title: Chronic plantar fasciitis: Plantar fasciotomy versus gastrocnemius recession

abstract: PURPOSE ::: The purpose of this study was to compare results of partial proximal fasciotomy (PPF) with proximal medial gastrocnemius release (PMGR) in the treatment of chronic plantar fasciitis (CPF). ::: ::: ::: METHOD ::: This retrospective study compares 30 patients with CPF that underwent PPF with 30 that underwent isolated PMGR. Both groups were matched in terms of previous treatments and time from onset of symptoms to surgery. Different standardised evaluation scales (VAS, Likert, AOFASh) were used to evaluate results. ::: ::: ::: RESULTS ::: Plantar fasciotomy had satisfactory results in just 60 % of patients, with an average ten weeks needed to resume work and sports. Patient satisfaction in the PMGR group reached 95 %, being back to work and sports at three weeks on average. Functional and pain scores were considerably better for PMGR and fewer complications registered. ::: ::: ::: CONCLUSION ::: In our series, isolated PMGR is a simple and reliable procedure to treat patients with CPF. It provides far better results than conventional fasciotomy with less morbidity and better patient satisfaction, and thus has become our surgical procedure of choice in recalcitrant CPF.


title: Distributed top-k monitoring

abstract: The querying and analysis of data streams has been a topic of much recent interest, motivated by applications from the fields of networking, web usage analysis, sensor instrumentation, telecommunications, and others. Many of these applications involve monitoring answers to continuous queries over data streams produced at physically distributed locations, and most previous approaches require streams to be transmitted to a single location for centralized processing. Unfortunately, the continual transmission of a large number of rapid data streams to a central location can be impractical or expensive. We study a useful class of queries that continuously report the k largest values obtained from distributed data streams ("top-k monitoring queries"), which are of particular interest because they can be used to reduce the overhead incurred while running other types of monitoring queries. We show that transmitting entire data streams is unnecessary to support these queries and present an alternative approach that reduces communication significantly. In our approach, arithmetic constraints are maintained at remote stream sources to ensure that the most recently provided top-k answer remains valid to within a user-specified error tolerance. Distributed communication is only necessary on occasion, when constraints are violated, and we show empirically through extensive simulation on real-world data that our approach reduces overall communication cost by an order of magnitude compared with alternatives that o er the same error guarantees.


title: Social Computing for Verifying Social Media Content in Breaking News

abstract: Social media is the place to go for both journalists and the general public when news events break, offering a real-time source of eyewitness images and videos through platforms like YouTube, Instagram, and Periscope. Yet, the value of such content as a means of documenting and disseminating breaking news is compromised by the increasing amount of content misuse and false claims in social media. To this end, cost-effective social computing solutions for user-generated content verification are crucial for retaining the value and trust in social media for breaking news.


title: Virtual reality for inducing empathy and reducing prejudice towards stigmatized groups: A survey

abstract: This paper gives an up-to-date overview of research about Virtual Reality (VR) for inducing empathy and reducing prejudice towards stigmatized groups and the measurements used in the studies. Outcomes from the studies reviewed provide only preliminary support for the use of VR for successfully inducing empathy into people and reducing their prejudice towards stigmatized groups and the preference for using self-report methods for the measurement of empathy and prejudice.


title: Characterization and performance comparison of ripple-based control for voltage regulator modules

abstract: Three recently developed control methods for voltage regulator modules, namely, V/sup 2/ control, enhanced V/sup 2/ control, and enhanced V/sup 2/ control without output voltage dynamic feedback, are analyzed and compared in this paper. All three methods utilize the output voltage switching ripple for pulse-width modulation (PWM), hence, are collectively referred to as ripple-based control. A general modeling method based on the Krylov-Bogoliubov-Mitropolsky ripple estimation technique is applied to develop averaged models for single-channel as well as multichannel buck converters employing each of the control methods. Unlike existing models that are limited to small-signal operation, the proposed models are valid for large-signal operation and are capable of predicting subharmonic instability without including any sample-and-hold block as used in previous models. The paper also shows that adding parallel, high-quality ceramic capacitors at the output, which are ignored in previous models, can lead to pulse skipping and ripple instability, and a solution based on proper selection of the ceramic capacitors and/or ramp compensation at the PWM is presented. The models are further applied to analyze and compare the performance of the three control methods in terms of ripple stability, effective load current feedforward gain, and output impedance.


title: Unified Modeling Language for Knowledge Modelling

abstract: Knowledge modelling techniques are widely adopted for designing knowledge-based systems (KBS) and these systems are important for knowledge management. This paper argues the need and importance of adopting Unified Modeling Language (UML) for knowledge modelling through the profile extension mechanism. KBS are developed using knowledge engineering (KE) techniques that are often similar to software engineering (SE), and both domains emphasise the use of conceptual models in designing software systems. Most of these knowledge models heavily rely on modelling notations used in software engineering; in this the UML is widely adopted. UML can be formally tailored for knowledge modelling by creating a UML profile for knowledge modelling


title: Datavis: a visual programming language for scientific visualization

abstract: A memory controller controls the operation of a number of memory module units and includes a number of queues which couple to the module units. Each queue includes an address queue register, a control queue register and a data queue register. Each address queue register has tristate control for independent operation. Control circuits which couple to the queue address, control and data registers assign memory cycles between queues on an alternate basis when the queue control registers store requests which are being processed. This enables the interleaving of memory requests which eliminates processing delays particularly in cases where such requests involve multiword transfers over successive memory cycles of operation.


title: Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest

abstract: Abstract Three-dimensional atlases and databases of the brain at different ages facilitate the description of neuroanatomy and the monitoring of cerebral growth and development. Brain segmentation is challenging in young children due to structural differences compared to adults. We have developed a method, based on established algorithms, for automatic segmentation of young children's brains into 83 regions of interest (ROIs), and applied this to an exemplar group of 33 2-year-old subjects who had been born prematurely. The algorithm uses prior information from 30 normal adult brain magnetic resonance (MR) images, which had been manually segmented to create 30 atlases, each labeling 83 anatomical structures. Each of these adult atlases was registered to each 2-year-old target MR image using non-rigid registration based on free-form deformations. Label propagation from each adult atlas yielded a segmentation of each 2-year-old brain into 83 ROIs. The final segmentation was obtained by combination of the 30 propagated adult atlases using decision fusion, improving accuracy over individual propagations. We validated this algorithm by comparing the automatic approach with three representative manually segmented volumetric regions (the subcortical caudate nucleus, the neocortical pre-central gyrus and the archicortical hippocampus) using similarity indices (SI), a measure of spatial overlap (intersection over average). SI results for automatic versus manual segmentations for these three structures were 0.90 ± 0.01, 0.90 ± 0.01 and 0.88 ± 0.03 respectively. This registration approach allows the rapid construction of automatically labelled age-specific brain atlases for children at the age of 2 years.


Otiŭm: A web based planner for tourism and leisure


title: Topographic Analysis of the Supratrochlear Artery and the Supraorbital Artery: Implication for Improving the Safety of Forehead Augmentation

abstract: BACKGROUND ::: The supratrochlear and supraorbital artery branches from the ophthalmic artery are the primary suppliers of blood to the forehead. Filler injection into the forehead without precise knowledge of its vascular topography poses a risk of severe complications. ::: ::: ::: METHODS ::: Twenty hemifaces from 11 cadavers were dissected. ::: ::: ::: RESULTS ::: Based on the presence of the deep branch of the supratrochlear artery, two main arterial distribution patterns of the forehead were observed. Type I (deep branch of the supratrochlear artery-present pattern) was classified into two subtypes: type Ia, in which the layer superficial to the frontalis was supplied medially by the superficial branch of the supratrochlear artery and laterally by the superficial branch of the supraorbital artery, and the deep branch of the supratrochlear artery and the deep branch of the supraorbital artery were distributed deep to the frontalis; and type Ib, in which the layer superficial to the frontalis was supplied by the superficial branch of the supratrochlear artery and the superficial branch of the supraorbital artery in addition to the central artery or the paracentral artery, and the layer deep to the frontalis was supplied the same with type Ia. For type II (deep branch of the supratrochlear artery-absent pattern), the layer superficial to the frontalis was supplied the same with type Ia; only the deep branch of the supraorbital artery supplied the layer deep to the frontalis. ::: ::: ::: CONCLUSION ::: This study yielded novel arterial systems of the forehead and provided guidance for the safe forehead augmentation.


title: 3D Laser Scanner for Underwater Manipulation

abstract: Nowadays, research in autonomous underwater manipulation has demonstrated simple applications like picking an object from the sea floor, turning a valve or plugging and unplugging a connector. These are fairly simple tasks compared with those already demonstrated by the mobile robotics community, which include, among others, safe arm motion within areas populated with a priori unknown obstacles or the recognition and location of objects based on their 3D model to grasp them. Kinect-like 3D sensors have contributed significantly to the advance of mobile manipulation providing 3D sensing capabilities in real-time at low cost. Unfortunately, the underwater robotics community is lacking a 3D sensor with similar capabilities to provide rich 3D information of the work space. In this paper, we present a new underwater 3D laser scanner and demonstrate its capabilities for underwater manipulation. In order to use this sensor in conjunction with manipulators, a calibration method to find the relative position between the manipulator and the 3D laser scanner is presented. Then, two different advanced underwater manipulation tasks beyond the state of the art are demonstrated using two different manipulation systems. First, an eight Degrees of Freedom (DoF) fixed-base manipulator system is used to demonstrate arm motion within a work space populated with a priori unknown fixed obstacles. Next, an eight DoF free floating Underwater Vehicle-Manipulator System (UVMS) is used to autonomously grasp an object from the bottom of a water tank.


