title: BROAD-RSI – educational recommender system using social networks interactions and linked data

abstract: There are several educational resources distributed in different repositories that address to a wide range of subjects and different educational goals. The proper choice of these educational resources is a challenge. Recommendation systems may help users in this task. In order to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her interests. The constant and ever-increasing use of social networks allows the identification of different information about profile, interests, preferences, style and behavior from the spontaneous interaction. This paper presents an infrastructure able to extract users’ profile and educational context, from the Facebook social network and recommend educational resources. The proposal is supported by Information Extraction Techniques and Semantic Web technologies for extraction, enrichment and definition of user’s profile and interests. The recommendation approach is based on learning objects repositories, linked data and video repositories. It takes advantage of user’s spent time at the web. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. The evaluation showed users’ acceptance of extracted information about their educational interests, automatically generated from social network and enriched to find implicit interests. It was also validated the possibility of people recommendation, enabling the establishment of interest network, based on a specific subject, showing good partners to study and research.


title: Annealed dropout training of deep networks

abstract: Recently it has been shown that when training neural networks on a limited amount of data, randomly zeroing, or “dropping out” a fixed percentage of the outputs of a given layer for each training case can improve test set performance significantly. Dropout training discourages the detectors in the network from co-adapting, which limits the capacity of the network and prevents overfitting. In this paper we show that annealing the dropout rate from a high initial value to zero over the course of training can substantially improve the quality of the resulting model. As dropout (approximately) implements model aggregation over an exponential number of networks, this procedure effectively initializes the ensemble of models that will be learned during a given iteration of training with an enemble of models that has a lower average number of neurons per network, and higher variance in the number of neurons per network-which regularizes the structure of the final model toward models that avoid unnecessary co-adaptation between neurons. Importantly, this regularization procedure is stochastic, and so promotes the learning of “balanced” networks with neurons that have high average entropy, and low variance in their entropy, by smoothly transitioning from “exploration” with high learning rates to “fine tuning” with full support for co-adaptation between neurons where necessary. Experimental results demonstrate that annealed dropout leads to significant reductions in word error rate over standard dropout training.


title: Eye tracking using monocular camera for gaze estimation applications

abstract: Gaze estimation reveals valuable information in terms of human behavior. For example, gaze location can be used as a mean of human computer interaction. In practice, locating the eyes position is a challenging task. Moreover, it is more challenging to estimate the location of the gaze. It requires a system to extract the information of the human eye location then estimate the direction of the gaze. There are existing systems that achieve similar tasks. Many of these systems rely on complex systems, such as wearable sensors or an infrared camera to find the exact focus of eyes gaze. Consequently, these systems are not only complex but also suitable for limited applications. In this paper, we present efficient eye focus methods using a monocular camera, which brings more flexibility and convenience to the system. Three different algorithms for locating the eye's focus have been examined and compared. These algorithms are highly efficient and able to discern the location of the eye's focus in real-time using a regular laptop or desktop computers.


title: Inferring Logical Forms From Denotations

abstract: A core problem in learning semantic parsers from denotations is picking out consistent logical forms--those that yield the correct denotation--from a combinatorially large space. To control the search space, previous work relied on restricted set of rules, which limits expressivity. In this paper, we consider a much more expressive class of logical forms, and show how to use dynamic programming to efficiently represent the complete set of consistent logical forms. Expressivity also introduces many more spurious logical forms which are consistent with the correct denotation but do not represent the meaning of the utterance. To address this, we generate fictitious worlds and use crowdsourced denotations on these worlds to filter out spurious logical forms. On the WikiTableQuestions dataset, we increase the coverage of answerable questions from 53.5% to 76%, and the additional crowdsourced supervision lets us rule out 92.1% of spurious logical forms.


title: Efficient Processing of Deep Neural Networks: A Tutorial and Survey

abstract: Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances toward the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic codesigns, being proposed in academia and industry. The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the tradeoffs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.


title: Experimental Evaluation of UWB Indoor Positioning for Sport Postures

abstract: Radio frequency (RF)-based indoor positioning systems (IPSs) use wireless technologies (including Wi-Fi, Zigbee, Bluetooth, and ultra-wide band (UWB)) to estimate the location of persons in areas where no Global Positioning System (GPS) reception is available, for example in indoor stadiums or sports halls. Of the above-mentioned forms of radio frequency (RF) technology, UWB is considered one of the most accurate approaches because it can provide positioning estimates with centimeter-level accuracy. However, it is not yet known whether UWB can also offer such accurate position estimates during strenuous dynamic activities in which moves are characterized by fast changes in direction and velocity. To answer this question, this paper investigates the capabilities of UWB indoor localization systems for tracking athletes during their complex (and most of the time unpredictable) movements. To this end, we analyze the impact of on-body tag placement locations and human movement patterns on localization accuracy and communication reliability. Moreover, two localization algorithms (particle filter and Kalman filter) with different optimizations (bias removal, non-line-of-sight (NLoS) detection, and path determination) are implemented. It is shown that although the optimal choice of optimization depends on the type of movement patterns, some of the improvements can reduce the localization error by up to 31%. Overall, depending on the selected optimization and on-body tag placement, our algorithms show good results in terms of positioning accuracy, with average errors in position estimates of 20 cm. This makes UWB a suitable approach for tracking dynamic athletic activities.


title: Children and adults with Attention-Deficit/Hyperactivity Disorder cannot move to the beat

abstract: Children and adults with Attention-Deficit Hyperactivity Disorder (ADHD) fail in simple tasks like telling whether two sounds have different durations, or in reproducing single durations. The deficit is linked to poor reading, attention, and language skills. Here we demonstrate that these timing distortions emerge also when tracking the beat of rhythmic sounds in perceptual and sensorimotor tasks. This contrasts with the common observation that durations are better perceived and produced when embedded in rhythmic stimuli. Children and adults with ADHD struggled when moving to the beat of rhythmic sounds, and when detecting deviations from the beat. Our findings point to failure in generating an internal beat in ADHD while listening to rhythmic sounds, a function typically associated with the basal ganglia. Rhythm-based interventions aimed at reinstating or compensating this malfunctioning circuitry may be particularly valuable in ADHD, as already shown for other neurodevelopmental disorders, such as dyslexia and Specific Language Impairment.


title: A 51.3-MHz 21.8-ppm/°C CMOS Relaxation Oscillator With Temperature Compensation

abstract: A 51.3-MHz 18- $\mu\text{W}$ 21.8-ppm/°C relaxation oscillator is presented in 90-nm CMOS. The proposed oscillator employs an integrated error feedback and composite resistors to minimize its sensitivity to temperature variations. For a temperature range from −20 °C to 100 °C, the fabricated circuit demonstrates a frequency variation less than ±0.13%, leading to an average frequency drift of 21.8 ppm/°C. As the supply voltage changes from 0.8 to 1.2 V, the frequency variation is ±0.53%. The measured rms jitter and phase noise at 1-MHz offset are 89.27 ps and −83.29 dBc/Hz, respectively.


title: Enabling the Sharing Economy: Privacy Respecting Contract based on Public Blockchain

abstract: Blockchain is a novel way to construct fully distributed systems and has the potential to disrupt many businesses including Uber and Airbnb within the sharing economy. Specifically, blockchain provides a method to enforce the agreement between a user and the physical property owner without using any trusted party, e.g., if a user pays the agreed money, the blockchain guarantees that he/she has access to the property. While a blockchain based system has many desirable features, it may leak privacy information of involved parties due to its openness to the public. To mitigate the privacy concern, we propose a privacy respecting approach for blockchain-based sharing economy applications, which leverages a zero-knowledge scheme. We also analyze the security features and performance of the proposed approach to demonstrate its effectiveness in these applications.


title: IFN/ENIT - DATABASE OF HANDWRITTEN ARABIC WORDS

abstract: Dans cet article on presente une nouvelle base de donnees, qui contient des noms manuscrits de villes/villages arabes. Pour chaque nom les informations de base, par exemple l'ordre des formes de caractere, les informations sur le style de l'ecriture et la ligne de base, sont codees. 411 auteurs ont rempli des formulaires avec plus de 26400 noms contenant plus de 210 000 caracteres. La base de donnees est decrite en detail, et elle est concue pour la formation et l'essai des systemes d'identification pour les mots arabes manuscrits. La base de donnees IFA/ENIT est disponible pour la recherche.


title: ZigBee based parameters monitoring system for induction motor

abstract: In this paper, a wireless monitoring system for three phase induction motor is realized using ZigBee protocol, where wired communication is either more expensive or impossible due to physical conditions and human hazards for safe and economic data communication in industrial fields. A low cost system for measuring the parameters of induction motor such as phase voltages, phase currents, active power, reactive power, motor temperature with ZigBee protocol connectivity is described in this paper. Moreover, a database is built to execute online monitoring and to save the motor parameters received by radio frequency (RF) data acquisition system. The aim of this paper is to monitor and acquire the remote electrical parameters like Voltages, Currents, Powers, Temperature, and send these real values over wireless network. To implement this, a ZigBee model is connected to a programmed digital signal controller which would transmit the data to ZigBee coordinator which is connected to a PC through RS232 serial communication. Experimental results show that the proposed system is less costly, provides higher accuracy as well as safe and gives visual environment.


