learning for Heterogeneous Face Recognition

abstract: This paper presents a coupled discriminative feature learning (CDFL) method for heterogeneous face recognition (HFR). Different from most existing HFR approaches which use hand-crafted feature descriptors for face representation, our CDFL directly learns discriminative features from raw pixels for face representation. In particular, a couple of image filters is learned in CDFL to simultaneously exploit discriminative information and to reduce the appearance difference of face images captured across different modalities. With the help of the learned filters, CDFL can maximize the interclass variations and minimize the intraclass variations of the learned feature vectors, and meanwhile maximize the correlation of face images of the same person from different modalities by solving a generalized eigenvalue problem. Experimental results on three different heterogeneous face recognition applications show the effectiveness of our proposed approach.


title: A Survey of Evaluation Methods and Measures for Interpretable Machine Learning.

abstract: The need for interpretable and accountable intelligent system gets sensible as artificial intelligence plays more role in human life. Explainable artificial intelligence systems can be a solution by self-explaining the reasoning behind the decisions and predictions of the intelligent system. Researchers from different disciplines work together to define, design and evaluate interpretable intelligent systems for the user. Our work supports the different evaluation goals in interpretable machine learning research by a thorough review of evaluation methodologies used in machine-explanation research across the fields of human-computer interaction, visual analytics, and machine learning. We present a 2D categorization of interpretable machine learning evaluation methods and show a mapping between user groups and evaluation measures. Further, we address the essential factors and steps for a right evaluation plan by proposing a nested model for design and evaluation of explainable artificial intelligence systems.


title: Computer visualization of three-dimensional image data using IMOD.

abstract: Abstract We have developed a computer software package, IMOD, as a tool for analyzing and viewing three-dimensional biological image data. IMOD is useful for studying and modeling data from tomographic, serial section, and optical section reconstructions. The software allows image data to be visualized by several different methods. Models of the image data can be visualized by volume or contour surface rendering and can yield quantitative information.


title: Asherman syndrome—one century later

abstract: Objective To provide an update on the current knowledge of Asherman syndrome. Design Literature review. Setting The worldwide reports of this disease. Patient(s) Patients with Asherman syndrome who presented with amenorrhea or hypomenorrhea, infertility, or recurrent pregnancy loss. Intervention(s) Hysteroscopy and hysteroscopic surgery have been the gold standard of diagnosis and treatment respectively for this condition. Main Outcome Measure(s) The etiology, pathology, symptomatology, diagnosis, treatment, and reproductive outcomes were analyzed. Result(s) This syndrome occurs mainly as a result of trauma to the gravid uterine cavity, which leads to the formation of intrauterine and/or intracervical adhesions. Despite the advances in hysteroscopic surgery, the treatment of moderate to severe Asherman syndrome still presents a challenge. Furthermore, pregnancy after treatment remains high risk with complications including spontaneous abortion, preterm delivery, intrauterine growth restriction, placenta accrete or praevia, or even uterine rupture. Conclusion(s) The management of moderate to severe disease still poses a challenge, and the prognosis of severe disease remains poor. Close antenatal surveillance and monitoring are necessary for women who conceive after treatment.


title: Background Extraction Using Random Walk Image Fusion

abstract: It is important to extract a clear background for computer vision and augmented reality. Generally, background extraction assumes the existence of a clean background shot through the input sequence, but realistically, situations may violate this assumption such as highway traffic videos. Therefore, our probabilistic model-based method formulates fusion of candidate background patches of the input sequence as a random walk problem and seeks a globally optimal solution based on their temporal and spatial relationship. Furthermore, we also design two quality measures to consider spatial and temporal coherence and contrast distinctness among pixels as background selection basis. A static background should have high temporal coherence among frames, and thus, we improve our fusion precision with a temporal contrast filter and an optical-flow-based motionless patch extractor. Experiments demonstrate that our algorithm can successfully extract artifact-free background images with low computational cost while comparing to state-of-the-art algorithms.


title: Product discovery from E-commerce listings via deep text parsing

abstract: Understanding unstructured text in e-commerce catalogs is important for product search and recommendations. In this paper, we tackle the product discovery problem for fashion e-commerce catalogs where each input listing text consists of descriptions of one or more products; each with its own set of attributes. For instance, [this RED printed short top paired with blue jeans makes you go green] contains two products: item top with attributes {pattern=printed, length=short, brand=RED} and item jeans with attributes {color=blue}. The task of product discovery is rendered quite challenging due to the complexity of fashion dictionary (e.g. RED is a brand or green is a metaphor) added to the difficulty of associating attributes to appropriate items (e.g. associating RED brand with item top). Beyond classical attribute extraction task, product discovery entails parsing multi-sentence listings to tag new items and attributes unknown to the underlying schema; at the same time, associating attributes to relevant items to form meaningful products. Towards solving this problem, we propose a novel composition of sequence labeling and multi-task learning as an end-to-end trainable deep neural architecture. We systematically evaluate our approach on one of the largest tagged datasets in e-commerce consisting of 25K listings labeled at word-level. Given 23 labels, we discover label-values with F1 score of 92.2%. To our knowledge, this is the first work to tackle product discovery and show effectiveness of neural architectures on a complex dataset that goes beyond popular datasets for POS tagging and NER.