title: Efficient mapping of quantum circuits to the IBM QX architectures

abstract: In March 2017, IBM launched the project IBM Q with the goal to provide access to quantum computers for a broad audience. This allowed users to conduct quantum experiments on a 5-qubit and, since June 2017, also on a 16-qubit quantum computer (called IBM QX2 and IBM QX3, respectively). In order to use these, the desired quantum functionality (e.g. provided in terms of a quantum circuit) has to properly be mapped so that the underlying physical constraints are satisfied - a complex task. This demands for solutions to automatically and efficiently conduct this mapping process. In this paper, we propose such an approach which satisfies all constraints given by the architecture and, at the same time, aims to keep the overhead in terms of additionally required quantum gates minimal. The proposed approach is generic and can easily be configured for future architectures. Experimental evaluations show that the proposed approach clearly outperforms IBM's own mapping solution. In fact, for many quantum circuits, the proposed approach determines a mapping to the IBM architecture within less than five minutes (and within a fraction of a second in most cases), while IBM's solution suffers from long runtimes and runs into a timeout of 1 hour in several cases. As an additional benefit, the proposed approach yields mapped circuits with smaller costs (i.e. fewer additional gates are required).


title: Low-Shot Learning from Imaginary Data

abstract: Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts might help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present a novel approach to low-shot learning that uses this idea. Our approach builds on recent progress in meta-learning ("learning to learn") by combining a meta-learner with a "hallucinator" that produces additional training examples, and optimizing both models jointly. Our hallucinator can be incorporated into a variety of meta-learners and provides significant gains: up to a 6 point boost in classification accuracy when only a single training example is available, yielding state-of-the-art performance on the challenging ImageNet low-shot classification benchmark.


A Comparative Survey of Activity-Based Methods for Information Systems Development


title: Investigation of Wideband Substrate-Integrated Vertically-Polarized Electric Dipole Antenna and Arrays for mm-Wave 5G Mobile Devices

abstract: In this work, a mm-Wave vertically-polarized electric dipole array solution for 5G wireless devices is presented. The dipole is fabricated using vias in a standard PCB process to fit at the phone or tablet edges featuring wideband operation with broad half-power beamwidth in the elevation plane (HPBWELEV), high gain and high front-to-back radiation ratio (F/B). For enhanced gain, parasitic-vias are added in front of the dipole as directors. To improve HPBW without sacrificing gain, the directors are implemented as V-shaped bisection parasitic-vias. A via-fence surrounds the dipole structure to suppress back radiation and enhance F/B. The dipole is connected to a parallel-strip line (PS) which is interfaced to the main SIW feed through a novel SIW-to-PS transition. Thorough investigation, optimization, and parametric study are provided for each design parameter of the proposed structure. A single dipole, $2\times 1$ , and $4\times 1$ arrays were designed and fabricated showing close agreement between the simulated and measured results. The single-dipole operates over 7.23-GHz bandwidth with stable radiation performance. The $4\times 1$ array achieves HPBWELEV of 133.1°, F/B of 36.6-dB, cross-polarization less than −39.6-dB and 12.61-dBi gain with 95.8% radiation efficiency. The low cost, compactness, and good performance of the proposed dipole make it a competing candidate for the future 5G mobile devices transceivers.


title: Role of multisensory stimuli in vigilance enhancement- a single trial event related potential study

abstract: Development of interventions to prevent vigilance decrement has important applications in sensitive areas like transportation and defence. The objective of this work is to use multisensory (visual and haptic) stimuli for cognitive enhancement during mundane tasks. Two different epoch intervals representing sensory perception and motor response were analysed using minimum variance distortionless response (MVDR) based single trial ERP estimation to understand the performance dependency on both factors. Bereitschaftspotential (BP) latency L3 (r=0.6 in phase 1 (visual) and r=0.71 in phase 2 (visual and haptic)) was significantly correlated with reaction time as compared to that of sensory ERP latency L2 (r=0.1 in both phase 1 and phase 2). This implies that low performance in monotonous tasks is predominantly dependent on the prolonged neural interaction with the muscles to initiate movement. Further, negative relationship was found between the ERP latencies related to sensory perception and Bereitschaftspotential (BP) and occurrence of epochs when multisensory cues are provided. This means that vigilance decrement is reduced with the help of multisensory stimulus presentation in prolonged monotonous tasks.


title: Interval-valued symbolic representation based method for off-line signature verification

abstract: The objective of this investigation is to present an interval-symbolic representation based method for offline signature verification. In the feature extraction stage, Connected Components (CC), Enclosed Regions (ER), Basic Features (BF) and Curvelet Feature (CF)-based approaches are used to characterize signatures. Considering the extracted feature vectors, an interval data value is created for each feature extracted from every individual's signatures as an interval-valued symbolic data. This process results in a signature model for each individual that consists of a set of interval values. A similarity measure is proposed as the classifier in this paper. The interval-valued symbolic representation based method has never been used for signature verification considering Indian script signatures. Therefore, to evaluate the proposed method, a Hindi signature database consisting of 2400 (100×24) genuine signatures and 3000 (100×30) skilled forgeries is employed for experimentation. Concerning this large Hindi signature dataset, the highest verification accuracy of 91.83% was obtained on a joint feature set considering all four sets of features, while 2.5%, 13.84% and 8.17% of FAR (False Acceptance Rate), FRR (False Rejection Rate), and AER (Average Error Rate) were achieved, respectively.


Radio and USRP for Problem-Based Learning in Communications : Case Studies in Cellular Phone Jamming and Modulation Classification


title: Circuit breaker protection considerations in power converter-fed DC Systems

abstract: Considerations in applying circuit breaker protection to DC Systems are capacitive discharge, circuit breaker coordination and impacts of double ground faults. Test and analysis results show the potential for equipment damage. Solutions are proposed at the cost of increased integration between power conversion and protection systems.


title: Computer forensic timeline visualization tool

abstract: Computer Forensics is mainly about investigating crime where computers have been involved. There are many tools available to aid the investigator with this task. We have created a prototype of a new type of tool called CyberForensic TimeLab where all evidence is indexed by their time variables and plotted on a timeline. We believed that this way of visualizing the evidence allows the investigators to find coherent evidence faster and more intuitively. We have performed a user test where a group of people has evaluated our prototype tool against a modern commercial computer forensic tool and the results of this preliminary test are very promising. The results show that users completed the task in shorter time, with greater accuracy and with less errors using CyberForensic TimeLab. The subjects also experienced that the prototype were more intuitive to use and that it allowed them to easier locate evidence that was coherent in time.


title: Constraining Word Embeddings by Prior Knowledge – Application to Medical Information Retrieval

abstract: Word embedding has been used in many NLP tasks and showed some capability to capture semantic features. It has also been used in several recent studies in IR. However, word embeddings trained in unsupervised manner may fail to capture some of the semantic relations in a specific area (e.g. healthcare). In this paper, we leverage the existing knowledge (word relations) in the medical domain to constrain word embeddings using the principle that related words should have similar embeddings. The resulting constrained word embeddings are used to rerank documents, showing superior effectiveness to unsupervised word embeddings.


title: Model-driven development of user interfaces for IoT systems via domain-specific components and patterns

abstract: Internet of Things technologies and applications are evolving and continuously gaining traction in all fields and environments, including homes, cities, services, industry and commercial enterprises. However, still many problems need to be addressed. For instance, the IoT vision is mainly focused on the technological and infrastructure aspect, and on the management and analysis of the huge amount of generated data, while so far the development of front-end and user interfaces for IoT has not played a relevant role in research. On the contrary, user interfaces can play a key role in the acceptance of IoT solutions by final adopters. In this paper we discuss the requirements and usage scenarios covering the front end aspects of IoT systems and we present a model-driven approach to the design of such interfaces by: defining specific components and design patterns using a visual modeling language for IoT applications; describing an implementation of the solution that comprises also automatic code generation from models; and by showing the solution at work.


