title: Attacking GSM Networks as a Script Kiddie Using Commodity Hardware and Software

abstract: With the emergence of widely available hardware and software tools for GSM hacking, the security of cellular networks is threatened even by script kiddies. In this paper we present four different attacks in GSM networks, using commodity hardware as well as open source and freely available software tools. All attacks are performed using a common DVB-T TV tuner, which is used as a sniffer for the GSM radio interface, as well as an Arduino combined with a GSM shield that is used as a software programmable mobile phone. The attacks target both mobile users and the network, ranging from sniffing the signaling traffic to tracking and performing denial of service to the subscribers. Despite the script kiddie style of the attacks, their consequences are critical and threaten the normal operation of the cellular networks.


title: Densely Connected Attention Propagation for Reading Comprehension

abstract: We propose DecaProp (Densely Connected Attention Propagation), a new densely connected neural architecture for reading comprehension (RC). There are two distinct characteristics of our model. Firstly, our model densely connects all pairwise layers of the network, modeling relationships between passage and query across all hierarchical levels. Secondly, the dense connectors in our network are learned via attention instead of standard residual skip-connectors. To this end, we propose novel Bidirectional Attention Connectors (BAC) for efficiently forging connections throughout the network. We conduct extensive experiments on four challenging RC benchmarks. Our proposed approach achieves state-of-the-art results on all four, outperforming existing baselines by up to $2.6\%-14.2\%$ in absolute F1 score.


title: An intelligent self-checkout system for smart retail

abstract: Most of current self-checkout systems rely on barcodes, RFID tags, or QR codes attached on items to distinguish products. This paper proposes an Intelligent Self-Checkout System (ISCOS) embedded with a single camera to detect multiple products without any labels in real-time performance. In addition, deep learning skill is applied to implement product detection, and data mining techniques construct the image database employed as training dataset. Product information gathered from a number of markets in Taiwan is utilized to make recommendation to customers. The bounding boxes are annotated by background subtraction with a fixed camera to avoid time-consuming process for each image. The contribution of this work is to combine deep learning and data mining approaches to real-time multi-object detection in image-based checkout system.


Applying Design Patterns in Game Programming


title: Surgical outcome of thoracolumbar burst fractures with flexion–distraction injury of the posterior elements

abstract: Between 1991 and 2002 we treated 48 patients surgically for thoracolumbar burst fractures associated with flexion–distraction injury of the posterior elements. The degree of kyphotic deformity and the degree of vertebral wedging deformity were measured on plain lateral radiographs. The spinal canal compromise was measured on computer tomography. The mean postoperative follow-up was 70 (24–108) months. The preoperative kyphosis averaged 25.7° and the mean sagittal index was 28.8°. The mean wedging deformity of the fractured vertebral body was 46% (24–66%). The mean preoperative spinal canal compromise secondary to retropulsed bony fragments was 64%. Immediately after surgery, the correction of kyphosis averaged 98%. There was no loss of correction at the final follow-up. A satisfactory reduction and good stabilisation with solid fusion were achieved in all cases.


title: Consumers' Kansei Needs Clustering Method for Product Emotional Design Based on Numerical Design Structure Matrix and Genetic Algorithms

abstract: Consumers’ Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design.


QuickUMLS: a Fast, Unsupervised Approach for Medical Concept Extraction


title: Promises and Lies: An Experiment on Detecting Deception

abstract: Although economic and social relationships can involve deception (Gneezy 2005), such relationships are often governed by informal contracts that require trust (Berg et al. 1995). While important advances have been made concerning deception in economics, the research has focused little on written forms of communication. Are there certain systematic cues that signal written communications as dishonest? Are those signals accurately detected and used by message receivers? We fill this gap by studying messages written in a novel three-person trust game (we call it the â€œMistress Gameâ€ ). We find that: (i) messages that use encompassing terms, or a greater number of words, are significantly more likely to be viewed as promises; and (ii) promises that mention money are significantly more likely to be trusted. Notwithstanding the latter finding, we find senders who mention money within their promises to be significantly less likely to keep their word than those who do not. Length: 36


title: Streaming Architecture for Large-Scale Quantized Neural Networks on an FPGA-Based Dataflow Platform

abstract: Deep neural networks (DNNs) are used by different applications that are executed on a range of computer architectures, from IoT devices to supercomputers. The footprint of these networks is huge as well as their computational and communication needs. In order to ease the pressure on resources, research indicates that in many cases a low precision representation (1-2 bit per parameter) of weights and other parameters can achieve similar accuracy while requiring less resources. Using quantized values enables the use of FPGAs to run NNs, since FPGAs are well fitted to these primitives; e.g., FPGAs provide efficient support for bitwise operations and can work with arbitrary-precision representation of numbers. This paper presents a new streaming architecture for running QNNs on FPGAs. The proposed architecture scales out better than alternatives, allowing us to take advantage of systems with multiple FPGAs. We also included support for skip connections, that are used in state-of-the art NNs, and shown that our architecture allows to add those connections almost for free. All this allowed us to implement an 18-layer ResNet for 224×224 images classification, achieving 57.5% top-1 accuracy. In addition, we implemented a full-sized quantized AlexNet. In contrast to previous works, we use 2-bit activations instead of 1-bit ones, which improves AlexNet's top-1 accuracy from 41.8% to 51.03% for the ImageNet classification. Both AlexNet and ResNet can handle 1000-class real-time classification on an FPGA. Our implementation of ResNet-18 consumes 5× less power and is 4× slower for ImageNet, when compared to the same NN on the latest Nvidia GPUs. Smaller NNs, that fit a single FPGA, are running faster then on GPUs on small (32×32) inputs, while consuming up to 20× less energy and power.


title: Interactive k-d tree GPU raytracing

abstract: Over the past few years, the powerful computation rates and high memory bandwidth of GPUs have attracted efforts to run raytracing on GPUs. Our work extends Foley et al.'s GPU k-d tree research. We port their kd-restart algorithm from multi-pass, using CPU load balancing, to single pass, using current GPUs' branching and looping abilities. We introduce three optimizations: a packetized formulation, a technique for restarting partially down the tree instead of at the root, and a small, fixed-size stack that is checked before resorting to restart. Our optimized implementation achieves 15 - 18 million primary rays per second and 16 - 27 million shadow rays per second on our test scenes. Our system also takes advantage of GPUs' strengths at rasterization and shading to offer a mode where rasterization replaces eye ray scene intersection, and primary hits and local shading are produced with standard Direct3D code. For 1024x1024 renderings of our scenes with shadows and Phong shading, we achieve 12-18 frames per second. Finally, we investigate the efficiency of our implementation relative to the computational resources of our GPUs and also compare it against conventional CPUs and the Cell processor, which both have been shown to raytrace well.


title: Comparison of 3D Joint Angles Measured With the Kinect 2.0 Skeletal Tracker Versus a Marker-Based Motion Capture System.

abstract: The Microsoft Kinect is becoming a widely used tool for inexpensive, portable measurement of human motion, with the potential to support clinical assessments of performance and function. In this study, the relative osteokinematic Cardan joint angles of the hip and knee were calculated using the Kinect 2.0 skeletal tracker. The pelvis segments of the default skeletal model were reoriented and 3-dimensional joint angles were compared with a marker-based system during a drop vertical jump and a hip abduction motion. Good agreement between the Kinect and marker-based system were found for knee (correlation coefficient = 0.96, cycle RMS error = 11°, peak flexion difference = 3°) and hip (correlation coefficient = 0.97, cycle RMS = 12°, peak flexion difference = 12°) flexion during the landing phase of the drop vertical jump and for hip abduction/adduction (correlation coefficient = 0.99, cycle RMS error = 7°, peak flexion difference = 8°) during isolated hip motion. Nonsagittal hip and knee angles did not corr...


title: Properties and performance of a center/surround retinex

abstract: The last version of Land's (1986) retinex model for human vision's lightness and color constancy has been implemented and tested in image processing experiments. Previous research has established the mathematical foundations of Land's retinex but has not subjected his lightness theory to extensive image processing experiments. We have sought to define a practical implementation of the retinex without particular concern for its validity as a model for human lightness and color perception. We describe the trade-off between rendition and dynamic range compression that is governed by the surround space constant. Further, unlike previous results, we find that the placement of the logarithmic function is important and produces best results when placed after the surround formation. Also unlike previous results, we find the best rendition for a "canonical" gain/offset applied after the retinex operation. Various functional forms for the retinex surround are evaluated, and a Gaussian form is found to perform better than the inverse square suggested by Land. Images that violate the gray world assumptions (implicit to this retinex) are investigated to provide insight into cases where this retinex fails to produce a good rendition.


title: Design and characterization of a 1.7–2.7 GHz quasi-MMIC Doherty power amplifier

abstract: This paper presents the design and characterization of a Doherty power amplifier for small cells applications in the 1.7–2.7 GHz band. A quasi-monolithic realization is chosen for its cost advantages when compared to a fully-monolithic solution, and relies on GaN HEMT active devices, but passive networks realized on GaAs substrate. A lumped element Doherty combiner is designed to maximize the bandwidth at which the power amplifier shows high back-off efficiency that results higher than 37% in the 1.7–2.7 GHz band. The dual-input topology permits high flexibility in the optimization of performance, in particular in terms of bandwidth. The fabricated Doherty favourably compares to previously published power amplifiers.


