title: A Novel Comprehensive Approach for Estimating Concept Semantic Similarity in WordNet

abstract: Computation of semantic similarity between concepts is an important foundation for many research works. This paper focuses on IC computing methods and IC measures, which estimate the semantic similarities between concepts by exploiting the topological parameters of the taxonomy. Based on analyzing representative IC computing methods and typical semantic similarity measures, we propose a new hybrid IC computing method. Through adopting the parameter dhyp and lch, we utilize the new IC computing method and propose a novel comprehensive measure of semantic similarity between concepts. An experiment based on WordNet"is a"taxonomy has been designed to test representative measures and our measure on benchmark dataset R&G, and the results show that our measure can obviously improve the similarity accuracy. We evaluate the proposed approach by comparing the correlation coefficients between five measures and the artificial data. The results show that our proposal outperforms the previous measures.


title: The Long-Short Story of Movie Description

abstract: Generating descriptions for videos has many applications including assisting blind people and human-robot interaction. The recent advances in image captioning as well as the release of large-scale movie description datasets such as MPII-MD [28] and M-VAD [31] allow to study this task in more depth. Many of the proposed methods for image captioning rely on pre-trained object classifier CNNs and Long Short-Term Memory recurrent networks (LSTMs) for generating descriptions. While image description focuses on objects, we argue that it is important to distinguish verbs, objects, and places in the setting of movie description. In this work we show how to learn robust visual classifiers from the weak annotations of the sentence descriptions. Based on these classifiers we generate a description using an LSTM. We explore different design choices to build and train the LSTM and achieve the best performance to date on the challenging MPII-MD and M-VAD datasets. We compare and analyze our approach and prior work along various dimensions to better understand the key challenges of the movie description task.


title: Understanding Graph-Based Trust Evaluation in Online Social Networks: Methodologies and Challenges

abstract: Online Social Networks (OSNs) are becoming a popular method of meeting people and keeping in touch with friends. OSNs resort to trust evaluation models and algorithms to improve service quality and enhance user experiences. Much research has been done to evaluate trust and predict the trustworthiness of a target, usually from the view of a source. Graph-based approaches make up a major portion of the existing works, in which the trust value is calculated through a trusted graph (or trusted network, web of trust, or multiple trust chains). In this article, we focus on graph-based trust evaluation models in OSNs, particularly in the computer science literature. We first summarize the features of OSNs and the properties of trust. Then we comparatively review two categories of graph-simplification-based and graph-analogy-based approaches and discuss their individual problems and challenges. We also analyze the common challenges of all graph-based models. To provide an integrated view of trust evaluation, we conduct a brief review of its pre- and postprocesses (i.e., the preparation and validation of trust models, including information collection, performance evaluation, and related applications). Finally, we identify some open challenges that all trust models are facing.


title: Predicting Student Academic Performance at Degree Level: A Case Study

abstract: Universities gather large volumes of data with reference to their students in electronic form. The advances in the data mining field make it possible to mine these educational data and find information that allow for innovative ways of supporting both teachers and students. This paper presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts comprising 347 undergraduate students have been mined with different classifiers. The results show that it is possible to predict the graduation performance in 4th year at university using only pre-university marks and marks of 1st and 2nd year courses, no socio-economic or demographic features, with a reasonable accuracy. Furthermore courses that are indicators of particularly good or poor performance have been identified.


title: Semantic-Aware Searching Over Encrypted Data for Cloud Computing

abstract: With the increasing adoption of cloud computing, a growing number of users outsource their datasets to cloud. To preserve privacy, the datasets are usually encrypted before outsourcing. However, the common practice of encryption makes the effective utilization of the data difficult. For example, it is difficult to search the given keywords in encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users’ retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we propose ECSED, a novel semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. ECSED uses two cloud servers. One is used to store the outsourced datasets and return the ranked results to data users. The other one is used to compute the similarity scores between the documents and the query and send the scores to the first server. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. We employ the multi-keyword ranked search over encrypted cloud data as our basic frame to propose two secure schemes. The experiment results based on the real world datasets show that the scheme is more efficient than previous schemes. We also prove that our schemes are secure under the known ciphertext model and the known background model.


title: Modeling and controller design of a bidirectional resonant converter battery charger

abstract: In this paper, a controller design methodology is proposed for a CLLLC -type bidirectional resonant converter. The soft switching of all devices in this topology and the very high operating frequency lead to increased overall system efficiency. However, the dynamic nature of this converter is highly dependent on loading conditions, which proves challenging when designing the voltage and current closed-loop controllers. System instability is mainly due to the high-Q resonant peaking, which is observed in the open-loop bode-plots. In this paper, the controller design methodology is proposed, which accounts for the dynamics behavior due to load variations. The controller stability will be evaluated against the entire range of operating switching frequency. Both battery charging and regeneration modes will be described and analyzed. The focal contribution of this paper will focus on defining the worst operating scenarios for the converter using system-level modeling and analysis. In addition, the controller will be defined based on these operating points. To validate and verify the controller design methodology proposed, a 3.5-kW converter is designed with the appropriate output voltage and current loop controllers. The step response verified a stable system designed and thus proving the proposed controller design methodology.


title: TaintTrace: Efficient Flow Tracing with Dynamic Binary Rewriting

abstract: TaintTrace is a high performance flow tracing tool that protects systems against security exploits. It is based on dynamic execution binary rewriting empowering our tool with fine-grained monitoring of system activities such as the tracking of the usage and propagation of data originated from the network. The challenge lies in minimizing the run-time overhead of the tool. TaintTrace uses a number of techniques such as direct memory mapping to optimize performance. In this paper, we demonstrate that TaintTrace is effective in protecting against various attacks while maintaining a modest slowdown of 5.5 times, offering significant improvements over similar tools.


Uniformization for Time-Inhomogeneous Markov Population Models


title: Amino acid determination of human intestinal mucosa obtained by endosopic biopsies

abstract: In this study we describe a method which makes possible the determination of the free amino acids in biopsies of human gastrointestinal mucosa. Biopsies of duodenal or colonic mucosa were obtained from internal patients without histologically proven pathological processes of duodenum or colon during routinely performed gastrointestinal endoscopy. After shock-freezing in fluid nitrogen and lyophilization, the analysis of the extracts was performed by ion exchange chromatography. Standard deviations of the individual amino acid concentrations of 3 parallel specimens was 3–13% (except histidine: 19%). The sum of all amino acids without taurine was 61.9 (5.4) mmol/kg dry weight (mean, SD of 7 patients) in duodenal biopsies, and 82.9 (0.6) in colonic specimen; 50% (44%) of which consisted of aspartate and glutamate, and 14% (12%) of total essential amino acids. The relative amino acid pattern differed completely from the muscle pattern: aspartate was fourfold higher; glutamate, phenylalanine, glycine, valine, leucine, isoleucine about twofold higher. In contrast, glutamine amounted only to 4% (duodenum)/ 14% (colon) of muscle glutamine. The results obtained from duodenum were compared with that of HIV-positive patients (n=3). We found enhanced intracellular levels of serine and glutamate (150% of normal), glutamine (220%), valine (125%), and ornithine (410%). Amino acid analysis of endoscopically obtained mucosa biopsies might be helpful to monitor intestinal amino acid metabolism, as well as the effect of nutrition therapy in critically ill patients.


title: Multiphysical simulation of a semi-autonomous solar powered high altitude pseudo-satellite

abstract: With advances made in the fields of energy generation from renewable sources, airborne electrical propulsion, and autonomous system operation, much activity has been directed towards the development of so called high altitude pseudo satellites (HAPS) in recent years, with Zephyr (Airbus) and Aquila (Facebook) as prominent examples. Compared to classical orbital satellites, these are designed to require lower deployment costs and to offer a high flexibility in operational tasks and a long mission endurance. In the project StraVARIA, the goal was to develop a high-fidelity multiphysical simulation of such a HAPS, including a long-term mission planner, a reactive guidance system for weather avoidance, a flight control system with protections, a 6-DoF model with solar-electric propulsion system, and a comprehensive environment simulation with 4-D wind and turbulence. Due to the long mission duration, the mission planner and guidance system offer an increased autonomy level compared to standard operator controlled UAVs, however human input is still required for high level planning. The acausal and object-oriented modeling language Modelica has been used to create the integrated simulation model, enabling a modular and detailed modeling approach. By automatic code generation and optimization, simulation efficiency is improved, which is an important factor when considering long-term missions. Results of the integrated simulation show that missions like area surveillance and communication relay are possible whenever adverse weather conditions can be avoided. Ascending to and descending from mission altitude of approximately 18 km also poses a threat to the lightweight HAPS construction since layers of stronger winds and atmospheric disturbance have to be passed. To this end, simulated example missions over Bavaria are presented showcasing these effects, where mission success is ensured by means of the long term mission planner, the reactive guidance, and the inner-level protections implemented in the flight control system.


title: Italian guidelines on rehabilitation treatment of adolescents with scoliosis or other spinal deformities.