title: Unsupervised stance classification in online debates

abstract: This paper proposes an unsupervised debate stance classification algorithm. In other words, finding the side a post author is taking in an online debate. Stance detection has a complementary role in information retrieval, opinion mining, text summarization, etc. Existing stance detection techniques are not able to effectively handle two challenges: determine whether a given post is a debate or not? If the post is a debate on a given topic, correctly classify the side that the post author is taking. In this paper, we propose techniques that addresses both the above issues. Compared to existing technique, our technique gives 30% improvement in detection of whether a post is a debate or not. Our technique is able to find the side that an author is taking in a debate by 10% higher F1 score compared to existing work. We achieve this improvement by using new syntactic rules, better aspect popularity detection, co-reference resolution, and a novel integer linear programming model to solve the problem.


title: Annual research review : conceptualising functional impairment in children and adolescents

abstract: Functional impairment is a key factor in the clinical importance of mental health problems in children. Yet, the nature of impairment and criteria for defining and assessing impairment in childhood disorders has been surprisingly overlooked in much of the literature. The current article examines the extant literature on the conceptualisation, nature and assessment of impairment in childhood disorders. Relations between diagnostic symptoms and functional impairment are discussed together with the influence of impairment on diagnostic decisions and prevalence rates. Several factors influencing impairment in childhood such as culture, development and gender are considered. This article concludes with a discussion of the utility of separating judgements of impairment from specific diagnoses, which is proposed for consideration in the forthcoming DSM-5.


title: Investigating serendipity in recommender systems based on real user feedback

abstract: Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-addressed question. According to the most common definition, serendipity consists of three components: relevance, novelty and unexpectedness, where each component has multiple variations. In this paper, we looked at eight different definitions of serendipity and asked users how they perceived them in the context of movie recommendations. We surveyed 475 users of the movie recommender system, MovieLens regarding 2146 movies in total and compared those definitions of serendipity based on user responses. We found that most kinds of serendipity and all the variations of serendipity components broaden user preferences, but one variation of unexpectedness hurts user satisfaction. We found effective features for detecting serendipitous movies according to definitions that do not include this variation of unexpectedness. We also found that different variations of unexpectedness and different kinds of serendipity have different effects on preference broadening and user satisfaction. Among movies users rate in our system, up to 8.5% are serendipitous according to at least one definition of serendipity, while among recommendations that users receive and follow in our system, this ratio is up to 69%.


title: Synthesizing Type-Detection Logic for Rich Semantic Data Types using Open-source Code

abstract: Given a table of data, existing systems can often detect basic atomic types (e.g., strings vs. numbers) for each column. A new generation of data-analytics and data-preparation systems are starting to automatically recognize rich semantic types such as date-time, email address, etc., for such metadata can bring an array of benefits including better table understanding, improved search relevance, precise data validation, and semantic data transformation. However, existing approaches only detect a limited number of types using regular-expression-like patterns, which are often inaccurate, and cannot handle rich semantic types such as credit card and ISBN numbers that encode semantic validations (e.g., checksum). We developed AUTOTYPE from open-source repositories like GitHub. Users only need to provide a set of positive examples for a target data type and a search keyword, our system will automatically identify relevant code, and synthesize type-detection functions using execution traces. We compiled a benchmark with 112 semantic types, out of which the proposed system can synthesize code to detect 84 such types at a high precision. Applying the synthesized type-detection logic on web table columns have also resulted in a significant increase in data types discovered compared to alternative approaches.


title: Inkjet-printed RF energy harvesting and wireless power trasmission devices on paper substrate

abstract: This paper presents an overview of the state-of-the-art inkjet-printed energy harvesting and wireless power transfer technology on paper substrate. RF energy harvesting devices utilizing a charge pump circuit or an active antenna based wireless power transmission device are presented. Inkjet printing technology on paper substrate and fabrication techniques, along with a variety of applications, are presented with particular focus on low-cost and eco-friendly wireless systems for wireless sensor networks.


title: Comparison of Fuzzy Control Rules Using MATLAB Toolbox and Simulink for DC Induction Motor-Speed Control

abstract: This paper presents a fuzzy logic control for a speed control of DC induction motor. The simulation developed by using Fuzzy MATLAB Toolbox and SIMULINK. The fuzzy logic controller is also introduced to the system for keeping the motor speed to be constant when the load varies. Because of the low maintenance and robustness induction motors have many applications in the industries. The speed control of induction motor is more important to achieve maximum torque and efficiency. The result of the 3x3 matrix fuzzy control rules and 5x5 matrix fuzzy control rules of the theta and speed will do comparison in this paper. Observation the effects of the fuzzy control rules on the performance of the DC- induction motor-speed control.


title: Board-level IT governance and organizational performance

abstract: Research on the strategic management of Information Technology (IT) resources has mostly focused on the oversight provided by the management team as a means to increase organizational performance. In recent years, boards of directors have also increased their involvement in IT matters, and various theoretical lenses suggest that this oversight too has the potential to influence organizational performance. Hence, this study synthesizes the resource-based and contingency views of MIS with corporate governance theories, and examines key antecedents and consequences of board-level IT governance (ITG) using a multi-method approach. Structural Equation Modelling analysis applied to organization-level data collected from 171 board members suggested that the level of ITG exercised by boards was contingent upon the organization's ‘IT use mode’, along the two dimensions of need for (a) fast and reliable IT, and (b) new innovative IT. But, the findings further suggested that the contingency approach may be suboptimal because it can cause new ways of leveraging IT to be ignored. High levels of board-level ITG, regardless of existing IT needs, increased organizational performance. This phenomenon was illuminated with applicability checks. Moreover, content analysis and structured interviews with board members further enriched these insights.


