title: Copper wire bond pad/IMC interfacial layer crack study during HTSL (high temperature storage life) test

abstract: Copper wire has been popular in these years when facing ever-increasing gold prices. The success of large scale conversion of gold to copper wire in microelectronics could achieve successfully when all the failure mechanisms that can be discerned during reliability testing. One of these mechanisms is corrosion of the contact between the copper (Cu) ball and the aluminum (Al) bond pad, consisting of various intermetallic compounds (IMCs), which are more sensitive to corrosion compared to gold (Au)-Al IMC. The occurrence of the related open circuit failures are often reported as a consequence of humidity related stress test, HAST or PCT. Most of paper has been published focusing on the mechanism of corrosion during humidity related stress test. Recently, wire bond pad/IMC interfacial layer crack issue was also found during high temperature storage test. The occurrence of the open circuit failures are also reported related to high temperature storage test. In this paper, a CMOS 90nm low k device with 66um fine pitch and 1.2um bond pad metal thickness was test vehicle to study the mechanism of IMC layer crack during high temperature storage test. First, failure analysis and control experiment were conducted on failed unit to dig out root cause. Failure analysis showed element sulphur was found on the periphery of bonded ball and under the bonded ball. The Sulphur was suspected to be root cause to the IMC layer crack. Next, an experiment was conducted to investigate the root cause. The factors cover different IMC coverage, different type of epoxy molding compound with different level of sulfur content. Wire pull, ball shear and IMC data were collected. Strip level thermal aging test at 225 degree C was also performed with recording pull strength and its failure mode. Molded units were subject to high temperature storage at 175 degree C. The cells with higher IMC coverage and molded by low sulfur content compound could pass electronic test and wire pull test post decamping without ball lifted issue. Last, confirmation run was performed with low Sulphur content reformulated molding compound. The units were subject to temperature cycle, HAST and HTSL. The test result showed the lot with IMC coverage over control limit could pass HTSL and T/C. No delamination was found post temperature cycle by SCAM check. Regarding copper wire bond pad/IMC interfacial layer crack, IMC coverage and sulfur content of epoxy molding compound should be considered seriously.


title: DCSLAM: A dynamically constrained real-time slam

abstract: We propose a new real-time CSLAM algorithm (Constrained Simultaneous Localization And Mapping), named DCSLAM (dynamically constrained SLAM), designed to dynamically adapt each optimization to the variable number of parameters families and heterogeneous constraints. An automatic method is used to generate, from an exhaustive list of constraints, a dedicated optimization algorithm. This is, to our knowledge, the only implementation that combines flexibility and performance. The proposed experiments show the effectiveness of our approach in terms of accuracy and execution time compared to the state of the art on several public benchmarks of varying complexity. An augmented reality application mixing heterogeneous objects and constraints is also available.


Chapter 0 Immersion in Digital Games : a Review of Gaming Experience Research


title: Investigation on the use of Hidden-Markov Models in automatic transcription of music

abstract: Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data, and have been widely used in two main tasks of Automatic Music Transcription (AMT): note segmentation, i.e. identifying the played notes after a multi-pitch estimation, and sequential post-processing, i.e. correcting note segmentation using training data. In this paper, we employ the multi-pitch estimation method called Probabilistic Latent Component Analysis (PLCA), and develop AMT systems by integrating different HMM-based modules in this framework. For note segmentation, we use two different twostate on/o? HMMs, including a higher-order one for duration modeling. For sequential post-processing, we focused on a musicological modeling of polyphonic harmonic transitions, using a first- and second-order HMMs whose states are defined through candidate note mixtures. These different PLCA plus HMM systems have been evaluated comparatively on two different instrument repertoires, namely the piano (using the MAPS database) and the marovany zither. Our results show that the use of HMMs could bring noticeable improvements to transcription results, depending on the instrument repertoire.


title: Bridging the Semantic Gap Between Image Contents and Tags

abstract: With the exponential growth of Web 2.0 applications, tags have been used extensively to describe the image contents on the Web. Due to the noisy and sparse nature in the human generated tags, how to understand and utilize these tags for image retrieval tasks has become an emerging research direction. As the low-level visual features can provide fruitful information, they are employed to improve the image retrieval results. However, it is challenging to bridge the semantic gap between image contents and tags. To attack this critical problem, we propose a unified framework in this paper which stems from a two-level data fusions between the image contents and tags: 1) A unified graph is built to fuse the visual feature-based image similarity graph with the image-tag bipartite graph; 2) A novel random walk model is then proposed, which utilizes a fusion parameter to balance the influences between the image contents and tags. Furthermore, the presented framework not only can naturally incorporate the pseudo relevance feedback process, but also it can be directly applied to applications such as content-based image retrieval, text-based image retrieval, and image annotation. Experimental analysis on a large Flickr dataset shows the effectiveness and efficiency of our proposed framework.


title: Social media competitive analysis and text mining: A case study in the pizza industry

abstract: Abstract Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites. To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, this paper describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Domino's Pizza and Papa John's Pizza. The results reveal the value of social media competitive analysis and the power of text mining as an effective technique to extract business value from the vast amount of available social media data. Recommendations are also provided to help companies develop their social media competitive analysis strategy.


title: Approximate Probabilistic Neural Networks with Gated Threshold Logic

abstract: Probabilistic Neural Network (PNN) is a feedforward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an approximated PNN (APNN) algorithm in which the conventional exponential function of the PNN is replaced with gated threshold logic. The weights of the PNN are approximated using a memristive crossbar architecture. In particular, the proposed algorithm performs normalization of the training weights, and quantization into 16 levels which significantly reduces the complexity of the circuit.


title: Adaptive polynomial rendering

abstract: In this paper, we propose a new adaptive rendering method to improve the performance of Monte Carlo ray tracing, by reducing noise contained in rendered images while preserving high-frequency edges. Our method locally approximates an image with polynomial functions and the optimal order of each polynomial function is estimated so that our reconstruction error can be minimized. To robustly estimate the optimal order, we propose a multi-stage error estimation process that iteratively estimates our reconstruction error. In addition, we present an energy-preserving outlier removal technique to remove spike noise without causing noticeable energy loss in our reconstruction result. Also, we adaptively allocate additional ray samples to high error regions guided by our error estimation. We demonstrate that our approach outperforms state-of-the-art methods by controlling the tradeoff between reconstruction bias and variance through locally defining our polynomial order, even without need for filtering bandwidth optimization, the common approach of other recent methods.


title: Smart answering Chatbot based on OCR and Overgenerating Transformations and Ranking

abstract: with rapid development of information and communication technology, people are very diverse in education, learning style, and knowledge improvement methods. This paper presents an approach of converting documents into knowledge of Chatbot system that enables users to make more benefits of it by asking and answering questions through the use of electronic documents integrated with simulate system. It is an integrated system for enrich contents of documents from popular format such as Portable Document Format (PDF) and digital photos. The workflow of this system is started from extracts texts using Optical Character Recognition (OCR) from files, then generates questions via Overgenerating Transformations and Ranking algorithm, and finally let Chatbot response to the user's question when it is matched with the String pattern.


title: Impact of nonuniform gate oxide shape on TFET performance: A reliability issue

abstract: Abstract Gate dielectric scaling is a vital key to improve the steep switching characteristics of TFETs. However, scaling down the oxide thickness causes high gate leakage current that cannot be neglected. The impact of the leakage current on the device operation can cause a serious reliability problem. In this paper, we have investigated the effect of nonuniform gate oxide thickness on the current as well as the high-frequency performance of TFETs. The focus of this paper is to demonstrate how to reduce effects of gate leakage current by using a taper shape gate oxide. The IV and CV characteristics are investigated regarding different nonuniform gate oxide graded shapes. Further, the ON and OFF currents and SS, as figures of merit for low standby power application, have been analyzed. Apart from this, the RF figure of merit in terms of unit-gain cutoff frequency (fT) is investigated.