abstract: A mandated by the Italian Ministry of Health, the Italian Society of Physical Medicine and Rehabilitation (Societa Italiana Medicina Fisica e Riabilitazione, SIMFER) appointed a Commission composed of SIMFER members to draw up guidelines for the rehabilitation treatment of adolescents with spinal deformities. Other medical organizations contributing to the redaction of the final guidelines version were the Italian Society of Orthopedics and Traumatology (Societa Italiana di Ortopedia e Traumatologia, SIOT), the Italian Society of Trau-matology and Pediatric Orthopedics (Societa Italiana di Traumatologia e Ortopedia Pediatrica, SITOP), the Italian Study Group on Scoliosis and Spinal Diseases (Gruppo Italiano di studio della Scoliosi e delle patologie vertebrali, GIS), the Study Group on Scoliosis and Spinal Diseases (Gruppo di Studio della Scoliosi e delle patologie vertebrali, GSS), which collaborated through society members nominated by the organizations’


title: Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data

abstract: Abstract Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techniques. Cluster analysis is often used as one of the major data analysis technique widely applied for many practical applications in emerging areas of data mining. Two of the most delegated, partition based clustering algorithms namely k-Means and Fuzzy C-Means are analyzed in this research work. These algorithms are implemented by means of practical approach to analyze its performance, based on their computational time. The telecommunication data is the source data for this analysis. The connection oriented broad band data is used to find the performance of the chosen algorithms. The distance (Euclidian distance) between the server locations and their connections are rearranged after processing the data. The computational complexity (execution time) of each algorithm is analyzed and the results are compared with one another. By comparing the result of this practical approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially high in Fuzzy C-Means algorithm than the k-Means.


title: Decentralized Overlapping Control of a Formation of Unmanned Aerial Vehicles

abstract: Decentralized overlapping feedback laws are designed for a formation of unmanned aerial vehicles. The dynamic model of the formation with an information structure constraint in which each vehicle, except the leader, only detects the vehicle directly in front of it, is treated as an interconnected system with overlapping subsystems. Using the mathematical framework of the inclusion principle, the interconnected system is expanded into a higher dimensional space in which the subsystems appear to be disjoint. Then, at each subsystem, a static state feedback controller is designed to robustly stabilize the perturbed nominal dynamics of the subsystem. The design procedure is based on the application of convex optimization tools involving linear matrix inequalities. As a final step, the decentralized controllers are contracted back to the original interconnected system for implementation.


title: The Worldwide Prevalence of ADHD: A Systematic Review and Metaregression Analysis

abstract: Objective: The worldwide prevalence estimates of attention deficit hyperactivity disorder (ADHD)/hyperkinetic disorder (HD) are highly heterogeneous. Presently, the reasons for this discrepancy remain poorly understood. The purpose of this study was to determine the possible causes of the varied worldwide estimates of the disorder and to compute its worldwide-pooled prevalence. Method: The authors searched MEDLINE and PsycINFO databases from January 1978 to December 2005 and reviewed textbooks and reference lists of the studies selected. Authors of relevant articles from North America, South America, Europe, Africa, Asia, Oceania, and the Middle East and ADHD/HD experts were contacted. Surveys were included if they reported point prevalence of ADHD/HD for subjects 18 years of age or younger from the general population or schools according to DSM or ICD criteria. Results: The literature search generated 9,105 records, and 303 full-text articles were reviewed. One hundred and two studies comprising 171,756 ...


title: Radiation Performance Enhancement of E-Band Antenna in Package

abstract: This paper presents an antenna-in-package (AiP) solution with embedded wafer Level ball grid array (eWLB) packaging technology. The primary antenna is realized in the redistribution layer of the eWLB package. It is composed of two patches driven by differential signals and supports seamless connection with differential output monolithic microwave integrated circuits in a compact package. A dielectric rod lens was designed to optimize the radiation performance of the whole package. The final lens has the dimensions 10 mm × 10 mm × 10.5 mm. Our measurements show that the whole package (chip+AiP) including the lens reaches 16-dBm effective isotropic radiated power for the frequency range from 71.4 to 81.7 GHz. The radiation beam is relatively symmetric for both the E- and H-plane. The proposed AiP solution has great potential for millimeter-wave applications.


title: A Formulation Development Approach to Identify and Select Stable Ultra–High-Concentration Monoclonal Antibody Formulations With Reduced Viscosities

abstract: High protein concentration formulations are required for low-volume administration of therapeutic antibodies targeted for subcutaneous, self-administration by patients. Ultra-high concentrations (≥150 mg/mL) can lead to dramatically increased solution viscosities, which in turn can lead to stability, manufacturing, and delivery challenges. In this study, various categories and individual types of pharmaceutical excipients and other additives (56 in total) were screened for their viscosity reducing effects on 2 different mAbs. The physicochemical stability profile, as well as viscosity ranges, of several candidate antibody formulations, identified and designed based on the results of the excipient screening, were evaluated over a 6-month time period under accelerated and real-time storage conditions. In addition to reducing the solution viscosities to acceptable levels for parenteral administration (using currently available and acceptable delivery devices), the candidate formulations did not result in notable losses of physicochemical stability of the 2 antibodies on storage for 6 months at 25°C. The experiments described here demonstrate the feasibility of a formulation development and selection approach to identify candidate high-concentration antibody formulations with viscosities within pharmaceutically acceptable ranges that do not adversely affect their physicochemical storage stability.


title: Hierarchical CP-ASBE Scheme in Cloud Computing for fine-Grained Access Control with Scalability and Flexibility

abstract: Cloud computing has widespread use in IT industry. Many Companies like Google, Microsoft, and IBM uses cloud to deliver services to its users because of its reliability, scalability and remote access. A cloud service provider (CSP) is an entity in cloud computing, associated with the Organization has different roles and capabilities. A cloud service provider is limited to organization so it's vulnerable to attacks. When requested data from the user is retrieved from CSP, Organization needs to secure their data & CSP. Access to CSP should be flexible so that it can adapt the different conditions easily. To maintain data integrity on CSP, Attribute based encryption (ABE) with KP- ABE and CP-ABE can be used with access control implementation for cloud computing. But these schemes also lack of scalability and flexibility so CP-ASBE scheme is described. A review of the all fine grained access control solution is presented. Existing system suffers from system performance, Data Security and flexible access control. A Hierarchical Attribute set-based-encryption scheme is proposed to deal with the issues in the existing system to achieve fine grained, flexible and scalable access control.


title: An analysis of security issues for cloud computing

abstract: Cloud Computing is a flexible, cost-effective, and proven delivery platform for providing business or consumer IT services over the Internet. However, cloud Computing presents an added level of risk because essential services are often outsourced to a third party, which makes it harder to maintain data security and privacy, support data and service availability, and demonstrate compliance. Cloud Computing leverages many technologies (SOA, virtualization, Web 2.0); it also inherits their security issues, which we discuss here, identifying the main vulnerabilities in this kind of systems and the most important threats found in the literature related to Cloud Computing and its environment as well as to identify and relate vulnerabilities and threats with possible solutions.


title: Human-Like Rewards to Train a Reinforcement Learning Controller for Planar Arm Movement

abstract: High-level spinal cord injury (SCI) in humans causes paralysis below the neck. Functional electrical stimulation (FES) technology applies electrical current to nerves and muscles to restore movement, and controllers for upper extremity FES neuroprostheses calculate stimulation patterns to produce desired arm movement. However, currently available FES controllers have yet to restore natural movements. Reinforcement learning (RL) is a reward-driven control technique; it can employ user-generated rewards, and human preferences can be used in training. To test this concept with FES, we conducted simulation experiments using computer-generated “pseudo-human” rewards. Rewards with varying properties were used with an actor-critic RL controller for a planar two-degree-of-freedom biomechanical human arm model performing reaching movements. Results demonstrate that sparse, delayed pseudo-human rewards permit stable and effective RL controller learning. The frequency of reward is proportional to learning success, and human-scale sparse rewards permit greater learning than exclusively automated rewards. Diversity of training task sets did not affect learning. Long-term stability of trained controllers was observed. Using human-generated rewards to train RL controllers for upper-extremity FES systems may be useful. Our findings represent progress toward achieving human–machine teaming in control of upper-extremity FES systems for more natural arm movements based on human user preferences and RL algorithm learning capabilities.


title: Improving the Accuracy of Pre-trained Word Embeddings for Sentiment Analysis

abstract: Sentiment analysis is one of the well-known tasks and fast growing research areas in natural language processing (NLP) and text classifications. This technique has become an essential part of a wide range of applications including politics, business, advertising and marketing. There are various techniques for sentiment analysis, but recently word embeddings methods have been widely used in sentiment classification tasks. Word2Vec and GloVe are currently among the most accurate and usable word embedding methods which can convert words into meaningful vectors. However, these methods ignore sentiment information of texts and need a huge corpus of texts for training and generating exact vectors which are used as inputs of deep learning models. As a result, because of the small size of some corpuses, researcher often have to use pre-trained word embeddings which were trained on other large text corpus such as Google News with about 100 billion words. The increasing accuracy of pre-trained word embeddings has a great impact on sentiment analysis research. In this paper we propose a novel method, Improved Word Vectors (IWV), which increases the accuracy of pre-trained word embeddings in sentiment analysis. Our method is based on Part-of-Speech (POS) tagging techniques, lexicon-based approaches and Word2Vec/GloVe methods. We tested the accuracy of our method via different deep learning models and sentiment datasets. Our experiment results show that Improved Word Vectors (IWV) are very effective for sentiment analysis.