title: Model migration with Epsilon Flock

abstract: In their recent book, Mens and Demeyer state that Model-Driven Engineering introduces additional challenges for controlling and managing software evolution. Today, tools exist for generating model editors and for managing models with transformation, validation, merging and weaving. There is limited support, however, for model migration - a development activity in which instance models are updated in response to metamodel evolution. In this paper, we describe Epsilon Flock, a model-to-model transformation language tailored for model migration that contributes a novel algorithm for relating source and target model elements. To demonstrate its conciseness, we compare Flock to other approaches.


title: Automatic classification of sentences to support Evidence Based Medicine

abstract: AimGiven a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels.MethodWe constructed a corpus of 1,000 medical abstracts annotated by hand with specified medical categories (e.g. Intervention, Outcome). We explored the use of various features based on lexical, semantic, structural, and sequential information in the data, using Conditional Random Fields (CRF) for classification.ResultsFor the classification tasks over all labels, our systems achieved micro-averaged f-scores of 80.9% and 66.9% over datasets of structured and unstructured abstracts respectively, using sequential features. In labeling only the key sentences, our systems produced f-scores of 89.3% and 74.0% over structured and unstructured abstracts respectively, using the same sequential features. The results over an external dataset were lower (f-scores of 63.1% for all labels, and 83.8% for key sentences).ConclusionsOf the features we used, the best for classifying any given sentence in an abstract were based on unigrams, section headings, and sequential information from preceding sentences. These features resulted in improved performance over a simple bag-of-words approach, and outperformed feature sets used in previous work.


title: AntNet: Distributed Stigmergetic Control for Communications Networks

abstract: This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.


title: Compressed cooperative reception for the uplink of C-RAN with wireless fronthaul

abstract: This work deals with a joint design of radio access and fronthaul links for the uplink of cloud radio access network (C-RAN) equipped with wireless fronthaul link. A conventional uplink system based on per-cell decode-and-forward (DF) relaying, in which the message sent by each user equipment (UE) is decoded by the closest remote radio head (RRH) and forwarded to a baseband processing unit (BBU) via wireless fronthaul link, is first reviewed. Since decoding at each RRH might be affected by the interference signals of the other UEs, we present a compress-and-forward (CF) based cooperative reception scheme whereby each RRH quantizes and compresses its received signal and transmits the quantization output to the BBU through the fronthaul link. For the both approaches based on the DF and CF relaying, we tackle the problem of maximizing the sum-rate of the UEs subject to the per-UE and per-RRH power constraints. Link-level simulation results are reported that validate the advantages of the CF-based cooperative scheme compared to the DF-based per-cell reception scheme.


A Redundancy-Aware Sentence Regression Framework for Extractive Summarization


title: Wavelet PSO-Based LQR Algorithm for Optimal Structural Control Using Active Tuned Mass Dampers

abstract: This study presents a new method to find the optimal control forces for active tuned mass damper. The method uses three algorithms: discrete wavelet transform (DWT), particle swarm optimization (PSO), and linear quadratic regulator (LQR). DWT is used to obtain the local energy distribution of the motivation over the frequency bands. PSO is used to determine the gain matrices through the online update of the weighting matrices used in the LQR controller while eliminating the trial and error. The method is tested on a 10-story structure subject to several historical pulse-like near-fault ground motions. The results indicate that the proposed method is more effective at reducing the displacement response of the structure in real time than conventional LQR controllers.Copyright © 2015 by ASME


title: Influence of social media on performance of movies

abstract: The performance of movies in terms of revenue depends on many factors such as its production studio, genre, script quality, pre-release promotion etc, - all of which are traditionally used to estimate their potential success at the box office. Recently however, the “Wisdom of Crowd” and social media have been acknowledged as a strong signal in understanding consumer behavior towards media. In this paper, we discuss the impact of socially generated meta-data mined from the social multimedia sites and study the influence of social media on box- office performance and profitability of movies. Our result shows that various social media signals have varying yet significant impact in predicting the performance of movies. Our research also reveals that popularity of actor captured by follower count on Twitter is most relevant to the success of movie at theaters, and Facebook `like' signal has noise which impedes its analytical credibility.


Data Acquisition, Curation, and Use for a Continuously Learning Health System


The Cannabis sativa Versus Cannabis indica Debate: An Interview with Ethan Russo, MD


title: Empathizing with robots: Fellow feeling along the anthropomorphic spectrum

abstract: A long-standing question within the robotics community is about the degree of human-likeness robots ought to have when interacting with humans. We explore an unexamined aspect of this problem: how people empathize with robots along the anthropomorphic spectrum. We conducted a web-based experiment (n = 120) that measured how people empathized with four different robots shown to be experiencing mistreatment by humans. Our results indicate that people empathize more strongly with more human-looking robots and less with mechanical looking robots. We also found that a person's general ability to empathize has no predictive value for expressed empathy toward robots.


title: Inferring low-dimensional microstructure representations using convolutional neural networks

abstract: We apply recent advances in machine learning and computer vision to a central problem in materials informatics: The statistical representation of microstructural images. We use activations in a pre-trained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images. Next, we use manifold learning to obtain a low-dimensional embedding of this statistical characterization. We show that the low-dimensional embedding extracts the parameters used to generate the images. According to a variety of metrics, the convolutional neural network method yields dramatically better embeddings than the analogous method derived from two-point correlations alone.


Production , Manufacturing and Logistics The multi-item newsvendor model with cross-selling and the solution when demand


title: Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining

abstract: Process mining is a research discipline that aims to discover, monitor and improve real processing using event logs. In this paper we describe a novel approach that (i) identifies partial process models by exploiting sequential pattern mining and (ii) uses the additional information about the activities matching a partial process model to train nested prediction models from event logs. Models can be used to predict the next activity and completion time of a new (running) process instance. We compare our approach with a model based on Transition Systems implemented in the ProM5 Suite and show that the attributes in the event log can improve the accuracy of the model without decreasing performances. The experimental results show how our algorithm improves of a large margin ProM5 in predicting the completion time of a process, while it presents competitive results for next activity prediction.


title: Entity Hierarchy Embedding

abstract: Existing distributed representations are limited in utilizing structured knowledge to improve semantic relatedness modeling. We propose a principled framework of embedding entities that integrates hierarchical information from large-scale knowledge bases. The novel embedding model associates each category node of the hierarchy with a distance metric. To capture structured semantics, the entity similarity of context prediction are measured under the aggregated metrics of relevant categories along all inter-entity paths. We show that both the entity vectors and category distance metrics encode meaningful semantics. Experiments in entity linking and entity search show superiority of the proposed method.


title: Individual differences in motives, preferences, and pathology in video games: the gaming attitudes, motives, and experiences scales (GAMES)

abstract: A new measure of individual habits and preferences in video game use is developed in order to better study the risk factors of pathological game use (i.e., excessively frequent or prolonged use, sometimes called “game addiction”). This measure was distributed to internet message boards for game enthusiasts and to college undergraduates. An exploratory factor analysis identified 9 factors: Story, Violent Catharsis, Violent Reward, Social Interaction, Escapism, Loss-Sensitivity, Customization, Grinding, and Autonomy. These factors demonstrated excellent fit in a subsequent confirmatory factor analysis, and, importantly, were found to reliably discriminate between inter-individual game preferences (e.g., Super Mario Brothers as compared to Call of Duty). Moreover, three factors were significantly related to pathological game use: the use of games to escape daily life, the use of games as a social outlet, and positive attitudes towards the steady accumulation of in-game rewards. The current research identifies individual preferences and motives relevant to understanding video game players’ evaluations of different games and risk factors for pathological video game use.


title: A 0.5V/1.0V fast lock-in ADPLL for DVFS battery-powered devices

abstract: In this paper, a 0.5V/1.0V low-power all-digital phase-locked loop (ADPLL) for battery-powered devices with a dynamic voltage and frequency scaling (DVFS) scheme is presented. The proposed frequency estimation algorithm with a fine-resolution monotonic response digitally controlled oscillator (DCO), can quickly calculate the target control code for the DCO, and thus, the ADPLL can achieve a fast lock time in four clock cycles. The proposed ADPLL is implemented in a standard performance 65nm CMOS process with standard cells. The power consumption is 52.69μW at 600MHz with a 0.5V power supply and is 1.26mW at 1.28GHz with a 1.0V supply.


title: Mitochondrial Biology and Parkinson's Disease

abstract: Mitochondria are highly dynamic organelles with complex structural features which play several important cellular functions, such as the production of energy by oxidative phosphorylation, the regulation of calcium homeostasis, or the control of programmed cell death (PCD). Given its essential role in neuronal viability, alterations in mitochondrial biology can lead to neuron dysfunction and cell death. Defects in mitochondrial respiration have long been implicated in the etiology and pathogenesis of Parkinson's disease (PD). However, the role of mitochondria in PD extends well beyond defective respiration and also involves perturbations in mitochondrial dynamics, leading to alterations in mitochondrial morphology, intracellular trafficking, or quality control. Whether a primary or secondary event, mitochondrial dysfunction holds promise as a potential therapeutic target to halt the progression of dopaminergic neurodegeneration in PD.