title: Hybrid recommendation approach for learning material based on sequential pattern of the accessed material and the learner’s preference tree

abstract: Abstract The explosion of the learning materials in personal learning environments has caused difficulties to locate appropriate learning materials to learners. Personalized recommendations have been used to support the activities of learners in personal learning environments and this technology can deliver suitable learning materials to learners. In order to improve the quality of recommendations, this research considers the multidimensional attributes of material, rating of learners, and the order and sequential patterns of the learner’s accessed material in a unified model. The proposed approach has two modules. In the sequential-based recommendation module, latent patterns of accessing materials are discovered and presented in two formats including the weighted association rules and the compact tree structure (called Pattern-tree). In the attribute-based module, after clustering the learners using latent patterns by K-means algorithm, the learner preference tree (LPT) is introduced to consider the multidimensional attributes of materials, rating of learners, and also order of the accessed materials. The mixed, weighted, and cascade hybrid methods are employed to generate the final combined recommendations. The experiments show that the proposed approach outperforms the previous algorithms in terms of precision, recall, and intra-list similarity measure. The main contributions are improvement of the recommendations’ quality and alleviation of the sparsity problem by combining the contextual information, including order and sequential patterns of the accessed material, rating of learners, and the multidimensional attributes of materials.


title: Abstract Atomic Volumes for Mesh Completion

abstract: The increased use of scanned geometry for applications in computer graphics and 3D hardcopy output has highlighted the need for general, robust algorithms for reconstruction of watertight 3D models given partial polygonal meshes as input. We present an algorithm for 3D hole filling based on a decomposition of space into atomic volumes, which are each determined to be either completely inside or completely outside the model. By defining the output model as the union of interior atomic volumes we guarantee that the resulting mesh is watertight. Individual volumes are labeled as "inside" or "outside" by computing a minimum-cost cut of a graph representation of the atomic volume structure, patching all the holes simultaneously in a globally sensitive manner. User control is provided to select between multiple topologically distinct, yet still valid, ways of filling holes. Finally, we use an octree decomposition of space to provide output-sensitive computation time. We demonstrate the ability of our algorithm to fill complex, non-planar holes in large meshes obtained from 3D scanning devices.


title: Opportunities and Challenges of Switched Reluctance Motor Drives for Electric Propulsion: A Comparative Study

abstract: Selection of the proper electric traction drive is an important step in design and performance optimization of electrified powertrains. Due to the use of high energy magnets, permanent magnet synchronous machines (PMSM) have been the primary choice in the electric traction motor market. However, manufacturers are very interested to find a permanent magnet-free alternative as a fallback option due to unstable cost of rare-earth metals and fault tolerance issues related to the constant permanent magnet excitation. In this paper, a new comprehensive review of electric machines (EMs) that includes various new switched reluctance machine topologies in addition to conventional EMs such as PMSM, induction machine, synchronous reluctance machine (SynRel), and PM-assisted SynRel is presented. This paper is based on performances such as power density, efficiency, torque ripple, vibration and noise, and fault tolerance. These systematic examinations prove that recently proposed magnetic configurations such as double-stator switched reluctance machine can be a reasonable substitute for permanent magnet machines in electric traction applications.


title: The Soft-Start Analysis of a Full-Bridge LLC Converter with Hybrid Control Strategy

abstract: The current spike in full-bridge LLC resonant converter during start-up may destroy the circuit. Usually, the frequency modulation is used to solve this problem. However the waveform at the start-up is still not good enough. In this paper, a hybrid modulation method of phase-shift and frequency control is employed for the start-up process of LLC converter. By using the phase-shift modulation, the start-up process can be optimized and there is no need to reduce switching frequency. Furthermore, all the MOSFETs can achieve zero-voltage switching (ZVS) easily. Finally, the feasibility of the control method is verified by simulation.


title: DisCO: A distributed and concurrent offloading framework for mobile edge cloud computing

abstract: The final goal of this paper is to develop a distributed and concurrent offloading framework, DisCO. A DisCO is to offload the computationally-intensive or data-intensive part of the IoT application executed or collected on the mobile edge device to the mobile edge server with high computing resources and processing capacity in real time and then returned the results of executing. A detailed research contents to achieve profiling IoT applications in mobile edge devices and migrating offloading units with overhead to mobile edge servers and concurrently executing them and returning results. Additionally, we designed a security mechanism technology to solve security issues that may occur when offloading a part of IoT application to mobile edge server or exchanging data for simultaneous processing between mobile edge servers


title: Observation and analysis of BGP behavior under stress

abstract: Despite BGP's critical importance as the de-facto Internet inter-domain routing protocol, there is little understanding of how BGP actually performs under stressful conditions when dependable routing is most needed. In this paper, we examine BGP's behavior during one stressful period, the Code Red/Nimda attack on September 18, 2001. The attack was correlated with a 30-fold increase in the BGP update messages at a monitoring point which peers with a number of Internet service providers. Our examination of BGP's behavior during the event concludes that BGP exhibited no significant abnormality, and that over 40% of the observed updates can be attributed to the monitoring artifact in current BGP measurement settings. Our analysis, however, does reveal several weak points in both the protocol and its implementation, such as BGP's sensitivity to the transport session reliability, its inability to avoid the global propagation of small local changes, and its certain implementation features whose otherwise benign effects only get amplified under stressful conditions. We also identify areas for improvement in the current network measurement and monitoring effort.


title: Motor Profile of Children With Developmental Speech and Language Disorders

abstract: OBJECTIVES ::: The purpose of this study was to investigate the motor profile of 125 children with developmental speech and language disorders and to test for differences, if any, in motor profile among subgroups of children with developmental speech and language disorders. ::: ::: ::: METHODS ::: The participants were 125 children with developmental speech and language disorders aged 6 to 9 years from 2 special schools for children with communication problems in the northern Netherlands. They were tested with the Movement Assessment Battery for Children. The children were classified by the schools' speech and language therapists into 3 subgroups on the basis of language tests, oral motor tests, and clinical examinations: children with speech disorders (n = 14), language disorders (n = 46), or both (n = 65). ::: ::: ::: RESULTS ::: Compared with the norms of the Movement Assessment Battery for Children, children with developmental speech and language disorders performed significantly less well. Results showed that 51% of the children with developmental speech and language disorders had borderline or definite motor problems. Children with language disorders had significantly lower scores (ie, better performance) on the ball-skills subtest and the total test than children with speech disorders and children with both speech and language disorders. Furthermore, children with language disorders had significantly better performance on the balance subtest than children with both speech and language disorders. ::: ::: ::: CONCLUSIONS ::: The findings of this study support the idea that developmental speech and language disorders are frequently associated with motor problems and that the kind of developmental speech and language disorders affects motor performance differently. Speech and language disorders seem to have more impact on motor performance than only language disorders, and it seems that when speech production is affected, motor problems are more pronounced. The findings support the need to give early and more attention to the motor skills of children with developmental speech and language disorders in the educational and home setting, with special attention to children whose speech is affected.


MEL-GENERALIZED CEPSTRAL ANALYSIS — A UNIFIED APPROACH TO SPEECH SPECTRAL ESTIMATION


title: Estimating cognitive load using remote eye tracking in a driving simulator

abstract: We report on the results of a study in which pairs of subjects were involved in spoken dialogues and one of the subjects also operated a simulated vehicle. We estimated the driver's cognitive load based on pupil size measurements from a remote eye tracker. We compared the cognitive load estimates based on the physiological pupillometric data and driving performance data. The physiological and performance measures show high correspondence suggesting that remote eye tracking might provide reliable driver cognitive load estimation, especially in simulators. We also introduced a new pupillometric cognitive load measure that shows promise in tracking cognitive load changes on time scales of several seconds.


title: Ecology and physiology of the intestinal tract.

abstract: The number of microorganisms inhabiting the human digestive tract exceeds the number of body cells by a factor of ten. This microbial community affects host physiology and host health. The metabolic potential of the gut microbiota is immense affording the extraction of energy from otherwise indigestible carbohydrates (dietary fiber) and the conversion of host-derived substances, non-nutritive dietary components and drugs. Recognized functions of the gut microbiota include provision of colonization resistance against pathogens and priming of both the innate and the acquired immune systems. However, the intestinal microbiota may also contribute to the development of diseases such as ulcerative colitis and colorectal cancer. Culture-dependent studies provided basic knowledge on the gut microbiota, but only the advent of culture-independent molecular methods led to a better understanding of host-microbe interactions. The application of metagenomics to the gut microbial ecosystem revealed truly remarkable correlations between certain diseases and the gut microbiome. It also led to the suggestion of the existence of a 'core microbiome' that encompasses key functions shared by each individual. However, the mechanisms underlying host-microbe interactions have not yet been unraveled.


title: Kinematic Condition for Maximizing the Thrust of a Robotic Fish Using a Compliant Caudal Fin

abstract: The compliance of a fin affects the thrust of underwater vehicles mimicking the undulatory motion of fish. Determining the optimal compliance of a fin to maximize thrust is an important issue in designing robotic fish using a compliant fin. We present a simple method to identify the condition for maximizing the thrust generated by a compliant fin propulsion system. When a fin oscillates in a sinusoidal manner, it also bends in a sinusoidal manner. We focus on a particular kinematic parameter of this motion: the phase difference between the sinusoidal motion of the driving angle and the fin-bending angle. By observing the relationship between the thrust and phase difference, we conclude that while satisfying the zero velocity condition, the maximum thrust is obtained when a compliance creates a phase difference of approximately π/2 at a certain undulation frequency. This half-pi phase delay condition is supported by thrust measurements from different compliant fins (four caudal-shaped fins with different aspect ratios) and a beam bending model of the compliant fin. This condition can be used as a guideline to select the proper compliance of a fin when designing a robotic fish.