title: Network Flow Watermarking: A Survey

abstract: Traffic analysis (TA) is a useful tool aimed at understanding network traffic behavior. Basic network administration often takes advantage of TA for purposes such as security, intrusion detection, traffic shaping and policing, diagnostic monitoring, provisioning, and resource management. Network flow watermarking is a type of TA in which packet features of selected flows are manipulated in order to add a specific pattern easily identifiable when the watermarked flows cross an observation point. While passive TA has been extensively studied with hundreds of papers found in the literature, active TA, and more specifically network flow watermarking, has only recently attracted attention. Enforced robustness against traffic perturbations due to either natural network noise or attacks against passive TA have enhanced the appeal of this technique. The contribution of this paper is a thorough review of the main watermarking algorithms implemented for traffic analysis purposes. We present an overview of the motivations and the objectives that have led to the use of network flow watermarking. We also describe the general architecture of a watermarking system. In addition, we impose clarity and order in this branch of TA by providing a taxonomy of the algorithms proposed in the literature over the years, and categorize and present them based on carrier, visibility, and robustness.


title: Attachment Styles and Relationship Quality among Young Couples

abstract: This research was aimed to study the relationship between attachment style and rela-tionship quality among young couples. A correlational research design was adopted to answer the objectives of this study. A total of 257 students of Universiti Malaysia Sarawak (UNIMAS), Sarawak participated in this study. Two types of questionnaire were used in this study; the Experience in Close Relationship (ECR) to measure the attachment styles, and Couples Satisfaction Index (CSI) to measure the relationship satisfaction of the couples. The results showed that there was a significant relation-ship between attachment style and relationship quality among the young couples in UNIMAS. Attachment avoidance and attachment anxiety had a significant effect on the relationship quality of the young couples. It is suggested for future studies to con-sider selecting the samples by using simple random sampling as the targeted popula-tion would be able to represent the whole population and the result would be more accurate.


title: Uptake and retention of microplastics by the shore crab Carcinus maenas.

abstract: Microplastics, plastics particles <5 mm in length, are a widespread pollutant of the marine environment. Oral ingestion of microplastics has been reported for a wide range of marine biota, but uptake into the body by other routes has received less attention. Here, we test the hypothesis that the shore crab (Carcinus maenas) can take up microplastics through inspiration across the gills as well as ingestion of pre-exposed food (common mussel Mytilus edulis). We used fluorescently labeled polystyrene microspheres (8–10 μm) to show that ingested microspheres were retained within the body tissues of the crabs for up to 14 days following ingestion and up to 21 days following inspiration across the gill, with uptake significantly higher into the posterior versus anterior gills. Multiphoton imaging suggested that most microspheres were retained in the foregut after dietary exposure due to adherence to the hairlike setae and were found on the external surface of gills following aqueous exposure. Results were used...


pyISC: A Bayesian Anomaly Detection Framework for Python.


title: A novel approach to evaluate and rank candidates in a recruitment process by estimating emotional intelligence through social media data

abstract: In this work, we put forward a system that automates the eligibility check and aptitude evaluation of prospective candidates in a recruitment process. Implemented as a web application, the system lets employers post new job openings. Interested candidates could apply by filling an online resume and inputting their twitter handle. The system estimates their emotional aptitude by analyzing the tweets while professional eligibility is verified through the entries given in the online resume. Big Five Personality Model (also known as Five Factor Model) is used to predict the personality traits of the users and assess their emotional quotient. Machine learning techniques such as supervised classification is used to model the personality predictor. Meta-attributes from the tweets are considered for the process, while the actual contents are ignored, thus protecting the privacy of the users. Regression techniques are then employed to assign a compatibility score to each applicant. The system outputs a list of candidates ranked in the order of the compatibility score to the employer who posted the job offer.


title: Exploitation of social IoT for recommendation services

abstract: Internet of Things (IoT) applications are generally developed in a vertical manner, i.e., each IoT application is developed for a certain scenario which generally does not share data with IoT applications for recommendation services. This leads to an inefficient exploitation of other IoT service applications. In fact, such recommendation services can be achieved with the help of Social IoT (SIoT) by using data generated by various IoT applications. SIoT builds a profile of objects based on IoT applications data that can be exchanged with SIoT network for accessible to other IoT applications. In this manner, the SIoT network provides recommendation services for reusability of IoT applications' data among various IoT applications, as well as adapting IoT services according to users' needs which improve user experience. Additionally, the profiles built by a SIoT network can also help a single IoT application by looking for similar conditions that have been addressed in the past for the same IoT application. We propose a concept for exploiting the SIoT for recommendation services among various IoT applications with the help of a sample application scenario that highlights how the SIoT can help by providing recommendations. We also provide some implementation challenges for this concept.


title: TencentRec: Real-time Stream Recommendation in Practice

abstract: With the arrival of the big data era, opportunities as well as challenges arise in both industry and academia. As an important service in most web applications, accurate real-time recommendation in the context of big data is of high demand. Traditional recommender systems that analyze data and update models at regular time intervals cannot satisfy the requirements of modern web applications, calling for real-time recommender systems. In this paper, we tackle the ``big", ``real-time" and ``accurate" challenges in real-time recommendation, and propose a general real-time stream recommender system built on Storm named TencentRec from three aspects, i.e., ``system", ``algorithm", and ``data". We analyze the large amount of data streams from a wide range of applications leveraging the considerable computation ability of Storm, together with a data access component and a data storage component developed by us. To deal with various application specific demands, we have implemented several classic practical recommendation algorithms in TencentRec, including the item-based collaborative filtering, the content based, and the demographic based algorithms. Specially, we present a practical scalable item-based CF algorithm in detail, with the super characteristics such as robust to the implicit feedback problem, incremental update and real-time pruning. With the enhancement of real-time data collection and processing, we can capture the recommendation changes in real-time. We deploy the TencentRec in a series of production applications, and observe the superiority of TencentRec in providing accurate real-time recommendations for 10 billion user requests everyday.


title: A review of methods supporting supplier selection

abstract: this paper we present a review of decision methods reported in the literature for supporting the supplier selection process. The review is based on an extensive search in the academic literature. We position the contributions in a framework that takes the diversity of procurement situations in terms of complexity and importance into account and covers all phases in the supplier selection process from initial problem definition, over the formulation of criteria, the qualification of potential suppliers, to the final choice among the qualified suppliers. Moreover, we propose decision methods and techniques that previously have not been suggested in a purchasing context. The proposed methods specifically accommodate for buying situations for which few or no decision models were published so far. This paper extends previous reviews by Weber et al. (Eur. J. Oper. Res. 50 (1991) 2), Holt (Int. J. Project Mange. 16 (1998) 153) and Degraeve et al. (Eur. J. Oper. Res. 125 (1) (2000a) 34) in that it classifies the models in a framework developed by De Boer (Ph. D. Thesis, University of Twente, Enschede, The Netherlands, 1998) which recognises more steps in the buying process than only the final among qualified suppliers and accommodates for the diversity of procurement situations.


title: Word Sense Induction with Neural biLM and Symmetric Patterns

abstract: An established method for Word Sense Induction (WSI) uses a language model to predict probable substitutes for target words, and induces senses by clustering these resulting substitute vectors. ::: We replace the ngram-based language model (LM) with a recurrent one. Beyond being more accurate, the use of the recurrent LM allows us to effectively query it in a creative way, using what we call dynamic symmetric patterns. ::: The combination of the RNN-LM and the dynamic symmetric patterns results in strong substitute vectors for WSI, allowing to surpass the current state-of-the-art on the SemEval 2013 WSI shared task by a large margin.


title: The influence of geometrical deviations of electrical machine systems on the signal quality of the variable reluctance resolver

abstract: Variable reluctance resolvers are needed to regulate electrical machines with inverter control. Due to several issues, e.g. discrete windings in the stator and geometric tolerances in the assembly of the electrical machine, the rotors angular position cannot be acquired properly. The subject of this paper is a variable reluctance resolver model to investigate manufacturing induced geometrical tolerances of the electrical machine in means of rotor position. For this purpose, a block diagram of a stator segment is built, connecting 24 segments to one stator core using PLECS. The rotor can be set freely in shape and position. By varying a permeance block the rotation, i.e. the position dependent airgap, is mirrored. The proposed model allows a sensitivity analysis of the sensor signal according to geometrical changes and the absolute position in the electrical machine system. To evaluate the investigations, the results are verified by measurements on a sensor test bench. The simulation results show a similar behavior as the measurement. Although the values differ by a factor of five, this absolute difference is discussed by means of neglected boundary conditions.