title: Child Sexual Abuse Confirmed by Forensic Examination in Salvador, Bahia, Brazil

abstract: Child sexual abuse is a worldwide public health issue. The objective of this study was to describe the demographic and clinical characteristics of children in cases of sexual abuse confirmed by material evidence at forensic examination. Cases of children younger than 12 years examined for suspected sexual abuse at the Institute of Forensic Medicine in Salvador, Bahia, Brazil, were reviewed retrospectively. The period evaluated was between 2005 and 2010. The inclusion criterion was the coroner's confirmation of sexual abuse based on material evidence. The mean age of the children in whom sexual abuse was confirmed was 8.2 (SD, 2.9) years, and 208 cases were female (83.9%). In 94% of the cases, the reason for carrying out the forensic examination was a report of sexual abuse. Less than 1% of the aggressors were female. A ruptured hymen was the finding that confirmed sexual abuse in 172 cases, corresponding to 83% of cases in girls. There were 2 cases of pregnancy. Recent anal or perianal lesions were present in 35 (87.5%) of the confirmed cases involving boys. Laboratory confirmation based on the detection of spermatozoa or prostate-specific antigen occurred in only 4.2% of cases. In all these cases, sample collection was performed within 24 hours of the alleged abuse. Thus, in the majority of cases with material evidence of sexual abuse, the confirmation criteria consisted of a ruptured hymen and recent perianal lesions.


title: Business process – attempts to find a definition

abstract: Abstract Definitions of business process given in much of the literature on Business Process Management are limited in depth and their related models of business processes are correspondingly constrained. After giving a brief history of the progress of business process modeling techniques from production systems to the office environment, this paper proposes that most definitions are based on machine metaphor type explorations of a process. While these techniques are often rich and illuminating it is suggested that they are too limited to express the true nature of business processes that need to develop and adapt to today's challenging environment.


title: Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones

abstract: The poses of base station antennas play an important role in cellular network optimization. Existing methods of pose estimation are based on physical measurements performed either by tower climbers or using additional sensors attached to antennas. In this paper, we present a novel non-contact method of antenna pose measurement based on multi-view images of the antenna and inertial measurement unit (IMU) data captured by a mobile phone. Given a known 3D model of the antenna, we first estimate the antenna pose relative to the phone camera from the multi-view images and then employ the corresponding IMU data to transform the pose from the camera coordinate frame into the Earth coordinate frame. To enhance the resulting accuracy, we improve existing camera-IMU calibration models by introducing additional degrees of freedom between the IMU sensors and defining a new error metric based on both the downtilt and azimuth angles, instead of a unified rotational error metric, to refine the calibration. In comparison with existing camera-IMU calibration methods, our method achieves an improvement in azimuth accuracy of approximately 1.0 degree on average while maintaining the same level of downtilt accuracy. For the pose estimation in the camera coordinate frame, we propose an automatic method of initializing the optimization solver and generating bounding constraints on the resulting pose to achieve better accuracy. With this initialization, state-of-the-art visual pose estimation methods yield satisfactory results in more than 75% of cases when plugged into our pipeline, and our solution, which takes advantage of the constraints, achieves even lower estimation errors on the downtilt and azimuth angles, both on average (0.13 and 0.3 degrees lower, respectively) and in the worst case (0.15 and 7.3 degrees lower, respectively), according to an evaluation conducted on a dataset consisting of 65 groups of data. We show that both of our enhancements contribute to the performance improvement offered by the proposed estimation pipeline, which achieves downtilt and azimuth accuracies of respectively 0.47 and 5.6 degrees on average and 1.38 and 12.0 degrees in the worst case, thereby satisfying the accuracy requirements for network optimization in the telecommunication industry.


title: BLEBeacon: A Real-Subject Trial Dataset from Mobile Bluetooth Low Energy Beacons

abstract: The BLEBeacon dataset is a collection of Bluetooth Low Energy (BLE) advertisement packets/traces generated from BLE beacons carried by people following their daily routine inside a university building. A network of Raspberry Pi 3 (RPi)-based edge devices were deployed inside a multi-floor facility continuously gathering BLE advertisement packets and storing them in a cloud-based environment. The data were collected during an IRB (Institutional Review Board forhe Protection of Human Subjects in Research) approved one-month trial. Each facility occupant/participant was handed a BLE beacon to carry with him at all times. The focus is on presenting a real-life realization of a location-aware sensing infrastructure, that can provide insights for smart sensing platforms, crowd-based applications, building management, and user-localization frameworks. This work describes and documents the published BLEBeacon dataset.


title: Change Detection in Satellite Images Using a Genetic Algorithm Approach

abstract: In this letter, we propose a novel method for unsupervised change detection in multitemporal satellite images by minimizing a cost function using a genetic algorithm (GA). The difference image computed from the multitemporal satellite images is partitioned into two distinct regions, namely, ?changed? and ?unchanged,? according to the binary change detection mask realization from the GA. For each region, the mean square error (MSE) between its difference image values and the average of its difference image values is calculated. The weighted sum of the MSE of the changed and unchanged regions is used as a cost value for the corresponding change detection mask realization. The GA is employed to find the final change detection mask with the minimum cost by evolving the initial realization of the binary change detection mask through generations. The proposed method is able to produce the change detection result on the difference image without a priori assumptions. Change detection results are shown on multitemporal Advanced Synthetic Aperture Radar images acquired by the ESA/Envisat satellite and on multitemporal optical images acquired by the Landsat multispectral scanner. The comparisons with the state-of-the-art change detection methods are provided.


title: Arousal-Biased Competition in Perception and Memory

abstract: “Selection is the very keel on which our mental ship is built. And in this case of memory its utility is obvious. If we remembered everything, we should on most occasions be as ill off as if we remembered nothing.” William James, The Principles of Psychology, p. 680. ::: ::: The brain’s ability to prioritize information allows us to think and take action without being overwhelmed by external stimuli or internal thoughts and feelings. Attending to what is important while ignoring extraneous detail can enhance performance in challenging situations, such as when facing a difficult task or a threat to one’s safety. A wide range of cognitive and emotional challenges increase autonomic arousal, affecting heart rate, galvanic skin response, and pupil dilation. Even something as simple as an emotional picture shown for a few seconds can increase autonomic arousal (e.g., Bradley, Miccoli, Escrig, & Lang, 2008). Emotional stimuli and cognitive challenges also increase levels of stress hormones such as epinephrine and cortisol. ::: ::: Given the importance of focused attention in challenging situations, it would be helpful if arousal increased how selective the brain is when processing information. Many studies have examined the role of arousal in the selectivity of information processing, eliciting arousal in a variety of ways, including via emotional stimuli, stress, and administration of stress hormones. Some prominent characterizations of how arousal affects the selectivity of memory are listed in Table 1. These arousal effects on memory are diverse and no previous theory we are aware of can account for them all. Furthermore, even at the specific level, each separate arousal effect characterization (such as “arousal induces retrograde amnesia”) faces contradictory findings in the literature (see right-hand column in Table 1). In this paper, we propose a new theory of arousal-biased competition to account for how arousal affects memory selectivity. We start by outlining the theory and how it operates in perception. Then we turn to the domain of memory to explain how the theory accounts for the eclectic and apparently contradictory findings outlined in Table 1. ::: ::: ::: ::: Table 1 ::: ::: Some prominent characterizations of how arousal affects memory, with related findings; see text for how arousal-biased competition accounts for consistent and inconsistent findings.


title: A Parallel Fusion Approach to Piano Music Transcription Based on Convolutional Neural Network

abstract: In this paper, a supervised approach based on Convolutional Neural Networks (CNN) for polyphonic piano transcription is presented. The system consists of pitch detection model, onset/offset detection model, and note search model. The pitch detection model is a single-channel CNN predicting the probabilities of pitches contained in one frame of the audio. The onset/offset model based on dual-channel CNN is used for estimating the probabilities of each pitch's onset or offset in a frame. The note search model is rule-based; it integrates the outputs of the pitch model and onset/offset model to determine the final onset, offset and pitch of notes in audio. Two experiments with different dataset conditions are accomplished to compare with state-of-the-art approaches on the same datasets. Experimental results reveal that the proposed approach preforms better in both frame- and note-based metrics.


title: In Defense of the Classification Loss for Person Re-Identification

abstract: The recent research for person re-identification has been focused on two trends. One is learning the part-based local features to form more informative feature descriptors. The other is designing effective metric learning loss functions such as the triplet loss family. We argue that learning global features with classification loss could achieve the same goal, even with some simple and cost-effective architecture design. In this paper, we first explain why the person re-id framework with standard classification loss usually has inferior performance compared to metric learning. Based on that, we further propose a person re-id framework featured by channel grouping and multi-branch strategy, which divides global features into multiple channel groups and learns the discriminative channel group features by multi-branch classification layers. The extensive experiments show that our framework outperforms prior state-of-the-arts in terms of both accuracy and inference speed.