title: Selecting the Top-Quality Item Through Crowd Scoring

abstract: We investigate crowdsourcing algorithms for finding the top-quality item within a large collection of objects with unknown intrinsic quality values. This is an important problem with many relevant applications, such as in networked recommendation systems. The core of the algorithms is that objects are distributed to crowd workers, who return a noisy and biased evaluation. All received evaluations are then combined to identify the top-quality object. We first present a simple probabilistic model for the system under investigation. Then we devise and study a class of efficient adaptive algorithms to assign in an effective way objects to workers. We compare the performance of several algorithms, which correspond to different choices of the design parameters/metrics. In the simulations, we show that some of the algorithms achieve near optimal performance for a suitable setting of the system parameters.


title: R loops: new modulators of genome dynamics and function

abstract: R loops form when a transcript hybridizes to a complementary DNA locus to result in an RNA–DNA hybrid and a displaced single DNA strand. Such structures can have detrimental cellular roles by causing genome instability. However, recent studies have provided detailed views of genome-wide R-loop occurrences and uncovered various apparently beneficial roles in gene regulation. This Review discusses our latest understanding of the contrasting functions of R loops and the implications for genome regulation and various diseases.


title: pdf2table: A Method to Extract Table Information from PDF Files

abstract: Tables are a common structuring element in many documents, such as PDF files. To reuse such tables, appropriate methods need to be develop, which capture the structure and the content information. We have developed several heuristics which together recognize and decompose tables in PDF files and store the extracted data in a structured data format (XML) for easier reuse. Additionally, we implemented a prototype, which gives the user the ability of making adjustments on the extracted data. Our work shows that purely heuristic-based approaches can achieve good results, especially for lucid tables.


title: A multibeam CRT

abstract: CRT devices, both for photorecording and real time display applications, can be improved in certain of their characteristics by utilizing a number of electron beams simultaneously modulated in the tube. A plurality of beams, when properly generated, focused and deflected, will display information with writing speed, video band-width and brightness generally improved by a factor related to the number of beams used.


title: DARE: Adaptive Data Replication for Efficient Cluster Scheduling

abstract: Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Face book, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25\%, respectively.


title: New Results on Stability of Discrete-Time Systems With Time-Varying State Delay

abstract: This note is concerned with the stability analysis of discrete-time systems with time-varying state delay. By defining new Lyapunov functions and by making use of novel techniques to achieve delay dependence, several new conditions are obtained for the asymptotic stability of these systems. The merit of the proposed conditions lies in their less conservativeness, which is achieved by circumventing the utilization of some bounding inequalities for cross products between two vectors and by paying careful attention to the subtle difference between the terms Sigmam=k-dk k-1(middot) and Sigma m=k-dM k-1(middot), which is largely ignored in the existing literature. These conditions are shown, via several examples, to be much less conservative than some existing result


title: Effective frameworks for delivering compliance with personal data privacy regulatory requirements

abstract: Increasing regulatory requirements in the personal data protection arena creates new challenges for global companies generating value for businesses and their customers when designing and deploying responsive data privacy and compliance programs, achieving comprehensive compliance with the laws, and maintaining principles in corporate governance regarding conducting business ethically, sustainably, and responsibly around the world. In this work, the author demonstrates a new approach that was designed and developed at the leading global energy management provider to achieve and sustain compliance with the General Data Protection Regulation and a variety of individual national personal data privacy requirements. All of this was able to be achieved together with a dedication to meeting high corporate responsibility standards and while also enabling new business opportunities across the globe. Not only will this approach strengthen trust of current clients, but it will also open the possibility of attracting new customers with an appreciation for how seriously their personal data are treated and protected.


title: Fast Bloom Filters and Their Generalization

abstract: Bloom filters have been extensively applied in many network functions. Their performance is judged by three criteria: query overhead, space requirement, and false positive ratio. Due to wide applicability, any improvement to the performance of Bloom filters can potentially have a broad impact in many areas of networking research. In this paper, we study Bloom-1, a data structure that performs membership check in one memory access, which compares favorably with the k memory accesses of a standard Bloom filter. We also generalize Bloom-1 to Bloom-g and Bloom-Q, allowing performance tradeoff between membership query overhead and false positive ratio. We thoroughly examine the variants in this family of filters, and show that they can be configured to outperform the Bloom filters with a smaller number of memory accesses, a smaller or equal number of hash bits, and a smaller or comparable false positive ratio in practical scenarios. We also perform experiments based on a real traffic trace to support our filter design.


Model catalytic oxide surfaces : a study of the LaAlO 3 ( 001 ) surface


title: A reproducible evaluation of ANTs similarity metric performance in brain image registration.

abstract: Abstract The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard = 0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard = 0.669 ± 0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling.


title: Electronic circuit simulation with ideal switches

abstract: A recent model of an internally or externally controlled ideal switch is described. General nonperiodical switching is considered. No limitations regarding the circuit structure and complexity are imposed. Application to variety of electronic circuits is demonstrated. Special attention is paid to electronic power circuits such as switched power supplies and converters.


title: Artifact-free High Dynamic Range imaging

abstract: The contrast in real world scenes is often beyond what consumer cameras can capture. For these situations, High Dynamic Range (HDR) images can be generated by taking multiple exposures of the same scene. When fusing information from different images, however, the slightest change in the scene can generate artifacts which dramatically limit the potential of this solution. We present a technique capable of dealing with a large amount of movement in the scene: we find, in all the available exposures, patches consistent with a reference image previously selected from the stack. We generate the HDR image by averaging the radiance estimates of all such regions and we compensate for camera calibration errors by removing potential seams. We show that our method works even in cases when many moving objects cover large regions of the scene.


title: E-hubs: the new B2B (business-to-business) marketplaces.

abstract: Electronic hubs--Internet-based intermediaries that host electronic marketplaces and mediate transactions among businesses--are generating a lot of interest. Companies like Ariba, Chemdex, and Commerce One have already attained breathtaking stock market capitalizations. Venture capitalists are pouring money into more business-to-business start-ups. Even industrial stalwarts like GM and Ford are making plans to set up their own Web markets. As new entrants with new business models pour into the business-to-business space, it's increasingly difficult to make sense of the landscape. This article provides a blueprint of the e-hub arena. The authors start by looking at the two dimensions of purchasing: what businesses buy--manufacturing inputs or operating inputs--and how they buy--through systematic sourcing or spot sourcing. They classify B2B e-hubs into four categories: MRO hubs, yield managers, exchanges, and catalog hubs, and they discuss each type in detail. Drilling deeper into this B2B matrix, the authors look at how e-hubs create value--through aggregation and matching--and explain when each mechanism works best. They also examine the biases of e-hubs. Although many e-hubs are neutral--they're operated by independent third parties--some favor the buyers or sellers. The authors explain the differences and discuss the pros and cons of each position. The B2B marketplace is changing rapidly. This framework helps buyers, sellers, and market makers navigate the landscape by explaining what the different hubs do and how they add the most value.


title: Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition

abstract: Deep-learning neural networks such as convolutional neural network (CNN) have shown great potential as a solution for difficult vision problems, such as object recognition. Spiking neural networks (SNN)-based architectures have shown great potential as a solution for realizing ultra-low power consumption using spike-based neuromorphic hardware. This work describes a novel approach for converting a deep CNN into a SNN that enables mapping CNN to spike-based hardware architectures. Our approach first tailors the CNN architecture to fit the requirements of SNN, then trains the tailored CNN in the same way as one would with CNN, and finally applies the learned network weights to an SNN architecture derived from the tailored CNN. We evaluate the resulting SNN on publicly available Defense Advanced Research Projects Agency (DARPA) Neovision2 Tower and CIFAR-10 datasets and show similar object recognition accuracy as the original CNN. Our SNN implementation is amenable to direct mapping to spike-based neuromorphic hardware, such as the ones being developed under the DARPA SyNAPSE program. Our hardware mapping analysis suggests that SNN implementation on such spike-based hardware is two orders of magnitude more energy-efficient than the original CNN implementation on off-the-shelf FPGA-based hardware.


title: Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks

abstract: Automatic analysis of fetal echocardiography screening images could aid in the identification of congenital heart diseases. The first step towards automatic fetal echocardiography analysis is locating the fetal heart in an image and identifying the viewing (imaging) plane. This is highly challenging since the fetal heart is small with relatively indistinct anatomical structural appearance. This is further compounded by the presence of artefacts in ultrasound images. Herein we provide a state-of-art solution for detecting the fetal heart and classifying each individual frame as belonging to one of the standard viewing planes using fully convolutional neural networks (FCNs). Our FCN model achieves a classification error rate of 23.48% on real-world clinical ultrasound data. We also present comparative performance for analysis of different FCN architectures.