title: Modeling and analyses of IP spoofing attack in 6LoWPAN network

abstract: Abstract 6LoWPAN (modified version of IPv6 for low power devices) inherits security threats from its predecessor protocols, IPv4 and IPv6. IP spoofing is one such classic attack. There are vulnerabilities in 6LoWPAN and associated routing protocol, which open up new spoofing paths to the attacker. This study aims at profiling the feasibility to carry IPv6 spoofing attack on the 6LoWPAN network. Two new different attack paths are identified, which associate wrong IPv6 address with the MAC address of a node. These two paths use spoofed RPL and 6LoWPAN-ND messages to perform the IPv6 spoofing attack in an unsecured wireless medium. Probability of attack success is analyzed using the radio propagation environment parameters which affect the correct reception of a signal. It is shown that the success of an attack is highly dependent on the signal path loss. To perform the systematic mathematical analysis, attack tree model is used and attack is simulated in cooja simulator as well as performed in real experimental network. Our mathematical and simulated analysis show that path loss exponent, which represents distance based path loss, affects the probability of attack success. Attack feasibility analysis is done to find the cost to the attacker with respect to energy and memory consumption. It is observed that attacker code can be accommodated in memory constrained devices, and uses less energy to perform the attack, which manifests its feasibility.


title: Distributed receding horizon estimation for sensor networks with improved consensus strategy

abstract: The distributed estimation problem is one of the most essential issues in sensor networks. This paper studies the consensus estimation problem of linear sensor networks based on the distributed receding horizon estimation (RHE) scheme. To design such a consensus estimation scheme, a novel optimization problem is first formulated for each sensor node, by proposing a new consensus strategy. The explicit solution to each optimization problem is provided and the iterative estimation scheme is established. Under the assumption that the communication graph contains a spanning tree, the sufficient conditions for ensuring robust consensus estimation are developed. It is shown that, the estimation error between any two sensor nodes is upper bounded by a value that is related with the energy bound of the sensor noise.


title: Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches

abstract: Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinder the subsequent statistical inference. The gap between these two processes can be reduced by an incremental population approach. We propose a new probabilistic knowledge graph factorisation method that benefits from the path structure of existing knowledge (e.g. syllogism) and enables a common modelling approach to be used for both incremental population and knowledge completion tasks. More specifically, the probabilistic formulation allows us to develop an incremental population algorithm that trades off exploitation-exploration. Experiments on three benchmark datasets show that the balanced exploitation-exploration helps the incremental population, and the additional path structure helps to predict missing information in knowledge completion.


title: Double-talk detection based on soft decision for acoustic echo suppression

abstract: In this paper, we propose a novel double-talk detection (DTD) technique based on a soft decision in the frequency domain. The proposed method provides an efficient procedure to detect the double-talk situation by the use of the global near-end speech presence probability (GNSPP) and voice activity detection (VAD) of the near-end and far-end signal. Specifically, the GNSPP is derived based on a statistical method of speech and is employed to determine the double-talk presence in a given frame. The performance of our approach is evaluated by objective tests under different environments, and it is found that the suggested method yields better results compared with the conventional scheme.


title: A soft pneumatic actuator that can sense grasp and touch

abstract: We present a fiber reinforced soft pneumatic actuator with integrated strain and pressure sensors. We demonstrate that combining these sensors into the same actuator enables proprioception of both actuator curvature and environmental contact forces. We describe the manufacture and integration of a simple liquid metal strain sensor, a pressure sensor, and electrical circuits used for the sensors. We derive a constant curvature model for the actuator which predicts actuator curvature from the air pressure, and other constants of manufacture. The utility of the sensor integration is demonstrated by using the actuator to distinguish successful grasps amongst a set of common cylindrical objects with varying diameter. The grasp radius is estimated from the relationship between the sensor pair. Contact forces with the environment (touch) may be inferred from sensor readings which deviate from unconstrained motion.


title: Snapshot difference imaging using correlation time-of-flight sensors

abstract: Computational photography encompasses a diversity of imaging techniques, but one of the core operations performed by many of them is to compute image differences. An intuitive approach to computing such differences is to capture several images sequentially and then process them jointly. In this paper, we introduce a snapshot difference imaging approach that is directly implemented in the sensor hardware of emerging time-of-flight cameras. With a variety of examples, we demonstrate that the proposed snapshot difference imaging technique is useful for direct-global illumination separation, for direct imaging of spatial and temporal image gradients, for direct depth edge imaging, and more.


title: Caught in the Middle: Access to State Government Records in the United States

abstract: Government archivists must balance two key factors that influence access to records in their custody. On one hand, archivists are committed to providing access to government records as a core principle of a democratic government. At the same time, they must respect the rights of privacy of individuals whose lives may be documented in intimate detail in those records. They must assess both the potential benefits and damages if records are released, as well as the potential benefits and damages if the records remain restricted.


title: Design aspects of a medium-voltage direct current (MVDC) grid for a university campus

abstract: Today's power systems use alternating current (ac) for transmission and distribution of electrical energy, although the first grids were based on direct current (dc). Due to the absence of appropriate equipment to change voltage levels dc technology did not become widely accepted and was finally ruled out by the more efficient ac infrastructure. However, as a result of considerable technical progress, high-voltage direct current (HVDC) transmission has found its way back into power systems. At lower voltage and power levels medium-voltage dc (MVDC) distribution has been proposed for offshore wind farms and industrial applications. This paper describes the design of an MVDC grid for the interconnection of high-power test benches at a university campus. Voltage control within the dc grid as well as the behavior in different fault scenarios is analyzed using numerical simulations. To assess the environmental impact of the grid the magnetic flux density emitted by the dc cable lines is calculated.


title: Exploring the Reasons behind the Low ISO 27001 Adoption in Public Organizations in Saudi Arabia

abstract: The main purpose of this paper is to investigate the reason behind the low adoption of the ISO 27001 standard in the public organizations in Saudi Arabia. Human resource management issues such as shortage of information security expertise, lack of training, education, and awareness programs, and high salary demand for security professionals were found to be the most significant barriers to the successful implementation of ISO 27001 in public organizations in Saudi Arabia. Implications for public organizations management and future research are discussed at the end of this paper.


A Deep Learning Based Approach to Transliteration


title: Dynamic resource allocation exploiting mobility prediction in mobile edge computing

abstract: In 5G mobile networks, computing and communication converge into a single concept. This convergence leads to introduction of Mobile Edge Computing, where computing resources are distributed at the edge of mobile network, i.e., in base stations. This approach significantly reduces delay for computation of tasks offloaded from users' devices to cloud and reduces load of backhaul. However, due to users' mobility, optimal allocation of the computational resources at the base stations might change over time. The computational resources are allocated in a form of Virtual Machines (VM), which emulate a given computer system. User's mobility can be solved by VM migration, i.e., transfer of VM from one base station to another. Another option is to find a new communication path for exchange of data between the VM and the user. In this paper we propose an algorithm enabling flexible selection of communication path together with VM placement. To handle dynamicity of the system, we exploit prediction of users' movement. The prediction is used for dynamic VM placement and to find the most suitable communication path according to expected users' movement. Comparing to state of the art approaches, the proposal leads to reduction of the task offloading delay between 10% and 66% while energy consumed by user's equipment is kept at similar level. The proposed algorithm also enables higher arrival rate of the offloading requirements.


Recycling Decisions in 2020, 2030, and 2040—When Can Substantial NdFeB Extraction be Expected in the EU?


title: A Hybrid Data Compression Scheme for Power Reduction in Wireless Sensors for IoT

abstract: This paper presents a novel data compression and transmission scheme for power reduction in Internet-of-Things (IoT) enabled wireless sensors. In the proposed scheme, data is compressed with both lossy and lossless techniques, so as to enable hybrid transmission mode , support adaptive data rate selection and save power in wireless transmission. Applying the method to electrocardiogram (ECG), the data is first compressed using a lossy compression technique with a high compression ratio (CR). The residual error between the original data and the decompressed lossy data is preserved using entropy coding, enabling a lossless restoration of the original data when required. Average CR of 2.1 $\times$ and 7.8 $\times$ were achieved for lossless and lossy compression respectively with MIT/BIH database. The power reduction is demonstrated using a Bluetooth transceiver and is found to be reduced to 18% for lossy and 53% for lossless transmission respectively. Options for hybrid transmission mode , adaptive rate selection and system level power reduction make the proposed scheme attractive for IoT wireless sensors in healthcare applications.