title: Uniport: A Uniform Programming Support Framework for Mobile Cloud Computing

abstract: Personal mobile devices (PMDs) have become the most used computing devices for many people. With the introduction of mobile cloud computing, we can augment the storage and computing capabilities of PMDs via cloud support. However, there are many challenges in developing mobile cloud applications (MCAs) that incorporate cloud computing efficiently, especially for developers targeting multiple mobile platforms. This paper presents Uniport, a uniform framework for developing MCAs. We introduce a uniform architecture for MCAs based on the Model-View-Controller (MVC) pattern and a set of programming primitives and runtime libraries. Not only can Uniport support the creation of new MCAs, it can also help transform existing mobile applications to MCAs efficiently. We demonstrate the applicability and flexibility of Uniport in a case study to transform three existing mobile applications on iOS, Android and Windows Phone, to their mobile cloud versions respectively. Evaluation results show that, with very few modifications, we can easily transform mobile applications to MCAs that can exploit the cloud support to improve performance by 3 -- 7x and save more than half of their energy consumption.


title: Boosting Self-Supervised Learning via Knowledge Transfer

abstract: In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset without the need for human annotation. The main objective, however, is to transfer this model to a target domain and task. Currently, the most effective transfer strategy is fine-tuning, which restricts one to use the same model or parts thereof for both pretext and target tasks. In this paper, we present a novel framework for self-supervised learning that overcomes limitations in designing and comparing different tasks, models, and data domains. In particular, our framework decouples the structure of the self-supervised model from the final task-specific fine-tuned model. This allows us to: 1) quantitatively assess previously incompatible models including handcrafted features; 2) show that deeper neural network models can learn better representations from the same pretext task; 3) transfer knowledge learned with a deep model to a shallower one and thus boost its learning. We use this framework to design a novel self-supervised task, which achieves state-of-the-art performance on the common benchmarks in PASCAL VOC 2007, ILSVRC12 and Places by a significant margin. Our learned features shrink the mAP gap between models trained via self-supervised learning and supervised learning from 5.9% to 2.6% in object detection on PASCAL VOC 2007.


title: Point Clouds: Lidar versus 3D Vision

abstract: Novel automated photogrammetry is based on four innovations. First is the cost-free increase of overlap between images when sensing digitally. Second is an improved radiometry. Third is multi-view matching. Fourth is the Graphics Processing Unit (GPU), making complex algorithms for image matching very practical. These innovations lead to improved automation of the photogrammetric workflow so that point clouds are created at sub-pixel accuracy, at very dense intervals, and in near real-time, thereby eroding the unique selling proposition of lidar scanners. Two test projects compare point clouds from aerial and street-side lidar systems with those created from images. We show that the photogrammetric accuracy compares well with the lidar-method, yet the density of surface points is much higher from images, and the throughput is commensurate with a fully automated all-digital approach. Beyond this density, we identify 15 additional advantages of the photogrammetric approach.


title: A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation

abstract: Reinforcement Learning (RL) provides a promising new approach to systems performance management that differs radically from standard queuing-theoretic approaches making use of explicit system performance models. In principle, RL can automatically learn high-quality management policies without an explicit performance model or traffic model and with little or no built-in system specific knowledge. In our original work [1], [2], [3] we showed the feasibility of using online RL to learn resource valuation estimates (in lookup table form) which can be used to make high-quality server allocation decisions in a multi-application prototype Data Center scenario. The present work shows how to combine the strengths of both RL and queuing models in a hybrid approach in which RL trains offline on data collected while a queuing model policy controls the system. By training offline we avoid suffering potentially poor performance in live online training. We also now use RL to train nonlinear function approximators (e.g. multi-layer perceptrons) instead of lookup tables; this enables scaling to substantially larger state spaces. Our results now show that in both open-loop and closed-loop traffic, hybrid RL training can achieve significant performance improvements over a variety of initial model-based policies. We also find that, as expected, RL can deal effectively with both transients and switching delays, which lie outside the scope of traditional steady-state queuing theory.


title: Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification

abstract: Person re-identification is to seek a correct match for a person of interest across different camera views among a large number of impostors. It typically involves two procedures of non-linear feature extractions against dramatic appearance changes, and subsequent discriminative analysis in order to reduce intra-personal variations while enlarging inter-personal differences. In this paper, we introduce a hybrid deep architecture which combines Fisher vectors and deep neural networks to learn non-linear transformations of pedestrian images to a deep space where data can be linearly separable. The proposed method starts from Fisher vector encoding which computes a sequence of local feature extraction, aggregation, and encoding. The resulting Fisher vector output are fed into stacked supervised layer to seek non-linear transformation into a deep space. On top of the deep neural network, Linear Discriminant Analysis (LDA) is reinforced such that linearly separable latent representations can be learned in an end-to-end fashion. By optimizing an objective function modified from LDA, the network is enforced to produce feature distributions which have a low variance within the same class and high variance between classes. The objective is essentially derived from the general LDA eigenvalue problem and allows to train the network with Stochastic Gradient Descent and back-propagate LDA gradients to compute Gaussian Mixture Model (GMM) gradients in Fisher vector encoding. For empirical evaluations, we test our approach on four benchmark data sets in person re-identification (VIPeR 1, CUHK03 2, CUHK01 3, and Market 1501 4). Extensive experiments on these benchmarks show that our method can achieve state-of-the-art results. HighlightsA hybrid architecture that combines Fisher vectors and deep neural networks.An end-to-end training with linear discriminant analysis as objective.Deep features are linearly separable and class separability is maximally preserved.


title: Detecting code clones in binary executables

abstract: Large software projects contain significant code duplication, mainly due to copying and pasting code. Many techniques have been developed to identify duplicated code to enable applications such as refactoring, detecting bugs, and protecting intellectual property. Because source code is often unavailable, especially for third-party software, finding duplicated code in binaries becomes particularly important. However, existing techniques operate primarily on source code, and no effective tool exists for binaries. In this paper, we describe the first practical clone detection algorithm for binary executables. Our algorithm extends an existing tree similarity framework based on clustering of characteristic vectors of labeled trees with novel techniques to normalize assembly instructions and to accurately and compactly model their structural information. We have implemented our technique and evaluated it on Windows XP system binaries totaling over 50 million assembly instructions. Results show that it is both scalable and precise: it analyzed Windows XP system binaries in a few hours and produced few false positives. We believe our technique is a practical, enabling technology for many applications dealing with binary code.


title: Inference Networks for Sequential Monte Carlo in Graphical Models

abstract: We introduce a new approach for amortizing inference in directed graphical models by learning heuristic approximations to stochastic inverses, designed specifically for use as proposal distributions in sequential Monte Carlo methods. We describe a procedure for constructing and learning a structured neural network which represents an inverse factorization of the graphical model, resulting in a conditional density estimator that takes as input particular values of the observed random variables, and returns an approximation to the distribution of the latent variables. This recognition model can be learned offline, independent from any particular dataset, prior to performing inference. The output of these networks can be used as automatically-learned high-quality proposal distributions to accelerate sequential Monte Carlo across a diverse range of problem settings.


title: Collaborative Learning Enhances Critical Thinking

abstract: The concept of collaborative learning, the grouping and pairing of students for the purpose of achieving an academic goal, has been widely researched and advocated throughout the professional literature. The term “collaborative learning” refers to an instruction method in which students at various performance levels work together in small groups toward a common goal. The students are responsible for one another’s learning as well as their own. Thus, the success of one student helps other students to be successful. Proponents of collaborative learning claim that the active exchange of ideas within small groups not only increases interest among the participants but also promotes critical thinking. According to Johnson and Johnson (1986), there is persuasive evidence that cooperative teams achieve at higher levels of thought and retain information longer than students who work quietly as individuals. The shared learning gives students an opportunity to engage in discussion, take responsibility for their own learning, and thus become critical thinkers (Totten, Sills, Digby, & Russ, 1991). In spite of these advantages, most of the research studies on collaborative learning have been done at the primary and secondary levels. As yet, there is little empirical evidence on its effectiveness at the college level. However, the need for noncompetitive, collaborative group work is emphasized in much of the higher education literature. Also, majority of the research in collaborative learning has been done in non-technical disciplines. The advances in technology and changes in the organizational infrastructure put an increased emphasis on teamwork within the workforce. Workers need to be able to think creatively, solve problems, and make decisions


A Kernel for Semi-Supervised Learning With Multi-View Point


title: Provenance in neuroimaging

abstract: Abstract Provenance, the description of the history of a set of data, has grown more important with the proliferation of research consortia-related efforts in neuroimaging. Knowledge about the origin and history of an image is crucial for establishing data and results quality; detailed information about how it was processed, including the specific software routines and operating systems that were used, is necessary for proper interpretation, high fidelity replication and re-use. We have drafted a mechanism for describing provenance in a simple and easy to use environment, alleviating the burden of documentation from the user while still providing a rich description of an image's provenance. This combination of ease of use and highly descriptive metadata should greatly facilitate the collection of provenance and subsequent sharing of data.


title: Epidemic Information Dissemination in Mobile Social Networks With Opportunistic Links

abstract: With the advancement of smartphones, mobile social networks (MSNs) have emerged where information can be shared among mobile users via opportunistic peer-to-peer links. Since the social ties and users’ behaviors in MSNs have diverse characteristics, the information dissemination in MSNs becomes a new challenge. In particular, mobile users’ interested information may vary, which can significantly affect the information dissemination. In this paper, we develop an analytical model to analyze the epidemic information dissemination in MSNs. We first adopt preimmunity and immunity to represent the features of mobile nodes when they change their interests. Then, the information dissemination mechanism is introduced with four proposed dissemination rules according to the process of the epidemic information dissemination. We develop the analytical model through ordinary differential equations to mimic epidemic information dissemination in MSNs. The trace-driven simulation demonstrates that our analytical model is more accurate to mimic epidemic information dissemination than other existing ones.