title: Semantic background subtraction

abstract: We introduce the notion of semantic background subtraction, a novel framework for motion detection in video sequences. The key innovation consists to leverage object-level semantics to address the variety of challenging scenarios for background subtraction. Our framework combines the information of a semantic segmentation algorithm, expressed by a probability for each pixel, with the output of any background subtraction algorithm to reduce false positive detections produced by illumination changes, dynamic backgrounds, strong shadows, and ghosts. In addition, it maintains a fully semantic background model to improve the detection of camouflaged foreground objects. Experiments led on the CDNet dataset show that we managed to improve, significantly, almost all background subtraction algorithms of the CDNet leaderboard, and reduce the mean overall error rate of all the 34 algorithms (resp. of the best 5 algorithms) by roughly 50% (resp. 20%).


title: A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs

abstract: Low-dimensional vector embeddings, computed using LSTMs or simpler techniques, are a popular approach for capturing the “meaning” of text and a form of unsupervised learning useful for downstream tasks. However, their power is not theoretically understood. The current paper derives formal understanding by looking at the subcase of linear embedding schemes. Using the theory of compressed sensing we show that representations combining the constituent word vectors are essentially information-preserving linear measurements of Bag-of-n-Grams (BonG) representations of text. This leads to a new theoretical result about LSTMs: low-dimensional embeddings derived from a low-memory LSTM are provably at least as powerful on classification tasks, up to small error, as a linear classifier over BonG vectors, a result that extensive empirical work has thus far been unable to show. Our experiments support these theoretical findings and establish strong, simple, and unsupervised baselines on standard benchmarks that in some cases are state of the art among word-level methods. We also show a surprising new property of word embeddings such as GloVe and word2vec: they form a good sensing matrix for text that is more efficient than random matrices, a standard sparse recovery tool, which may explain why they lead to better representations in practice.


title: Measurement of Commercial Peer-to-Peer Live Video Streaming

abstract: Peer-to-peer technologies have proved to be effective for various bandwidth intensive, large scale applications such as file-transfer. For many years, there has been tremendous interest in academic environments for live video streaming as another application of P2P. Recently, a number of new commercial scale video streaming systems have cropped up. These systems differ from others in the type of content that they provide and attract a large number of users from across the globe. These are proprietary systems and very little is known about their architecture and behavior. This study is one of the first of its kind to analyze the performance and characteristics of P2P live streaming applications. In particular, we analyze PPLive and SOPCast, two of the most popular systems in this class. In this paper, we (1) present a framework in which to analyze these P2P applications from a single observable point, (2) analyze control traffic to present a probable operation model and (3) present analysis of resource usage, locality and stability of data distribution. We conclude that P2P live streaming has an even greater impact on network bandwidth utilization and control than P2P file transfer applications.


Taking the Initiative: Political Parties, Primary Elections, and the Constitutional Guarantee of Republican Governance


title: Organizational readiness for implementing change: a psychometric assessment of a new measure

abstract: Organizational readiness for change in healthcare settings is an important factor in successful implementation of new policies, programs, and practices. However, research on the topic is hindered by the absence of a brief, reliable, and valid measure. Until such a measure is developed, we cannot advance scientific knowledge about readiness or provide evidence-based guidance to organizational leaders about how to increase readiness. This article presents results of a psychometric assessment of a new measure called Organizational Readiness for Implementing Change (ORIC), which we developed based on Weiner’s theory of organizational readiness for change. We conducted four studies to assess the psychometric properties of ORIC. In study one, we assessed the content adequacy of the new measure using quantitative methods. In study two, we examined the measure’s factor structure and reliability in a laboratory simulation. In study three, we assessed the reliability and validity of an organization-level measure of readiness based on aggregated individual-level data from study two. In study four, we conducted a small field study utilizing the same analytic methods as in study three. Content adequacy assessment indicated that the items developed to measure change commitment and change efficacy reflected the theoretical content of these two facets of organizational readiness and distinguished the facets from hypothesized determinants of readiness. Exploratory and confirmatory factor analysis in the lab and field studies revealed two correlated factors, as expected, with good model fit and high item loadings. Reliability analysis in the lab and field studies showed high inter-item consistency for the resulting individual-level scales for change commitment and change efficacy. Inter-rater reliability and inter-rater agreement statistics supported the aggregation of individual level readiness perceptions to the organizational level of analysis. This article provides evidence in support of the ORIC measure. We believe this measure will enable testing of theories about determinants and consequences of organizational readiness and, ultimately, assist healthcare leaders to reduce the number of health organization change efforts that do not achieve desired benefits. Although ORIC shows promise, further assessment is needed to test for convergent, discriminant, and predictive validity.


Model of a Heat Recovery Steam Generator ( HRSG ) Process and Simulation in Steady State and Transient Operation


title: Sex Differences in the Developmental Trajectories of Impulse Control and Sensation-Seeking from Early Adolescence to Early Adulthood

abstract: It has been proposed that high rates of risk-taking in adolescence are partly attributable to patterns of neurobiological development that promote an increase in sensation-seeking tendencies at a time when impulse control is still developing. It is not known, however, whether this pattern is the same for males and females. The present study investigates sex differences in the developmental trajectories of self-reported impulse control and sensation-seeking between the ages of 10 and 25 using longitudinal data from the National Longitudinal Study of Youth 1979 Child and Young Adult Survey (N = 8,270; 49 % female; 33 % Black, 22 % Hispanic, 45 % Non-Black, Non-Hispanic). Prior work has found that, consistent with the dual-systems model of adolescent neurobiological development, sensation-seeking rises and falls across this age span, whereas impulse control increases into the 20s. In the present study, we find that this same general pattern holds for both males and females, but with some key differences. As expected, males exhibit higher levels of sensation-seeking and lower levels of impulse control than females. Differences also emerged in the shapes of the developmental trajectories. Females reach peak levels of sensation-seeking earlier than males (consistent with the idea that sensation-seeking is linked to pubertal development) and decline in sensation-seeking more rapidly thereafter. Also, males increase in impulse control more gradually than females. Consequently, sex differences in both impulse control and sensation-seeking increase with age. The findings suggest that the window of heightened vulnerability to risk-taking during adolescence may be greater in magnitude and more protracted for males than for females.