title: A 0.6-V 38-nW 9.4-ENOB 20-kS/s SAR ADC in 0.18-$\mu{\rm m}$ CMOS for Medical Implant Devices

abstract: This paper presents a 10-bit ultra-low power successive approximation register (SAR) analog-to-digital converter (ADC) for implantable medical devices. To achieve the nanowatt range power consumption, a novel switching scheme is proposed, which can accomplish the first three comparisons without consuming any energy and thus improve the energy efficiency significantly. In addition, to boost the offset performance of the comparator working under low supply voltage, a detailed theoretical analysis of comparator offset voltages is made. Based on the analysis, the comparator is optimized by only adjusting transistor sizes without any particular offset cancellation. As a result, when the common-mode input voltage varies from 300 mV to 450 mV at a 0.6 V supply, the $3\times\sigma$ offset voltage is optimized to be about 6 mV with a fluctuation of only 0.15 mV, as revealed by Monte Carlo simulations. A prototype of the proposed ADC was fabricated in 0.18 $\mu{\rm m}$ 1P6M CMOS technology, which occupies an active area of only $380\times 430\ \mu{\rm m}^{2}$ . At a 0.6-V supply and 20 kS/s sampling rate, the ADC achieves an SNDR of 58.3 dB and a power consumption of 38 nW, resulting in a figure of merit (FOM) of 2.8 fJ/conversion-step.


title: Enhanced privacy of a remote data integrity-checking protocol for secure cloud storage

abstract: Remote data integrity checking (RDIC) enables a server to prove to an auditor the integrity of a stored file. It is a useful technology for remote storage such as cloud storage. The auditor could be a party other than the data owner; hence, an RDIC proof is based usually on publicly available information. To capture the need of data privacy against an untrusted auditor, Hao et al. formally defined "privacy against third party verifiers" as one of the security requirements and proposed a protocol satisfying this definition. However, we observe that all existing protocols with public verifiability supporting data update, including Hao et al.'s proposal, require the data owner to publish some meta-data related to the stored data. We show that the auditor can tell whether or not a client has stored a specific file and link various parts of those files based solely on the published meta-data in Hao et al.'s protocol. In other words, the notion "privacy against third party verifiers" is not sufficient in protecting data privacy, and hence, we introduce "zero-knowledge privacy" to ensure the third party verifier learns nothing about the client's data from all available information. We enhance the privacy of Hao et al.'s protocol, develop a prototype to evaluate the performance and perform experiment to demonstrate the practicality of our proposal.


title: A roadmap for graphene

abstract: Graphene, the one-atom-thick, two-dimensional form of carbon, boasts a range of properties with potential practical applications. Now that economic means for its mass production have been developed, some of those applications may soon become realities. Time, therefore, for a review of progress in graphene research and production, and a critical look at probable applications. Among the first applications to reach the market, the authors speculate, will be technologies such as printable and flexible electronics, flexible solar cells and supercapacitors.


title: Associations between emotional intelligence, depression and suicide risk in nursing students

abstract: Summary Background The most important factor which predisposes young people to suicide is depression, although protective factors such as self-esteem, emotional adaptation and social support may reduce the probability of suicidal ideation and suicide attempts. Several studies have indicated an elevated risk of suicide for health-related professions. Little is known, however, about the relationship between perceived emotional intelligence and suicide risk among nursing students. Objectives The main goals were to determine the prevalence of suicide risk in a sample of nursing students, to examine the relationship between suicide risk and perceived emotional intelligence, depression, trait anxiety and self-esteem, and to identify any gender differences in relation to these variables. Method Cross-sectional study of nursing students (n = 93) who completed self-report measures of perceived emotional intelligence (Trait Meta-Mood Scale, which evaluates three dimensions: emotional attention, clarity and repair), suicide risk (Plutchik Suicide Risk Scale), self-esteem (Rosenberg Self-esteem Scale), depression (Zung Self-Rating Depression Scale) and anxiety (Trait scale of the State-Trait Anxiety Inventory). Results Linear regression analysis confirmed that depression and emotional attention are significant predictors of suicidal ideation. Moreover, suicide risk showed a significant negative association with self-esteem and with emotional clarity and repair. Gender differences were only observed in relation to depression, on which women scored significantly higher. Overall, 14% of the students were considered to present a substantial suicide risk. Conclusions The findings suggest that interventions to prevent suicidal ideation among nursing students should include strategies to detect mood disorders (especially depression) and to improve emotional coping skills. In line with previous research the results indicate that high scores on emotional attention are linked to heightened emotional susceptibility and an increased risk of suicide. The identification and prevention of factors associated with suicidal behaviour in nursing students should be regarded as a priority.


title: A SUBSTRATE INTEGRATED WAVEGUIDE BANDPASS FILTER USING NOVEL DEFECTED GROUND STRUCTURE SHAPE

abstract: In this paper, a X-band wideband bandpass fllter based on a novel substrate integrated waveguide-to-defected ground structure (SIW-DGS) cell is presented. In the cell, the DGS is etched on the top plane of the SIW with high accuracy, so that the performance of the fllter can be kept as good as possible. Finally, the fllter, consisting of three cascaded cells, is designed and measured to meet compact size, low insertion loss, good return loss as well as smooth group delay. There is good agreement between the measurement and simulation results.


title: Image Captioning with Object Detection and Localization

abstract: Automatically generating a natural language description of an image is a task close to the heart of image understanding. In this paper, we present a multi-model neural network method closely related to the human visual system that automatically learns to describe the content of images. Our model consists of two sub-models: an object detection and localization model, which extract the information of objects and their spatial relationship in images respectively; Besides, a deep recurrent neural network (RNN) based on long short-term memory (LSTM) units with attention mechanism for sentences generation. Each word of the description will be automatically aligned to different objects of the input image when it is generated. This is similar to the attention mechanism of the human visual system. Experimental results on the COCO dataset showcase the merit of the proposed method, which outperforms previous benchmark models.


title: Entity-Centric Topic Extraction and Exploration: A Network-Based Approach

abstract: Topic modeling is an important tool in the analysis of corpora and the classification and clustering of documents. Various extensions of the underlying graphical models have been proposed to address hierarchical or dynamical topics. However, despite their popularity, topic models face problems in the exploration and correlation of the (often unknown number of) topics extracted from a document collection, and rely on compute-intensive graphical models. In this paper, we present a novel framework for exploring evolving corpora of news articles in terms of topics covered over time. Our approach is based on implicit networks representing the cooccurrences of entities and terms in the documents as weighted edges. Edges with high weight between entities are indicative of topics, allowing the context of a topic to be explored incrementally by growing network sub-structures. Since the exploration of topics corresponds to local operations in the network, it is efficient and interactive. Adding new news articles to the collection simply updates the network, thus avoiding expensive recomputations of term and topic distributions.


title: Determination of Five Polyphenols by HPLC/DAD and Discrimination of Apple Varieties

abstract: The objective of this study was to set up a method to detect five compounds in fresh smashed apples by HPLC/DAD simultaneously. Different methods have been tested to control browning and ascorbic acid with ultrasonication was adopted. Methanol–water–acetic acid (30:69:1, v/v) containing 2.0 g of ascorbic acid L−1 was chosen as the extract solvent. The method effectively simplified the sample treatment compared with the traditional ways. And primarily, the results were used to identify between different varieties. The chromatographic separation was performed on an Atlantis C18 (250 mm × 4.5 mm, particle size 5 μm) with a gradient elution program using a mixture of acetonitrile and 2% aqueous acetic acid (v/v) as mobile phase within 20 min at 270 nm wavelength. The variation of the content of five compounds was gallic acid (ND ~1.81 μg g−1), protocatechuic acid (ND ~1.79 μg g−1), chlorogenic acid (13.81–189.4 μg g−1), caffeic acid (6.82–45.02 μg g−1) and rutin (0.96–18.55 μg g−1). The results could successfully be used to discriminate between different apple varieties (Gala, Fuji, Delicious, 8th Apple US, Golden Apple, Green Apple and Red Rose); chlorogenic acid and rutin being the polyphenols that contribute most to the differentiation.


title: Parametric and nonparametric linkage analysis: a unified multipoint approach.

abstract: In complex disease studies, it is crucial to perform multipoint linkage analysis with many markers and to use robust nonparametric methods that take account of all pedigree information. Currently available methods fall short in both regards. In this paper, we describe how to extract complete multipoint inheritance information from general pedigrees of moderate size. This information is captured in the multipoint inheritance distribution, which provides a framework for a unified approach to both parametric and nonparametric methods of linkage analysis. Specifically, the approach includes the following: (1) Rapid exact computation of multipoint LOD scores involving dozens of highly polymorphic markers, even in the presence of loops and missing data. (2) Non-parametric linkage (NPL) analysis, a powerful new approach to pedigree analysis. We show that NPL is robust to uncertainty about mode of inheritance, is much more powerful than commonly used nonparametric methods, and loses little power relative to parametric linkage analysis. NPL thus appears to be the method of choice for pedigree studies of complex traits. (3) Information-content mapping, which measures the fraction of the total inheritance information extracted by the available marker data and points out the regions in which typing additional markers is most useful. (4) Maximum-likelihood reconstruction of many-marker haplotypes, even in pedigrees with missing data. We have implemented NPL analysis, LOD-score computation, information-content mapping, and haplotype reconstruction in a new computer package, GENEHUNTER. The package allows efficient multipoint analysis of pedigree data to be performed rapidly in a single user-friendly environment.