title: Collecting Image Annotations Using Amazon’s Mechanical Turk

abstract: Crowd-sourcing approaches such as Amazon's Mechanical Turk (MTurk) make it possible to annotate or collect large amounts of linguistic data at a relatively low cost and high speed. However, MTurk offers only limited control over who is allowed to particpate in a particular task. This is particularly problematic for tasks requiring free-form text entry. Unlike multiple-choice tasks there is no correct answer, and therefore control items for which the correct answer is known cannot be used. Furthermore, MTurk has no effective built-in mechanism to guarantee workers are proficient English writers. We describe our experience in creating corpora of images annotated with multiple one-sentence descriptions on MTurk and explore the effectiveness of different quality control strategies for collecting linguistic data using Mechanical MTurk. We find that the use of a qualification test provides the highest improvement of quality, whereas refining the annotations through follow-up tasks works rather poorly. Using our best setup, we construct two image corpora, totaling more than 40,000 descriptive captions for 9000 images.


title: Design and Modeling of a Textile Pressure Sensor for Sitting Posture Classification

abstract: A textile pressure sensor has been designed for measuring pressure distribution on the human body. Electrodes built with conductive textiles are arranged on both sides of a compressible spacer, forming a variable capacitor. The use of textiles makes unobtrusive, comfortable, lightweight, and washable sensors possible. This simplifies the goal to integrate such sensors into clothing in the future, to be simple, and fast to mount just as getting dressed. Hysteresis induced by the spacer of the sensor has been modeled with the Preisach model to reduce the measurement error from 24% to 5% on average and the maximal error from above 50% to below 10%. Standard textiles that are not specially optimized for low hysteresis can be used for designing the sensor due to the modeling. The model may also be used for other pressure or even strain sensors to reduce their hysteresis. The modeling enhances the accuracy of the textile sensor to those of commercial, nontextile pressure sensing mats. Furthermore, we used the sensor system for the classification of sitting postures in a chair. The data of nine subjects have been classified with Naive Bayes classifier, achieving an average recognition rate of 82%. We show that the textile sensor performs similarly to a commercial, nontextile pressure sensing mat for this application.


title: Scenario and countermeasure for replay attack using join request messages in LoRaWAN

abstract: LPWAN (Low Power Wide Area Networks) technologies have been attracting attention continuously in IoT (Internet of Things). LoRaWAN is present on the market as a LPWAN technology and it has features such as low power consumption, low transceiver chip cost and wide coverage area. In the LoRaWAN, end devices must perform a join procedure for participating in the network. Attackers could exploit the join procedure because it has vulnerability in terms of security. Replay attack is a method of exploiting the vulnerability in the join procedure. In this paper, we propose a attack scenario and a countermeasure against replay attack that may occur in the join request transfer process.


title: As Time Goes By: Comprehensive Tagging of Textual Phrases with Temporal Scopes

abstract: Temporal expressions (TempEx's for short) are increasingly important in search, question answering, information extraction, and more. Techniques for identifying and normalizing explicit temporal expressions work well, but are not designed for and cannot cope with textual phrases that denote named events, such as "Clinton's term as secretary of state". This paper addresses the problem of detecting such temponyms, inferring their temporal scopes, and mapping them to events in a knowledge base if present there. ::: ::: We present methods for this kind of temponym resolution, using an entity- and TempEx-oriented document model and the Yago knowledge base for distant supervision. We develop a family of Integer Linear Programs for jointly inferring temponym mappings to the timeline and knowledge base. This enriches the document representation and also extends the knowledge base by obtaining new alias names for events. Experiments with three different corpora demonstrate the viability of our methods.


Jemez Tones and Stress


title: Early stages (P100) of face perception in humans as measured with event-related potentials (ERPs)

abstract: Summary.According to current ERP literature, face specific activity is reflected by a negative component over the inferior occipito-temporal cortex between 140 and 180 ms after stimulus onset (N170). A recently published study (Liu et al., 2002) using magnetoencephalography (MEG) clearly indicated that a face-selective component can be observed at 100 ms (M100) which is about 70 ms earlier than reported in most previous studies. Here we report these early differences at 107 ms between the ERPs of faces and buildings over the occipito-temporal cortex using electroencephalography. To exclude contrast differences as the main factor for this P100 differences we replicated this study using pictures of faces and scrambled faces. Both studies indicated that face processing starts already at ∼100 ms with an initial stage which can be measured not only with MEG but also with ERPs.


title: HfO2-based memristors for neuromorphic applications

abstract: In recent years, biologically inspired systems, which emulate the nervous system of living beings, are becoming more and more requested due to their ability to solve ill-posed problems such as pattern recognition or interaction with the external environment. By virtue of their nanoscaled size and their tunable conductance, memristors are key elements to emulate high-density networks of biological synapses that regulate the communication efficacy among neurons and implement learning capability. We propose a TiN/ HfO2/Ti/TiN memristor as artificial synapse for neuromorphic architectures. The device can gradually change its conductance upon application of proper electrical stimuli. More specifically, it features gradual potentiation and depression when stimulated by trains of identical potentiating or depressing spikes, which are easy to be implemented on-chip. Moreover, we demonstrate that the memristor conductance can be regulated according to the delay time between two spikes incoming to the device terminals. This regulation of memristor conductance implements the typical biological learning process named Spike-Time-Dependent-Plasticity (STDP). Finally, collected STDP data were used to simulate a simple fully connected Spiking Neural Network (SNN) for pattern recognition.


title: Quality versus Quantity: An Investigation into Electronic Word of Mouth’s Influence on Consumer Buying Intention

abstract: Online consumer reviews play an increasingly crucial role in shaping purchasing intention. With the wide-spread reviews available around the world on sites like TripAdvisor or Yelp, companies’ reputation and hence survival are potentially at risk. The purpose of this study is to investigate the impacts of electronic word-of-mouth (eWOM) on the individual’s attitudes towards the quantity and quality of online store reviews and consider how it impacts their behavior. We incorporate brand trust as a mediator in the relationship between eWOM and purchase intention. Using a vignette survey experiment centered on coffee shop reviews in Hanoi, Vietnam, we test our hypothesis with 223 respondents. Results showed that the number and quality of review does significantly affect purchase intention. Findings also show that brand trust has a mediating effect on the relationship between online reviews and buying intention. This study assists practitioners to develop user-oriented information review format in order to manage online consumer reviews properly and effectively.


title: Unsupervised Domain Adaptation for Semantic Segmentation with GANs

abstract: Visual Domain Adaptation is a problem of immense importance in computer vision. Previous approaches showcase the inability of even deep neural networks to learn informative representations across domain shift. This problem is more severe for tasks where acquiring hand labeled data is extremely hard and tedious. In this work, we focus on adapting the representations learned by segmentation networks across synthetic and real domains. Contrary to previous approaches that use a simple adversarial objective or superpixel information to aid the process, we propose an approach based on Generative Adversarial Networks (GANs) that brings the embeddings closer in the learned feature space. To showcase the generality and scalability of our approach, we show that we can achieve state of the art results on two challenging scenarios of synthetic to real domain adaptation. Additional exploratory experiments show that our approach: (1) generalizes to unseen domains and (2) results in improved alignment of source and target distributions.


title: Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems

abstract: Fault detection problem for closed loop uncertain dynamical systems, is investigated in this paper, using different deep learning based methods. Traditional classifier based method does not perform well, because of the inherent difficulty of detecting system level faults for closed loop dynamical system. Specifically, acting controller in any closed loop dynamical system, works to reduce the effect of system level faults. A novel Generative Adversarial based deep Autoencoder is designed to classify datasets under normal and faulty operating conditions. This proposed network performs significantly well when compared to any available classifier based methods, and moreover, does not require labeled fault incorporated datasets for training purpose. Finally, this aforementioned network's performance is tested on a high complexity building energy system dataset.


title: Poster: Smart buildings, sensor networks, and the Internet of Things

abstract: In contrast to traditional sensor networks, the "Internet of Things" focuses on interactions between humans and physical objects rather than on sensing and reporting low level information. While several middle-ware systems have been created to simplify the task of managing and aggregating data from multiple sensor networks that use different hardware and software, management of the data is not sufficient to build an Internet of Things. To build a smart home or office application, information from sensors and existing infrastructure, such as networked power supplies and printers, need to be associated with physical objects. As low-level data is processed, it can be fed back into the system to support higher-level results. In this paper we introduce Octopus, an open-source, dynamically extensible system that supports data management and fusion for Internet of Things applications.


title: Substrate Integrated Folded Waveguide Filter with Out-of-Band Rejection Controlled by Resonant-Mode Suppression

abstract: This letter presents a new filter, based on substrate integrated folded waveguide (SIFW) technology, which exhibits compact size and good out-of-band rejection. The filter is based on a SIFW cavity, which guarantees size reduction, and the out-of-band rejection is controlled by the suppression and tuning of the high-order cavity modes. A detailed investigation of the cavity mode spectrum is presented, to illustrate the operation principle and the design of the filter. The interesting feature of this filter is the possibility to design the pass band and the return band by simply tuning the mode spectrum of the cavity, which is practically unaffected by the connection to the excitation ports. The fabrication and testing of a prototype operating at 4.5 GHz validate the proposed filter topology.