title: Design and implementation of home automation system

abstract: M2M wireless communication of various machines and devices in mobile networks is a fast growing business and application area in industry, maintenance business, customer service, security, and banking areas. This paper presents design and implementation of remote control system by means of GSM cellular communication network. The design integrates the device to be controlled, the microcontroller, and the GSM module so that it can be used for a wide range of applications. Detailed description and implementation of each design element are presented. To verify the principle operation of the M2M design, two home applications are experimentally tested using PC-based environment.


title: Antecedents of Information and System Quality: An Empirical Examination Within the Context of Data Warehousing

abstract: Understanding the successful adoption of information technology is largely based upon understanding the linkages among quality, satisfaction, and usage. Although the satisfaction and usage constructs have been well studied in the information systems literature, there has been only limited attention to information and system quality over the past decade. To address this shortcoming, we developed a model consisting of nine fundamental determinants of quality in an information technology context, four under the rubric of information quality (the output of an information system) and five that describe system quality (the information processing system required to produce the output). We then empirically examined the aptness of our model using a sample of 465 data warehouse users from seven different organizations that employed report-based, query-based, and analytical business intelligence tools. The results suggest that our determinants are indeed predictive of overall information and system quality in data warehouse environments, and that our model strikes a balance between comprehensiveness and parsimony. We conclude with a discussion of the implications for both theory and the development and implementation of information technology applications in practice.


title: Building and scaling virtual clusters with residual resources from interactive clouds

abstract: The popularity of cloud-based interactive computing services (e.g., virtual desktops) brings new management challenges. Each interactive user leaves abundant but fluctuating residual resources while being intolerant to latency, precluding the use of aggressive VM consolidation. In this paper, we present the Resource Harvester for Interactive Clouds (RHIC), an autonomous management framework that harnesses dynamic residual resources aggressively without slowing the harvested interactive services. RHIC builds ad-hoc clusters for running throughput-oriented "background" workloads using a hybrid of residual and dedicated resources. For a given background job, RHIC intelligently discovers/maintains the ideal cluster size and composition, to meet user-specified goals such as cost/energy minimization or deadlines. RHIC employs black-box workload performance modeling, requiring only system-level metrics and incorporating techniques to improve modeling accuracy under bursty and heterogeneous residual resources. Our results show that RHIC finds near-ideal cluster sizes/compositions across a wide range of workload/goal combinations, significantly outperforms alternative approaches, tolerates high instability in the harvested interactive cloud, works with heterogeneous hardware and imposes minimal overhead.


Is this a Child, a Girl or a Car? Exploring the Contribution of Distributional Similarity to Learning Referential Word Meanings


title: Evolving imaging model for super-resolution reconstruction

abstract: Super-resolution reconstruction (SRR) allows for enhancing image spatial resolution from low-resolution (LR) observations, which are assumed to have been derived from a hypothetical high-resolution image by applying a certain imaging model (IM). However, if the actual degradation is different from the assumed IM, which is often the case in real-world scenarios, then the reconstruction quality is affected. We introduce a genetic algorithm to optimize the SRR hyper-parameters and to discover the actual IM by evolving the kernels exploited in the IM. The reported experimental results indicate that our approach outperforms the state of the art for a variety of images, including difficult real-life satellite data.


title: 2sRanking-CNN: A 2-stage ranking-CNN for diagnosis of glaucoma from fundus images using CAM-extracted ROI as an intermediate input

abstract: Glaucoma is a disease in which the optic nerve is chronically damaged by the elevation of the intra-ocular pressure, resulting in visual field defect. Therefore, it is important to monitor and treat suspected patients before they are confirmed with glaucoma. In this paper, we propose a 2-stage ranking-CNN that classifies fundus images as normal, suspicious, and glaucoma. Furthermore, we propose a method of using the class activation map as a mask filter and combining it with the original fundus image as an intermediate input. Our results have improved the average accuracy by about 10% over the existing 3-class CNN and ranking-CNN, and especially improved the sensitivity of suspicious class by more than 20% over 3-class CNN. In addition, the extracted ROI was also found to overlap with the diagnostic criteria of the physician. The method we propose is expected to be efficiently applied to any medical data where there is a suspicious condition between normal and disease.


title: Answer Set Programming Modulo `Space-Time'

abstract: We present ASP Modulo `Space-Time', a declarative representational and computational framework to perform commonsense reasoning about regions with both spatial and temporal components. Supported are capabilities for mixed qualitative-quantitative reasoning, consistency checking, and inferring compositions of space-time relations; these capabilities combine and synergise for applications in a range of AI application areas where the processing and interpretation of spatio-temporal data is crucial. The framework and resulting system is the only general KR-based method for declaratively reasoning about the dynamics of `space-time' regions as first-class objects. We present an empirical evaluation (with scalability and robustness results), and include diverse application examples involving interpretation and control tasks.


title: SemWIQ - Semantic Web Integrator and Query Engine.

abstract: One of the most popular applications of Semantic Web technology is the integration of data from distributed locations over the Web. With wrappers, screen scrapers, and information extraction tools it is possible to access, merge, and reason over RDF data from various different, heterogeneous sources. SPARQL can be used to access RDF data in a declarative manner and as will be shown, it can also be used to virtually integrate distributed RDF graphs. In this contribution a mediator-wrapper based middleware is shown which virtually integrates distributed, heterogeneous data sources based on RDF and a slightly modified SPARQL algebra. Schema information available in RDF Schema and OWL vocabularies is used for query federation and optimization. Compared to traditional data integration systems, SemWIQ can fully exploit the semantics of available RDF data. Additionally, RDF has many interesting properties which provide advanced query capabilities based on OWL DL inference.


title: A-Optimality for Active Learning of Logistic Regression Classifiers ∗

abstract: Over the last decade there has been growing interest in pool-based active learning techniques, where instead of receiving an i.i.d. sample from a pool of unlabeled data, a learner may take an active role in selecting examples from the pool. Queries to an oracle (a human annotator in most applications) provide label information for the selected observations, but at a cost. The challenge is to end up with a model that provides the best possible generalization error at the least cost. Popular methods such as uncertainty sampling often work well, but sometimes fail badly. We take the A-optimality criterion used in optimal experimental design, and extend it so that it can be used for pool-based active learning of logistic regression classifiers. A-optimality has attractive theoretical properties, and empirical evaluation confirms that it offers a more robust approach to active learning for logistic regression than alternatives.


title: Energy efficiency analysis of small cell networks

abstract: Small cell networks have recently been proposed as an important evolution path for the next-generation cellular networks. While such approach has the potential of meeting the growing network throughput requirement, the energy efficiency of small cell networks is of great concern as the base station (BS) density will be significantly increased. The objective of this paper is to analyze the energy efficiency in small cell networks. To do so, we adopt a random spatial network model, where BSs and users are modeled as two independent spatial Poisson point processes (PPPs). We shall derive analytical results for the network energy efficiency, which show that the BS power consumption model plays a critical role. In particular, it will be shown that increasing the BS density can actually improve the energy efficiency if the BS power consumption that is not related to signal transmission is less than a certain threshold. By comparing the cases between single-antenna and multi-antenna BSs, we find that single-antenna BSs provide a higher energy efficiency if the circuit power is larger than a threshold. Simulation results will demonstrate that our conclusions which are based on the random network model also hold in a regular grid-based model.


title: Union and Difference of Models, 10 years later

abstract: This paper contains a summary of the talk given by the author on the occasion of the MODELS 2013 most influential paper award. The talk discussed the original paper as published in 2003, the research work done by others afterwards and the author’s personal reflection on the award. 1 Version Control of Software and System Models There are two main usage scenarios for design models in software and system development: models as sketches, that serve as a communication aid in informal discussions, and models as formal artifacts, to be analyzed, transformed into other artifacts, maintained and evolved during the whole software and system development process. In this second scenario, models are valuable assets that should be kept in a trusted repository. In a complex development project, these models will be updated often and concurrently by different developers. Therefore, there is a need for a version control system for models with optimistic locking. This is a system to compare, merge and store all versions of all models created within a development project. We can illustrate the use of a version control system for models as follows. Let us assume that the original model shown at the top of Figure 1 is edited simultaneously by two developers. One developer has decided that the subclass B is no longer necessary in the model. Simultaneously, the other developer has decided that class C should have a subclass D. The problem is to combine the contributions of both developers into a single model. This is the model shown at the bottom of Fig. 1. We presented the basic algorithms to solve this problem in the original paper published in the proceedings of the UML 2003 conference [1]. The proposed solution is based on calculating the final model as the merge of the differences between the original and the edited models. Figure 2 shows an example of the difference of two models, in this case the difference between the models edited by the developers and the original model. The result of the difference is not always a model, in a similar way that the difference between two natural numbers is not a natural number but a negative one. An example of this is shown in the bottom B C A Original Model


title: Regression Based Approach for Measurement of Current in Single-Phase Smart Energy Meter

abstract: In the present scenario rate of utilization of electrical energy is continuously increasing, to preserve the non-renewable energy resources, efficient consumption of electrical energy is one of the most important needs of this era. Smart Energy Meter (SEM) plays a vital role in efficient use of electrical energy in consumer premises. Accuracy in the measurement of current is one of the key factors to be considered while measuring energy consumption. This paper introduces Arduino based low cost single-phase SEM and proposes a linear regression based technique for accurate measurement of current. The proposed current measurement approach gives accurate measurement for linear as well as highly non-linear loads. The SEM built in the laboratory has data transfer capability and also embedded with Demand Response (DR) feature. Block Rate Tariff structure used by Tamil Nadu Generation and Distribution Company is considered to incorporate DR that warns the consumers whenever their energy consumption reaches the boundary of a block.