In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation


title: Microservices and Their Design Trade-Offs: A Self-Adaptive Roadmap

abstract: Migrating to microservices (microservitization) enablesoptimising the autonomy, replaceability, decentralisedgovernance and traceability of software architectures. Despitethe hype for microservitization, the state of the art still lacksconsensus on the definition of microservices, their propertiesand their modelling techniques. This paper summarises viewsof microservices from informal literature to reflect on the foundationalcontext of this paradigm shift. A strong foundationalcontext can advance our understanding of microservitizationand help guide software architects in addressing its designproblems. One such design problem is finalising the optimallevel of granularity of a microservice architecture. Relateddesign trade-offs include: balancing the size and numberof microservices in an architecture and balancing the nonfunctionalrequirement satisfaction levels of the individualmicroservices as well as their satisfaction for the overall system. We propose how self-adaptivity can assist in addressing thesedesign trade-offs and discuss some of the challenges such a selfadaptivesolution. We use a hypothetical online movie streamingsystem to motivate these design trade-offs. A solution roadmapis presented in terms of the phases of a feedback control loop.


title: A business driven cloud optimization architecture

abstract: In this paper, we discuss several facets of optimization in cloud computing, the corresponding challenges and propose an architecture for addressing those challenges. We consider a layered cloud where various cloud layers virtualize parts of the cloud infrastructure. The architecture takes into account different stakeholders in the cloud (infrastructure providers, platform providers, application providers and end users). The architecture supports self-management by automating most of the activities pertaining to optimization: monitoring, analysis and prediction, planning and execution.


title: Hidradenitis suppurativa: an update

abstract: Hidradenitis suppurativa (HS) is a chronic, painful skin disease characterised by recurrent inflammatory lesions in flexural locations such as the axillae, groins and perineum. The papules, nodules and abscesses may discharge blood-stained pus which, combined with pain, results in marked quality-of-life reduction. Sinus tracts and scarring may also result. Onset of HS is typically in the second to fourth decades and it affects about 1% of young European adults. There are links with smoking and obesity and an autosomal dominant pattern of inheritance is reported by one-third of patients. Medical management escalates from topical antimicrobials to oral tetracyclines, a combination of clindamycin and rifampicin typically given for 10 weeks, oral disease modifiers, and anti-tumour necrosis factor-alpha therapies. Excision of individual lesions has high recurrence rates which can be minimised by wider excisions, at the expense of longer healing times. Treatment of pain is a relatively neglected aspect of therapy.


title: An insight into assistive technology for the visually impaired and blind people: state-of-the-art and future trends

abstract: Assistive technology for the visually impaired and blind people is a research field that is gaining increasing prominence owing to an explosion of new interest in it from disparate disciplines. The field has a very relevant social impact on our ever-increasing aging and blind populations. While many excellent state-of-the-art accounts have been written till date, all of them are subjective in nature. We performed an objective statistical survey across the various sub-disciplines in the field and applied information analysis and network-theory techniques to answer several key questions relevant to the field. To analyze the field we compiled an extensive database of scientific research publications over the last two decades. We inferred interesting patterns and statistics concerning the main research areas and underlying themes, identified leading journals and conferences, captured growth patterns of the research field; identified active research communities and present our interpretation of trends in the field for the near future. Our results reveal that there has been a sustained growth in this field; from less than 50 publications per year in the mid 1990s to close to 400 scientific publications per year in 2014. Assistive Technology for persons with visually impairments is expected to grow at a swift pace and impact the lives of individuals and the elderly in ways not previously possible.


title: Polar clusters in relaxor (Pb,La)(Zr,Ti)O3 revealed by second harmonic generation

abstract: Polarization ordering in Pb1−xLax(Zr0.65Ti0.35)0.98O3 ceramics for x=8%, 9% and 9.5% (PLZT x/65/35) was detected from the temperature dependence of the averaged second harmonic generation (SHG) intensity. In PLZT 8/65/35 it shows two drops near the freezing transition (at about 50 °C) and slightly below the Burns temperature (about 350 °C). The SHG intensity exhibits peaks at 90 and 355 °C detected in both PLZT compositions using slow heating runs, which disappear for faster temperature runs. This indicates an unusual increase in dipole ordering with a very slow relaxation time.


title: Phase space vector modulation for a four-leg voltage source inverter

abstract: The purpose of this paper is to obtain the high performance modulation that combines all features in one: the power efficiency, the robustness to switching disturbances and the currents balancing under an unbalanced load. Due to the high power efficiency, this type of modulation is very attractive for high dynamic applications such as electrical vehicles. The performance efficiency of the proposed modulation in comparison with a conventional one is shown.


title: FuturesMEX: Secure, Distributed Futures Market Exchange

abstract: In a Futures-Exchange, such as the Chicago Mercantile Exchange, traders buy and sell contractual promises (futures) to acquire or deliver, at some future pre-specified date, assets ranging from wheat to crude oil and from bacon to cash in a desired currency. The interactions between economic and security properties and the exchange's essentially non-monotonic security behavior; a valid trader's valid action can invalidate other traders' previously valid positions, are a challenge for security research. We show the security properties that guarantee an Exchange's economic viability (availability of trading information, liquidity, confidentiality of positions, absence of price discrimination, risk-management) and an attack when traders' anonymity is broken. We describe all key operations for a secure, fully distributed Futures-Exchange, hereafter referred to as simply the ""Exchange"". Our distributed, asynchronous protocol simulates the centralized functionality under the assumptions of anonymity of the physical layer and availability of a distributed ledger. We consider security with abort (in absence of honest majority) and extend it to penalties. Our proof of concept implementation and its optimization (based on zk-SNARKs and SPDZ) demonstrate that the computation of actual trading days (along Thomson-Reuters Tick History DB) is feasible for low-frequency markets; however, more research is needed for high-frequency ones.


title: Laparoscopy or fast-track surgery, or both?

abstract: There is no doubt that the laparoscopic revolution could beconsidered one of the major improvements for the man-agement of colorectal diseases. Since its introduction in the1990s, several meta-analyses have clearly demonstrated itsfeasibility and efﬁcacy [1, 2]. It is becoming the new ‘‘goldstandard’’ for the care of colonic diseases with a high levelof evidence [3] because of its mini-invasiveness and betterpostoperative comfort for surgical patients. At the sametime, another improvement in colorectal surgery, initiatedby Kehlet and coworkers, the so-called enhanced recoveryprotocol or the fast-track surgery (FTS) approach [4, 5],initially less recognized, has gradually gained worldwideacceptance [6]. This new paradigm, a parallel revolution inthe care of surgical patients (based on a multimodalapproach of patients), was initiated in the ﬁeld of opensurgery. However, the evidence-based literature showswith a high level of evidence that FTS is feasible, safe, andoffers a better postoperative recovery compared with tra-ditional care [5], even for laparoscopic surgery.Generally speaking, beside the ﬁeld of colorectal sur-gery, one of the most important lessons learned from thelaparoscopic revolution has been that some of the olddogma has to be questioned: this has sparked a review ofseveral practices in open surgery. As an example, surgicalstress and inﬂammatory response to surgery have beenbetter evaluated and cell-mediated immunosuppression hasbeen highlighted as an important component in this setting[7]. Thanks to the laparoscopic approach, ‘‘modern open’’surgery also has become less ‘‘aggressive.’’Two alternatives for colorectal surgeryWe are now faced with two alternative options, bothdesigned to improve perioperative surgical care: ‘‘tradi-tional’’ laparoscopic surgery and ‘‘open’’ FTS.It is noteworthy that in the major, randomized trials [8–11] that served to establish the superiority of laparoscopiccolon surgery compared with its open counterpart, the con-trol patients underwent open traditional approach withoutFTS—the standard when those trials were initiated. There-fore, from anevidence-basedpoint ofview, one can only saythat laparoscopic surgery is superior to open surgery as itwas conceptualized in the 1990s. Without trials comparingtraditional laparoscopic surgery to open FTS, whether thereis sound evidence that the traditional laparoscopic surgery issuperior to open FTS remains to be shown.When we consider the respective results of these twoapproaches and compare for example the length of post-operative hospital stay (LOS), the results of randomizedtrials on conventional laparoscopic surgery (without amultimodal perioperative approach) were never superior tothose of FTS. Indeed, LOS is an outcome subjected tovariations related to socioeconomic factors, but all pub-lished large trials showed concordant results with a meanLOS exceeding 5 days in all cases [8–11], whereas in mostrandomized trials on ‘‘open’’ FTS, the mean LOS did notexceed 5 days [references on request to KS].