title: Shift: a technique for operating pen-based interfaces using touch

abstract: Retrieving the stylus of a pen-based device takes time and requires a second hand. Especially for short intermittent interactions many users therefore choose to use their bare fingers. Although convenient, this increases targeting times and error rates. We argue that the main reasons are the occlusion of the target by the user's finger and ambiguity about which part of the finger defines the selection point. We propose a pointing technique we call Shift that is designed to address these issues. When the user touches the screen, Shift creates a callout showing a copy of the occluded screen area and places it in a non-occluded location. The callout also shows a pointer representing the selection point of the finger. Using this visual feedback, users guide the pointer into the target by moving their finger on the screen surface and commit the target acquisition by lifting the finger. Unlike existing techniques, Shift is only invoked when necessary--over large targets no callout is created and users enjoy the full performance of an unaltered touch screen. We report the results of a user study showing that with Shift participants can select small targets with much lower error rates than an unaided touch screen and that Shift is faster than Offset Cursor for larger targets.


title: A Neural Network for Factoid Question Answering over Paragraphs

abstract: Text classification methods for tasks like factoid question answering typically use manually defined string matching rules or bag of words representations. These methods are ineective when question text contains very few individual words (e.g., named entities) that are indicative of the answer. We introduce a recursive neural network (rnn) model that can reason over such input by modeling textual compositionality. We apply our model, qanta, to a dataset of questions from a trivia competition called quiz bowl. Unlike previous rnn models, qanta learns word and phrase-level representations that combine across sentences to reason about entities. The model outperforms multiple baselines and, when combined with information retrieval methods, rivals the best human players.


A study of birth order, academic performance, and personality


title: (Date) CAD Tools for Creating Space-filling 3D Escher Tiles

abstract: We discuss the design and implementation of CAD tools for creating decorative solids that tile 3-space in a regular, isohedral manner. Starting with the simplest case of extruded 2D tilings, we describe geometric algorithms used for maintaining boundary representations of 3D tiles, including a Java implementation of an interactive constrained Delaunay triangulation library and a mesh-cutting algorithm used in layering extruded tiles to create more intricate designs. Finally, we demonstrate a CAD tool for creating 3D tilings that are derived from cubic lattices. The design process for these 3D tiles is more constrained, and hence more difficult, than in the 2D case, and it raises additional user interface issues.


title: BIM: an open ontology for the annotation of biomedical images

abstract: Biomedical images published within the scientific literature play a central role in reporting and facilitating life science discoveries. Existing ontologies and vocabularies describing biomedical imag-‐ es, particularly sequence images, do not provide sufficient seman-‐ tic representation for image annotations generated automatically and/or semi-‐automatically. We present an open ontology for the annotation of biomedical images (BIM) scripted in OWL/RDF. The BIM ontology provides semantic vocabularies to describe the manually curated image annotations as well as annotations gener-‐ ated by online bioinformatics services using content extracted from images by the Semantic Enrichment of Biomedical Images (SEBI) system. The BIM ontology is represented in three parts; (i) image vocabularies -‐ which holds vocabularies for the annotation of an image and/or region of interests (ROI) inside an image, as well as vocabularies to represent the pre and post processing states of an image, (ii) text entities -‐ covers annotations from the text that are associated with an image (e.g. image captions) and provides semantic representation for NLP algorithm outputs, (iii) a provenance model -‐ that contributes towards the maintenance of annotation versioning. To illustrate the BIM ontology’s utility, we provide three annotation cases generated by BIM in conjunction with the SEBI image annotation engine.


title: 16 IFAR – The International Forum for Aviation Research

abstract: The future challenges of air transport motivated the leading worldwide aviation research institutions to found IFAR - the International Forum for Aviation Research which aims at the development of a Road Map outlining worldwide research. Climate change is currently the most relevant topic and was the motivation to set up IFAR. However, IFAR also addresses other topics relevant for a global air transport system (e.g. noise, security, safety, efficient operations). IFAR connects and repre-sents worldwide aviation research and provides a common voice for their members in the interna-tional dialogue. IFAR interacts with the public, politics and industry, takes up the challenges identi-fied by them and acts as independent advisor to policy makers worldwide. The idea of IFAR was born at the Berlin Summit 2008 where key leaders of 12 international aeronautical research organi-sations met to address the question of the Air Transport of the Future in the context of climate change. In this regard, the participants agreed that any research and strategy contributing to new solutions will have to reconcile the increasing need for international mobility in a globalized work-sharing economy with the challenge of simultaneously developing new solutions to balance the climate effects of the accompanying world-wide air traffic growth. At the second Berlin Summit in 2010 16 international aeronautical research organisations met and eventually set up IFAR. The main objective of IFAR is the development of a Road Map agreed upon by research institutions worldwide. Within this Road Map promising technologies are identified which contribute to an im-proved Air Transport System. IFAR as research representative focuses on the identification of new technologies up to the development of TRL level 6. The IFAR members agreed at the Berlin Sum-mit 2010 to focus in 2010/2011 on the topics related to climate change and to present potential solutions for an ecologically and economically efficient air transport system. Within the next years this Road Map is going to be extended by taking the other topics noise, safety, security and effi-cient operations into account.This paper deals with the objectives, state-of-the art and future planning of IFAR.


title: aHDFS: An Erasure-Coded Data Archival System for Hadoop Clusters

abstract: In this paper, we propose an erasure-coded data archival system called aHDFS for Hadoop clusters, where $RS(k+r,k)$ codes are employed to archive data replicas in the Hadoop distributed file system or HDFS. We develop two archival strategies (i.e., aHDFS-Grouping and aHDFS-Pipeline ) in aHDFS to speed up the data archival process. aHDFS-Grouping - a MapReduce-based data archiving scheme - keeps each mapper’s intermediate output Key-Value pairs in a local key-value store. With the local store in place, aHDFS-Grouping merges all the intermediate key-value pairs with the same key into one single key-value pair, followed by shuffling the single Key-Value pair to reducers to generate final parity blocks. aHDFS-Pipeline forms a data archival pipeline using multiple data node in a Hadoop cluster. aHDFS-Pipeline delivers the merged single key-value pair to a subsequent node’s local key-value store. Last node in the pipeline is responsible for outputting parity blocks. We implement aHDFS in a real-world Hadoop cluster. The experimental results show that aHDFS-Grouping and aHDFS-Pipeline speed up Baseline ’s shuffle and reduce phases by a factor of 10 and 5, respectively. When block size is larger than 32 MB, aHDFS improves the performance of HDFS-RAID and HDFS-EC by approximately 31.8 and 15.7 percent, respectively.