title: Unreeling netflix: Understanding and improving multi-CDN movie delivery

abstract: Netflix is the leading provider of on-demand Internet video streaming in the US and Canada, accounting for 29.7% of the peak downstream traffic in US. Understanding the Netflix architecture and its performance can shed light on how to best optimize its design as well as on the design of similar on-demand streaming services. In this paper, we perform a measurement study of Netflix to uncover its architecture and service strategy. We find that Netflix employs a blend of data centers and Content Delivery Networks (CDNs) for content distribution. We also perform active measurements of the three CDNs employed by Netflix to quantify the video delivery bandwidth available to users across the US. Finally, as improvements to Netflix's current CDN assignment strategy, we propose a measurement-based adaptive CDN selection strategy and a multiple-CDN-based video delivery strategy, and demonstrate their potentials in significantly increasing user's average bandwidth.


title: Speech Recognition using Hidden Markov Model

abstract: 3 Abstract.Speech technology and systems in human computer interaction have witnessed a steady and important advancement over last two decades. Today, speech technologies are commercially available for boundless but interesting range of tasks. These technologies permit machines to respond correctly and consistently to human voices, and provide useful and valuable services. In the present era, mainly Hidden Markov Model (HMMs) based speech recognizers are used. This paper aims to present a speech recognition system using Hidden Markov Model. Hidden Markov Model Toolkit (HTK) is used to develop the system. It is used to recognize the isolated words using acoustic word model.


title: Distributed Recurrent Neural Networks for Cooperative Control of Manipulators: A Game-Theoretic Perspective

abstract: This paper considers cooperative kinematic control of multiple manipulators using distributed recurrent neural networks and provides a tractable way to extend existing results on individual manipulator control using recurrent neural networks to the scenario with the coordination of multiple manipulators. The problem is formulated as a constrained game, where energy consumptions for each manipulator, saturations of control input, and the topological constraints imposed by the communication graph are considered. An implicit form of the Nash equilibrium for the game is obtained by converting the problem into its dual space. Then, a distributed dynamic controller based on recurrent neural networks is devised to drive the system toward the desired Nash equilibrium to seek the optimal solution of the cooperative control. Global stability and solution optimality of the proposed neural networks are proved in the theory. Simulations demonstrate the effectiveness of the proposed method.


title: REALM: A rule-based evolutionary computation agent that learns to play Mario

abstract: REALM is a rule-based evolutionary computation agent for playing a modified version of Super Mario Bros. according to the rules stipulated in the Mario AI Competition held in the 2010 IEEE Symposium on Computational Intelligence and Games. Two alternate representations for the REALM rule sets are reported here, in both hand-coded and learned versions. Results indicate that the second version, with an abstracted action set, tends to perform better overall, but the first version shows a steeper learning curve. In both cases, learning quickly surpasses the hand-coded rule sets.


title: Unsupervised Multi-Domain Adaptation with Feature Embeddings

abstract: Representation learning is the dominant technique for unsupervised domain adaptation, but existing approaches often require the specification of "pivot features" that generalize across domains, which are selected by task-specific heuristics. We show that a novel but simple feature embedding approach provides better performance, by exploiting the feature template structure common in NLP problems.


title: A Novel Bottom Spacer FinFET Structure for Improved Short-Channel, Power-Delay, and Thermal Performance

abstract: For the first time, we propose a novel bottom spacer fin-shaped field-effect-transistor (FinFET) structure for logic applications suitable for system-on-chip (SoC) requirements. The proposed device achieved improved short-channel, power-delay, and self-heating performance compared with standard silicon-on-insulator FinFETs. Process aspects of the proposed device are also discussed in this paper. Physical insight into the improvement toward the short-channel performance and power dissipation is given through a detailed 3-D device/mixed-mode simulation. The self-heating behavior of the proposed device is compared with standard FinFETs by using detailed electrothermal simulations. The proposed device requires an extra process step but enables smaller electrical width for self-loaded circuits and is an excellent option for SoC applications.


title: Convergent activation dynamics in continuous time networks

abstract: Abstract The activation dynamics of nets are considered from a rigorous mathematical point of view. A net is identified with the dynamical system defined by a continuously differentiable vector field on the space of activation vectors, with fixed weights, biases, and inputs. Chaotic and oscillatory nets are briefly discussed, but the main goal is to find conditions guaranteeing that the trajectory of every (or almost every) initial activation state converges to an equilibrium. Several new results of this type are proved. These are illustrated with applications to additive nets. Cascades of nets are considered and a cascade decomposition theorem is proved. An extension of the Cohen-Grossberg convergence theorem is proved for certain nets with nonsymmetric weight matrices.


title: Line-of-sight stabilization system based on fractional-order control

abstract: The line-of-sight (LOS) stabilization control is required to isolate LOS from the movement and vibration of carrier and ensure pointing and tracking for target in electro-optical tracking system. In this paper, a fractional-order PI (FOPI) controller is applied in the design of stabilization loop of LOS stabilization system based on the analysis of the fractional calculus theory. This controller exhibits excellent performance even in presence of nonlinearity and uncertainty. Details of this controller along with the performance comparisons between FOPI and conventional integer-order PI (IOPI) are presented. Simulation results indicate that the FOPI controller can improve the carrier disturbance rejection performance of LOS stabilization system.