title: Clustering Using Monte Carlo Cross-Validation

abstract: Finding the "right" number of clusters, k, for a data set is a difficult, and often ill-posed, problem. In a probabilistic clustering context, likelihood-ratios, penalized likelihoods, and Bayesian techniques are among the more popular techniques. In this paper a new cross-validated likelihood criterion is investigated for determining cluster structure. A practical clustering algorithm based on Monte Carlo cross-validation (MCCV) is introduced. The algorithm permits the data analyst to judge if there is strong evidence for a particular k, or perhaps weaker evidence over a sub-range of k values. Experimental results with Gaussian mixtures on real and simulated data suggest that MCCV provides genuine insight into cluster structure, v-fold cross-validation appears inferior to the penalized likelihood method (BIC), a Bayesian algorithm (AutoClass v2.0), and the new MCCV algorithm. Overall, MCCV and AutoClass appear the most reliable of the methods. MCCV provides the data-miner with a useful data-driven clustering tool which complements the fully Bayesian approach.


title: Do You See What I Mean? Visual Resolution of Linguistic Ambiguities

abstract: Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception. In this work, we present a novel task for grounded language understanding: disambiguating a sentence given a visual scene which depicts one of the possible interpretations of that sentence. To this end, we introduce a new multimodal corpus containing ambiguous sentences, representing a wide range of syntactic, semantic and discourse ambiguities, coupled with videos that visualize the different interpretations for each sentence. We address this task by extending a vision model which determines if a sentence is depicted by a video. We demonstrate how such a model can be adjusted to recognize different interpretations of the same underlying sentence, allowing to disambiguate sentences in a unified fashion across the different ambiguity types.


title: The Incredible Shrinking Lesbian World and other Queer Conundra

abstract: This is a reflection on the state of lesbian identity politics, focusing on claims that lesbian subcultures are shrinking and even dying. Current fears focus on the increasingly permeable boundaries between butch lesbians and female-to-male transgender individuals, but its history is much longer. What we are seeing, quite possibly, is the exhaustion of a particular historical construction: a group of individuals who are defined primarily on the basis of their sexuality, and the rise of more specific identities which combine sexual preference, gender presentation and other modes of identification. This was the first annual Sally Miller Gearhart Lecture in Lesbian Studies, sponsored by the Department of Women’s and Gender Studies at the University of Oregon, in May 2009


title: Reproducible Experiments for Comparing Apache Flink and Apache Spark on Public Clouds

abstract: Big data processing is a hot topic in today's computer science world. There is a significant demand for analysing big data to satisfy many requirements of many industries. Emergence of the Kappa architecture created a strong requirement for a highly capable and efficient data processing engine. Therefore data processing engines such as Apache Flink and Apache Spark emerged in open source world to fulfil that efficient and high performing data processing requirement. There are many available benchmarks to evaluate those two data processing engines. But complex deployment patterns and dependencies make those benchmarks very difficult to reproduce by our own. This project has two main goals. They are making few of community accepted benchmarks easily reproducible on cloud and validate the performance claimed by those studies.


title: CATERPILLAR: A Business Process Execution Engine on the Ethereum Blockchain

abstract: Blockchain platforms, such as Ethereum, allow a set of actors to maintain a ledger of transactions without relying on a central authority and to deploy scripts, called smart contracts, that are executed whenever certain transactions occur. These features can be used as basic building blocks for executing collaborative business processes between mutually untrusting parties. However, implementing business processes using the low-level primitives provided by blockchain platforms is cumbersome and error-prone. In contrast, established business process management systems, such as those based on the standard Business Process Model and Notation (BPMN), provide convenient abstractions for rapid development of process-oriented applications. This article demonstrates how to combine the advantages of a business process management system with those of a blockchain platform. The article introduces a blockchain-based BPMN execution engine, namely Caterpillar. Like any BPMN execution engine, Caterpillar supports the creation of instances of a process model and allows users to monitor the state of process instances and to execute tasks thereof. The specificity of Caterpillar is that the state of each process instance is maintained on the (Ethereum) blockchain and the workflow routing is performed by smart contracts generated by a BPMN-to-Solidity compiler. The Caterpillar compiler supports a large array of BPMN constructs, including subprocesses, multi-instances activities and event handlers. The paper describes the architecture of Caterpillar, and the interfaces it provides to support the monitoring of process instances, the allocation and execution of work items, and the execution of service tasks.


Formal ontology of space, time, and physical entities in classical mechanics


title: Interpreting Neural Networks to Improve Politeness Comprehension

abstract: We present an interpretable neural network approach to predicting and understanding politeness in natural language requests. Our models are based on simple convolutional neural networks directly on raw text, avoiding any manual identification of complex sentiment or syntactic features, while performing better than such feature-based models from previous work. More importantly, we use the challenging task of politeness prediction as a testbed to next present a much-needed understanding of what these successful networks are actually learning. For this, we present several network visualizations based on activation clusters, first derivative saliency, and embedding space transformations, helping us automatically identify several subtle linguistics markers of politeness theories. Further, this analysis reveals multiple novel, high-scoring politeness strategies which, when added back as new features, reduce the accuracy gap between the original featurized system and the neural model, thus providing a clear quantitative interpretation of the success of these neural networks.


title: KeLP: a Kernel-based Learning Platform for Natural Language Processing

abstract: Kernel-based learning algorithms have been shown to achieve state-of-the-art results in many Natural Language Processing (NLP) tasks. We present KELP, a Java framework that supports the implementation of both kernel-based learning algorithms and kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms: once a new kernel function has been implemented it can be adopted in all the available kernelmachine algorithms. The platform includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. This paper will show the main aspects of the framework by applying it to different NLP tasks.


title: Anatomical landmarks of mandibular interforaminal region related to dental implant placement with 3D CBCT: comparison between edentulous and dental mandibles

abstract: BACKGROUND ::: Anterior mandibular (interforaminal) region is important in implant applications as it serves a basis for neurovascular bedding and holds the prosthesis for patients. Treatment planning for dental implant patients is often complicated by the unknown extent of the anterior loop of the neurovascular bundle. Anatomical structures including mandibular incisive canal (MIC) and lingual foramen (LF) should also be examined as part of the detailed analysis for their neurovascular structures. This study aimed to detect the positions of LF and MIC as well as the prolongation of interforaminal region in Anatolian population to supply the reference data of the surgical safe zone in chin for the clinicians. ::: ::: ::: MATERIALS AND METHODS ::: Mandibles of 70 adult specimens (35 edentulous + 35 dentate) were retrieved from the Department of Anatomy, Ege University. Images of the dry mandibles were obtained using a cone beam computed tomography unit applying a standardized exposure protocol. Afterwards, mandibles were sawn into vertical sections according to the respective tomographic cross-sections. Images were evaluated for the absence/presence of the MIC, its dimensions and antero-posterior length for both edentulous and dentate groups. In addition; the presence, number, location, labial canal and LF diameter and height of the LF were determined for both groups. ::: ::: ::: RESULTS ::: The MIC was observed in 80 and 68.6% of the dentate and edentulous groups, consecutively (p > 0.05). The MIC continued towards the incisor region in a slightly downward direction. The LF was observed in all dentate mandibles (100%), while it was present in 94.3% of the edentulous mandibles (p > 0.05). For the dentate group, 62.9% of the specimens had two foramens and 20% had three foramens in the mandibular midline. Mean length of the MIC in dentate groups and edentulous groups was measured as 2.55 ± 0.809 and 3.08 ± 1.745 mm, respectively. Well-defined MIC mean diameter in dentate groups and edentulous groups were measured as 2.44 ± 0.702 and 2.35 ± 0.652 mm, respectively. Significant difference was found between dentate and edentulous group in most of the parameters except for the LF and the diameter of the MIC (p > 0.05). The correlation between observers' measurements ranged between 0.742 and 0.993 for all anatomical landmarks and mandible groups. ::: ::: ::: CONCLUSION ::: The MIC and LF are associated with neurovascular bundle variations in number, location and size. Therefore, clinicians should determine each of these anatomical structures on a case-by-case basis to recognize their presence and to take measures for the possible implications of various treatment options. These guidelines included leaving a 2 mm safety zone between an implant and the coronal aspect of the neurovascular bundle. To avoid neurovascular injury during surgery in the interforaminal area, guidelines were developed with respect to validating the presence of an anterior loop of the neurovascular bundle.