title: DDR4 transmitter with AC-boost equalization and wide-band voltage regulators for thin-oxide protection in 14-nm SOI CMOS technology

abstract: A DDR4 transmitter (TX) for direct-attach memory on a processor chip is presented as well as the design of the associated low-dropout linear voltage regulators (LDO) that generate the split-mode supply voltages for the thin-oxide protection of the TX output stages operated from the 1.2 V DDR4-supply. The TX uses AC-boost equalization. Signal-integrity (SI) simulations have shown that pre-emphasis equalization is better suited to meet the DRAM eye mask specification than de-emphasis equalization. The LDO design is optimized for good frequency compensation at large load variations, which typically occur during burst-mode transmissions in DDR memory links. A wide-band low-output impedance buffer located between the LDO's error amplifier and the power transistor is proposed that implements a load-sensing and current-injection scheme to extend the low-output impedance range of the buffer, which in turn stabilizes the dominant output pole over a wider di/dt-range. The design is implemented in 14-nm silicon-on-insulator (SOI) CMOS technology, and the key performance measures are 2.8 pJ/b efficiency of the TX when driving with 34 Ω into a 40 Ω DRAM load and a figure-of-merit (FOM) of 96 ps for the LDO.


title: An antimicrobial prescription surveillance system that learns from experience

abstract: Inappropriate prescribing of antimicrobials is a major clinical and health concern, as well as a financial burden, in hospitals worldwide. In this paper, we describe a deployed automated antimicrobial prescription surveillance system that has been assisting hospital pharmacists in identifying and reporting inappropriate antimicrobial prescriptions. One of the key characteristics of this system is its ability to learn new rules for detecting inappropriate prescriptions based on previous false alerts. The supervised learning algorithm combines instance-based learning and rule induction techniques. It exploits temporal abstraction to extract a meaningful time interval representation from raw clinical data, and applies nearest neighbor classification with a distance function on both temporal and non-temporal parameters. The learning capability is valuable both in configuring the system for initial deployment and improving its long term use. We give an overview of the application, point to lessons learned so far and provide insight into the machine learning capability.


Chapter 01: The Increasing Importance of Learning How to Learn


title: Structural Data De-anonymization: Quantification, Practice, and Implications

abstract: In this paper, we study the quantification, practice, and implications of structural data (e.g., social data, mobility traces) De-Anonymization (DA). First, we address several open problems in structural data DA by quantifying perfect and (1-e)-perfect structural data DA}, where e is the error tolerated by a DA scheme. To the best of our knowledge, this is the first work on quantifying structural data DA under a general data model, which closes the gap between structural data DA practice and theory. Second, we conduct the first large-scale study on the de-anonymizability of 26 real world structural datasets, including Social Networks (SNs), Collaborations Networks, Communication Networks, Autonomous Systems, and Peer-to-Peer networks. We also quantitatively show the conditions for perfect and (1-e)-perfect DA of the 26 datasets. Third, following our quantification, we design a practical and novel single-phase cold start Optimization based DA} (ODA) algorithm. Experimental analysis of ODA shows that about 77.7% - 83.3% of the users in Gowalla (.2M users and 1M edges) and 86.9% - 95.5% of the users in Google+ (4.7M users and 90.8M edges) are de-anonymizable in different scenarios, which implies optimization based DA is implementable and powerful in practice. Finally, we discuss the implications of our DA quantification and ODA and provide some general suggestions for future secure data publishing.


title: Monitoring of Blood Pressure Using Photoplethysmographic (PPG) Sensor with Aromatherapy Diffusion

abstract: Continuous blood pressure monitoring is an important procedure for checking the conditions of the elderly people, especially those who experience hypertension or hypotension. However, existing Blood Pressure measuring devices neither have a way to automatically indicate whether a Blood Pressure reading is either above or below the acceptable normal range, nor a way to provide “first aid” for it; nor activate an alerting device to the people who are directly caring for them. In this paper, the proponents developed a system, which uses Photoplethysmography to sense the pulse of a person, non-invasively and cufflessly, to derive the blood pressure reading. The indication of an occurrence of a high blood pressure or a low blood pressure is determined by an algorithm that may trigger the alerting mechanism of the system to activate a quick application of aromatherapy by inhalation (using essential oils) to the elder to make his/her blood pressure lower (when hypertension occurs) or vice versa.


title: Gait planning based on kinematics for a quadruped gecko model with redundancy

abstract: Recent research on mobile robots has focused on locomotion in various environments. In this paper, a gait-generation algorithm for a mobile robot that can travel from the ground to a wall and climb vertical surfaces is proposed. The algorithm was inspired by a gecko lizard. Our gait planning was based on inverse kinematics using the Jacobian of the whole body, where the redundancy was solved by defining an object function for the gecko posture to avoid collisions with the surface. The optimal scalar factor for these two objects was obtained by defining a superior object function to minimize the angular acceleration of joints. The algorithm was verified through simulation of the gecko model travelling on given task paths and avoiding abnormal joint movements and collisions.


title: Cutting-plane training of structural SVMs

abstract: Discriminative training approaches like structural SVMs have shown much promise for building highly complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. However, current training algorithms are computationally expensive or intractable on large datasets. To overcome this bottleneck, this paper explores how cutting-plane methods can provide fast training not only for classification SVMs, but also for structural SVMs. We show that for an equivalent "1-slack" reformulation of the linear SVM training problem, our cutting-plane method has time complexity linear in the number of training examples. In particular, the number of iterations does not depend on the number of training examples, and it is linear in the desired precision and the regularization parameter. Furthermore, we present an extensive empirical evaluation of the method applied to binary classification, multi-class classification, HMM sequence tagging, and CFG parsing. The experiments show that the cutting-plane algorithm is broadly applicable and fast in practice. On large datasets, it is typically several orders of magnitude faster than conventional training methods derived from decomposition methods like SVM-light, or conventional cutting-plane methods. Implementations of our methods are available at www.joachims.org .


title: 1.5°C accurate CMOS temperature sensor with a single point trim at 85°C

abstract: This paper presents a temperature sensor with an accuracy of 1.5°C. The paper analyzes inaccuracy of the existing temperature sensors and outlines the techinques to overcome them. The sensor is based on a CMOS current reference which is almost constant over process/corner and voltage variation. Most of the techniques known today for generating a process and voltage independent current source have very stringent constraints on sizing of the transistors. The current source proposed in the paper relaxes such constraints thus easing the portability across various technology nodes. The sensor designed in TSMC 28 nm CMOS process offers the accuracy of 1.5°C over a temperature range of −40°C to 125°C with a single point trim at 85°C. The sensor consumes a power of 90 μW of power when operating on 1.8 V supply.


abstract: In this paper, we present a first of a kind system, called Business Intelligence from Voice of Customer (BIVoC), that can: 1) combine unstructured information and structured information in an information intensive enterprise and 2) derive richer business insights from the combined data. Unstructured information, in this paper, refers to Voice of Customer (VoC) obtained from interaction of customer with enterprise namely, conversation with call-center agents, email, and sms. Structured database reflect only those business variables that are static over (a longer window of) time such as, educational qualification, age group, and employment details. In contrast, a combination of unstructured and structured data provide access to business variables that reflect upto date dynamic requirements of the customers and more importantly indicate trends that are difficult to derive from a larger population of customers through any other means. For example, some of the variables reflected in unstructured data are problem/interest in a certain product, expression of dissatisfaction with the business provided, and some unexplored category of people showing certain interest/problem. This gives the BIVoC system the ability to derive business insights that are richer, more valuable and crucial to the enterprises than the traditional business intelligence systems which utilize onlystructured information. We demostrate the effectiveness of BIVoC system through one of our real-life engagements where the problem is to determine how to improve agent productivity in a call center scenario. We also highlight major challenges faced while dealing with unstructured information such as handling noise and linking with structured data.


title: Complex Unconstrained Three-Dimensional Hand Movement and Constant Equi-Affine Speed

abstract: One long-established simplifying principle behind the large repertoire and high versatility of human hand movements is the two-thirds power law—an empirical law stating a relationship between local geometry and kinematics of human hand trajectories during planar curved movements. It was further generalized not only to various types of human movements, but also to motion perception and prediction, although it was unsuccessful in explaining unconstrained three-dimensional (3D) movements. Recently, movement obeying the power law was proved to be equivalent to moving with constant planar equi-affine speed. Generalizing such motion to 3D space—i.e., to movement at constant spatial equi-affine speed—predicts the emergence of a new power law, whose utility for describing spatial scribbling movements we have previously demonstrated. In this empirical investigation of the new power law, subjects repetitively traced six different 3D geometrical shapes with their hand. We show that the 3D power law explains the data...