title: Design and construction of a DAB using SiC MOSFETs with an isolation of 24 kV for PET applications

abstract: This paper analyzes the design and construction of a SiC-based Dual Active Bridge (DAB) converter for its use in a three-stage Power Electronic Transformer (PET) based on a Modular Multilevel Converter (MMC). The galvanic isolation between primary and secondary of the PET provided by the DAB High Frequency Transformer (HFT) is 24 kV. Challenges for the HFT design are discussed. A 5 kW DAB prototype using commercial 1.2-kV SiC MOSFETs is built to confirm the correctness of the design process. Parallelization of the DAB HFT to increase the power being transferred is also discussed.


title: MCS Selection for Throughput Improvement in Downlink LTE Systems

abstract: In this paper, we investigate resource block (RB) assignment and modulation-and-coding scheme (MCS) selection to maximize downlink throughput of long-term evolution (LTE) systems, where all RB's assigned to the same user in any given transmission time interval (TTI) must use the same MCS. We develop several effective MCS selection schemes by using the effective packet-level SINR based on exponential effective SINR mapping (EESM), arithmetic mean, geometric mean, and harmonic mean. From both analysis and simulation results, we show that the system throughput of all the proposed schemes are better than that of the scheme in [7]. Furthermore, the MCS selection scheme using harmonic mean based effective packet-level SINR almost reaches the optimal performance and significantly outperforms the other proposed schemes.


title: Energy-Based Metrics for Arthroscopic Skills Assessment

abstract: Minimally invasive skills assessment methods are essential in developing efficient surgical simulators and implementing consistent skills evaluation. Although numerous methods have been investigated in the literature, there is still a need to further improve the accuracy of surgical skills assessment. Energy expenditure can be an indication of motor skills proficiency. The goals of this study are to develop objective metrics based on energy expenditure, normalize these metrics, and investigate classifying trainees using these metrics. To this end, different forms of energy consisting of mechanical energy and work were considered and their values were divided by the related value of an ideal performance to develop normalized metrics. These metrics were used as inputs for various machine learning algorithms including support vector machines (SVM) and neural networks (NNs) for classification. The accuracy of the combination of the normalized energy-based metrics with these classifiers was evaluated through a leave-one-subject-out cross-validation. The proposed method was validated using 26 subjects at two experience levels (novices and experts) in three arthroscopic tasks. The results showed that there are statistically significant differences between novices and experts for almost all of the normalized energy-based metrics. The accuracy of classification using SVM and NN methods was between 70% and 95% for the various tasks. The results show that the normalized energy-based metrics and their combination with SVM and NN classifiers are capable of providing accurate classification of trainees. The assessment method proposed in this study can enhance surgical training by providing appropriate feedback to trainees about their level of expertise and can be used in the evaluation of proficiency.


title: A search-based method for forecasting ad impression in contextual advertising

abstract: Contextual advertising (also called content match) refers to the placement of small textual ads within the content of a generic web page. It has become a significant source of revenue for publishers ranging from individual bloggers to major newspapers. At the same time it is an important way for advertisers to reach their intended audience. This reach depends on the total number of exposures of the ad (impressions) and its click-through-rate (CTR) that can be viewed as the probability of an end-user clicking on the ad when shown. These two orthogonal, critical factors are both difficult to estimate and even individually can still be very informative and useful in planning and budgeting advertising campaigns. In this paper, we address the problem of forecasting the number of impressions for new or changed ads in the system. Producing such forecasts, even within large margins of error, is quite challenging: 1) ad selection in contextual advertising is a complicated process based on tens or even hundreds of page and ad features; 2) the publishers' content and traffic vary over time; and 3) the scale of the problem is daunting: over a course of a week it involves billions of impressions, hundreds of millions of distinct pages, hundreds of millions of ads, and varying bids of other competing advertisers. We tackle these complexities by simulating the presence of a given ad with its associated bid over weeks of historical data. We obtain an impression estimate by counting how many times the ad would have been displayed if it were in the system over that period of time. We estimate this count by an efficient two-level search algorithm over the distinct pages in the data set. Experimental results show that our approach can accurately forecast the expected number of impressions of contextual ads in real time. We also show how this method can be used in tools for bid selection and ad evaluation.


title: Understanding Obfuscated Code

abstract: Code obfuscation makes it harder for a security analyst to understand the malicious payload of a program. In most cases an analyst needs to study the program at the machine code level, with little or no extra information available, apart from his experience. An unexperienced analyst is confronted with a steep learning curve, as understanding unobfuscated machine code already requires some skills. We have built LOCO, a graphical, interactive environment to help a security analyst improving his skills in understanding obfuscated code.


title: A New Approach to Design Domain Specific Ontology Based Web Crawler

abstract: Coal is restructured by extrusion into a tube like article that has a hollow core, which may contain igniter material to facilitate ignition of the coal. The hollow core and possibly other deformities, such as ribs, flutes or the like in the inner or outer walls of the tube-like article and/or slotted, circular or like openings through the tube wall artifically create an environment that enhances the burning characteristics in a relatively open or uncontrolled environment that is ordinarily hostile to the burning of coal. The article may be burned according to a novel process that creates coke.


title: Detecting source code similarity using code abstraction

abstract: Various approaches have been proposed to develop effective methods to measure program similarity. Even commercial tools and freeware tools are available for measuring program similarity based on source code comparison. These tools are quite useful to handle small to middle scale software products, but limited for large scale software products. In addition, these tools may report similarity measures with less credentials for the source code either obfuscated by malicious users or generated by automatic program template generation tools. To handle large scale software, more drastic measures should be provided. In this paper, we propose an automatic abstraction method to summarize source code. We eliminate a large portion of source code which is less relevant to similarity comparison. With this abstraction, our similarity comparison method can provide more robust measures for obfuscation and automatic code generation. We evaluate our abstraction method by running through source comparison tool --- MOSS, a web-based similarity detection tool. According to our experiment with multiple versions of Apache HTTP server, Putty SSH client, and Lighttpd server, our abstraction method reports quite reliable results with abstracted source code, which are only 23--35% of original source code. As the execution time for pattern match is linearly proportional to the length of the source code, our method can reduce the execution time as much as the percentage of source code reduction.


title: Computer aided detection (CAD): an overview

abstract: Computer aided detection (CAD) is a technology designed to decrease observational oversights--and thus the false negative rates--of physicians interpreting medical images. Prospective clinical studies have demonstrated an increase in breast cancer detection with CAD assistance. This overview briefly describes the metrics that have been used to define CAD system performance.


title: Extracting Fuzzy Rules from Data for Function Approximation and Pattern Classification

abstract: Extracting fuzzy rules from data allows relationships in the data to be modeled by "if-then" rules that are easy to understand, verify, and extend. This paper presents methods for extracting fuzzy rules for both function approximation and pattern classification. The rule extraction methods are based on estimating clusters in the data; each cluster obtained corresponds to a fuzzy rule that relates a region in the input space to an output region (or, in the case of pattern classification, to an output class). After the number of rules and initial rule parameters are obtained by cluster estimation, the rule parameters are optimized by gradient descent. Applications to a function approximation problem and to a pattern classification problem are also illustrated.


title: A 67-mW 10.7-Gb/s 60-GHz OOK CMOS Transceiver for Short-Range Wireless Communications

abstract: A low-power and high data-rate fully integrated 60-GHz on-off keying (OOK) transceiver for short-range wireless communication is demonstrated. The transceiver consists of a switch, transmitter, and receiver and uses OOK modulation for a compact and low-power design. With a highly efficient Tx, high-speed modulator/demodulator, and wideband characteristic, the transceiver has low-power and high data-rate capability. Implemented in 90-nm CMOS technology, the transmitter and the receiver consume 31 and 36 mW at 10.7 Gb/s and occupy an active footprint of 0.15 and 0.29 mm2, respectively. The transceiver with an on-board Yagi-Uda antenna achieves 10.7-Gb/s wireless OOK data transmission over 10 cm at a bit-error rate of less than 10-12 for 27 -1 pseudorandom binary sequence. As a result, the proposed transceiver achieves energy efficiency of 6.26 pJ/bit.


title: Pre-Translation for Neural Machine Translation

abstract: Recently, the development of neural machine translation (NMT) has significantly improved the translation quality of automatic machine translation. While most sentences are more accurate and fluent than translations by statistical machine translation (SMT)-based systems, in some cases, the NMT system produces translations that have a completely different meaning. This is especially the case when rare words occur. ::: When using statistical machine translation, it has already been shown that significant gains can be achieved by simplifying the input in a preprocessing step. A commonly used example is the pre-reordering approach. ::: In this work, we used phrase-based machine translation to pre-translate the input into the target language. Then a neural machine translation system generates the final hypothesis using the pre-translation. Thereby, we use either only the output of the phrase-based machine translation (PBMT) system or a combination of the PBMT output and the source sentence. ::: We evaluate the technique on the English to German translation task. Using this approach we are able to outperform the PBMT system as well as the baseline neural MT system by up to 2 BLEU points. We analyzed the influence of the quality of the initial system on the final result.