title: Experimental Protocol for Biodiesel Production with Isolation of Alkenones as Coproducts from Commercial Isochrysis Algal Biomass

abstract: The need to replace petroleum fuels with alternatives from renewable and more environmentally sustainable sources is of growing importance. Biomass-derived biofuels have gained considerable attention in this regard, however first generation biofuels from edible crops like corn ethanol or soybean biodiesel have generally fallen out of favor. There is thus great interest in the development of methods for the production of liquid fuels from domestic and superior non-edible sources. Here we describe a detailed procedure for the production of a purified biodiesel from the marine microalgae Isochrysis. Additionally, a unique suite of lipids known as polyunsaturated long-chain alkenones are isolated in parallel as potentially valuable coproducts to offset the cost of biodiesel production. Multi-kilogram quantities of Isochrysis are purchased from two commercial sources, one as a wet paste (80% water) that is first dried prior to processing, and the other a dry milled powder (95% dry). Lipids are extracted with hexanes in a Soxhlet apparatus to produce an algal oil ("hexane algal oil") containing both traditional fats (i.e., triglycerides, 46-60% w/w) and alkenones (16-25% w/w). Saponification of the triglycerides in the algal oil allows for separation of the resulting free fatty acids (FFAs) from alkenone-containing neutral lipids. FFAs are then converted to biodiesel (i.e., fatty acid methyl esters, FAMEs) by acid-catalyzed esterification while alkenones are isolated and purified from the neutral lipids by crystallization. We demonstrate that biodiesel from both commercial Isochrysis biomasses have similar but not identical FAME profiles, characterized by elevated polyunsaturated fatty acid contents (approximately 40% w/w). Yields of biodiesel were consistently higher when starting from the Isochrysis wet paste (12% w/w vs. 7% w/w), which can be traced to lower amounts of hexane algal oil obtained from the powdered Isochrysis product.


title: IoT security: A layered approach for attacks & defenses

abstract: Internet of Things (IoT) has been a massive advancement in the Information and Communication Technology (ICT). It is projected that over 50 billion devices will become part of the IoT in the next few years. Security of the IoT network should be the foremost priority. In this paper, we evaluate the security challenges in the four layers of the IoT architecture and their solutions proposed from 2010 to 2016. Furthermore, important security technologies like encryption are also analyzed in the IoT context. Finally, we discuss countermeasures of the security attacks on different layers of IoT and highlight the future research directions within the IoT architecture.


title: Drift Compensation for Electronic Nose by Semi-Supervised Domain Adaption

abstract: Drift compensation is an important issue for electronic nose systems. Traditional methods are costly and laborious because they need to frequently recalibrate referred gases or continually provide data labeling. In this paper, a new drift compensation method is proposed. The inspiration of our method is originated from semi-supervised domain adaption that can effectively tackle the mismatches between source domain and target domain. In our approach, a weighted geodesic flow kernel is initially constructed, then the combination of such kind of kernels is proposed considering that there are intermediate unlabeled data between the source and target domains. We will discuss how unlabeled data is selected from the target domain. The selected unlabeled data is used to provide incremental knowledge in order to dynamically adapt classifier to the target domain. Based on the kernel combination and selected unlabeled data, manifold regularization is used to train the classifier. To the best of our knowledge, we are the first to apply domain adaption to deal with the sensor drift problem. The advantages of our method include degrading recalibration rate, requiring few labeled data, and the robustness in handling the drift. Our experiments show that the proposed method significantly outperforms the baseline methods.


title: Global Optimization issues in Supervised Learning. An overview

abstract: The paper presents an overview of global issues in optimization methods for Supervised Learning (SL). We focus on Feedforward Neural Networks with the aim of reviewing global methods specifically devised for the class of continuous unconstrained optimization problems arising both in Multi Layer Perceptron/Deep Networks and in Radial Basis Networks. We first recall the learning optimization paradigm for FNN and we briefly discuss global scheme for the joined choice of the network topologies and of the network parameters. The main part of the paper focus on the core subproblem which is the unconstrained regularized weight optimization problem. We review some recent results on the existence of local-non global solutions of the unconstrained nonlinear problem and the role of determining a global solution in a Machine Learning paradigm. Local algorithms that are widespread used to solve the continuous unconstrained problems are addressed with focus on possible improvements to exploit the global properties. Hybrid global methods specifically devised for SL optimization problems which embed local algorithms are discussed at the end.


title: Integrated collaborative care for comorbid major depression in patients with cancer (SMaRT Oncology-2): a multicentre randomised controlled effectiveness trial

abstract: Summary Background Medical conditions are often complicated by major depression, with consequent additional impairment of quality of life. We aimed to compare the effectiveness of an integrated treatment programme for major depression in patients with cancer (depression care for people with cancer) with usual care. Methods SMaRT Oncology-2 is a parallel-group, multicentre, randomised controlled effectiveness trial. We enrolled outpatients with major depression from three cancer centres and their associated clinics in Scotland, UK. Participants were randomly assigned in a 1:1 ratio to the depression care for people with cancer intervention or usual care, with stratification (by trial centre) and minimisation (by age, primary cancer, and sex) with allocation concealment. Depression care for people with cancer is a manualised, multicomponent collaborative care treatment that is delivered systematically by a team of cancer nurses and psychiatrists in collaboration with primary care physicians. Usual care is provided by primary care physicians. Outcome data were collected up until 48 weeks. The primary outcome was treatment response (≥50% reduction in Symptom Checklist Depression Scale [SCL-20] score, range 0–4) at 24 weeks. Trial statisticians and data collection staff were masked to treatment allocation, but participants could not be masked to the allocations. Analyses were by intention to treat. This trial is registered with Current Controlled Trials, number ISRCTN40568538. Findings 500 participants were enrolled between May 12, 2008, and May 13, 2011; 253 were randomly allocated to depression care for people with cancer and 247 to usual care. 143 (62%) of 231 participants in the depression care for people with cancer group and 40 (17%) of 231 in the usual care group responded to treatment: absolute difference 45% (95% CI 37–53), adjusted odds ratio 8·5 (95% CI 5·5–13·4), p Interpretation Our findings suggest that depression care for people with cancer is an effective treatment for major depression in patients with cancer. It offers a model for the treatment of depression comorbid with other medical conditions. Funding Cancer Research UK and Chief Scientist Office of the Scottish Government.


title: Theory and algorithm for generalized memory partitioning in high-level synthesis

abstract: The significant development of high-level synthesis tools has greatly facilitated FPGAs as general computing platforms. During the parallelism optimization for the data path, memory becomes a crucial bottleneck that impedes performance enhancement. Simultaneous data access is highly restricted by the data mapping strategy and memory port constraint. Memory partitioning can efficiently map data elements in the same logical array onto multiple physical banks so that the accesses to the array are parallelized. Previous methods for memory partitioning mainly focused on cyclic partitioning for single-port memory. In this work we propose a generalized memory-partitioning framework to provide high data throughput of on-chip memories. We generalize cyclic partitioning into block-cyclic partitioning for a larger design space exploration. We build the conflict detection algorithm on polytope emptiness testing, and use integer points counting in polytopes for intra-bank offset generation. Memory partitioning for multi-port memory is supported in this framework. Experimental results demonstrate that compared to the state-of-art partitioning algorithm, our proposed algorithm can reduce the number of block RAM by 19.58%, slice by 20.26% and DSP by 50%.


title: UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems

abstract: We describe three semantic text similarity systems developed for the *SEM 2013 STS shared task and the results of the corresponding three runs. All of them shared a word similarity feature that combined LSA word similarity and WordNet knowledge. The first, which achieved the best mean score of the 89 submitted runs, used a simple term alignment algorithm augmented with penalty terms. The other two runs, ranked second and fourth, used support vector regression models to combine larger sets of features.


title: Thin is Green : Leveraging the Thin-Client Paradigm for Sustainable Mobile Computing

abstract: Display Omitted An analytical model for lifecycle energy consumption of mobile devices is proposed and evaluated.A derivation of the theoretical conditions for energy savings under thin client paradigm.A comparative empirical evaluation of performance and energy consumption of old and new Android-based smartphone operating under the thin client paradigm was performed. Progress in the area of environmental sustainability for the mobile computing industry could be achieved by making advancement on two fronts: reducing the energy consumed by individual devices throughout their life cycle and reducing the rate at which these devices are discarded. In this work, to address both fronts, we propose the use of a thin-client approach, whereby a mobile device relies mainly on the resources at a remote server to carry out application tasks. To assess the benefits of the proposed approach, this work develops an analytical model as well as performs an empirical evaluation of performance and energy consumption on Android-based smartphones. In terms of energy, a reduction of approximately 11% in the average life cycle energy (LCE) is seen by increasing the device's usage life by even three months through a thin-client approach. In terms of performance, a thin-client device is shown to improve execution by 57% compared to a self-reliant device.