title: A millimeter-wave conical conformal low sidelobe microstrip antenna array

abstract: Design of a millimeter-wave conformal low sidelobe microstrip patch antenna 1times8 elements array on a cone surface is presented. The hemline feed method (HFM) is used to design the patch element. This kind of elements and parallel feed network is used for forming the conformal low sidelobe microstrip array based on the design principle of Dolph-Chebyshev distribution and T-junction power divider. The radiation features of the array are discussed to illuminate the realization of a low sidelobe level (-30.5 dB) of the array. Simulations by the CST MICROWAVE STUDIO are given to verify this present design.


title: Augmenting data when training a CNN for retinal vessel segmentation: How to warp?

abstract: The retinal vascular condition is a trustworthy biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation is a crucial step to diagnose and monitor these problems. Deep Learning models have recently revolutionized the state-of-the-art in several fields, since they can learn features with multiple levels of abstraction from the data itself. However, these methods can easily fall into overfitting, since a huge number of parameters must be learned. Having bigger datasets may act as regularization and lead to better models. Yet, acquiring and manually annotating images, especially in the medical field, can be a long and costly procedure. Hence, when using regular datasets, people heavily need to apply artificial data augmentation. In this work, we use a fully convolutional neural network capable of reaching the state-of-the-art. Also, we investigate the benefits of augmenting data with new samples created by warping retinal fundus images with nonlinear transformations. Our results hint that may be possible to halve the amount of data, while maintaining the same performance.


title: Can embodied interaction and virtual peer customization in a virtual programming environment enhance computational thinking?

abstract: To address the problem of underexposure, underrepresentation, and underproduction of diverse professionals in the field of computing, we target middle school education using an idea that combines computational thinking with dance and movement choreography. This lightning talk delves into a virtual reality education and entertainment application named Virtual Environment Interactions (VEnvI). Our in vivo study examines how VEnvI can be used to teach fundamental computer science concepts such as sequences, loops, variables, conditionals, functions, and parallel programming. We aim to reach younger students through a fun and intuitive interface for choreographing dance movements with a virtual character. Our study contrasts the highly immersive and embodied virtual reality metaphor of using VEnvI with a non-immersive desktop metaphor. Additionally, we examine the effects of user attachment by comparing the learning results gained with customizable virtual characters in contrast with character presets. By analyzing qualitative and quantitative user responses measuring cognition, presence, usability, and satisfaction, we hope to find how virtual reality can enhance interest in the field of computer science among middle school students.


title: Blind estimation of scrambler offset using encoder redundancy

abstract: A self-recovering receiver for encoded and scrambled binary data streams is proposed in this paper. The generating polynomial of the scrambling sequence is known, as well as the encoder structure and coefficients, but the scrambler time offset is unknown. Taking profit of redundancy introduced by the encoder, we proposed a method which is able to estimate the scrambling sequence offset from the observed scrambled stream. The method is based on the projection of the observed data on the encoder orthogonal subspace. Once the offset has been estimated, classical data descrambling and decoding can be used to recover the information stream.


title: The immune contexture in human tumours: impact on clinical outcome

abstract: The infiltration of various types of immune cells is common to most tumour microenvironments. As discussed in this Opinion article, the pattern of immune cell infiltration varies between cancer type and individual tumours of the same type, and this pattern can be used to indicate prognosis and response to therapy.


title: ADS-B encryption: confidentiality in the friendly skies

abstract: The US Federal Aviation Administration is proposing an upgrade to air transportation that will fundamentally overhaul the current, aging system. A key component, the automatic dependent surveillance broadcast (ADS-B) system, will enhance air traffic monitoring and control by requiring aircraft to continually broadcast position, identity and velocity via unencrypted data links to ground stations. Although ADS-B may enhance air traffic safety and support the increase in traffic demands, open broadcast of clear aircraft data points raise serious security concerns. The ability to encrypt ADS-B message transactions would afford protection to ensure that the confidentiality of aircraft data is not compromised. The implementation of an encryption framework for a large, distributed and dynamic system, however, is nontrivial. This paper examines encryption schemes and highlights challenges associated with implementing confidentiality security protections for the ADS-B environment.


title: SAP HANA database: data management for modern business applications

abstract: The SAP HANA database is positioned as the core of the SAP HANA Appliance to support complex business analytical processes in combination with transactionally consistent operational workloads. Within this paper, we outline the basic characteristics of the SAP HANA database, emphasizing the distinctive features that differentiate the SAP HANA database from other classical relational database management systems. On the technical side, the SAP HANA database consists of multiple data processing engines with a distributed query processing environment to provide the full spectrum of data processing -- from classical relational data supporting both row- and column-oriented physical representations in a hybrid engine, to graph and text processing for semi- and unstructured data management within the same system. From a more application-oriented perspective, we outline the specific support provided by the SAP HANA database of multiple domain-specific languages with a built-in set of natively implemented business functions. SQL -- as the lingua franca for relational database systems -- can no longer be considered to meet all requirements of modern applications, which demand the tight interaction with the data management layer. Therefore, the SAP HANA database permits the exchange of application semantics with the underlying data management platform that can be exploited to increase query expressiveness and to reduce the number of individual application-to-database round trips.


title: Deep Cascade Learning

abstract: In this paper, we propose a novel approach for efficient training of deep neural networks in a bottom-up fashion using a layered structure. Our algorithm, which we refer to as deep cascade learning, is motivated by the cascade correlation approach of Fahlman and Lebiere, who introduced it in the context of perceptrons. We demonstrate our algorithm on networks of convolutional layers, though its applicability is more general. Such training of deep networks in a cascade directly circumvents the well-known vanishing gradient problem by ensuring that the output is always adjacent to the layer being trained. We present empirical evaluations comparing our deep cascade training with standard end-end training using back propagation of two convolutional neural network architectures on benchmark image classification tasks (CIFAR-10 and CIFAR-100). We then investigate the features learned by the approach and find that better, domain-specific, representations are learned in early layers when compared to what is learned in end-end training. This is partially attributable to the vanishing gradient problem that inhibits early layer filters to change significantly from their initial settings. While both networks perform similarly overall, recognition accuracy increases progressively with each added layer, with discriminative features learned in every stage of the network, whereas in end-end training, no such systematic feature representation was observed. We also show that such cascade training has significant computational and memory advantages over end-end training, and can be used as a pretraining algorithm to obtain a better performance.


title: What is a True Gamer? The Male Gamer Stereotype and the Marginalization of Women in Video Game Culture

abstract: Women and men play video games in approximately equal numbers. Despite this similarity, video gaming is still strongly associated with men. A common justification for this stereotype is that, although women might play games, they should not be considered “true” or “hard-core” gamers because they play more casually and less skillfully compared to their male counterparts. In this contribution, we review the existing literature on gender and gaming to investigate the male gamer stereotype in terms of its accuracy, persistence, effects, and future perspective. We conclude that the stereotype varies in accuracy depending on the definition of “gamer.” We further argue that the persistence of this stereotype can be explained by the fact that almost all professional and highly visible figures in gaming culture are male. On the other hand, female players who achieve a moderate level of competence are rendered invisible or are actively marginalized. We argue that the effects of the male gamer stereotype can be harmful to women, precluding them from the positive outcomes of video game play such as enhanced access to fields of science, technology, and engineering.


title: Adverse drug events and medication errors: detection and classification methods

abstract: Investigating the incidence, type, and preventability of adverse drug events (ADEs) and medication errors is crucial to improving the quality of health care delivery. ADEs, potential ADEs, and medication errors can be collected by extraction from practice data, solicitation of incidents from health professionals, and patient surveys. Practice data include charts, laboratory, prescription data, and administrative databases, and can be reviewed manually or screened by computer systems to identify signals. Research nurses, pharmacists, or research assistants review these signals, and those that are likely to represent an ADE or medication error are presented to reviewers who independently categorize them into ADEs, potential ADEs, medication errors, or exclusions. These incidents are also classified according to preventability, ameliorability, disability, severity, stage, and responsible person. These classifications, as well as the initial selection of incidents, have been evaluated for agreement between reviewers and the level of agreement found ranged from satisfactory to excellent (kappa = 0.32-0.98). The method of ADE and medication error detection and classification described is feasible and has good reliability. It can be used in various clinical settings to measure and improve medication safety.


title: Public space, collective memory and intercultural dialogue in a (UK) city of culture

abstract: Using the case study of Derry~Londonderry and its designation as ‘UK City of Culture 2013’, the primary objective of this research paper is to critically analyse the challenges associated with the production of a year-long cultural programme in a culturally and politically divided place. Given that Northern Ireland’s second largest city has been understood in terms of a conflict between ‘two traditions’, Irish/Catholic and British/Protestant, we critically assess the dialogue and policy negotiations with reference to public places as well as representations of collective memory and traditional music during the year. Fieldwork over two years has enabled us to investigate how culture and identity politics are played out in the context of a city undergoing a process of reconciliation. Placing our case study in a strongly comparative context, we argue that cultural concerns are pivotal points of (re-)negotiation in any society transitioning from conflict to ‘peace’ and that this issue, therefore, is of vital concern to academics and policymakers alike internationally.