title: Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

abstract: Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions-like taxation, justice, and child protection-are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning-absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the 'street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications.


title: Palladium(0) nanoparticles supported on polydopamine coated Fe3O4as magnetically isolable, highly active and reusable catalysts for hydrolytic dehydrogenation of ammonia borane

abstract: Magnetic ferrite nanopowders were coated with polydopamine which is inert against the hydrolysis of ammonia borane. Coating of ferrite powders was achieved by pH-induced self-polymerization of dopamine hydrochloride at room temperature. Palladium(0) nanoparticles supported on polydopamine coated ferrite (Pd0/PDA–Fe3O4) were prepared by impregnation of palladium(II) ions on the surface of PDA–Fe3O4 followed by their reduction with sodium borohydride in aqueous solution at room temperature. Magnetically isolable Pd0/PDA–Fe3O4 catalysts were characterized by a combination of advanced analytical techniques. The results reveal that palladium nanoparticles with an average size of 2.0 ± 0.4 nm are well dispersed on polydopamine layer with a thickness of 3.0 ± 0.5 nm on the surface of ferrite nanopowders. Pd0/PDA–Fe3O4 with a palladium loading of 3.81% wt was found to be a highly active and reusable catalyst in hydrogen generation from the hydrolysis of ammonia borane with a turnover frequency value of 14.5 min−1 at 25.0 ± 0.1 °C. Magnetically isolable Pd0/PDA–Fe3O4 catalysts preserve their initial catalytic activity even after the tenth use providing the release of 3 equivalent H2 per mole of ammonia borane. The report also includes the results of kinetic studies on the hydrolytic dehydrogenation of ammonia borane performed at various temperatures and different catalyst concentrations.


title: INFORMATION CAMPAIGNS – MEANS OF COMMUNICATION WITH CUSTOMERS

abstract: Within a company’s activities, an important role is granted to the performance and implementation of information campaigns for the final consumers. The paper has the general objective to identify specific issues that constitute the premises for the preparation and conduct of such activities, and especially the expected results. The analysis is performed and based on a direct research among several companies, which operate in various fields, for the identification of a method of performance and implementation of information campaigns that are necessary for ensuring visibility on the reference market. The aim of the study was to identify the importance that the firms from Resita give to information campaigns addressed to a target audience. The study was carried from April to May 2013 on a sample of small, medium and large firms, which operate in trade, manufacturing and services. The questions and the discussions addressed to the public were meant to outline the specific aspects of the exact way in which the information campaigns were done for their clients, but also to underline the results obtained with the help of these campaigns.


title: A Hamilton-Jacobi-Bellman approach to optimal trade execution

abstract: The optimal trade execution problem is formulated in terms of a mean-variance tradeoff, as seen at the initial time. The mean-variance problem can be embedded in a linear-quadratic (LQ) optimal stochastic control problem. A semi-Lagrangian scheme is used to solve the resulting nonlinear Hamilton-Jacobi-Bellman (HJB) PDE. This method is essentially independent of the form for the price impact functions. Provided a strong comparison property holds, we prove that the numerical scheme converges to the viscosity solution of the HJB PDE. Numerical examples are presented in terms of the efficient trading frontier and the trading strategy. The numerical results indicate that in some cases there are many different trading strategies which generate almost identical efficient frontiers.


title: CLOSED-FORM DESIGN METHOD OF AN N-WAY DUAL-BAND WILKINSON HYBRID POWER DIVIDER

abstract: In this paper, the closed-form design method of an N- way dual-band Wilkinson hybrid power divider is proposed. This symmetric structure including N groups of two sections of transmission lines and two isolated resistors is described which can split a signal into N equiphase equiamplitude parts at two arbitrary frequencies (dual-band) simultaneously, where N can be odd or even. Based on the rigorous even- and odd-mode analysis, the closed-form design equations are derived. For veriflcation, various numerical examples are designed, calculated and compared while two practical examples including two ways and three ways dual-band microstrip power dividers are fabricated and measured. It is very interesting that this generalized power divider with analytical design equations can be designed for wideband applications when the frequency-ratio is relatively small. In addition, it is found that the conventional N-way hybrid Wilkinson power divider for single-band applications is a special case (the frequency-ratio equals to 3) of this generalized power divider.


title: Long-range GPS-denied aerial inertial navigation with LIDAR localization

abstract: Despite significant progress in GPS-denied autonomous flight, long-distance traversals (> 100 km) in the absence of GPS remain elusive. This paper demonstrates a method capable of accurately estimating the aircraft state over a 218 km flight with a final position error of 27 m, 0.012% of the distance traveled. Our technique efficiently captures the full state dynamics of the air vehicle with semi-intermittent global corrections using LIDAR measurements matched against an a priori Digital Elevation Model (DEM). Using an error-state Kalman filter with IMU bias estimation, we are able to maintain a high-certainty state estimate, reducing the computation time to search over a global elevation map. A sub region of the DEM is scanned with the latest LIDAR projection providing a correlation map of landscape symmetry. The optimal position is extracted from the correlation map to produce a position correction that is applied to the state estimate in the filter. This method provides a GPS-denied state estimate for long range drift-free navigation. We demonstrate this method on two flight data sets from a full-sized helicopter, showing significantly longer flight distances over the current state of the art.


title: Inference Attacks on Location Tracks

abstract: Although the privacy threats and countermeasures associated with location data are well known, there has not been a thorough experiment to assess the effectiveness of either. We examine location data gathered from volunteer subjects to quantify how well four different algorithms can identify the subjects' home locations and then their identities using a freely available, programmable Web search engine. Our procedure can identify at least a small fraction of the subjects and a larger fraction of their home addresses. We then apply three different obscuration countermeasures designed to foil the privacy attacks: spatial cloaking, noise, and rounding. We show how much obscuration is necessary to maintain the privacy of all the subjects.


title: An iterated local search heuristic for the split delivery vehicle routing problem

abstract: This paper concerns the Split Delivery Vehicle Routing Problem (SDVRP). This problem is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) since the customers' demands are allowed to be split. We deal with the cases where the fleet is unlimited (SDVRP-UF) and limited (SDVRP-LF). In order to solve them, we implemented a multi-start Iterated Local Search (ILS) based heuristic that includes a novel perturbation mechanism. Extensive computational experiments were carried out on benchmark instances available in the literature. The results obtained are highly competitive, more precisely, 55 best known solutions were equaled and new improved solutions were found for 243 out of 324 instances, with an average and maximum improvement of 1.15% and 2.81%, respectively.


title: Study of 2D DOA Estimation for Uniform Circular Array in Wireless Location System

abstract: in this paper, the use of a uniform circular antenna arrays (UCA) for high resolution of two dimensional (2D) direction of arrivals (DOAs) estimation in wireless location system is investigated. Performance of 2D DOA estimation based on the real-valued unitary transformation MUSIC algorithm for UCA is presented, especially focusing on DOA estimation of multiple correlated signals. The determination of the number of incident signals on an antenna array is addressed in the condition of colored noise and coherent signal sources. Selected method for estimating the number of these sources is formulated based on the modified eigenvectors of the covariance matrix of the received signal at the antenna array. The calibration procedure is also presented for UCA especially. Simulation results are presented to confirm the performance analysis of algorithm, then the validations of Unitary Transformation MUSIC algorithm are performed based on the measurement data in a wireless location system.


title: Calibration of Rogowski Coils With an Integrator at High Currents

abstract: This paper describes the calibration of Rogowski coils with an integrator for current measurement in the current range of up to 30 kA, at mains frequency. A current loop of ten turns with a current of 3 kA was used for calibration. The magnetic field generated in this way differs from the field of one turn placed at the center and passed with a current of 30 kA. The influence of the unevenness of winding and the cross section of the Rogowski coil may cause a calibration error when using a current loop. The difference in the results between using a current loop and one turn, and the measurement uncertainties are given here. Calculations of the mutual inductances of typical Rogowski coils and their uncertainties are presented in the Appendix.


title: A High-Performance Dual-Mode Feed Horn for Parabolic Reflectors with a Stepped-Septum Polarizer in a Circular Waveguide [Antenna Designer's Notebook]

abstract: This article describes a circularly polarized feed horn designed to feed shallow, prime-focus or offset-fed parabolic reflectors. Both circular polarization senses are available at two individual coaxial ports. The design provides good circularity, a very clean radiation pattern with excellent sidelobe suppression, outstanding isolation between ports, and needs no adjustments. An L-band example is presented, including both simulation and experimental results. The complete set of dimensions, not previously found in the literature for septum polarizers in circular waveguide, is provided for the practical engineer who desires to duplicate or scale this feed horn to other frequencies.


title: Modified TEM horn antenna for wideband applications

abstract: This paper presents a novel method to enhance the pattern characteristics of the TEM horn antenna for 2−19 GHz frequency band. The conventional TEM horn antenna introduces some fluctuations in the main lobe radiation pattern over the center frequencies, which are 10−15 GHz. This motivated us to propose a new method to remove the aforementioned impact by carving an arc shape to the open end of exponentially tapered plates. The associated curvature of this arc is optimized to completely remove the aforementioned fluctuation. The simulation results show that the improved TEM horn antenna structure exhibits low VSWR as well as good radiation pattern over 2−19 GHz frequency band.