Imitation Learning with THOR


title: A compact modified corporate feed network for antenna arrays with non-identical rectangular microstrip antenna elements

abstract: An antenna array with modified compact feed has been designed with non-identical elements using cosine square amplitude distribution with the purpose of reducing the first side lobe level. The proposed feed network uses an overall area of 25.47% less than that of a similar array with conventional corporate feed network. Experimental results of this six element array validate the theoretical parameters.


title: IT asset management of industrial automation systems

abstract: The installation and administration of large heterogeneous IT infrastructures, for enterprises as well as industrial automation systems, are becoming more and more complex and time consuming. Industrial automation systems, such as those delivered by ABB Inc., present an additional challenge, in that these control and supervise mission critical production sites. Nevertheless, it is common practice to manually install and maintain industrial networks and the process control software running on them, which can be both expensive and error prone. In order to address these challenges, we believe that in the long term such systems must behave autonomously. As preliminary steps to the realization of this vision, automated IT asset management tools and practices will be highlighted in this contribution. We will point out the advantages of combining process control and network management in the domain of industrial automation technology. Furthermore we will propose a new component model for autonomic network management applied to industrial automation systems.


[12]Annulene Gemini Surfactants: Structure and Self‐Assembly


title: Predicting collective sentiment dynamics from time-series social media

abstract: More and more people express their opinions on social media such as Facebook and Twitter. Predictive analysis on social media time-series allows the stake-holders to leverage this immediate, accessible and vast reachable communication channel to react and proact against the public opinion. In particular, understanding and predicting the sentiment change of the public opinions will allow business and government agencies to react against negative sentiment and design strategies such as dispelling rumors and post balanced messages to revert the public opinion. In this paper, we present a strategy of building statistical models from the social media dynamics to predict collective sentiment dynamics. We model the collective sentiment change without delving into micro analysis of individual tweets or users and their corresponding low level network structures. Experiments on large-scale Twitter data show that the model can achieve above 85% accuracy on directional sentiment prediction.


title: A Bayesian approach to unsupervised one-shot learning of object categories

abstract: Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and richness of object appearance which requires models containing hundreds of parameters. We present a method for learning object categories from just a few images (1 /spl sim/ 5). It is based on incorporating "generic" knowledge which may be obtained from previously learnt models of unrelated categories. We operate in a variational Bayesian framework: object categories are represented by probabilistic models, and "prior" knowledge is represented as a probability density function on the parameters of these models. The "posterior" model for an object category is obtained by updating the prior in the light of one or more observations. Our ideas are demonstrated on four diverse categories (human faces, airplanes, motorcycles, spotted cats). Initially three categories are learnt from hundreds of training examples, and a "prior" is estimated from these. Then the model of the fourth category is learnt from 1 to 5 training examples, and is used for detecting new exemplars a set of test images.


title: The characteristics and effects of motivational music in exercise settings: the possible influence of gender, age, frequency of attendance, and time of attendance.

abstract: AIM ::: The purpose of the present study was to investigate the characteristics and effects of motivational music in British gymnasia. The secondary purpose was to determine whether the characteristics and effects of motivational music were invariant in relation to gender, age, frequency of gymnasium attendance, and the time of day at which exercise participants attended gymnasia. ::: ::: ::: METHODS ::: Participants (n=532) from 29 David Lloyd Leisure exercise facilities across Britain responded to a questionnaire that was designed to assess music preferences during exercise via 2 open-ended questions and 1 scaled-response item. ::: ::: ::: RESULTS ::: A content analysis of the questionnaire data yielded 45 analytic properties that were grouped into the following categories: specific music factors, general music factors, music programme factors, delivery factors, televisual factors, personal factors, contextual factors, and psychophysical response factors. The relative incidence of these analytic properties across gender groups (male/female), age groups (16-26 y, 27-34 y, 35-45 y, 46+ y), frequency of attendance groups (low, medium, high), and time of attendance groups (morning, afternoon, evening) was tested by use of chi(2) analyses. Of the personal variables tested, age exerted the greatest influence on musical preference during exercise; older participants expressed a preference for quieter, slower, and generally less overtly stimulative music. ::: ::: ::: CONCLUSION ::: Music programmes that are prescribed to accompany exercise should be varied in terms of musical idiom and date of release. Such programmes will account for the preferences of different groups of exercise participants that attend gymnasia at different times of the day. Further, the music chosen should be characterised by a strong rhythmical component.


title: Endovascular Coil Trapping of a Ruptured Dissecting Aneurysm of the Vertebral Artery Using Detachable Coils and Micro-Tornado® Coils

abstract: We experienced a patient with a ruptured dissecting aneurysm of the vertebral artery who was treated by trapping of the lesion using Guglielmi detachable coils (GDCs) with micro-tornado® coils (MTCs). An 80-year-old male was transferred with a ruptured left vertebral artery dissecting aneurysm (VADA). The dissected portion of the vertebral artery was effectively trapped using GDCs and MTCs. The MTCs used for neurointervention were comprised of various types of coils and we successfully placed them into the parent artery of the dissected segment. The author suggests that this case demonstrates the usefulness of endovascular coil trapping of VADAs using MTCs in achievement of embolization.