title: Web page revisitation revisited: implications of a long-term click-stream study of browser usage

abstract: This paper presents results of an extensive long-term click-stream study of Web browser usage. Focusing on character and challenges of page revisitation, previous findings from seven to thirteen years ago are updated. The term page re-visit had to be differentiated, since the recurrence rate--the key measure for the share of page revisits--turns out to strongly depend on interpretation. We identify different types of revisitation that allow assessing the quality of current user support and developing concepts for new tools. Individual navigation strategies differ dramatically and are strongly influenced by personal habits and type of site visited. Based on user action logs and interviews, we distinguished short-term revisits (backtrack or undo) from medium-term (re-utilize or observe) and long-term revisits (rediscover). We analyze current problems and provide suggestions for improving support for different revisitation types.


title: Cancer statistics, 2012.

abstract: Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data from the National Center for Health Statistics. A total of 1,638,910 new cancer cases and 577,190 deaths from cancer are projected to occur in the United States in 2012. During the most recent 5 years for which there are data (2004-2008), overall cancer incidence rates declined slightly in men (by 0.6% per year) and were stable in women, while cancer death rates decreased by 1.8% per year in men and by 1.6% per year in women. Over the past 10 years of available data (1999-2008), cancer death rates have declined by more than 1% per year in men and women of every racial/ethnic group with the exception of American Indians/Alaska Natives, among whom rates have remained stable. The most rapid declines in death rates occurred among African American and Hispanic men (2.4% and 2.3% per year, respectively). Death rates continue to decline for all 4 major cancer sites (lung, colorectum, breast, and prostate), with lung cancer accounting for almost 40% of the total decline in men and breast cancer accounting for 34% of the total decline in women. The reduction in overall cancer death rates since 1990 in men and 1991 in women translates to the avoidance of about 1,024,400 deaths from cancer. Further progress can be accelerated by applying existing cancer control knowledge across all segments of the population, with an emphasis on those groups in the lowest socioeconomic bracket.


title: Performance Comparison of Dual Connectivity and Hard Handover for LTE-5G Tight Integration

abstract: This Thesis will propose an LTE-5G tight integration architecture, based on mobile terminals' dual connectivity to LTE and 5G radio access networks, and will evaluate which are the new network procedures that will be needed to support it. Moreover, this new architecture will be implemented in the ns-3 simulator, and a thorough simulation campaign will be conducted in order to evaluate its performance, with respect to the baseline of handover between LTE and 5G


title: Requisite Variety, Autopoiesis, and Self-organization

abstract: Purpose – Autopoiesis is a concept originally used to define living systems. However, no measure for autopoiesis has been proposed so far. Moreover, how can we build systems with a higher autopoiesis value? The paper aims to discuss these issues. Design/methodology/approach – Relating autopoiesis with Ashby’s law of requisite variety, self-organization is put forward as a way in which systems can be designed to match the variety of their environment. Findings – Guided self-organization has been shown to produce systems which can adapt to the requisite variety of their environment, offering more efficient solutions for problems that change in time than those obtained with traditional techniques. Originality/value – Being able to measure autopoiesis allows us to apply this measure to all systems. More “living” systems will be fitter to survive in their environments: biological, social, technological, or urban.


title: Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries

abstract: Much recent work focuses on formal interpretation of natural question utterances, with the goal of executing the resulting structured queries on knowledge graphs (KGs) such as Freebase. Here we address two limitations of this approach when applied to open-domain, entity-oriented Web queries. First, Web queries are rarely wellformed questions. They are “telegraphic”, with missing verbs, prepositions, clauses, case and phrase clues. Second, the KG is always incomplete, unable to directly answer many queries. We propose a novel technique to segment a telegraphic query and assign a coarse-grained purpose to each segment: a base entity e1, a relation type r, a target entity type t2, and contextual words s. The query seeks entity e2 2 t2 where r(e1,e2) holds, further evidenced by schema-agnostic words s. Query segmentation is integrated with the KG and an unstructured corpus where mentions of entities have been linked to the KG. We do not trust the best or any specific query segmentation. Instead, evidence in favor of candidate e2s are aggregated across several segmentations. Extensive experiments on the ClueWeb corpus and parts of Freebase as our KG, using over a thousand telegraphic queries adapted from TREC, INEX, and WebQuestions, show the efficacy of our approach. For one benchmark, MAP improves from 0.2‐0.29 (competitive baselines) to 0.42 (our system). NDCG@10 improves from 0.29‐0.36 to 0.54.


Automatic detection and verification of rumors on Twitter


Preoperative localization of low-density foreign bodies under CT guidance.


title: Comparison and Characterization of Android-Based Fall Detection Systems

abstract: Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones' potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems.


title: Ethics and the professional practice of psychologists: the role of virtues and principles.

abstract: We evaluate the potential relevance of virtue ethics to the training and practice of professional psychologists, and we contrast them with principle ethics. Typically, principles are used to facilitate the selection of socially and historically acceptable answers to the question "What shall I do?" when confronted by ethical dilemmas. Virtue ethics, however, generally focus on the question "Who shall I be?" Strengths and weaknesses of each approach are presented. The impact of each is discussed with respect to informed consent and the therapeutic construct "genuineness." We conclude that virtue ethics are an essential component of responsible ethical training and practice.


title: A hybrid design for high performance large-scale sorting on FPGA

abstract: Sorting is a key kernel in numerous big data application including database operations, graphs and text analytics. Due to low control overhead, parallel bitonic sorting networks are usually employed for hardware implementations to accelerate sorting. Although a typical implementation of merge sort network can lead to low latency and small memory usage, it suffers from low throughput due to the lack of parallelism in the final stage. We analyze a pipelined merge sort network, showing its theoretical limits in terms of latency, memory and, throughput. To increase the throughput, we propose a merge sort based hybrid design where the final few stages in the merge sort network are replaced with “folded” bitonic merge networks. In these “folded” networks, all the interconnection patterns are realized by streaming permutation networks (SPN). We present a theoretical analysis to quantify latency, memory and throughput of our proposed design. Performance evaluations are performed by experiments on Xilinx Virtex-7 FPGA with post place-androute results. We demonstrate that our implementation achieves a throughput close to 10 GBps, outperforming state-of-the-art implementation of sorting on the same hardware by 1.2x, while preserving lower latency and higher memory efficiency.


title: Electoral Predictions with Twitter: A Machine-Learning approach

abstract: Several studies have shown how to approximately predict public opinion, ::: such as in political elections, by analyzing user activities in blogging platforms ::: and on-line social networks. The task is challenging for several reasons. ::: Sample bias and automatic understanding of textual content are two of several ::: non trivial issues. ::: In this work we study how Twitter can provide some interesting insights concerning ::: the primary elections of an Italian political party. State-of-the-art approaches ::: rely on indicators based on tweet and user volumes, often including sentiment ::: analysis. We investigate how to exploit and improve those indicators in order to ::: reduce the bias of the Twitter users sample. We propose novel indicators and a ::: novel content-based method. Furthermore, we study how a machine learning approach ::: can learn correction factors for those indicators. Experimental results on ::: Twitter data support the validity of the proposed methods and their improvement ::: over the state of the art.


title: Compressive sensing for subsurface imaging using ground penetrating radar

abstract: The theory of compressive sensing (CS) enables the reconstruction of sparse signals from a small set of non-adaptive linear measurements by solving a convex @?"1 minimization problem. This paper presents a novel data acquisition system for wideband synthetic aperture imaging based on CS by exploiting sparseness of point-like targets in the image space. Instead of measuring sensor returns by sampling at the Nyquist rate, linear projections of the returned signals with random vectors are used as measurements. Furthermore, random sampling along the synthetic aperture scan points can be incorporated into the data acquisition scheme. The required number of CS measurements can be an order of magnitude less than uniform sampling of the space-time data. For the application of underground imaging with ground penetrating radars (GPR), typical images contain only a few targets. Thus we show, using simulated and experimental GPR data, that sparser target space images are obtained which are also less cluttered when compared to standard imaging results.


title: Facial age estimation using BSIF and LBP

abstract: Human face aging is irreversible process causing changes in human face characteristics such us hair whitening, muscles drop and wrinkles. Due to the importance of human face aging in biometrics systems, age estimation became an attractive area for researchers. This paper presents a novel method to estimate the age from face images, using binarized statistical image features (BSIF) and local binary patterns (LBP)histograms as features performed by support vector regression (SVR) and kernel ridge regression (KRR). We applied our method on FG-NET and PAL datasets. Our proposed method has shown superiority to that of the state-of-the-art methods when using the whole PAL database.