Nod Detection in Human-Robot Interaction


Brilliance Improvement of a Laser-Produced Soft X-Ray Plasma


Handwritten Digit Recognition using Convolutional Neural Networks and Gabor filters


title: Chemical Reaction Optimization: a tutorial

abstract: Chemical Reaction Optimization (CRO) is a recently established metaheuristics for optimization, inspired by the nature of chemical reactions. A chemical reaction is a natural process of transforming the unstable substances to the stable ones. In microscopic view, a chemical reaction starts with some unstable molecules with excessive energy. The molecules interact with each other through a sequence of elementary reactions. At the end, they are converted to those with minimum energy to support their existence. This property is embedded in CRO to solve optimization problems. CRO can be applied to tackle problems in both the discrete and continuous domains. We have successfully exploited CRO to solve a broad range of engineering problems, including the quadratic assignment problem, neural network training, multimodal continuous problems, etc. The simulation results demonstrate that CRO has superior performance when compared with other existing optimization algorithms. This tutorial aims to assist the readers in implementing CRO to solve their problems. It also serves as a technical overview of the current development of CRO and provides potential future research directions.


title: MS-LSTM: A multi-scale LSTM model for BGP anomaly detection

abstract: Detecting anomalous Border Gateway Protocol (BGP) traffic is significantly important in improving both security and robustness of the Internet. Existing solutions apply classic classifiers to make real-time decision based on the traffic features of present moment. However, due to the frequently happening burst and noise in dynamic Internet traffic, the decision based on short-term features is not reliable. To address this problem, we propose MS-LSTM, a multi-scale Long Short-Term Memory (LSTM) model to consider the Internet flow as a multi-dimensional time sequence and learn the traffic pattern from historical features in a sliding time window. In addition, we find that adopting different time scale to preprocess the traffic flow has great impact on the performance of all classifiers. In this paper, comprehensive experiments are conducted and the results show that a proper time scale can improve about 10% accuracy of LSTM as well as all conventional machine learning methods. Particularly, MS-LSTM with optimal time scale 8 can achieve 99.5% accuracy in the best case.


An Expressive Utility Account of Partisan Cue Receptivity : Cognitive Resources in the Service of Identity Expression


title: UNDERSTANDING USER INTENTION TO SHARE INFORMATION IN ONLINE SOCIAL SHOPPING COMMUNITIES: THE MODERATING EFFECT OF COMMUNITY EQUITY

abstract: Online social shopping community has substantially changed the way organizations, communities, and individuals communicate. The advance of online social shopping communities facilitates individuals to share and exchange consumption related information. In this study, we attempt to address the question of why individuals intend to share information in social shopping communities. Despite the growing popularity of social shopping communities, theoretical understanding of information sharing in this new form of social media remains unknown. We extend prior knowledge sharing literature into the context of online social shopping communities, and to examine the impacts of psychological motivations on user intention to share information. In addition, we explore the moderating role of community equity in the relationship between psychological motivations and user intention to share information. Analyses of survey data of 1089 users from an online social shopping community confirm our hypotheses. This study not only enriches our theoretical understanding of information sharing in this new social media, but also provides guidelines for online social shopping community administrators to better design their community features.


title: Conceptual Graph Matching for Semantic Search

abstract: Semantic search becomes a research hotspot. The combined use of linguistic ontologies and structured semantic matching is one of the promising ways to improve both recall and precision. In this paper, we propose an approach for semantic search by matching conceptual graphs. The detailed definitions of semantic similarities between concepts, relations and conceptual graphs are given. According to these definitions of semantic similarity, we propose our conceptual graph matching algorithm that calculates the semantic similarity. The computation complexity of this algorithm is constrained to be polynomial. A prototype of our approach is currently under development with IBM China Research Lab.


title: A Taxonomy for Human-Robot Interaction

abstract: This paper integrates research and ideas in the fields of Human-Computer Interaction and Robotics for the creation of a taxonomy for human-robot interaction. By drawing from multiple research fields, a more complete taxonomy is attained. Taxonomy categories include team composition (ratio of people to robots, types of robots), amount of required interaction, decision support provided for the user, and space-time location.


title: EEG based biometric authentication using new spectral features

abstract: From the viewpoint of user management, continuous authentication is effective. However, general biometrics such as fingerprint, iris, vein, and so on are not suitable for the continuous verification since they require conscious presentation of biometric data. As unconscious biometrics, to use a brain wave (electroencephalogram: EEG) has been proposed but conventional approaches require heavy computational load for feature extraction and verification and it becomes a problem for practical applications. In this paper, we propose new features: the concavity and convexity of spectral distribution in the alpha band and propose to fuse them with a spectral variance in verification. The proposed methods are achieved with low computational load. In our experiments using 23 subjects, EER of 11% is obtained and it is comparable with the performance of conventional approaches.


title: Geodetic Graphs Homeomorphic to a Given Geodetic Graph

abstract: This paper describes a new approach to the problem of generating the class of all geodetic graphs homeomorphic to a given geodetic one. An algorithmic procedure is elaborated to carry out a systematic finding of such a class of graphs. As a result, the enumeration of the class of geodetic graphs homeomorphic to certain Moore graphs has been performed.


title: Self-sustaining strategy for a hybrid electric bike

abstract: Electrically Power Assisted Cycles (EPACs) have been gaining attention as an efficient and clean means of transportation. In this work, the concept of full hybrid bike presented in previous research is further developed by introducing a charge sustaining strategy. The main idea is to consider the load-dependent efficiency of the cyclist and let the electric motor assist the cyclist in low efficiency regions while letting the cyclist recharge the battery in high efficiency regions. To this end a State-of-Charge (SoC) controller is designed. Its tuning is analyzed and compared in terms of efficiency and feeling against the acausal strategy available in the literature.


Aggression and Violence: Definitions and Distinctions


title: Gamifying requirement elicitation: Practical implications and outcomes in improving stakeholders collaboration ☆

abstract: Abstract The requirements engineering process is a key phase of the Information System development since it determines its functionalities and its operation. Before requirements can be analyzed, modeled, or specified they must be gathered through an elicitation process. Requirements elicitation is non-trivial because you can never be sure you get all requirements from the user or stakeholder by just asking them what the system should do. Requirements elicitation practices include interviews, questionnaires, user observation, workshops, brainstorming, use cases, role playing and prototyping. However, these common procedures are still prone to be ambiguous or incorrect which can lead the Information Systems to failure. It is consensual that one of the major problem of this activity relates to the communication and collaboration between different and distant stakeholders. Thus, recent studies have been proposing web collaborative tools to gather these stakeholders in order to elicit requirements. The paper aims to evaluate the effectiveness and acceptance of such a collaborative tool which was developed by using a gamification approach and the Six Thinking Hats method. The document also makes a discussion of the implication and outcomes of improving stakeholders collaboration.


title: Unobstrusive heart rate and respiratory rate monitor embedded on a wheelchair

abstract: A biomedical system embedded on a wheelchair able to measure heart rate, respiratory rate, and the wheelchair motion state is presented. A wireless multi-channel acquisition module is connected to a BCG (ballistocardiography) unit and to an inertial 3-axial MEMS accelerometer based unit. The sensing units are embedded on the seat and backrest of the wheelchair and the acquired data is transmitted through Wi-Fi connection to a remote processing unit expressed by a laptop PC. Considering the different motion situations of the wheelchair when utilized by the user, an important part of the work was focused on BCG noise and artifacts removal as well as to heart rate and respiratory rate accurate estimation from BCG signal using wavelet based filtering and ICA (Independent Component Analysis) algorithms. Experimental results for different scenarios of wheelchair utilization are included in the paper.


Semigroups of operators that describe a Feller process on the line, which is the result of pasting together two diffusion processes


title: Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multiresolution Signal Decomposition and Two-Stage Support Vector Machine

abstract: Fault detection in photovoltaic (PV) arrays becomes difficult as the number of PV panels increases. Particularly, under low irradiance conditions with an active maximum power point tracking algorithm, line-to-line (L-L) faults may remain undetected because of low fault currents, resulting in loss of energy and potential fire hazards. This paper proposes a fault detection algorithm based on multiresolution signal decomposition for feature extraction, and two-stage support vector machine (SVM) classifiers for decision making. This detection method only requires data of the total voltage and current from a PV array and a limited amount of labeled data for training the SVM. Both simulation and experimental case studies verify the accuracy of the proposed method.


Contrastive Focus in Mandarin Chinese


Automatic multi-view texture mapping of 3D surface projections