title: Digitizing paper forms with mobile imaging technologies

abstract: In low-resource settings in developing countries, most records are still captured and maintained using paper forms. Despite a recent proliferation of digital data collection systems, paper forms remain a trusted, low-cost and ubiquitous medium that will continue to be utilized in these communities for years to come. However, it can be challenging to aggregate, share, and analyze the data collected using paper forms. This paper presents mScan, a mobile smartphone application that uses computer vision to capture data from paper forms that use a multiple choice or bubble format. The initial mScan implementation targets the task of digitizing paper forms used to record vaccine statistics in rural health centers in Mozambique. We have evaluated the accuracy and performance of mScan under a variety of different environmental conditions, and our results show that mScan is a robust tool that is capable of accurately capturing and digitizing data from paper forms.


title: Modeling Avoidance in Mood and Anxiety Disorders Using Reinforcement Learning

abstract: Abstract Background Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behavior—avoiding social situations for fear of embarrassment, for instance—is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear. Methods Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. Results We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress. Conclusions This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders.


title: Obstacle avoidance strategies for autonomous surface vehicles

abstract: Autonomous Surface Vehicles (ASVs) rely on two layers of guidance for effective path planning: global and local methods. Global methods require a macroscopic view of the mission's goals in order to efficiently navigate an environment. For this research, different global path planner algorithms are explored both in simulation and in field tests to observe path planner qualities for modularity, practicality, and robustness for future integration with local, reactive obstacle avoidance. Three obstacle configurations are tested to study situational approaches for each algorithm. With appropriate inputs for path planning parameters (i.e. connection distance, number of nodes, and iterations), numerical simulations are able to find a feasible path. Results demonstrate path following robustness in response to a generic ASV craft with only a roughly tuned heading and speed controller.


title: Opcode sequences as representation of executables for data-mining-based unknown malware detection

abstract: Malware can be defined as any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing faster every year and poses a serious global security threat. Consequently, malware detection has become a critical topic in computer security. Currently, signature-based detection is the most widespread method used in commercial antivirus. In spite of the broad use of this method, it can detect malware only after the malicious executable has already caused damage and provided the malware is adequately documented. Therefore, the signature-based method consistently fails to detect new malware. In this paper, we propose a new method to detect unknown malware families. This model is based on the frequency of the appearance of opcode sequences. Furthermore, we describe a technique to mine the relevance of each opcode and assess the frequency of each opcode sequence. In addition, we provide empirical validation that this new method is capable of detecting unknown malware.


title: Resource Allocation for Energy Harvesting-Powered D2D Communication Underlaying UAV-Assisted Networks

abstract: In this paper, we investigate the resource allocation problem for unmanned aerial vehicle (UAV)-assisted networks, where a UAV acting as an energy source provides radio frequency energy for multiple energy harvesting-powered device-to-device (D2D) pairs with much information to be transmitted. The goal is to maximize the average throughput within a time horizon while satisfying the energy causality constraint under a generalized harvest-transmit-store model, which results in a non-convex problem. By introducing the Lagrangian relaxation method, we analytically show that the behavior of all D2D pairs at each time slot is exclusive: harvesting energy or transmitting information signals. The formulated non-convex optimization problem is thus transformed into a mixed integer nonlinear programming (MINIP). We then design an efficient resource allocation algorithm to solve this MINIP, where D.C. (difference of two convex functions) programming and golden section method are combined to achieve a suboptimal solution. Furthermore, we provide an idea to reduce the computational complexity for facilitating the application in practice. Simulations are conducted to validate the effectiveness of the proposed algorithm and evaluate the system throughput performance.


title: The Impact of Power Allocation on Cooperative Non-orthogonal Multiple Access Networks With SWIPT

abstract: In this paper, a cooperative non-orthogonal multiple access (NOMA) network is considered, where a source communicates with two users through an energy harvesting relay. The impact of two types of NOMA power allocation policies, namely NOMA with fixed power allocation (F-NOMA) and cognitive radio inspired NOMA (CR-NOMA), on the considered cooperative simultaneous wireless information and power transfer (SWIPT) system is investigated. In particular, closed-form expressions for the outage probability and their high SNR approximations are derived to characterize the performance of SWIPT-F-NOMA and SWIPT-CR-NOMA. These developed analytical results demonstrate that the two power allocation policies realize different tradeoffs between the reception reliability, user fairness and system complexity. Compared with the conventional SWIPT relaying networks with orthogonal multiple access (OMA), the proposed NOMA schemes can effectively reduce the outage probability, although all of them realize the same diversity gain.


Generalizing data to provide anonymity when disclosing information (abstract)


title: Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

abstract: We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.