title: Tensor decomposition of dense SIFT descriptors in object recognition

abstract: In machine vision, Scale-invariant feature transform (SIFT) and its variants have been widely used in image classification task. How- ever, the high dimensionality nature of SIFT features, usually in the or- der of multiple thousands per image, would require careful consideration in place to achieve accurate and timely categorization of objects within images. This paper explores the possibility of processing SIFT features as tensors and uses tensor decomposition techniques on high-order SIFT tensors for dimensionality reduction. The method focuses on both accu- racy and efficiency aspects and the validation result with the Caltech 101 dataset confirms the improvement with notable margins.


title: Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

abstract: Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed.


title: Authenticating Primary Users' Signals in Cognitive Radio Networks via Integrated Cryptographic and Wireless Link Signatures

abstract: To address the increasing demand for wireless bandwidth, cognitive radio networks (CRNs) have been proposed to increase the efficiency of channel utilization; they enable the sharing of channels among secondary (unlicensed) and primary (licensed) users on a non-interference basis. A secondary user in a CRN should constantly monitor for the presence of a primary user's signal to avoid interfering with the primary user. However, to gain unfair share of radio channels, an attacker (e.g., a selfish secondary user) may mimic a primary user's signal to evict other secondary users. Therefore, a secure primary user detection method that can distinguish a primary user's signal from an attacker's signal is needed. A unique challenge in addressing this problem is that Federal Communications Commission (FCC) prohibits any modification to primary users. Consequently, existing cryptographic techniques cannot be used directly. In this paper, we develop a novel approach for authenticating primary users' signals in CRNs, which conforms to FCC's requirement. Our approach integrates cryptographic signatures and wireless link signatures (derived from physical radio channel characteristics) to enable primary user detection in the presence of attackers. Essential to our approach is a {\em helper node} placed physically close to a primary user. The helper node serves as a "bridge" to enable a secondary user to verify cryptographic signatures carried by the helper node's signals and then obtain the helper node's authentic link signatures to verify the primary user's signals. A key contribution in our paper is a novel physical layer authentication technique that enables the helper node to authenticate signals from its associated primary user. Unlike previous techniques for link signatures, our approach explores the geographical proximity of the helper node to the primary user, and thus does not require any training process.


title: Deep Belief Networks Are Compact Universal Approximators

abstract: Deep belief networks (DBN) are generative models with many layers of hidden causal variables, recently introduced by Hinton, Osindero, and Teh (2006), along with a greedy layer-wise unsupervised learning algorithm. Building on Le Roux and Bengio (2008) and Sutskever and Hinton (2008), we show that deep but narrow generative networks do not require more parameters than shallow ones to achieve universal approximation. Exploiting the proof technique, we prove that deep but narrow feedforward neural networks with sigmoidal units can represent any Boolean expression.


title: Efficient SKIP Mode Detection for Coarse Grain Quality Scalable Video Coding

abstract: Scalable video coding (SVC) was recently standardized by the Joint Video Team as an extension of H.264. In SVC, a computationally expensive exhaustive mode decision is employed to select the best coding mode for each macroblock (MB), which achieves a high coding efficiency. In order to reduce computational complexity, we propose an efficient SKIP mode detection approach for coarse grain quality SVC. It makes use of the coding information of spatial neighboring MBs and the co-located MB in base layer to predict the SKIP mode MB and early terminate its mode decision procedure. Experimental results show that the proposed early SKIP mode decision approach can achieve the average computational saving about 54% with almost no loss of rate distortion (RD) performance in the enhancement layer.


title: Adaptive impulse response modeling for interactive sound propagation

abstract: We present novel techniques to accelerate the computation of impulse responses for interactive sound rendering. Our formulation is based on geometric acoustic algorithms that use ray tracing to compute the propagation paths from each source to the listener in large, dynamic scenes. In order to accelerate generation of realistic acoustic effects in multi-source scenes, we introduce two novel concepts: the impulse response cache and an adaptive frequency-driven ray tracing algorithm that exploits psychoacoustic characteristics of the impulse response length. As compared to prior approaches, we trace relatively fewer rays while maintaining high simulation fidelity for real-time applications. Furthermore, our approach can handle highly reverberant scenes and high-dynamic-range sources. We demonstrate its application in many scenarios and have observed a 5x speedup in computation time and about two orders of magnitude reduction in memory overhead compared to previous approaches. We also present the results of a preliminary user evaluation of our approach.


title: Unsupervised neural and Bayesian models for zero-resource speech processing

abstract: In settings where only unlabelled speech data is available, zero-resource speech technology needs to be developed without transcriptions, pronunciation dictionaries, or language modelling text. There are two central problems in zero-resource speech processing: (i) finding frame-level feature representations which make it easier to discriminate between linguistic units (phones or words), and (ii) segmenting and clustering unlabelled speech into meaningful units. In this thesis, we argue that a combination of top-down and bottom-up modelling is advantageous in tackling these two problems. ::: To address the problem of frame-level representation learning, we present the correspondence autoencoder (cAE), a neural network trained with weak top-down supervision from an unsupervised term discovery system. By combining this top-down supervision with unsupervised bottom-up initialization, the cAE yields much more discriminative features than previous approaches. We then present our unsupervised segmental Bayesian model that segments and clusters unlabelled speech into hypothesized words. By imposing a consistent top-down segmentation while also using bottom-up knowledge from detected syllable boundaries, our system outperforms several others on multi-speaker conversational English and Xitsonga speech data. Finally, we show that the clusters discovered by the segmental Bayesian model can be made less speaker- and gender-specific by using features from the cAE instead of traditional acoustic features. ::: In summary, the different models and systems presented in this thesis show that both top-down and bottom-up modelling can improve representation learning, segmentation and clustering of unlabelled speech data.


title: A Paradigm Shift: Alternative Interaction Techniques for Use with Mobile & Wearable Devices

abstract: Desktop user interface design originates from the fact that users are stationary and can devote all of their visual resource to the application with which they are interacting. In contrast, users of mobile and wearable devices are typically in motion whilst using their device which means that they cannot devote all or any of their visual resource to interaction with the mobile application -- it must remain with the primary task, often for safety reasons. Additionally, such devices have limited screen real estate and traditional input and output capabilities are generally restricted. Consequently, if we are to develop effective applications for use on mobile or wearable technology, we must embrace a paradigm shift with respect to the interaction techniques we employ for communication with such devices.This paper discusses why it is necessary to embrace a paradigm shift in terms of interaction techniques for mobile technology and presents two novel multimodal interaction techniques which are effective alternatives to traditional, visual-centric interface designs on mobile devices as empirical examples of the potential to achieve this shift.


title: Neonatal gonadotropin therapy in male congenital hypogonadotropic hypogonadism

abstract: Congenital hypogonadotropic hypogonadism (CHH) causes pubertal failure and infertility in both women and men due to partial or total secretory failure of the two pituitary gonadotropins lutropin (LH) and follitropin (FSH) during periods of physiological activation of the gonadotropic axis. Men and women with CHH frequently seek treatment for infertility after hypogonadism therapy. Some etiologies, such as autosomal dominant or X-linked Kallmann syndrome, raise the question of hereditary transmission, leading to increasing demands for genetic counseling and monitoring of medically assisted pregnancies. Diagnosis and treatment of newborn boys is, therefore, becoming an increasingly important issue. In male individuals with complete forms of CHH, the antenatal and neonatal gonadotropin deficit leads to formation of a micropenis and cryptorchidism, which could undermine future sexual and reproductive functions. Standard treatments, usually started after the age of puberty, often only partially correct the genital abnormalities and spermatogenesis. The aim of this Review is to examine the possible additional benefits of neonatal gonadotropin therapy in male patients with CHH. Encouraging results of neonatal therapy, together with a few reports of prepubertal treatment, support the use of this novel therapeutic strategy aimed at improving sexual and reproductive functions in adulthood.


title: Compressive Sensing for Background Subtraction

abstract: Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reconstructed from a small set of random projections, provided that the signal is sparse in some basis, e.g., wavelets. In this paper, we describe a method to directly recover background subtracted images using CS and discuss its applications in some communication constrained multi-camera computer vision problems. We show how to apply the CS theory to recover object silhouettes (binary background subtracted images) when the objects of interest occupy a small portion of the camera view, i.e., when they are sparse in the spatial domain. We cast the background subtraction as a sparse approximation problem and provide different solutions based on convex optimization and total variation. In our method, as opposed to learning the background, we learn and adapt a low dimensional compressed representation of it, which is sufficient to determine spatial innovations; object silhouettes are then estimated directly using the compressive samples without any auxiliary image reconstruction. We also discuss simultaneous appearance recovery of the objects using compressive measurements. In this case, we show that it may be necessary to reconstruct one auxiliary image. To demonstrate the performance of the proposed algorithm, we provide results on data captured using a compressive single-pixel camera. We also illustrate that our approach is suitable for image coding in communication constrained problems by using data captured by multiple conventional cameras to provide 2D tracking and 3D shape reconstruction results with compressive measurements.