Privacy protection, control of information, and privacy-enhancing technologies


title: Video image processing for moving object detection and segmentation using background subtraction

abstract: Moving objects often contain almost important information for surveillance videos, traffic monitoring, human motion capture etc. Background subtraction methods are widely exploited for moving object detection in videos in many applications. Moving object segmentation is the application in video processing. Segmentation helps in detecting various features of moving objects for further video/image processing. In this paper object detection and segmentation is proposed and they are compared using background subtraction algorithm (object detection) and segmentation algorithm (edge detection and thresholding). The experiment results show that the proposed method gives better results.


title: OOK/BPSK-Modulated Impulse Transmitters Integrated With Leakage-Cancelling Circuit

abstract: A time-gating technique is adopted to design an on-off keying (OOK) and a binary phase-shift keying (BPSK) ultra-wideband impulse transmitters, respectively. A leakage-cancelling technique is proposed to suppress leakage signal from the oscillator, which is implemented by integrating a leakage-cancelling circuit with the output buffer of the OOK transmitter and the modulator of the BPSK transmitter, respectively. These two types of transmitters have been implemented using 0.18-μ m RF CMOS technology. The measured leakage overshoot is less then 1.5 dBc in the OOK transmitter, and is negligible in the BPSK version.


Text Classification through Statistical and Machine Learning Methods: A Survey


title: Tri-Polarized 12-Antenna MIMO Array for Future 5G Smartphone Applications

abstract: A tri-polarized 12-antenna array working in the 3.5-GHz band (3.4–3.6 GHz) for future 5G (the fifth generation mobile communication) multiple-input multiple-output (MIMO) operations in the smartphone is presented. In order to reduce the mutual couplings and simplify the design process, orthogonal polarization technique is utilized. By combining a quarter mode substrate integrated wave-guide antenna and two open-end slots, a compact 3-antenna tri-polarization block operating in the 3.5-GHz band is achieved within a small volume of $17\times 17\times6$ mm3. Thanks to the orthogonal polarization features, the three antennas within the block are able to have good impedance matchings and low mutual couplings between antennas. By integrating four such tri-polarization blocks, a 12-antenna MIMO array is then designed for smartphone applications. It is also due to the tri-polarization feature, the proposed array could attain acceptable isolations and low correlations between antennas with only two additional decoupling structures. The proposed array is fabricated and tested, good antenna performances, such as return loss better than 10 dB, isolation higher than 12.5 dB, and antenna efficiencies higher than 50%, are obtained. The channel capacity of the 12-antenna array is calculated to be about 57 bps/Hz in a $12\times12$ MIMO system with 20-dB signal-to-noise ratio, which indicates the proposed array using tri-polarization technique is a good choice for future 5G terminals.


title: A Comparison of Algorithms for TV Ad Targeting

abstract: Television is the largest advertising category in the United States with 70 billion spent by advertisers per year. We compare a variety of different targeting algorithms, ranging from the traditional age-gender targeting methods employed based on Nielsen ratings, to new approaches that attempt to target high probability buyers using Set Top Box data. We show the performance of these different algorithms on a real television campaign, and discuss the advantages and limitations of each method. In contrast to other theoretical work, all methods presented in this paper are implementable on current television delivery systems.


title: A comparison between the axial flux and the radial flux structures for PM synchronous motors

abstract: The aim of this paper is the comparison of the axial flux (AF) structures versus the conventional radial flux (RF) structures for permanent-magnet synchronous motors. The comparison procedure is based on simple thermal considerations. Two motor typologies are chosen and compared in terms of delivered electromagnetic torque. The comparison is developed for different motor dimensions and the pole number influence is put into evidence. The paper reports the complete comparison procedure and the related results analysis. The obtained results show that, when the axial length is very short and the pole number is high, the AF motors can be an attractive alternative to the conventional RF solutions.


title: Psychological constraints on form-bearing dimensions in music

abstract: In raising the question of form-bearing dimensions in music, we are trying to understand the possibilities and limits of the apprehension of musical form in terms of the psychological mechanisms that operate on a received acoustic structure. To approach this understanding theoretically and experimentally, we need to define the notion of form-bearing dimension and to develop some ideas of the interactions that take place between perceptual processes and memory structures as form is accumulated in the mind of a listener. Three areas of psychological concern are discussed: perceptual grouping processes, abstract musical knowledge structures and event structure processing. For each area, the constraints on different musical dimensions such as pitch, duration, dynamics and timbre are examined in light of their potential to carry musical form.


title: Design and analysis of C shaped chipless RFID tag

abstract: In this paper, we demonstrate the passive chipless RFID tag having C shaped copper resonators operating in ultra wide band (2.5–5.5 GHz). The efficient bandwidth utilization is performed by using the frequency coding technique. The number of possible unique combinations is 210. Measurements and calculations are performed in frequency domain and the results are shown with the help of radar cross section (RCS) plot. The tag design is optimized and analyzed for three different substrates having same compact geometry of 20 × 40 mm2. The comparative analysis shows that the overall bandwidth of FR4, Rogers RT5880 and Taconic TLX-0 is 2.85 GHz, 2.5 GHz and 2.03 GHz respectively. The proposed tag is a suitable candidate for applications requiring high data capacity with an average tag size and simple resonating structures.


title: Verification of Computer Switching Networks: An Overview

abstract: Formal verification has seen much success in several domains of hardware and software design. For example, in hardware verification there has been much work in the verification of microprocessors (e.g. [1]) and memory systems (e.g. [2]). Similarly, software verification has seen success in device-drivers (e.g. [3]) and concurrent software (e.g. [4]). The area of network verification, which consists of both hardware and software components, has received relatively less attention. Traditionally, the focus in this domain has been on performance and security, with less emphasis on functional correctness. However, increasing complexity is resulting in increasing functional failures and thus prompting interest in verification of key correctness properties. This paper reviews the formal verification techniques that have been used here thus far, with the goal of understanding the characteristics of the problem domain that are helpful for each of the techniques, as well as those that pose specific challenges. Finally, it highlights some interesting research challenges that need to be addressed in this important emerging domain.


title: Algorithms For Vertex Partitioning Problems On Partial k-Trees

abstract: In this paper, we consider a large class of vertex partitioning problems and apply to them the theory of algorithm design for problems restricted to partial k-trees. We carefully describe the details of algorithms and analyze their complexity in an attempt to make the algorithms feasible as solutions for practical applications. ::: We give a precise characterization of vertex partitioning problems, which include domination, coloring and packing problems, and their variants. Several new graph parameters are introduced as generalizations of classical parameters. This characterization provides a basis for a taxonomy of a large class of problems, facilitating their common algorithmic treatment and allowing their uniform complexity classification. ::: We present a design methodology of practical solution algorithms for generally $\NP$-hard problems when restricted to partial k-trees (graphs with treewidth bounded by k). This "practicality" accounts for dependency on the parameter k of the computational complexity of the resulting algorithms. ::: By adapting the algorithm design methodology on partial k-trees to vertex partitioning problems, we obtain the first algorithms for these problems with reasonable time complexity as a function of treewidth. As an application of the methodology, we give the first polynomial-time algorithm on partial k-trees for computation of the Grundy number.


title: Seeing the self in the washing machine: the deep affordance of 2.0 philosophy in the household appliance domain

abstract: The acceleration of rhythm of everyday life requires efficiency and flexibility in daily routines. The real expectations and needs of people concerning intelligent home devices should be carefully researched. The project Moon 2.0 by Indesit Company presents alternative ways of producing household appliance services developing a 2.0 Human Machine Interface and programs setting unit for washing machines, totally manageable by smart phones or I-Phones. Users cannot explicitly control washing machines when they would like to use a feature combination that has not application in a current washing program. The application of the Web 2.0 philosophy to the washing machine let the user the possibility to directly control all the existing features of the washing programs and to decide time by time how many programs their machine should have, with regards to the transparency and interactivity concepts of the ambient intelligence. Moon 2.0 should not be confused with an hand held personal home assistant capable of controlling a wide range of electronic home devices. The smart phone behaves the intelligence of the washing machine and offers the user endless customisation possibilities.


title: High-Performance Adaptive Perturb and Observe MPPT Technique for Photovoltaic-Based Microgrids

abstract: Solar photovoltaic (PV) energy has witnessed double-digit growth in the past decade. The penetration of PV systems as distributed generators in low-voltage grids has also seen significant attention. In addition, the need for higher overall grid efficiency and reliability has boosted the interest in the microgrid concept. High-efficiency PV-based microgrids require maximum power point tracking (MPPT) controllers to maximize the harvested energy due to the nonlinearity in PV module characteristics. Perturb and observe (PO second, no steady-state oscillations around the MPP; and lastly, no need for predefined system-dependent constants, hence provides a generic design core. A design example is presented by experimental implementation of the proposed technique. Practical results for the implemented setup at different irradiance levels are illustrated to validate the proposed technique.