A Bayesian Analysis of Some Forms of Inductive Reasoning


title: Direct Model Reference Adaptive Internal Model Controller using Perrin equation Adjustment Mechanism for DFIG Wind Farms

abstract: This paper presents the direct model reference adaptive internal model controller for doubly fed induction generator used in wind farms based on MIT rule adjustment mechanism in addition to Perrin equation adjustment mechanisms with improved internal model controller filter design. Direct model reference adaptive internal model controller is developed and it is tuned using MIT rule adjustment mechanism with adjustment mechanism derived from Perrin equation. Rotor current controller is designed using direct model reference adaptive internal model controller to improve the robust performance of the system. The performance is compared with both method and it is described. Flexibility of proposed method in shunt connection is illustrated, dynamic response under fault causes sag condition, like sudden drop in terminal voltage changes affecting the behavior of the machine, this phenomenon is simulated using MATLAB/ SIMULINK and it is presented.


title: Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia

abstract: Detection of cognitive impairment, especially at the early stages, is critical. Such detection has traditionally been performed manually by one or more clinicians based on reports and test results. Machine learning algorithms offer an alternative method of detection that may provide an automated process and valuable insights into diagnosis and classification. In this paper, we explore the use of neuropsychological and demographic data to predict Clinical Dementia Rating (CDR) scores (no dementia, very mild dementia, dementia) and clinical diagnoses (cognitively healthy, mild cognitive impairment, dementia) through the implementation of four machine learning algorithms, naive Bayes (NB), C4.5 decision tree (DT), back-propagation neural network (NN), and support vector machine (SVM). Additionally, a feature selection method for reducing the number of neuropsychological and demographic data needed to make an accurate diagnosis was investigated. The NB classifier provided the best accuracies, while the SVM classifier proved to offer some of the lowest accuracies. We also illustrate that with the use of feature selection, accuracies can be improved. The experiments reported in this paper indicate that artificial intelligence techniques can be used to automate aspects of clinical diagnosis of individuals with cognitive impairment.


title: In-Air Ultrasonic 3D-Touchscreen with Gesture Recognition Using Existing Hardware for Smart Devices

abstract: A versatile and intuitive way of interacting with smart devices consists of a 3D-touchscreen in the air above the device, combined with gesture recognition. In this paper, the limitations of hand tracking and gesture recognition using existing hardware in smartphones will be investigated. To accomplish hand tracking and gesture recognition, the two loudspeakers of the device send ultrasonic tones in combination with a Maximum LengthSequence(MLS), after which the microphones pick up the potentially altered reflected signal again. The Doppler-response on the tones yields information about the speed of the hand above the device and the acoustic channel estimation obtained from the MLS estimates the distance at which the hand is present. The position estimation module estimates the 3D-position of the hand in a predefined grid of eight regions and hereby achieves an accuracy above 77 % in all cases. For the gesture recognition, features are extracted from both the Doppler-response as well as the acoustic channel estimation. A simple decision tree then classifies the performed gesture. Experimental results show that the accuracy of the gesture recognition module achieves an accuracy between 87 and 100 %.


title: Rule-based system for automated classification of non-functional requirements from requirement specifications

abstract: Unmasking the non-functional requirements (NFRs) such as quality attributes, interface requirements and design constraints of software is crucial in finding the architectural alternatives for software starting from early design opinions. For developing quality software product, extraction of NFRs from requirement documents is needed to be carried out and it's beneficiary if this process becomes automated, reducing the human efforts, time and mental fatigue involved in identifying specific requirements from a large number of requirements in a document. The proposal presented in this paper combines automated identification and classification of requirement sentences into NFR sub-classes with the help of rule-based classification technique using thematic roles and identifying the priority of extracted NFR sentences within the document according to their occurrence in multiple NFR classes. F1-measure of 97% is obtained on PROMISE corpus and 94% F1-Measure on Concordia RE corpus. The results established validates the claim that proposal provides specific and higher results than the previous state of art approaches.


title: Digital signatures with RSA and other public-key cryptosystems

abstract: Public-key signature systems can be vulnerable to attack if the protocols for signing messages allow a cryptanalyst to obtain signatures on arbitrary messages of the cryptanalyst's choice. This vulnerability is shown to arise from the homomorphic structure of public- key systems. A method of foiling the attack is described.


title: The Research and Analysis in Decision Tree Algorithm Based on C4.5 Algorithm

abstract: On the base of introducing the Decision Tree, this article lists two main algorithms of Decision Tree. After describing the principle of briefly, it reveals the essential idea and the computing process of C4.5 Algorithm in detail. As constructing a Decision Tree on a group of instance data with the two algorithms, we get the principle of classification about the data. By comparing the two algorithms during the process of constructing the tree, we analysis the advantages and disadvantages of them finally. With the conclusion of the applied scope of each algorithm, it provides a theoretical basis for choosing an appropriate method in practical application.


title: Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy

abstract: We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the large-scale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.


title: ZMP Support Areas for Multicontact Mobility Under Frictional Constraints

abstract: We propose a method for checking and enforcing multi-contact stability based on the Zero-tilting Moment Point (ZMP). The key to our development is the generalization of ZMP support areas to take into account (a) frictional constraints and (b) multiple non-coplanar contacts. We introduce and investigate two kinds of ZMP support areas. First, we characterize and provide a fast geometric construction for the support area generated by valid contact forces, with no other constraint on the robot motion. We call this set the full support area. Next, we consider the control of humanoid robots using the Linear Pendulum Mode (LPM). We observe that the constraints stemming from the LPM induce a shrinking of the support area, even for walking on horizontal floors. We propose an algorithm to compute the new area, which we call pendular support area. We show that, in the LPM, having the ZMP in the pendular support area is a necessary and sufficient condition for contact stability. Based on these developments, we implement a whole-body controller and generate feasible multi-contact motions where an HRP-4 humanoid locomotes in challenging multi-contact scenarios.


title: Responses to depression and their effects on the duration of depressive episodes.