title: FPGA-Based Real-Time Simulation of Finite-Element Analysis Permanent Magnet Synchronous Machine Drives

abstract: This paper presents a real-time simulator of a permanent magnet synchronous motor (PMSM) drive based on a finite-element analysis (FEA) method and implemented on an FPGA card for HIL testing of motor drive controllers. The proposed PMSM model is a phase domain model with inductances and flux profiles computed from the JMAG-RT finite element analysis software. A 3-phase IGBT inverter drives the PMSM machine. Both models are implemented on an FPGA chip, with no VHDL coding, using the RT-LAB real-time simulation platform from Opal-RT and a Simulink blockset called xilinx system generator (XSG). The PMSM drive, along with an open-loop test source for the pulse width modulation, is coded for an FPGA card. The PMSM drive is completed with various encoder models (quadrature, Hall effects and resolver). The overall model compilation and simulation is entirely automated by RT-LAB. The drive is designed to run in a closed loop with a HIL-interfaced controller connected to the I/O of the real-time simulator. The PMSM drive model runs with an equivalent 10 nanosecond time step (100 MHz FPGA card) and has a latency of 300 ns (PMSM machine and inverter) with the exception of the FEA-computed inductance matrix routines which are updated in parallel on a CPU of the real-time simulator at a 40 us rate. The motor drive is directly connected to digital inputs and analog outputs with 1 microsecond settling time on the FPGA card and has a resulting total hardware-in-the-loop latency of 1.3 microseconds.


title: A cognitive hierarchy model of games

abstract: Players in a game are "in equilibrium" if they are rational, and accurately predict other players' strategies. In many experiments, however, players are not in equilibrium. An alternative is "cognitive hierarchy" (CH) theory, where each player assumes that his strategy is the most sophisticated. The CH model has inductively defined strategic categories: step 0 players randomize; and step k thinkers best-respond, assuming that other players are distributed over step 0 through step k - 1. This model fits empirical data, and explains why equilibrium theory predicts behavior well in some games and poorly in others. An average of 1.5 steps fits data from many games.


title: ( U ) SimMonitor : A Mobile Application for Security Evaluation of Cellular Networks

abstract: The lack of precise directives in 3GPP specifications allows mobile operators to configure and deploy security mechanisms at their sole discretion. This may lead to the adoption of bad security practices and insecure configurations. Based on this observation, this paper presents the design and implementation of a novel mobile application named (U)SimMonitor that captures and analyzes the security policy that a cellular operator enforces, i.e., the invocation and employment of the specified security measures to protect its users. (U)SimMonitor achieves this by executing AT commands to extract network related parameters including encryption keys, identities, and location of users. Using (U)SimMonitor as our basic analysis tool, we have conducted a set of experiments for three mobile operators in Greece in a time period of 9 months. The obtained results allow us to quantify, compare and evaluate their applied security as well as pinpoint a set of generic critical observations. Numerical results and security measurements show that mobile networks have poor security configurations and practices, exposing subscribers to several attacks.


title: A Post-Wall Waveguide Center-Feed Parallel Plate Slot Array Antenna in the Millimeter-Wave Band

abstract: A center-feed structure using cross-junctions is designed for a post-wall waveguide-fed parallel plate slot array antenna. The length of the radiating waveguide in the center-feed antenna is half of that of a conventional end-feed antenna, and the center-feed antenna is expected to enhance bandwidth depending on the long line effect. The symmetrical structure of the antenna can keep the main beam at boresight as the frequency is changed. The cross-junction is analogous to that previously proposed for a single-mode waveguide array, but it excites plane TEM waves in both directions in the oversized parallel plate. The authors have designed and fabricated a slot array with uniform aperture distribution at 61.25 GHz. The bandwidth for 1-dB gain reduction is 1.7 times as wide as that of a conventional end-feed type antenna. The sidelobe level in the E-plane increases to -7.8 dB because of the slot-free area over the center-feed waveguide, which is suppressed to -11.1 dB by introducing a -8.3 dB amplitude tapered distribution in the slot design of the array.


title: Uncertainty, Expectations, and Foundations of the Theory of Planning

abstract: Presents information on a study which outlined some fundamental theoretical concepts which form an important part of the basis of any general theory of planning. Distinction between risk and uncertainty in modern decision theory; Foundations of planning theory; Conclusions.


title: A Systematic Review of the Evidence for Hyporesponsivity in ASD

abstract: Sensory modulation difficulties are common in children with Autism spectrum disorder (ASD). Whilst both hyporesponsivity and hyperresponsivity have been established in ASD comparative to typically developing controls, it has been proposed that hyporesponsivity may distinguish the sensory profile of ASD from other neurodevelopmental conditions. This paper aimed to systematically evaluate evidence for a syndrome-specific profile of hyporesponsivity in individuals with ASD when compared to individuals from clinical comparison groups, evaluating 10 eligible papers. Support was cautiously identified for a syndrome-specific sensory profile of hyporesponsivity. Four factors that reduced variability in findings were as follows: chronological age, type of comparison group, sensory measure and quality of study. Whilst hyporesponsivity in ASD was identified, the use of poorly defined comparison groups, over-representation of children with ASD and intellectual disabilities, and younger age ranges complicate generalisation of this body of work. Recommendations for further research in this field are offered.


title: The effect of cosmetic surgery reality tv shows on adolescent girls’ body image.

abstract: Cosmetic surgery media coverage has become common in recent years, with surgery featuring in advertising and reality TV. Concerns have been expressed by the American and British Associations for Plastic Surgery about the nature of this coverage, particularly with respect to the impact on adolescents (ASPS, 2004; BAAPS, 2004). This study was the first to investigate adolescent girls’ responses to a cosmetic surgery TV show using an experimental design. Girls (N = 99) aged 15 to 18 (M = 16.6) years were randomly allocated to one of three conditions: a cosmetic surgery TV show, which (1) mentioned risks associated with surgery, (2) did not mention risks, or (3) to the control condition, a home makeover show. Results showed that exposure to cosmetic surgery shows resulted in girls reporting more dissatisfaction with their weight and appearance, but no changes were observed in attitudes toward cosmetic surgery. Girls’ responses to cosmetic surgery shows varied according to their materialistic values and the extent that they derived self-worth from their appearance. Results suggest that cosmetic surgery reality TV can be damaging to adolescent girls’ body image and that there is a need for research to consider factors that may affect how girls respond to such shows.


title: Machine learning for astronomical big data processing

abstract: Recently, more and more high resolution, high precision telescopes have been developing in the world, such as SKA[1], Arecibo[2], ALMA[2], Muser[4][5]. By aid of these modern telescopes, human acquired more and in-depth knowledge about the universe; meanwhile, a “big data” challenge was raised for astronomical big data processing. For example, the MingantU SpEctral Radioheliograph (Muser) records about 100TB raw data per month for solar radio observation. The big data firstly causes a big challenge for archiving and classifying recorded data, especially as we need the fast processing of daily recorded data. The traditional data processing was usually implemented manually before the emergence of big data, so it is no longer applicable to current big data. It is urgent to develop automatic algorithms for processing the daily recorded data efficiently and timely. Secondly, the big data also cause great difficulty in the storage and transmission of data, so data compression is highly desirable. This paper reports our efforts on big data archiving, classification and activity forecast by using machine learning, especially deep learning.


title: Face Feature Extraction: A Complete Review

abstract: Feature extraction is vital for face recognition. In this paper, we focus on the general feature extraction framework for robust face recognition. We collect about 300 papers regarding face feature extraction. While some works apply handcrafted features, other works employ statistical learning methods. We believe that a general framework for face feature extraction consists of four major components: filtering, encoding, spatial pooling, and holistic representation. We analyze each component in detail. Each component could be applied in a task with multiple levels. Then, we provide a brief review of deep learning networks, which can be seen as a hierarchical extension of the framework above. Finally, we provide a detailed performance comparison of various features on LFW and FERET face database.