title: Graph Regularized Nonnegative Matrix Factorization for Data Representation

abstract: Abstract Recently, multiple graph regularizer based methods have shown promising performances in data representation. However, the parameter choice of the regularizer is crucial to the performance of clustering and its optimal value changes for different real datasets. To deal with this problem, we propose a novel method called Parameter-less Auto-weighted Multiple Graph regularized Nonnegative Matrix Factorization (PAMGNMF) in this paper. PAMGNMF employs the linear combination of multiple simple graphs to approximate the manifold structure of data as previous methods do. Moreover, the proposed method can automatically learn an optimal weight for each graph without introducing an additive parameter. Therefore, the proposed PAMGNMF method is easily applied to practical problems. Extensive experimental results on different real-world datasets have demonstrated that the proposed method achieves better performance than the state-of-the-art approaches.


title: Magnetic resonance imaging study of hippocampal volume in chronic, combat-related posttraumatic stress disorder

abstract: This study used quantitative volumetric magnetic resonance imaging techniques to explore the neuroanatomic correlates of chronic, combat-related posttraumatic stress disorder (PTSD) in seven Vietnam veterans with PTSD compared with seven nonPTSD combat veterans and eight normal nonveterans. Both left and right hippocampi were significantly smaller in the PTSD subjects compared to the Combat Control and Normal subjects, even after adjusting for age, whole brain volume, and lifetime alcohol consumption. There were no statistically significant group differences in intracranial cavity, whole brain, ventricles, ventricle:brain ratio, or amygdala. Subarachnoidal cerebrospinal fluid was increased in both veteran groups. Our finding of decreased hippocampal volume in PTSD subjects is consistent with results of other investigations which utilized only trauma-unexposed control groups. Hippocampal volume was directly correlated with combat exposure, which suggests that traumatic stress may damage the hippocampus. Alternatively, smaller hippocampi volume may be a pre-existing risk factor for combat exposure and/or the development of PTSD upon combat exposure.


title: Using machine learning to secure IoT systems

abstract: The Internet of Things (IoT) is a massive group of devices containing sensors or actuators connected together over wired or wireless networks. With an estimate of over 25 billion devices connected together by 2020, IoT has been rapidly growing over the past decade. During the growth, security has been identified as one of the weakest areas in IoT. When implementing security within an IoT network, there are several challenges including heterogeneity within the system as well as the quantity of devices that need to be addressed. To approach the challenges in securing IoT devices, we propose using machine learning within an IoT gateway to help secure the system. We investigate using Artificial Neural Networks in a gateway to detect anomalies in the data sent from the edge devices. We are convinced that this approach can improve the security of IoT systems.


title: Scatter Search for Vehicle Routing Problem with Time Windows and Split Deliveries

abstract: The classical vehicle routing problem (VRP) aims to find a set of routes at a minimal cost (finding the shortest path, minimizing the number of vehicles, etc) beginning and ending the route at the depot, so that the known demand of all nodes are fulfilled. Each node is visited only once, by only one vehicle, and each vehicle has a limited capacity. Some formulations also present constraints on the maximum traveling time. The VRPSD is a variation of the classical VRP, where each customer can be served by more than one vehicle. Thus, for the VRPSD, besides the delivery routes, the amount to be delivered to each customer in each vehicle must also be determined. The option of splitting a demand makes it possible to service a customer whose demand exceeds the vehicle capacity. Splitting may also allow decreasing costs. The vehicle routing problem with time windows and split deliveries (VRPTWSD) is an extension of the VRPSD, adding to it the time window restraints. Lenstra and Rinnooy Kan (1981) have analyzed the complexity of the vehicle routing problem and have concluded that practically all the vehicle routing problems are NP-hard (among them the classical vehicle routing problem), since they are not solved in polynomial time. According to Solomon and Desrosiers (1988), the vehicle routing problem with time windows (VRPTW) is also NP-hard because it is an extension of the VRP. Although the vehicle routing problem with split deliveries (VRPSD) is a relaxation of the VRP, it is still NP-hard (Dror and Trudeau, 1990, Archetti et al., 2005). Therefore, the VRPTWSD is NP-hard, since it is a combination of the vehicle routing problem with time windows (VRPTW) and the vehicle routing problem with split delivery (VRPSD), and that makes a strong point for applying heuristics and metaheuristic in order to solve the problem. This work develops a scatter search (SS) algorithm to solve a vehicle routing problem with time windows and split deliveries (VRPTWSD). To generate the initial solutions of SS we propose an adaptation of the sequential insertion heuristic of Solomon (1987). Ho and Haugland (2004) modified the customers’ demands of the Solomon’s test problems in order to perform split deliveries. Numerical results of SS are reported as well as comparisons with the Ho and Haugland algorithm. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m


title: Preventing Gradient Explosions in Gated Recurrent Units

abstract: A gated recurrent unit (GRU) is a successful recurrent neural network architecture for time-series data. The GRU is typically trained using a gradient-based method, which is subject to the exploding gradient problem in which the gradient increases significantly. This problem is caused by an abrupt change in the dynamics of the GRU due to a small variation in the parameters. In this paper, we find a condition under which the dynamics of the GRU changes drastically and propose a learning method to address the exploding gradient problem. Our method constrains the dynamics of the GRU so that it does not drastically change. We evaluated our method in experiments on language modeling and polyphonic music modeling. Our experiments showed that our method can prevent the exploding gradient problem and improve modeling accuracy.


title: Killing the Gatekeeper: Introducing a Continuous Integration System

abstract: This is the story of how the Launchpad (https://launchpad.net) development team switched to a continuous integration system to increase several flows in their development process: flow of changes on trunk; flow of changes requiring database schema upgrade; flow of deployed changes to end users. The switch to a buildbot-based system meant violating a very old company taboo: a trunk that doesn’t pass its test suite. The risk of a broken trunk was offset by allowing each developer to run the full test suite in the Amazon EC2 cloud.


title: Virtual reality enhanced mannequin (VREM) that is well received by resuscitation experts

abstract: Summary: The objective of this study was to test acceptance of, and interest in, a newly developed prototype of virtual reality enhanced mannequin (VREM) on a sample of congress attendees who volunteered to participate in the evaluation session and to respond to a specifically designed questionnaire. Methods: A commercial Laerdal HeartSim 4000 mannequin was developed to integrate virtual reality (VR) technologies with specially developed virtual reality software to increase the immersive perception of emergency scenarios. To evaluate the acceptance of a virtual reality enhanced mannequin (VREM), we presented it to a sample of 39 possible users. Each evaluation session involved one trainee and two instructors with a standardized procedure and scenario: the operator was invited by the instructor to wear the data-gloves and the head mounted display and was briefly introduced to the scope of the simulation. The instructor helped the operator familiarize himself with the environment. After the patient’s collapse, the operator was asked to check the patient’s clinical conditions and start CPR. Finally, the patient started to recover signs of circulation and the evaluation session was concluded. Each participant was then asked to respond to a questionnaire designed to explore the trainee’s perception in the areas of user-friendliness, realism, and interaction/immersion. Results: Overall, the evaluation of the system was very positive, as was the feeling of immersion and realism of the environment and simulation. Overall, 84.6% of the participants judged the virtual reality experience as interesting and believed that its development could be very useful for healthcare training. Conclusions: The prototype of the virtual reality enhanced mannequin was well-liked, without interfence by interaction devices, and deserves full technological development and validation in emergency medical training.


title: Robust method to retrieve the constitutive effective parameters of metamaterials

abstract: We propose an improved method to retrieve the effective constitutive parameters (permittivity and permeability) of a slab of metamaterial from the measurement of S parameters. Improvements over existing methods include the determination of the first boundary and the thickness of the effective slab, the selection of the correct sign of effective impedance, and a mathematical method to choose the correct branch of the real part of the refractive index. The sensitivity of the effective constitutive parameters to the accuracy of the S parameters is also discussed. The method has been applied to various metamaterials and the successful retrieval results prove its effectiveness and robustness.


title: Association study of stuttering candidate genes GNPTAB, GNPTG and NAGPA with dyslexia in Chinese population

abstract: BackgroundDyslexia is a polygenic speech and language disorder characterized by an unexpected difficulty in reading in children and adults despite normal intelligence and schooling. Increasing evidence reveals that different speech and language disorders could share common genetic factors. As previous study reported association of GNPTAB, GNPTG and NAGPA with stuttering, we investigated these genes with dyslexia through association analysis.ResultsThe study was carried out in an unrelated Chinese cohort with 502 dyslexic individuals and 522 healthy controls. In all, 21 Tag SNPs covering GNPTAB, GNPTG and NAGPA were subjected to genotyping. Association analysis was performed on all SNPs. Significant association of rs17031962 in GNPTAB and rs882294 in NAGPA with developmental dyslexia was identified after FDR correction for multiple comparisons.ConclusionOur results revealed that the stuttering risk genes GNPTAB and NAGPA might also associate with developmental dyslexia in the Chinese population.


title: Vision under mesopic and scotopic illumination

abstract: Evidence has accumulated that rod activation under mesopic and scotopic light levels alters visual perception and performance. Here we review the most recent developments in the measurement of rod and cone contributions to mesopic color perception and temporal processing, with a focus on data measured using the four-primary photostimulator method that independently controls rod and cone excitations. We discuss the findings in the context of rod inputs to the three primary retinogeniculate pathways to understand rod ::: contributions to mesopic vision. Additionally, we present evidence that hue perception is possible under scotopic, pure rod-mediated conditions that involves cortical mechanisms.