abstract: I propose that the ways people respond to their own symptoms of depression influence the duration of these symptoms. People who engage in ruminative responses to depression, focusing on their symptoms and the possible causes and consequences of their symptoms, will show longer depressions than people who take action to distract themselves from their symptoms. Ruminative responses prolong depression because they allow the depressed mood to negatively bias thinking and interfere with instrumental behavior and problem-solving. Laboratory and field studies directly testing this theory have supported its predictions. I discuss how response styles can explain the greater likelihood of depression in women than men. Then I intergrate this response styles theory with studies of coping with discrete events. The response styles theory is compared to other theories of the duration of depression. Finally, I suggest what may help a depressed person to stop engaging in ruminative responses and how response styles for depression may develop.


title: mrFPGA: A novel FPGA architecture with memristor-based reconfiguration

abstract: In this paper, we introduce a novel FPGA architecture with memristor-based reconfiguration (mrFPGA). The proposed architecture is based on the existing CMOS-compatible memristor fabrication process. The programmable interconnects of mrFPGA use only memristors and metal wires so that the interconnects can be fabricated over logic blocks, resulting in significant reduction of overall area and interconnect delay but without using a 3D die-stacking process. Using memristors to build up the interconnects can also provide capacitance shielding from unused routing paths and reduce interconnect delay further. Moreover we propose an improved architecture that allows adaptive buffer insertion in interconnects to achieve more speedup. Compared to the fixed buffer pattern in conventional FPGAs, the positions of inserted buffers in mrFPGA are optimized on demand. A complete CAD flow is provided for mrFPGA, with an advanced P&R tool named mrVPR that was developed for mrFPGA. The tool can deal with the novel routing structure of mrFPGA, the memristor shielding effect, and the algorithm for optimal buffer insertion. We evaluate the area, performance and power consumption of mrFPGA based on the 20 largest MCNC benchmark circuits. Results show that mrFPGA achieves 5.18x area savings, 2.28x speedup and 1.63x power savings. Further improvement is expected with combination of 3D technologies and mrFPGA.


title: Analysis of focus measure operators for shape-from-focus

abstract: Shape-from-focus (SFF) has widely been studied in computer vision as a passive depth recovery and 3D reconstruction method. One of the main stages in SFF is the computation of the focus level for every pixel of an image by means of a focus measure operator. In this work, a methodology to compare the performance of different focus measure operators for shape-from-focus is presented and applied. The selected operators have been chosen from an extensive review of the state-of-the-art. The performance of the different operators has been assessed through experiments carried out under different conditions, such as image noise level, contrast, saturation and window size. Such performance is discussed in terms of the working principles of the analyzed operators.


title: The Value of Intraoperative Near-Infrared Fluorescence Imaging Based on Enhanced Permeability and Retention of Indocyanine Green: Feasibility and False-Positives in Ovarian Cancer

abstract: OBJECTIVE ::: In ovarian cancer, two of the most important prognostic factors for survival are completeness of staging and completeness of cytoreductive surgery. Therefore, intra-operative visualization of tumor lesions is of great importance. Preclinical data already demonstrated tumor visualization in a mouse-model using near-infrared (NIR) fluorescence imaging and indocyanine green (ICG) as a result of enhanced permeability and retention (EPR). The aim of this study was to determine feasibility of intraoperative ovarian cancer metastases imaging using NIR fluorescence imaging and ICG in a clinical setting. ::: ::: ::: METHODS ::: Ten patients suspected of ovarian cancer scheduled for staging or cytoreductive surgery were included. Patients received 20 mg ICG intravenously after opening the abdominal cavity. The mini-FLARE NIR fluorescence imaging system was used to detect NIR fluorescent lesions. ::: ::: ::: RESULTS ::: 6 out of 10 patients had malignant disease of the ovary or fallopian tube, of which 2 had metastatic disease outside the pelvis. Eight metastatic lesions were detected in these 2 patients, which were all NIR fluorescent. However, 13 non-malignant lesions were also NIR fluorescent, resulting in a false-positive rate of 62%. There was no significant difference in tumor-to-background ratio between malignant and benign lesions (2.0 vs 2.0; P=0.99). ::: ::: ::: CONCLUSIONS ::: This is the first clinical trial demonstrating intraoperative detection of ovarian cancer metastases using NIR fluorescence imaging and ICG. Despite detection of all malignant lesions, a high false-positive rate was observed. Therefore, NIR fluorescence imaging using ICG based on the EPR effect is not satisfactory for the detection of ovarian cancer metastases. The need for tumor-specific intraoperative agents remains. ::: ::: ::: TRIAL REGISTRATION ::: ISRCTN Registry ISRCTN16945066.


title: JSForce: A Forced Execution Engine for Malicious JavaScript Detection

abstract: The drastic increase of JavaScript exploitation attacks has led to a strong interest in developing techniques to enable malicious JavaScript analysis. Existing analysis tech- niques fall into two general categories: static analysis and dynamic analysis. Static analysis tends to produce inaccurate results (both false positive and false negative) and is vulnerable to a wide series of obfuscation techniques. Thus, dynamic analysis is constantly gaining popularity for exposing the typical features of malicious JavaScript. However, existing dynamic analysis techniques possess limitations such as limited code coverage and incomplete environment setup, leaving a broad attack surface for evading the detection. To overcome these limitations, we present the design and implementation of a novel JavaScript forced execution engine named JSForce which drives an arbitrary JavaScript snippet to execute along different paths without any input or environment setup. We evaluate JSForce using 220,587 HTML and 23,509 PDF real- world samples. Experimental results show that by adopting our forced execution engine, the malicious JavaScript detection rate can be substantially boosted by 206.29% using same detection policy without any noticeable false positive increase. We also make JSForce publicly available as an online service and will release the source code to the security community upon the acceptance for publication.