A New approach to Requirements Elicitation Using Paper Prototype


title: Sacrifice, Atonement, and Legal Ethics

abstract: Lawyers surely understand sacrifice. The business of representation requires a willingness to subjugate, at least temporarily, one’s own priorities, beliefs, and comforts to those of another. Today, that willingness is tested and demanded with unprecedented force. Corporate litigators toil around the clock to perfect their clients’ cases. Public defenders stagger under the heaviest caseloads their mandate and conviction will permit. In nearly every legal market, heightened competition has tipped the balance of power toward clients and caused practitioners to surrender more of their autonomy and time. Examined more closely, however, the increasingly routine actions of attorneys are only sacrifices in a certain sense of the word—they are the exchange of one thing for something else. Corporate lawyers are richly compensated. The public defender’s reward is in a different but no less valuable currency. In either case, the aggregate benefits that accrue to the lawyer roughly compensate her for her costs. This is a truism of the rational-actor school of economics, and there is no reason to suppose it does not generally prevail. There is another brand of sacrifice, less common but equally familiar, perfected by American litigators of the last century: the representation of a worthy but unpopular cause or group. John Quincy Adams was an early


title: Adaptive content generation for games

abstract: From a simple entertainment activity to a learning tool, it is undeniable that video games are one of the most active and relevant areas in today's society. Since their inception, video games experienced an evolution unmatched by almost any other area and today's video games are complex pieces of software built by expensive multidisciplinary teams. Procedural Content Generation (PCG) offers an alternative to manual design of video game content, optimizing the process of development and thus reducing its cost. However, the content that is generated by traditional PCG techniques is usually very generic and it struggles to offer meaningful game experiences to a diverse player base. Recent years have brought some new PCG techniques that try to solve this problem by dynamically adjusting the generation of content to suit the needs of each individual player. The work presented in this paper focuses on the development of a new PCG methodology that aims to close the gap between game developers and their players. This is achieved by providing the developers with relevant real time player and playing context information and thus creating an easier way for developers to adjust their content generation process in run time to better suit the needs of each player. This was achieved by first designing a methodology that models the player, their context and the game. It was also developed a simple game to showcase the potential of such methodology. The end result was a game that adapts some of its content to different types of players and contexts.


title: Using Structured Events to Predict Stock Price Movement: An Empirical Investigation

abstract: It has been shown that news events influence the trends of stock price movements. However, previous work on news-driven stock market prediction rely on shallow features (such as bags-of-words, named entities and noun phrases), which do not capture structured entity-relation information, and hence cannot represent complete and exact events. Recent advances in Open Information Extraction (Open IE) techniques enable the extraction of structured events from web-scale data. We propose to adapt Open IE technology for event-based stock price movement prediction, extracting structured events from large-scale public news without manual efforts. Both linear and nonlinear models are employed to empirically investigate the hidden and complex relationships between events and the stock market. Largescale experiments show that the accuracy of S&P 500 index prediction is 60%, and that of individual stock prediction can be over 70%. Our event-based system outperforms bags-of-words-based baselines, and previously reported systems trained on S&P 500 stock historical data.


title: Using SKOS vocabularies for improving web search

abstract: Knowledge organization systems such as thesauri or taxonomies are increasingly being expressed using the Simple Knowledge Organization System (SKOS) and published as structured data on the Web. Search engines can exploit these vocabularies and improve search by expanding terms at query or document indexing time. We propose a SKOS-based term expansion and scoring technique that leverages labels and semantic relationships of SKOS concept definitions. We also implemented this technique for Apache Lucene and Solr. Experiments with the Medical Subject Headings vocabulary and an early evaluation with Library of Congress Subject Headings indicated gains in precision when using SKOS-based expansion compared to pseudo relevance feedback and no expansion. Our findings are important for publishers and consumer of Web vocabularies who want to use them for improving search over Web documents.


title: Who did What: A Large-Scale Person-Centered Cloze Dataset

abstract: We have constructed a new "Who-did-What" dataset of over 200,000 fill-in-the-gap (cloze) multiple choice reading comprehension problems constructed from the LDC English Gigaword newswire corpus. The WDW dataset has a variety of novel features. First, in contrast with the CNN and Daily Mail datasets (Hermann et al., 2015) we avoid using article summaries for question formation. Instead, each problem is formed from two independent articles --- an article given as the passage to be read and a separate article on the same events used to form the question. Second, we avoid anonymization --- each choice is a person named entity. Third, the problems have been filtered to remove a fraction that are easily solved by simple baselines, while remaining 84% solvable by humans. We report performance benchmarks of standard systems and propose the WDW dataset as a challenge task for the community.


title: Fast Training of Multilayer Perceptrons

abstract: Training a multilayer perceptron by an error backpropagation algorithm is slow and uncertain. This paper describes a new approach which is much faster and certain than error backpropagation. The proposed approach is based on combined iterative and direct solution methods. In this approach, we use an inverse transformation for linearization of nonlinear output activation functions, direct solution matrix methods for training the weights of the output layer; and gradient descent, the delta rule, and other proposed techniques for training the weights of the hidden layers. The approach has been implemented and tested on many problems. Experimental results, including training times and recognition accuracy, are given. Generally, the approach achieves accuracy as good as or better than perceptrons trained using error backpropagation, and the training process is much faster than the error backpropagation algorithm and also avoids local minima and paralysis.


title: Modeling Retinal Ganglion Cell Population Activity with Restricted Boltzmann Machines

abstract: The retina is a complex nervous system which encodes visual stimuli before higher order processing occurs in the visual cortex. In this study we evaluated whether information about the stimuli received by the retina can be retrieved from the firing rate distribution of Retinal Ganglion Cells (RGCs), exploiting High-Density 64x64 MEA technology. To this end, we modeled the RGC population activity using mean-covariance Restricted Boltzmann Machines, latent variable models capable of learning the joint distribution of a set of continuous observed random variables and a set of binary unobserved random units. The idea was to figure out if binary latent states encode the regularities associated to different visual stimuli, as modes in the joint distribution. We measured the goodness of mcRBM encoding by calculating the Mutual Information between the latent states and the stimuli shown to the retina. Results show that binary states can encode the regularities associated to different stimuli, using both gratings and natural scenes as stimuli. We also discovered that hidden variables encode interesting properties of retinal activity, interpreted as population receptive fields. We further investigated the ability of the model to learn different modes in population activity by comparing results associated to a retina in normal conditions and after pharmacologically blocking GABA receptors (GABAC at first, and then also GABAA and GABAB). As expected, Mutual Information tends to decrease if we pharmacologically block receptors. We finally stress that the computational method described in this work could potentially be applied to any kind of neural data obtained through MEA technology, though different techniques should be applied to interpret the results.