title: Cling: A Memory Allocator to Mitigate Dangling Pointers

abstract: Use-after-free vulnerabilities exploiting so-called dangling pointers to deallocated objects are just as dangerous as buffer overflows: they may enable arbitrary code execution. Unfortunately, state-of-the-art defenses against use-after-free vulnerabilities require compiler support, pervasive source code modifications, or incur high performance overheads. This paper presents and evaluates Cling, a memory allocator designed to thwart these attacks at runtime. Cling utilizes more address space, a plentiful resource on modern machines, to prevent type-unsafe address space reuse among objects of different types. It infers type information about allocated objects at runtime by inspecting the call stack of memory allocation routines. Cling disrupts a large class of attacks against use-after-free vulnerabilities, notably including those hijacking the C++ virtual function dispatch mechanism, with low CPU and physical memory overhead even for allocation intensive applications.


title: JINS MEME algorithm for estimation and tracking of concentration of users

abstract: Activity tracking using a wearable device is an emerging research field. Large-scale studies on activity tracking performed with eyewear-type wearable devices remains a challenging area owing to the negative effect such devices have on users' looks. To cope with this challenge, JINS Inc., an eyewear retailer in Japan, has developed a state-of-the-art smart eyewear called JINS MEME. The design of JINS MEME is almost the same as that of Wellington-type eyeglasses so that people can use it in their daily lives. JINS MEME is equipped with sensors to detect a user's eye movement and head motion. In addition to these functions of JINS MEME, JINS developed an application to measure concentration levels of users. In this demonstration, users will experience wearing the JINS MEME glasses and their concentration will be measured while they perform a certain task at our booth.


title: Fine-grained semi-supervised labeling of large shape collections

abstract: In this paper we consider the problem of classifying shapes within a given category (e.g., chairs) into finer-grained classes (e.g., chairs with arms, rocking chairs, swivel chairs). We introduce a multi-label (i.e., shapes can belong to multiple classes) semi-supervised approach that takes as input a large shape collection of a given category with associated sparse and noisy labels, and outputs cleaned and complete labels for each shape. The key idea of the proposed approach is to jointly learn a distance metric for each class which captures the underlying geometric similarity within that class, e.g., the distance metric for swivel chairs evaluates the global geometric resemblance of chair bases. We show how to achieve this objective by first geometrically aligning the input shapes, and then learning the class-specific distance metrics by exploiting the feature consistency provided by this alignment. The learning objectives consider both labeled data and the mutual relations between the distance metrics. Given the learned metrics, we apply a graph-based semi-supervised classification technique to generate the final classification results. In order to evaluate the performance of our approach, we have created a benchmark data set where each shape is provided with a set of ground truth labels generated by Amazon's Mechanical Turk users. The benchmark contains a rich variety of shapes in a number of categories. Experimental results show that despite this variety, given very sparse and noisy initial labels, the new method yields results that are superior to state-of-the-art semi-supervised learning techniques.


title: Machine Learning Techniques for the Analysis of Magnetic Flux Leakage Images in Pipeline Inspection

abstract: The magnetic flux leakage (MFL) technique, commonly used for nondestructive testing of oil and gas pipelines, involves the detection of defects and anomalies in the pipe wall and the evaluation of the severity of these defects. The difficulty with the MFL method is the extent and complexity of the analysis of the MFL images. In this paper, we show how modern machine learning techniques can be used to considerable advantage in this respect. We apply the methods of support vector regression, kernelization techniques, principal component analysis, partial least squares, and methods for reducing the dimensionality of the feature space. We demonstrate the adequacy of the performance of these methods using real MFL data collected from pipelines, with regard to the performance of both the detection of defects and the accuracy in the estimation of the severity of the defects. We also show how low-dimensional latent variable structures can be effective for visualizing the clustering behavior of the classifier.


title: Uncertainty handling in power system expansion planning under a robust multi-objective framework

abstract: In this study, a substantial idea has been reviewed which is useful in the investigation of the planning uncertainties. The concept relies on the optimality of an expansion plan in different conditions rather than the condition it has been optimised for. The method is developed in a manner that can be used in all sub-systems (i.e. generation, transmission and distribution) expansion planning. However, in this study the idea has been assessed for generation expansion planning (GEP). Cost and reliability have been considered as two major objectives of the planning and robustness has been added as a supplementary objective. The method can deal with uncertainty in both coefficients of the objective functions and the constraints. Two GEP models, one static and the other dynamic, have been proposed to examine the performance of the method in the uncertainty handling. In addition, the efficiency of the Taguchi's orthogonal array testing method has been compared with Monte Carlo simulation in the scenario generation. Two case studies have been provided to simplify the justification on the efficiency of the method.


title: Frequent subgraph pattern mining on uncertain graph data

abstract: Graph data are subject to uncertainties in many applications due to incompleteness and imprecision of data. Mining uncertain graph data is semantically different from and computationally more challenging than mining exact graph data. This paper investigates the problem of mining frequent subgraph patterns from uncertain graph data. The frequent subgraph pattern mining problem is formalized by designing a new measure called expected support. An approximate mining algorithm is proposed to find an approximate set of frequent subgraph patterns by allowing an error tolerance on the expected supports of the discovered subgraph patterns. The algorithm uses an efficient approximation algorithm to determine whether a subgraph pattern can be output or not. The analytical and experimental results show that the algorithm is very efficient, accurate and scalable for large uncertain graph databases.


title: Rationale for the RBAC96 family of access control models

abstract: A family of role-based access control (RBAC) models, referred to here as the RBAC96 models, was recently published by the author and his colleagues. This paper gives our rationale for the major decisions in developing these models and discusses alternatives that were considered.


title: User Acceptance of Footfall Analytics with Aggregated and Anonymized Mobile Phone Data

abstract: Monitoring and analyzing pedestrian traffic in and around retail stores has become an important tool for discovering underutilized operational and marketing opportunities of retail localities. Since a large proportion of pedestrians nowadays carry mobile phones, visual observation methods of the past could give way to cell-tower and WiFi based capture of passers-by, optionally augmented by aggregated or anonymized demographic data about them coming from their service providers. A major mobile phone operator recently announced the introduction of such a service in Germany, the U.K. and Brazil, but had to cancel its plans for Germany since the revelation caused nationwide privacy uproar.


Parameter Free Bursty Events Detection in Text Streams


title: Deep recurrent neural network for seizure detection

abstract: EEG is one the most effective tools used in the diagnosis of epilepsy. However, proper diagnosis of epilepsy requires the detection and analysis of epileptic seizures for a long period of time. Manual monitoring of long term EEG is tedious and costly. Therefore, a reliable automated seizure detection system is desirable. Most current state-of-the-art methods use hand crafted feature extraction and simple classification techniques, which often leads to sub-par performance due to lack of generalizability across patient dataset. Consequently, this work introduces novel deep recurrent neural network (DRNN) architecture to perform automated patient specific seizure detection using scalp EEG. We further propose a unique mapping of seizure EEG signal for efficient processing with the DRNN. This mapping allows the proposed deep architecture to simultaneously learn both temporal and spatial features of raw seizure EEG respectively. The proposed DRNN architecture is tested with long term patient specific scalp EEG data of 5 subjects with approximately 34 hours of EEG extracted from a publically available dataset. Overall, the proposed network successfully detects 100% of total seizure events with an average detection delay of ~7.0 sec. The results demonstrate superior performance to that of the current state-of-art seizure detection methods. The proposed DRNN architecture also obtains a runtime of approximately 30ms for 1 sec segment of 18 channel EEG. The low processing time with sparse use of computing resources and superior performance make the proposed architecture appropriate for real-time use.


title: Ethereum: State of Knowledge and Research Perspectives

abstract: Ethereum is a major blockchain-based platform for smart contracts – Turing complete programs that are executed in a decentralized network and usually manipulate digital units of value. A peer-to-peer network of mutually distrusting nodes maintains a common view of the global state and executes code upon request. The stated is stored in a blockchain secured by a proof-of-work consensus mechanism similar to that in Bitcoin. The core value proposition of Ethereum is a full-featured programming language suitable for implementing complex business logic.


title: Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing with Untrusted Server

abstract: With spatial crowdsourcing (SC), requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted parties (SC server) for effective assignments of tasks to workers. In this paper we propose a framework for assigning tasks to workers in an online manner without compromising the location privacy of workers and tasks. We perturb the locations of both tasks and workers based on geo-indistinguishability and then devise techniques to quantify the probability of reachability between a task and a worker, given their perturbed locations. We investigate both analytical and empirical models for quantifying the worker-task pair reachability and propose task assignment strategies that strike a balance among various metrics such as the number of completed tasks, worker travel distance and system overhead. Extensive experiments on real-world datasets show that our proposed techniques result in minimal disclosure of task locations and no disclosure of worker locations without significantly sacrificing the total number of assigned tasks.