title: Revealing daily travel patterns and city structure with taxi trip data

abstract: Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of taxi-trip data from Shanghai, we built spatially embedded networks to model intra-city spatial interactions and to introduce network science methods into the analysis. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long- and short-distance trips, we reveal a two-level hierarchical polycentric city structure in Shanghai. Further explorations of sub-network structures demonstrate that urban sub-regions have broader internal spatial interactions, while suburban centers are more influential on local traffic. By incorporating the land use of centers from a travel-pattern perspective, we investigate sub-region formation and the interaction patterns of center–local places. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in developing and applying urban transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than for other structures, such as administrative divisions.


title: Improved feature processing for Deep Neural Networks

abstract: In this paper, we investigate alternative ways of processing MFCC-based features to use as the input to Deep Neural Networks (DNNs). Our baseline is a conventional feature pipeline that involves splicing the 13-dimensional front-end MFCCs across 9 frames, followed by applying LDA to reduce the dimension to 40 and then further decorrelation using MLLT. Confirming the results of other groups, we show that speaker adaptation applied on the top of these features using feature-space MLLR is helpful. The fact that the number of parameters of a DNN is not strongly sensitive to the input feature dimension (unlike GMM-based systems) motivated us to investigate ways to increase the dimension of the features. In this paper, we investigate several approaches to derive higher-dimensional features and verify their performance with DNN. Our best result is obtained from splicing our baseline 40-dimensional speaker adapted features again across 9 frames, followed by reducing the dimension to 200 or 300 using another LDA. Our final result is about 3% absolute better than our best GMM system, which is a discriminatively trained model.


title: A low profile single dipole antenna radiating circularly polarized waves

abstract: A low profile single dipole antenna that can generate circularly polarized (CP) radiation patterns is proposed in this paper. The CP patterns and low profile configuration are achieved using a specially designed artificial ground plane: a thin grounded slab loaded with periodic rectangular patches. The artificial ground plane exhibits in-phase reflection coefficients with polarization-dependent feature. The radiation mechanism of the antenna is described, and experimental results verify the antenna concept.


title: Dynamic Reconfiguration of Primary/Backup Clusters

abstract: Dynamically changing (reconfiguring) the membership of a replicated distributed system while preserving data consistency and system availability is a challenging problem. In this paper, we show that reconfiguration can be simplified by taking advantage of certain properties commonly provided by Primary/Backup systems. We describe a new reconfiguration protocol, recently implemented in Apache Zookeeper. It fully automates configuration changes and minimizes any interruption in service to clients while maintaining data consistency. By leveraging the properties already provided by Zookeeper our protocol is considerably simpler than state of the art.


title: A systematic assessment of rigor in information systems ranking-type Delphi studies

abstract: A B S T R A C T The ranking-type Delphi method is well suited as a means for consensus-building by using a series of questionnaires to collect data from a panel of geographically dispersed participants. This method allows a group of experts to systematically approach a particular task or problem. While information systems researchers have been using this method for almost three decades, no research to date has attempted to assess the extent to which Delphi studies have been rigorously conducted. Using the guidelines that have been prescribed by the leading Delphi methodologists, our descriptive review reveals many positive signs of rigor such as ensuring the anonymity of experts and providing clear and precise instructions to participants. Nevertheless, there are still several areas for improvement, such as reporting response and retention rates, instrument pretesting, and explicitly justifying modifications to the ranking-type Delphi method.


title: Role congruity theory of prejudice toward female leaders.

abstract: A role congruity theory of prejudice toward female leaders proposes that perceived incongruity between the female gender role and leadership roles leads to 2 forms of prejudice: (a) perceiving women less favorably than men as potential occupants of leadership roles and (b) evaluating behavior that fulfills the prescriptions of a leader role less favorably when it is enacted by a woman. One consequence is that attitudes are less positive toward female than male leaders and potential leaders. Other consequences are that it is more difficult for women to become leaders and to achieve success in leadership roles. Evidence from varied research paradigms substantiates that these consequences occur, especially in situations that heighten perceptions of incongruity between the female gender role and leadership roles. Leadership has been predominantly a male prerogative in corporate, political, military, and other sectors of society. Although women have gained increased access to supervisory and middle management positions, they remain quite rare as elite leaders and top executives. To explain this phenomenon, public and scientific discussion has centered on the idea of a “glass ceiling”—a barrier of prejudice and discrimination that excludes women from higher level leadership positions (Federal Glass Ceiling Commission, 1995; Morrison, White, & Van Velsor, 1987). To further this discussion, we advance a theory of prejudice toward female leaders and test the theory in relation to available empirical research. This integrative theory builds on social psychologists’ tradition of studying prejudice and stereotyping and industrial– organizational psychologists’ tradition of studying perceptions of managerial roles. The popularity of the glass ceiling concept may stem from the rarity of women in major leadership posts, despite the presence of equality or near equality of the sexes on many other indicators. A number of statistics thus suggest equality: In the United States, women make up 46% of all workers (U.S. Bureau of Labor Statistics, 2001b) and 45% of those in executive, administrative, and managerial occupations (U.S. Bureau of Labor Statistics, 2001a); women possess 51% of bachelor’s degrees and 45% of all advanced degrees (U.S. Bureau of the Census, 2000). In contrast, statistics pertaining to major leadership roles consistently show inequality: Women constitute 4% of the five highest earning officers in Fortune 500 companies and 0.4% of the CEOs (Catalyst, 2000); 13% of senators, 14% of congressional representatives, and 10% of state governors (Center for the American Woman and Politics, 2001); and 2% of military officers at the level of brigadier general and rear admiral or higher (U.S. Department of Defense,


Serverless Programming (Function as a Service)


title: Validation of the Sinhala translation of the International Consultation on Incontinence Modular Questionnaire for female lower urinary tract symptoms among women in Sri Lanka

abstract: Introduction and hypothesis ::: The objective of this study was to translate the International Consultation on Incontinence Modular Questionnaire for female lower urinary tract symptoms (ICIQ-FLUTS) into Sinhala and validate the Sinhala translation for use in clinical practice.


title: Recognising spontaneous facial micro-expressions

abstract: Facial micro-expressions are rapid involuntary facial expressions which reveal suppressed affect. To the best knowledge of the authors, there is no previous work that successfully recognises spontaneous facial micro-expressions. In this paper we show how a temporal interpolation model together with the first comprehensive spontaneous micro-expression corpus enable us to accurately recognise these very short expressions. We designed an induced emotion suppression experiment to collect the new corpus using a high-speed camera. The system is the first to recognise spontaneous facial micro-expressions and achieves very promising results that compare favourably with the human micro-expression detection accuracy.


title: Broadband 55–95 GHz microstrip to waveguide transition based on a dielectric tip and a tapered double-ridged waveguide section

abstract: As applications in E-band (60–90 GHz) are gaining increasing commercial interest, a full-band, low-cost and high performance microstrip to rectangular waveguide standard (WR12) transition is desired. The presented design covers a bandwidth from 55 GHz to 95 GHz thanks to smooth impedance transitions. A dielectric tip is machined at the end of the microstrip line to couple with a double ridged waveguide section, which is then linearly tapered to a standard WR12 section. Measurements on back-to-back transitions confirm wideband operation beyond E-band with a 3.6 dB average insertion loss (1.8 dB per transition) and return loss lower than 10 dB in the 55 to 95 GHz frequency range. Additionally, group delay is measured showing broadband operation in line with the simulation.


title: Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification

abstract: Classifying videos according to content semantics is an important problem with a wide range of applications. In this paper, we propose a hybrid deep learning framework for video classification, which is able to model static spatial information, short-term motion, as well as long-term temporal clues in the videos. Specifically, the spatial and the short-term motion features are extracted separately by two Convolutional Neural Networks (CNN). These two types of CNN-based features are then combined in a regularized feature fusion network for classification, which is able to learn and utilize feature relationships for improved performance. In addition, Long Short Term Memory (LSTM) networks are applied on top of the two features to further model longer-term temporal clues. The main contribution of this work is the hybrid learning framework that can model several important aspects of the video data. We also show that (1) combining the spatial and the short-term motion features in the regularized fusion network is better than direct classification and fusion using the CNN with a softmax layer, and (2) the sequence-based LSTM is highly complementary to the traditional classification strategy without considering the temporal frame orders. Extensive experiments are conducted on two popular and challenging benchmarks, the UCF-101 Human Actions and the Columbia Consumer Videos (CCV). On both benchmarks, our framework achieves very competitive performance: 91.3% on the UCF-101 and 83.5% on the CCV.


title: A causal test of the motor theory of speech perception: a case of impaired speech production and spared speech perception

abstract: The debate about the causal role of the motor system in speech perception has been reignited by demonstrations that motor processes are engaged during the processing of speech sounds. Here, we evaluate which aspects of auditory speech processing are affected, and which are not, in a stroke patient with dysfunction of the speech motor system. We found that the patient showed a normal phonemic categorical boundary when discriminating two non-words that differ by a minimal pair (e.g., ADA–AGA). However, using the same stimuli, the patient was unable to identify or label the non-word stimuli (using a button-press response). A control task showed that he could identify speech sounds by speaker gender, ruling out a general labelling impairment. These data suggest that while the motor system is not causally involved in perception of the speech signal, it may be used when other cues (e.g., meaning, context) are not available.