title: Bluetooth based home automation system

abstract: The past decade has seen significant advancement in the field of consumer electronics. Various ‘intelligent’ appliances such as cellular phones, air-conditioners, home security devices, home theatres, etc. are set to realize the concept of a smart home. They have given rise to a Personal Area Network in home environment, where all these appliances can be interconnected and monitored using a single controller. Busy families and individuals with physical limitation represent an attractive market for home automation and networking. A wireless home network that does not incur additional costs of wiring would be desirable. Bluetooth technology, which has emerged in late 1990s, is an ideal solution for this purpose. This paper describes an application of Bluetooth technology in home automation and networking environment. It proposes a network, which contains a remote, mobile host controller and several client modules (home appliances). The client modules communicate with the host controller through Bluetooth devices. q 2002 Elsevier Science B.V. All rights reserved.


title: Culture and Cognition

abstract: Recent work in cognitive psychology and social cognition bears heavily on concerns of sociologists of culture. Cognitive research confirms views of culture as fragmented; clarifies the roles of institutions and agency; and illuminates supraindividual aspects of culture. Individuals experience culture as disparate bits of information and as schematic structures that organize that information. Culture carried by institutions, networks, and social movements diffuses, activates, and selects among available schemata. Implications for the study of identity, collective memory, social classification, and logics of action are developed.


title: An improved Prophet routing protocol for underwater communication

abstract: In opportunistic networks, rather than accepting an end-to-end communication in the conventional Internet model. Opportunistic networking stems from the research area of mobile ad-hoc networks (MANET). Opportunistic networks are wireless networks in which a completely connected path from source to destination is not likely to exist. Mobile ad hoc routing protocol allows nodes with wireless adaptors to communicate with one another without any pre-existing network infrastructure. An improved routing protocol based on Prophet routing protocol is being proposed for underwater communication. Our results show that the proposed routing protocol is able to provide better results in term of message delivery probability and overhead ratio.


title: Green Component Procurement Collaboration for Improving Supply Chain Management in the High Technology Industries: A Case Study from the Systems Perspective

abstract: The impacts of high technology industries have been growing increasingly to technological innovations and global economic developments, while the concerns in sustainability are calling for facilitating green materials and cleaner production in the industrial value chains. Today’s manufacturing companies are not striving for individual capacities but for the effective working with green supply chains. However, in addition to environmental and social objectives, cost and economic feasibility has become one of the most critical success factors for improving supply chain management with green component procurement collaboration, especially for the electronics OEM (original equipment manufacturing) companies whose procurement costs often make up a very high proportion of final product prices. This paper presents a case study from the systems perspective by using System Dynamics simulation analysis and statistical validations with empirical data. Empirical data were collected from Taiwanese manufacturing chains—among the world’s largest manufacturing clusters of high technology components and products—and their global green suppliers to examine the benefits of green component procurement collaborations in terms of shared costs and improved shipping time performance. Two different supply chain collaboration models, from multi-layer ceramic capacitor (MLCC) and universal serial bus 3.0 (USB 3.0) cable procurements, were benchmarked and statistically validated. The results suggest that the practices of collaborative planning for procurement quantity and accurate fulfillment by suppliers are significantly related to cost effectiveness and shipping time efficiency. Although the price negotiation of upstream raw materials for the collaborative suppliers has no statistically significant benefit to the shipping time efficiency, the shared cost reduction of component procurement is significantly positive for supply chain collaboration among green manufacturers. Managerial implications toward sustainable supply chain management were also discussed.


title: GO-Elite: a flexible solution for pathway and ontology over-representation

abstract: Summary: We introduce GO-Elite, a flexible and powerful pathway analysis tool for a wide array of species, identifiers (IDs), pathways, ontologies and gene sets. In addition to the Gene Ontology (GO), GO-Elite allows the user to perform over-representation analysis on any structured ontology annotations, pathway database or biological IDs (e.g. gene, protein or metabolite). GO-Elite exploits the structured nature of biological ontologies to report a minimal set of non-overlapping terms. The results can be visualized on WikiPathways or as networks. Built-in support is provided for over 60 species and 50 ID systems, covering gene, disease and phenotype ontologies, multiple pathway databases, biomarkers, and transcription factor and microRNA targets. GO-Elite is available as a web interface, GenMAPP-CS plugin and as a cross-platform application. ::: ::: Availability: http://www.genmapp.org/go_elite ::: ::: Contact: nsalomonis@gladstone.ucsf.edu ::: ::: Supplementary Information:Supplementary data are available at Bioinformatics online.


title: An improved density peaks-based clustering method for social circle discovery in social networks

abstract: With the development of Internet, social networks have become important platforms which allow users to follow streams of posts generated by their friends and acquaintances. Through mining a collection of nodes with similarities, community detection can make us understand the characteristics of complex network deeply. Therefore, community detection has attracted increasing attention in recent years. Since the problem of discovering social circles is posed as a community detecting problem, hence, in this paper, targeted at on-line social networks, we investigate how to exploit user's profile and topological structure information in social circle discovery. Firstly, according to directionality of linkages, we put forward in-link Salton metric and out-link Salton metric to measure user's topological structure. Then we propose an improved density peaks-based clustering method and deploy it to discover social circles with overlap on account of user's profile- and topological structure-based features. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of different parameters and different features in social circle discovery.


title: Moringa oleifera extract enhances sexual performance in stressed rats

abstract: Aphrodisiacs are required to improve male sexual function under stressful conditions. Due to the effects of oxidative stress and dopamine on male sexual function, we hypothesized that Moringa oleifera leaves might improve male sexual dysfunction induced by stress. Therefore, the effects on various factors playing important roles in male sexual behavior, such as antioxidant effects, the suppression of monoamine and phosphodiesterase type 5 (PDE-5) activities, serum testosterone and corticosterone levels, and histomorphological changes in the testes, of a hydroethanolic extract of M. oleifera leaves were investigated. Various doses of extract including 10, 50, and 250 mg/kg body weight (BW) were given orally to male Wistar rats before exposure to 12 h-immobilization stress for 7 d. The results demonstrated that the extract showed both antioxidant and monoamine oxidase type B (MAO-B) suppression activities. At 7 d of treatment, the low dose of extract improved sexual performance in stress-exposed rats by decreasing intromission latency and increasing intromission frequency. It also suppressed PDE-5 activity, decreased serum corticosterone level, but increased serum testosterone, numbers of interstitial cells of Leydig and spermatozoa. The increased numbers of interstitial cells of Leydig and spermatozoa might have been due to the antioxidant effect of the extract. The increased sexual performance during the intromission phase might have been due to the suppression of MAO-B and PDE-5 activities and increased testosterone. Therefore, M. oleifera is a potential aphrodisiac, but further research concerning the precise underlying mechanisms is still needed.


title: Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

abstract: Unmanned aerial vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas.


title: Attention-based Adaptive Model to Unify Warm and Cold Starts Recommendation

abstract: Nowadays, recommender systems provide essential web services on the Internet. There are mainly two categories of traditional recommendation algorithms: Content-Based (CB) and Collaborative Filtering (CF). CF methods make recommendations mainly according to the historical feedback information. They usually perform better when there is sufficient feedback information but less successful on new users and items, which is called the "cold-start'' problem. However, CB methods help in this scenario because of using content information. To take both advantages of CF and CB, how to combine them is a challenging issue. To the best of our knowledge, little previous work has been done to solve the problem in one unified recommendation model. In this work, we study how to integrate CF and CB, which utilizes both types of information in model-level but not in result-level and makes recommendations adaptively. A novel attention-based model named Attentional Content&Collaborate Model (ACCM) is proposed. Attention mechanism helps adaptively adjust for each user-item pair from which source information the recommendation is made. Especially, a "cold sampling'' learning strategy is designed to handle the cold-start problem. Experimental results on two benchmark datasets show that the ACCM performs better on both warm and cold tests compared to the state-of-the-art algorithms.


title: Study habits, skills, and attitudes: The third pillar supporting collegiate academic performance.