title: The MovieLens Datasets: History and Context

abstract: The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.


title: The Use of Technology for Second Language Learning and Teaching: A Retrospective

abstract: As one of the oldest and most influential foreign language pedagogical journals, The Modern Language Journal (MLJ) offers valuable insights into how technological advances have affected language teaching and learning at various points in history. The present article will review the proposed pedagogical use of technological resources by means of a critical analysis of articles published in the MLJ since its first edition in 1916. The assessment of how previous technical capabilities have been implemented for pedagogical purposes represents a necessary background for the assessment of the pedagogical potential of present-day technologies. In this article I argue that, whereas most “new technologies” (radio, television, VCR, computers) may have been revolutionary in the overall context of human interaction, it is not clear that they have achieved equal degrees of pedagogical benefit in the realm of second language teaching. I further claim that the pedagogical effectiveness of different technologies is related to four major questions: (a) Is increased technological sophistication correlated to increased pedagogical effectiveness? (b) Which technical attributes specific to new technologies can be profitably exploited for pedagogical purposes? (c) How can new technologies be successfully integrated into the curriculum? and (d) Do new technologies provide for an efficient use of human and material resources?


title: An application of simultaneous Diophantine approximation in combinatorial optimization

abstract: We present a preprocessing algorithm to make certain polynomial time algorithms strongly polynomial time. The running time of some of the known combinatorial optimization algorithms depends on the size of the objective functionw. Our preprocessing algorithm replacesw by an integral valued-w whose size is polynomially bounded in the size of the combinatorial structure and which yields the same set of optimal solutions asw.


title: Sensor networks: evolution, opportunities, and challenges

abstract: Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. This paper traces the history of research in sensor networks over the past three decades, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor Information Technology (SensIT) programs. Technology trends that impact the development of sensor networks are reviewed, and new applications such as infrastructure security, habitat monitoring, and traffic control are presented. Technical challenges in sensor network development include network discovery, control and routing, collaborative signal and information processing, tasking and querying, and security. The paper concludes by presenting some recent research results in sensor network algorithms, including localized algorithms and directed diffusion, distributed tracking in wireless ad hoc networks, and distributed classification using local agents.


title: Wireless Power Transfer for Vehicular Applications: Overview and Challenges

abstract: More than a century-old gasoline internal combustion engine is a major contributor to greenhouse gases. Electric vehicles (EVs) have the potential to achieve eco-friendly transportation. However, the major limitation in achieving this vision is the battery technology. It suffers from drawbacks such as high cost, rare material, low energy density, and large weight. The problems related to battery technology can be addressed by dynamically charging the EV while on the move. In-motion charging can reduce the battery storage requirement, which could significantly extend the driving range of an EV. This paper reviews recent advances in stationary and dynamic wireless charging of EVs. A comprehensive review of charging pad, power electronics configurations, compensation networks, controls, and standards is presented.


title: Missing carbon reductions? Exploring rebound and backfire effects in UK households (vol 39, pg 3572, 2011)

abstract: Households are expected to play a pivotal role in reducing the UK’s greenhouse gas (GHG) emissions, and the UK Government is encouraging specific household actions to help meet its targets. However, due to there bound effect, only a portion of the GHG emission reductions estimated by simple engineering calculations are generally achieved in practice. For example, replacing short car journeys by walking or cycling reduces consumption of motorfuels. But this frees up money that may be spent on, for example, purchasing extra clothes or flying on vacation. Alternatively, the money may be put into savings. Since all of these options lead to GHG emissions, total GHG savings may be less than anticipated. Indeed, in some instances, emissions may increase — a phenomenon known as ‘backfire’. We estimate that there bound effect for a combination of three abatement actions by UK households is approximately 34%. Targeting re-spending on goods and services with a low GHG intensity reduces this to a minimum of around 12%, while re-spending on goods and services with a high GHG intensity leads to backfire. Our study highlights the importance of shifting consumption to lower GHG intensive categories and investing in low carbon investments.


title: Modifiable Socio-Behavioural Factors Associated with Overweight and Hypertension among Persons Aged 35 to 60 Years in Eastern Uganda

abstract: BACKGROUND ::: Few studies have examined the behavioural correlates of non-communicable, chronic disease risk in low-income countries. The objective of this study was to identify socio-behavioural characteristics associated with being overweight or being hypertensive in a low-income setting, so as to highlight possible interventions and target groups. ::: ::: ::: METHODS ::: A population based survey was conducted in a Health and Demographic Surveillance Site (HDSS) in eastern Uganda. 1656 individuals aged 35 to 60 years had their Body Mass Index (BMI) and blood pressure (BP) assessed. Seven lifestyle factors were also assessed, using a validated questionnaire. Logistic regression was used to identify socio-behavioural factors associated with being overweight or being hypertensive. ::: ::: ::: RESULTS ::: Prevalence of overweight was found to be 18% (25.2% of women; 9.7% of men; p<0.001) while prevalence of obesity was 5.3% (8.3% of women; 2.2% of men). The prevalence of hypertension was 20.5%. Factors associated with being overweight included being female (OR 3.7; 95% CI 2.69-5.08), peri-urban residence (OR 2.5; 95% CI 1.46-3.01), higher socio-economic status (OR 4.1; 95% CI 2.40-6.98), and increasing age (OR 1.8; 95% CI 1.12-2.79). Those who met the recommended minimum physical activity level, and those with moderate dietary diversity were less likely to be overweight (OR 0.5; 95% CI 0.35-0.65 and OR 0.7; 95% CI 0.49-3.01). Factors associated with being hypertensive included peri-urban residence (OR 2.4; 95%CI 1.60-3.66), increasing age (OR 4.5; 95% CI 2.94-6.96) and being over-weight (OR 2.8; 95% CI 1.98-3.98). Overweight persons in rural areas were significantly more likely to be hypertensive than those in peri-urban areas (p = 0.013). ::: ::: ::: CONCLUSIONS ::: Being overweight in low-income settings is associated with sex, physical activity and dietary diversity and being hypertensive is associated with being overweight; these factors are modifiable. There is need for context-specific health education addressing disparities in lifestyles at community levels in rural Africa.


title: Fame for sale: efficient detection of fake Twitter followers

abstract: Fake followers are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere-hence impacting on economy, politics, and society. In this paper, we contribute along different dimensions. First, we review some of the most relevant existing features and rules (proposed by Academia and Media) for anomalous Twitter accounts detection. Second, we create a baseline dataset of verified human and fake follower accounts. Such baseline dataset is publicly available to the scientific community. Then, we exploit the baseline dataset to train a set of machine-learning classifiers built over the reviewed rules and features. Our results show that most of the rules proposed by Media provide unsatisfactory performance in revealing fake followers, while features proposed in the past by Academia for spam detection provide good results. Building on the most promising features, we revise the classifiers both in terms of reduction of overfitting and cost for gathering the data needed to compute the features. The final result is a novel Class A classifier, general enough to thwart overfitting, lightweight thanks to the usage of the less costly features, and still able to correctly classify more than 95% of the accounts of the original training set. We ultimately perform an information fusion-based sensitivity analysis, to assess the global sensitivity of each of the features employed by the classifier.The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the novel issue of fake Twitter followers.


Circulating TGF-β1 and VEGF and risk of cancer among liver transplant recipients


title: An Oscillatory Hierarchy Controlling Neuronal Excitability and Stimulus Processing in the Auditory Cortex

abstract: EEG oscillations are hypothesized to reflect cyclical variations in the neuronal excitability, with particular frequency bands reflecting differing spatial scales of brain operation. However, despite decades of clinical and scientific investigation, there is no unifying theory of EEG organization, and the role of ongoing activity in sensory processing remains controversial. This study analyzed laminar profiles of synaptic activity [current source density CSD] and multiunit activity (MUA), both spontaneous and stimulus-driven, in primary auditory cortex of awake macaque monkeys. Our results reveal that the EEG is hierarchically organized; delta (1–4 Hz) phase modulates theta (4–10 Hz) amplitude, and theta phase modulates gamma (30–50 Hz) amplitude. This oscillatory hierarchy controls baseline excitability and thus stimulus-related responses in a neuronal ensemble. We propose that the hierarchical organization of ambient oscillatory activity allows auditory cortex to structure its temporal activity pattern ...


title: Learning Joint Representations of Videos and Sentences with Web Image Search

abstract: Our objective is video retrieval based on natural language queries. In addition, we consider the analogous problem of retrieving sentences or generating descriptions given an input video. Recent work has addressed the problem by embedding visual and textual inputs into a common space where semantic similarities correlate to distances. We also adopt the embedding approach, and make the following contributions: First, we utilize web image search in sentence embedding process to disambiguate fine-grained visual concepts. Second, we propose embedding models for sentence, image, and video inputs whose parameters are learned simultaneously. Finally, we show how the proposed model can be applied to description generation. Overall, we observe a clear improvement over the state-of-the-art methods in the video and sentence retrieval tasks. In description generation, the performance level is comparable to the current state-of-the-art, although our embeddings were trained for the retrieval tasks.