title: Efficient detection of projected concentric circles using four intersection points on a secant line

abstract: Concentric circles are often used for calibration. Based on the geometric properties of concentric circles, we proved that only four intersection edge points on one secant line of the two images of the concentric circles (ICC) are sufficient to determine the whole parameters of the ICC, when the ratio of the radii of two concentric circles is given. Experimental results validate the proposed approach.


title: Optimal Consumption and Investment Strategies with Stochastic Interest Rates

abstract: We characterize the solution to the consumption and investment problem of a time-additive power utility investor in a continuous-time dynamically complete market with stochastic changes in the opportunity set. It is demonstrated that under stochastic interest rates the investor optimally hedges against changes in the term structure of interest rates by investing in a coupon bond, or portfolio of bonds, with a payment schedule that matches the forward-expected (i.e. certainty equivalent) consumption pattern. This is of conceptual importance since the hedge portfolio only depends on the specic term structure dynamics through the consequences for the optimal consumption pattern. We consider two explicit examples where the term structure dynamics are given by the Vasicek model and a three factor non-Markovian Heath-Jarrow-Morton model.


title: A secure address resolution protocol

abstract: We propose an architecture for securely resolving IP addresses into hardware addresses over an Ethernet. The proposed architecture consists of a secure server connected to the Ethernet and two protocols: an invite-accept protocol and a request-reply protocol. Each computer connected to the Ethernet can use the invite-accept protocol to periodically record its IP address and its hardware address in the database of the secure server. Each computer can later use the request-reply protocol, to obtain the hardware address of any other computer connected to the Ethernet from the database of the secure server. These two protocols are designed to overcome the actions of any adversary that can lose sent messages, arbitrarily modify the fields of sent messages, and replay old messages.


title: Change Detection Based on Gabor Wavelet Features for Very High Resolution Remote Sensing Images

abstract: In this letter, we propose a change detection method based on Gabor wavelet features for very high resolution (VHR) remote sensing images. First, Gabor wavelet features are extracted from two temporal VHR images to obtain spatial and contextual information. Then, the Gabor-wavelet-based difference measure (GWDM) is designed to generate the difference image. In GWDM, a new local similarity measure is defined, in which the Markov random field neighborhood system is incorporated to obtain a local relationship, and the coefficient of variation method is applied to discriminate contributions from different features. Finally, the fuzzy c-means cluster algorithm is employed to obtain the final change map. Experiments employing QuickBird and SPOT5 images demonstrate the effectiveness of the proposed approach.


title: SPARSE: A symptom-based antipattern retrieval knowledge-based system using Semantic Web technologies

abstract: Abstract Antipatterns provide information on commonly occurring solutions to problems that generate negative consequences. The number of software project management antipatterns that appears in the literature and the Web increases to the extent that makes using antipatterns problematic. Furthermore, antipatterns are usually inter-related and rarely appear in isolation. As a result, detecting which antipatterns exist in a software project is a challenging task which requires expert knowledge. This paper proposes SPARSE, an OWL ontology based knowledge-based system that aims to assist software project managers in the antipattern detection process. The antipattern ontology documents antipatterns and how they are related with other antipatterns through their causes, symptoms and consequences. The semantic relationships that derive from the antipattern definitions are determined using the Pellet DL reasoner and they are transformed into the COOL language of the CLIPS production rule engine. The purpose of this transformation is to create a compact representation of the antipattern knowledge, enabling a set of object-oriented CLIPS production rules to run and retrieve antipatterns relevant to some initial symptoms. SPARSE is exemplified through 31 OWL ontology antipattern instances of software development antipatterns that appear on the Web.


title: Monitoring body temperature of newborn infants at neonatal intensive care units using wearable sensors

abstract: Body temperature is one of the key parameters for health monitoring of premature infants at the neonatal intensive care unit (NICU). In this paper, we propose and demonstrate a design of non-invasive neonatal temperature monitoring with wearable sensors. A negative temperature coefficient (NTC) resistor is applied as the temperature sensor due to its accuracy and small size. Conductive textile wires are used to make the sensor integration compatible for a wearable non-invasive monitoring platform, such as a neonatal smart jacket. Location of the sensor, materials and appearance are designed to optimize the functionality, patient comfort and the possibilities for aesthetic features. A prototype belt is built of soft bamboo fabrics with NTC sensor integrated to demonstrate the temperature monitoring. Experimental results from the testing on neonates at NICU of Maxima Medical Center (MMC), Veldhoven, the Netherlands, show the accurate temperature monitoring by the prototype belt comparing with the standard patient monitor.


title: The 12-Core POWER8™ Processor With 7.6 Tb/s IO Bandwidth, Integrated Voltage Regulation, and Resonant Clocking

abstract: POWER8™ is a 12-core processor fabricated in IBM's 22 nm SOI technology with core and cache improvements driven by big data applications, providing 2.5× socket performance over POWER7+™. Core throughput is supported by 7.6 Tb/s of off-chip I/O bandwidth which is provided by three primary interfaces, including two new variants of Elastic Interface as well as embedded PCI Gen-3. Power efficiency is improved with several techniques. An on-chip controller based on an embedded PowerPC™ 405 processor applies per-core DVFS by adjusting DPLLs and fully integrated voltage regulators. Each voltage regulator is a highly distributed system of digitally controlled microregulators, which achieves a peak power efficiency of 90.5%. A wide frequency range resonant clock design is used in 13 clock meshes and demonstrates a minimum power savings of 4%. Power and delay efficiency is achieved through the use of pulsed-clock latches, which require statistical validation to ensure robust yield.


title: High income improves evaluation of life but not emotional well-being

abstract: aspects of well-being. We report an analysis ofmore than 450,000 responsesto the Gallup-HealthwaysWell-BeingIndex, adailysurveyof 1,000 US residents conducted by the Gallup Organization. We find that emotional well-being (measured by questions about emotional experiencesyesterday)andlifeevaluation(measuredbyCantril’sSelfAnchoringScale) have different correlates. Income andeducation are more closely related to life evaluation, but health, care giving, loneliness,andsmokingarerelativelystrongerpredictorsofdailyemotions. When plotted against log income, life evaluation rises steadily. Emotional well-being also rises with log income, but there is no further progress beyond an annual income of ∼$75,000. Low income exacerbates the emotional pain associated with such misfortunes as divorce, ill health, and being alone. We conclude that high income buys life satisfaction but not happiness, and that low income is associated both with low life evaluation and low emotional well-being.


title: Composite Templates for Cloth Modeling and Sketching

abstract: Cloth modeling and recognition is an important and challenging problem in both vision and graphics tasks, such as dressed human recognition and tracking, human sketch and portrait. In this paper, we present a context sensitive grammar in an And-Or graph representation which will produce a large set of composite graphical templates to account for the wide variabilities of cloth configurations, such as T-shirts, jackets, etc. In a supervised learning phase, we ask an artist to draw sketches on a set of dressed people, and we decompose the sketches into categories of cloth and body components: collars, shoulders, cuff, hands, pants, shoes etc. Each component has a number of distinct subtemplates (sub-graphs). These sub-templates serve as leafnodes in a big And-Or graph where an And-node represents a decomposition of the graph into sub-configurations with Markov relations for context and constraints (soft or hard), and an Or-node is a switch for choosing one out of a set of alternative And-nodes (sub-configurations) - similar to a node in stochastic context free grammar (SCFG). This representation integrates the SCFG for structural variability and the Markov (graphical) model for context. An algorithm which integrates the bottom-up proposals and the topdown information is proposed to infer the composite cloth template from the image.


title: Extended Object Tracking using a Radar Resolution Model

abstract: The work presented here concerns the problem of vehicle tracking when multiple radar reflection centers could be resolved on each vehicle. For this extended target tracking problem we propose a radar sensor model, capable of describing such measurements, incorporating sensor resolution. Furthermore, we introduce approximations to handle the inherently complex data association problem. The evaluation in terms of describing measured data and resulting tracking performance shows that the model effectively exploits the information in multiple vehicle detections.


INFORMATION PROCESSING IN COMPUTER AND MAN.


title: Foreign Particles Testing in Orally Inhaled and Nasal Drug Products

abstract: The International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS) presents this paper in order to contribute to public discussion regarding best approaches to foreign particles testing in orally inhaled and nasal drug products (OINDPs) and to help facilitate development of consensus views on this subject. We performed a comprehensive review of industry experience and best practices regarding foreign particles testing in OINDPs, reviewed current guidances and techniques, and considered health and safety perspectives. We also conducted and assessed results of an industry survey on U.S. Food and Drug Administration requirements for foreign particles testing. We provide here a result of our review and survey: a summary of industry best practices for testing and controlling foreign particles in OINDPs and proposals for developmental characterization and quality control strategies for foreign particles. We believe that clear consensus-based recommendations and standards for foreign particles testing and control in OINDPs are needed. The proposals contained in this paper could provide a starting point for developing such consensus recommendations and standards.


title: Learning to Communicate with Deep Multi-Agent Reinforcement Learning

abstract: We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can backpropagate error derivatives through (noisy) communication channels. Hence, this approach uses centralised learning but decentralised execution. Our experiments introduce new environments for studying the learning of communication protocols and present a set of engineering innovations that are essential for success in these domains.


title: Efficient partial order preserving unsupervised feature selection on networks

abstract: In the past decade, research on network data has attracted much attention and many interesting phenomena have been discovered. Such data are often characterized by high dimensionality but how to select meaningful and more succinct features for network data received relatively less attention. In this paper, we investigate unsupervised feature selection problem on networks. To effectively incorporate linkage information, we propose a Partial Order Preserving (POP) principle for evaluating features. We show the advantage of this novel formulation in several respects: effectiveness, efficiency and its connection to optimizing AUC. We propose three instantiations derived from the POP principle and evaluate them using three real-world datasets. Experimental results show that our approach has significantly better performance than state-of-the-art methods under several different metrics.