abstract: Study habit, skill, and attitude inventories and constructs were found to rival standardized tests and previous grades as predictors of academic performance, yielding substantial incremental validi...


title: Learning Semantically Coherent and Reusable Kernels in Convolution Neural Nets for Sentence Classification

abstract: The state-of-the-art CNN models give good performance on sentence classification tasks. The purpose of this work is to empirically study desirable properties such as semantic coherence, attention mechanism and reusability of CNNs in these tasks. Semantically coherent kernels are preferable as they are a lot more interpretable for explaining the decision of the learned CNN model. We observe that the learned kernels do not have semantic coherence. Motivated by this observation, we propose to learn kernels with semantic coherence using clustering scheme combined with Word2Vec representation and domain knowledge such as SentiWordNet. We suggest a technique to visualize attention mechanism of CNNs for decision explanation purpose. Reusable property enables kernels learned on one problem to be used in another problem. This helps in efficient learning as only a few additional domain specific filters may have to be learned. We demonstrate the efficacy of our core ideas of learning semantically coherent kernels and leveraging reusable kernels for efficient learning on several benchmark datasets. Experimental results show the usefulness of our approach by achieving performance close to the state-of-the-art methods but with semantic and reusable properties.


title: A Proactive DDoS Attack Detection Approach Using Data Mining Cluster Analysis

abstract: Detection and preventing Distributed Denial of Service Attack (DDoS) becomes a crucial process for the commercial organization that using the internet these days. Different approaches have been adopted to process traffic information collected by a monitoring stations (Routers and Servers) to distinguish the misbehaving of malicious traffic of DDoS attacks in Intrusion Detection Systems (IDS). In general, data mining techniques can be designed and implemented with the intrusion systems to protect the organizations from malicious. Specifically, unsupervised data mining clustering techniques allow to effectively distinguish the normal traffic from malicious traffic in a good accuracy. In this paper, we present a hybrid approach called centroid-based rules to detect and prevent a real-world DDoS attacks collected from “CAIDA UCSD " DDoS Attack 2007 Dataset” and normal traffic traces from “CAIDA Anonymized Internet Traces 2008 Dataset” using unsupervised k-means data mining clustering techniques with proactive rules method. Centroid-based rules are used to effectively detect the DDoS attack in an efficient time. The Result of experiments shows that the centroid-based rules method perform better than the centroid-based method in term of accuracy and detection rate. In term of false alarm rates, the proposed solution obtains very low false positive rate in the training process and testing phases. Results of accuracy were more than 99% in training and testing processes. The proposed centroid-based rules method can be used in a real-time monitoring as DDoS defense system.


title: Transfer learning in heterogeneous collaborative filtering domains

abstract: A major challenge for collaborative filtering (CF) techniques in recommender systems is the data sparsity that is caused by missing and noisy ratings. This problem is even more serious for CF domains where the ratings are expressed numerically, e.g. as 5-star grades. We assume the 5-star ratings are unordered bins instead of ordinal relative preferences. We observe that, while we may lack the information in numerical ratings, we sometimes have additional auxiliary data in the form of binary ratings. This is especially true given that users can easily express themselves with their preferences expressed as likes or dislikes for items. In this paper, we explore how to use these binary auxiliary preference data to help reduce the impact of data sparsity for CF domains expressed in numerical ratings. We solve this problem by transferring the rating knowledge from some auxiliary data source in binary form (that is, likes or dislikes), to a target numerical rating matrix. In particular, our solution is to model both the numerical ratings and ratings expressed as like or dislike in a principled way. We present a novel framework of Transfer by Collective Factorization (TCF), in which we construct a shared latent space collectively and learn the data-dependent effect separately. A major advantage of the TCF approach over the previous bilinear method of collective matrix factorization is that we are able to capture the data-dependent effect when sharing the data-independent knowledge. This allows us to increase the overall quality of knowledge transfer. We present extensive experimental results to demonstrate the effectiveness of TCF at various sparsity levels, and show improvements of our approach as compared to several state-of-the-art methods.


title: The OpenGrm open-source finite-state grammar software libraries

abstract: In this paper, we present a new collection of open-source software libraries that provides command line binary utilities and library classes and functions for compiling regular expression and context-sensitive rewrite rules into finite-state transducers, and for n-gram language modeling. The OpenGrm libraries use the OpenFst library to provide an efficient encoding of grammars and general algorithms for building, modifying and applying models.


title: Improving the Energy-Efficiency of GPS Based Location Sensing Smartphone Applications

abstract: Smartphones with an embedded GPS sensor are being increasingly used for location determination to enable Location based services (LBS) deliver location context pervasive computing services such as maps and navigation. Although a Smartphone GPS provides adequate accuracy, it has limitations such as high energy consumption and is unavailable in locations with an obscured view of GPS satellites. Use of alternate location sensors such as Wi-Fi and GSM can be used to augment GPS and to alleviate these GPS limitations, but they can increase the average localization error. The novelty of our contribution is twofold. First we present an accelerometer based architecture that reduces GPS energy-consumption without compromising on either the location accuracy or sampling rate. Evaluation of our system shows energy-savings of up to 27% in typical circumstances. Second, as a user's mobility state is complex we also propose a method to not only detect that a user is non-stationary but also classify a representative set of mobility states.


title: Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences

abstract: We present a systematic approach to the mathematical treatment of the t-distributed stochastic neighbor embedding (t-SNE) and the stochastic neighbor embedding (SNE) method. This allows an easy adaptation of the methods or exchange of their respective modules. In particular, the divergence which measures the difference between probability distributions in the original and the embedding space can be treated independently from other components like, e.g. the similarity of data points or the data distribution. We focus on the extension for different divergences and propose a general framework based on the consideration of Frechet-derivatives. This way the general approach can be adapted to the user specific needs.


title: Private database queries using somewhat homomorphic encryption

abstract: In a private database query system, a client issues queries to a database and obtains the results without learning anything else about the database and without the server learning the query. While previous work has yielded systems that can efficiently support disjunction queries, performing conjunction queries privately remains an open problem. In this work, we show that using a polynomial encoding of the database enables efficient implementations of conjunction queries using somewhat homomorphic encryption. We describe a three-party protocol that supports efficient evaluation of conjunction queries. Then, we present two implementations of our protocol using Paillier's additively homomorphic system as well as Brakerski's somewhat homomorphic cryptosystem. Finally, we show that the additional homomorphic properties of the Brakerski cryptosystem allow us to handle queries involving several thousand elements over a million-record database in just a few minutes, far outperforming the implementation using the additively homomorphic system.


title: Tavaxy: Integrating Taverna and Galaxy workflows with cloud computing support

abstract: BackgroundOver the past decade the workflow system paradigm has evolved as an efficient and user-friendly approach for developing complex bioinformatics applications. Two popular workflow systems that have gained acceptance by the bioinformatics community are Taverna and Galaxy. Each system has a large user-base and supports an ever-growing repository of application workflows. However, workflows developed for one system cannot be imported and executed easily on the other. The lack of interoperability is due to differences in the models of computation, workflow languages, and architectures of both systems. This lack of interoperability limits sharing of workflows between the user communities and leads to duplication of development efforts.ResultsIn this paper, we present Tavaxy, a stand-alone system for creating and executing workflows based on using an extensible set of re-usable workflow patterns. Tavaxy offers a set of new features that simplify and enhance the development of sequence analysis applications: It allows the integration of existing Taverna and Galaxy workflows in a single environment, and supports the use of cloud computing capabilities. The integration of existing Taverna and Galaxy workflows is supported seamlessly at both run-time and design-time levels, based on the concepts of hierarchical workflows and workflow patterns. The use of cloud computing in Tavaxy is flexible, where the users can either instantiate the whole system on the cloud, or delegate the execution of certain sub-workflows to the cloud infrastructure.ConclusionsTavaxy reduces the workflow development cycle by introducing the use of workflow patterns to simplify workflow creation. It enables the re-use and integration of existing (sub-) workflows from Taverna and Galaxy, and allows the creation of hybrid workflows. Its additional features exploit recent advances in high performance cloud computing to cope with the increasing data size and complexity of analysis.The system can be accessed either through a cloud-enabled web-interface or downloaded and installed to run within the user's local environment. All resources related to Tavaxy are available at http://www.tavaxy.org.