Molecular Plant Membrane Trafficking in Plant Immunity


title: Open source cable models for EMI simulations

abstract: This paper describes the progress of work towards an Open Source software toolset suitable for developing Spice based multi-conductor cable models. The issues related to creating a transmission line model for implementation in Spice which include the frequency dependent properties of real cables are presented and the viability of spice cable models is demonstrated through application to a three conductor crosstalk model. Development of the techniques to include models of shielded cables and incident field excitation has been demonstrated.


title: V3I-STAL: Visual Vehicle-to-Vehicle Interaction via Simultaneous Tracking and Localization

abstract: This paper investigates a visual interaction system for vehicle-to-vehicle (V2V) platform, called V3I. Our system employs common visual cameras that are mounted on connected vehicles to perceive the existence of isolated vehicles in the same roadway, and provides human drivers with imagery situational awareness. This allows effective interactions between vehicles even with a low permeation rate of V2V devices. The underlying research problem for V3I includes two aspects: i) tracking isolated vehicles of interest over time through local cameras; ii) at each time-step fusing the results of local visual perceptions to obtain a global location map that involves both isolated and connected vehicles. In this paper, we introduce a unified probabilistic approach to solve the above two problems, i.e., tracking and localization, in a joint fashion. Our approach will explore both the visual features of individual vehicles in images and the pair-wise spatial relationships between vehicles. We develop a fast Markov Chain Monte Carlo (MCMC) algorithm to search the joint solution space efficiently, which enables real-time application. To evaluate the performance of the proposed approach, we collect and annotate a set of video sequences captured with a group of vehicle-resident cameras. Extensive experiments with comparisons clearly demonstrate that the proposed V3I approach can precisely recover the dynamic location map of the surrounding and thus enable direct visual interactions between vehicles .


title: An Anomaly Detector for Keystroke Dynamics Based on Medians Vector Proximity

abstract: This paper presents an anomaly detector for keystroke dynamics authentication, based on a statistical measure of proximity, evaluated through the empirical study of an independent benchmark of keystroke data. A password typing-rhythm classifier is presented, to be used as an anomaly detector in the authentication process of genuine users and impostors. The proposed user authentication method involves two phases. First a training phase in which a user typing profile is created through repeated entry of password. In the testing phase, the password typing rhythm of the user is compared with the stored typing profile, to determine whether it is a genuine user or an impostor. The typing rhythm is obtained through keystroke timings of key-down / key-up of individual keys and the latency between keys. The training data is stored as a typing profile, consisting of a vector of median values of elements of the feature set, and as a vector of standard deviations for the same elements. The proposed classifier algorithm computes a score for the typing of a password to determine authenticity. A measure of proximity is used in the comparison between feature set medians vector and feature set testing vector. Each feature in the testing vector is given a binary score of 1 if it is within a proximity distance threshold from the stored median of that feature, otherwise the score is 0. The proximity distance threshold for a feature is chosen to be the standard deviation of that feature in the training data. The typing of a password is classified as genuine if the accumulated score for all features meet a minimum acceptance threshold. Analysis of the benchmark dataset using the proposed classifier has given an improved anomaly detection performance in comparison with results of 14 algorithms that were previously tested using the same benchmark.


title: Entrepreneurial orientation and small business performance: a configurational approach

abstract: The strategy and entrepreneurship literatures suggest that an entrepreneurial orientation (EO) improves firm performance, but the empirical results are mixed. In this article, we investigate the EO of small businesses and find that a main-effects-only analysis provides an incomplete picture of performance. Access to capital and the dynamism of the environment are important to small businesses, and we find that when combined with EO (a three-way interaction model) the configurational approach explains variance in performance over and above a contingency model (two-way interactions) and a main-effects-only model.


title: Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

abstract: Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.


title: Low-Cost CPW Meander Inductors Utilizing Ink-Jet Printing on Flexible Substrate for High-Frequency Applications

abstract: This paper describes the design and fabrication of low-cost coplanar waveguide (CPW) miniature meander inductors. Inductors are fabricated on a flexible plastic polyimide foil in ink-jet printed technology with silver nanoparticle ink in a single layer. For the first time, the detailed characterization and simulation of CPW inductors in this technology is reported. The inductors are developed with impressive measured self-resonance frequency up to 18.6 GHz. The 2.107-nH inductor measures only 1 mm × 1.7 mm × 0.075 mm and demonstrates a high level of miniaturization in ink-jet printing technology. The measured response characteristics are in excellent agreement with the predicted simulation response.


title: Thermal Efficiency Analysis of SkyFuel's Advanced, Large-aperture, Parabolic Trough Collector

abstract: Abstract SkyFuel has empirically established the thermal efficiency of the SkyTrough ® DSP : an advanced, low-cost parabolic trough collector recently developed by SkyFuel, Inc., and funded in part by DOE grant DE-EE0003584.000. A full-scale demonstration module has been built at SkyFuel's headquarters in Colorado for the purpose of comprehensive performance and efficiency testing. This testing is complete, andincludes the analysis of the collector's optical laser intercept, heat loss of the molten salt-compatible receivers, optical efficiency while tracking the sun, operation with molten salt, and molten salt freeze recovery. Complete results are presented herein for the SkyTrough ® DSP parabolic trough.


A Survey on Clustering High Dimensional Data Techniques


title: IEEE 802.22: An Introduction to the First Wireless Standard based on Cognitive Radios

abstract: In November/2004, we witnessed the formation of the first worldwide effort to define a novel wireless air interface (i.e., MAC and PHY) standard based on Cognitive Radios (CRs): the IEEE 802.22 Working Group (WG). The IEEE 802.22 WG is chartered with the development of a CR-based Wireless Regional Area Network (WRAN) Physical (PHY) and Medium Access Control (MAC) layers for use by license-exempt devices in the spectrum that is currently allocated to the Television (TV) service. Since 802.22 is required to reuse the fallow TV spectrum without causing any harmful interference to incumbents (i.e., the TV receivers), cognitive radio techniques are of primary importance in order to sense and measure the spectrum and detect the presence/absence of incumbent signals. On top of that, other advanced techniques that facilitate coexistence such as dynamic spectrum management and radio environment characterization could be designed. In this paper, we provide a detailed overview of the 802.22 draft specification, its architecture, requirements, applications, and coexistence considerations. These not only form the basis for the definition of this groundbreaking wireless air interface standard, but will also serve as foundation for future research in the promising area of CRs.


title: Brahmastra: driving apps to test the security of third-party components

abstract: We present an app automation tool called Brahmastra for helping app stores and security researchers to test third-party components in mobile apps at runtime. The main challenge is that call sites that invoke third-party code may be deeply embedded in the app, beyond the reach of traditional GUI testing tools. Our approach uses static analysis to construct a page transition graph and discover execution paths to invoke third-party code. We then perform binary rewriting to "jump start" the third-party code by following the execution path, efficiently pruning out undesired executions. Compared with the state-of-the-art GUI testing tools, Brahmastra is able to successfully analyse third-party code in 2.7× more apps and decrease test duration by a factor of 7. We use Brahmastra to uncover interesting results for two use cases: 175 out of 220 children's apps we tested display ads that point to web pages that attempt to collect personal information, which is a potential violation of the Children's Online Privacy Protection Act (COPPA); and 13 of the 200 apps with the Facebook SDK that we tested are vulnerable to a known access token attack.


title: Inventory Rebalancing and Vehicle Routing in Bike Sharing Systems

abstract: Bike sharing systems have been installed in many cities around the world and are increasing in popularity. A major operational cost driver in these systems is rebalancing the bikes over time such that the appropriate number of bikes and open docks are available to users. We combine two aspects that have previously been handled separately in the literature: determining service level requirements at each bike sharing station, and designing (near-)optimal vehicle routes to rebalance the inventory. Since finding provably optimal solutions is practically intractable, we propose a new cluster-first route-second heuristic, in which a polynomial-size Clustering Problem simultaneously considers the service level feasibility and approximate routing costs. Extensive computational results on real-world data from Hubway (Boston, MA) and Capital Bikeshare (Washington, DC) are provided, which show that our heuristic outperforms a pure mixed-integer programming formulation and a constraint programming approach.