title: ARSH 6: Reproductive Health Needs Assessment of Adolescents and Young People (15–24 y): A Qualitative Study on ‘Perceptions of Program Managers and Health Providers’

abstract: OBJECTIVES ::: To understand the perceptions of program managers and service providers using in depth interview technique, a well-accepted qualitative research that can also offer semi quantitative input. ::: ::: ::: METHODS ::: Need assessment was done qualitatively using in-depth interviews, among program managers of health care system including District Medical Officers and RCH Officers and program service providers, both in rural and urban areas. ::: ::: ::: RESULTS ::: In total 34 in-depth interviews were conducted. Nearly half (2+) of the program managers and service providers of adolescent programs opined that the important problems faced by adolescents were issues related to sexuality, psychosocial conflict, identity crisis, adjustment problems and scholastic problems. Approximately half of them thought that improper parenting, negative attitude of parents, separated parents, ignorance of parents, family background, nuclear family setup etc. are the most important factors, which influence adolescent problems and that friends and media are their major source of reproductive sexual health information. Nearly half of them pointed out that pain and psychological disturbances like anxiety, tension and anger were the important menstrual problems faced by adolescents. Again nearly half of them, felt that FLE (Family Life Education) should be given at school and ARSH services at PHCs, but there was little consensus on provision of contraceptive service and abortion services to adolescents. ::: ::: ::: CONCLUSION ::: All the service providers and program managers are ready to cooperate but they had varied opinions about who should impart adolescent reproductive sexual health education and how the program should be done.


title: Low-Loss Feedome Design for Axially Displaced Ellipse (ADE) Reflectors

abstract: To address the continuing demand for high-performance advanced extremely high frequency (EHF) antennas for satellite communications, we developed a low-loss feedome for the axially displaced ellipse (ADE) reflector antenna for improved aperture efficiency. To examine the feasibility of the approach, a feedome is designed and a prototype is built in the frequency range of 43.545.5 GHz (Q-band). Simulation results using a linear feed horn correlate very well with experimental results employing an right-hand circularly polarized (RHCP) feed horn.


title: Question focus extraction and answer passage retrieval

abstract: Question Analysis is an important task in Question Answering Systems (QAS). It consists generally in identifying the semantic type of the question and extracting the main focus of the question. The goal is to better specify the required information by the question. In this context and as part of a framework aiming to implement an Arabic opinion QAS for political debates, this paper addresses the problem of defining the focus of opinion questions and proposes particularly an approach for extracting the focus of attitude questions. The proposed approach is based on semi-automatically constructed lexico-syntactic patterns. Furthermore, the paper presents an adapted Vector Space Model (VSM) based method to retrieve candidate answer passages from a transcribed TV political show. Several experiments were carried out and showed that the focus extraction approach has achieved over 72% as F1 score for holder and target extraction, and has improved the baseline passage retrieval task by over than 25%.


title: Challenges and business models for mobile location-based services and advertising

abstract: Mobile advertising will become more pervasive and profitable, but not before addressing key technical and business challenges.


title: Restoring function in exhausted CD8 T cells during chronic viral infection

abstract: Functional impairment of antigen-specific T cells is a defining characteristic of many chronic infections, but the underlying mechanisms of T-cell dysfunction are not well understood. To address this question, we analysed genes expressed in functionally impaired virus-specific CD8 T cells present in mice chronically infected with lymphocytic choriomeningitis virus (LCMV), and compared these with the gene profile of functional memory CD8 T cells. Here we report that PD-1 (programmed death 1; also known as Pdcd1) was selectively upregulated by the exhausted T cells, and that in vivo administration of antibodies that blocked the interaction of this inhibitory receptor with its ligand, PD-L1 (also known as B7-H1), enhanced T-cell responses. Notably, we found that even in persistently infected mice that were lacking CD4 T-cell help, blockade of the PD-1/PD-L1 inhibitory pathway had a beneficial effect on the 'helpless' CD8 T cells, restoring their ability to undergo proliferation, secrete cytokines, kill infected cells and decrease viral load. Blockade of the CTLA-4 (cytotoxic T-lymphocyte-associated protein 4) inhibitory pathway had no effect on either T-cell function or viral control. These studies identify a specific mechanism of T-cell exhaustion and define a potentially effective immunological strategy for the treatment of chronic viral infections.


title: Origin and physiological roles of inflammation

abstract: Inflammation underlies a wide variety of physiological and pathological processes. Although the pathological aspects of many types of inflammation are well appreciated, their physiological functions are mostly unknown. The classic instigators of inflammation - infection and tissue injury - are at one end of a large range of adverse conditions that induce inflammation, and they trigger the recruitment of leukocytes and plasma proteins to the affected tissue site. Tissue stress or malfunction similarly induces an adaptive response, which is referred to here as para-inflammation. This response relies mainly on tissue-resident macrophages and is intermediate between the basal homeostatic state and a classic inflammatory response. Para-inflammation is probably responsible for the chronic inflammatory conditions that are associated with modern human diseases.


title: Ontology alignment design patterns

abstract: Interoperability between heterogeneous ontological descriptions can be performed through ontology mediation techniques. At the heart of ontology mediation lies the alignment: a specification of correspondences between ontology entities. Ontology matching can bring some automation but are limited to finding simple correspondences. Design patterns have proven themselves useful to capture experience in design problems. In this article, we introduce ontology alignment patterns as reusable templates of recurring correspondences. Based on a detailed analysis of frequent ontology mismatches, we develop a library of common patterns. Ontology alignment patterns can be used to refine correspondences, either by the alignment designer or via pattern detection algorithms. We distinguish three levels of abstraction for ontology alignment representation, going from executable transformation rules, to concrete correspondences between two ontologies, to ontology alignment patterns at the third level. We express patterns using an ontology alignment representation language, making them ready to use in practical mediation tasks. We extract mismatches from vocabularies associated with data sets published as linked open data, and we evaluate the ability of correspondence patterns to provide proper alignments for these mismatches. Finally, we describe an application of ontology alignment patterns for an ontology transformation service.


title: Detecting Distributed Denial of Service Attacks: Methods, Tools and Future Directions

abstract: The minimal processing and best-e↵ort forwarding of any packet, malicious or not, was the prime concern when the Internet was designed. This architecture creates an unregulated network path, which can be exploited by any cyber attacker motivated by revenge, prestige, politics or money. Denial-of-service (DoS) attacks exploit this to target critical Web services [1, 2, 3, 4, 5]. This type of attack is intended to make a computer resource unavailable to its legitimate users. Denial of service attack programs have been around for many years. Old single source attacks are now countered easily by many defense mechanisms and the source of these attacks can be easily rebu↵ed or shut down with improved tracking capabilities. However, with the astounding growth of the Internet during the last decade, an increasingly large number of vulnerable systems are now available to attackers. Attackers can now employ a large number of these vulnerable hosts to launch an attack instead of using a single server, an approach which is not very e↵ective and detected easily. A distributed denial of service (DDoS) attack [1, 6] is a large-scale, coordinated attack on the availability of services of a victim system or network resources, launched indirectly through many compromised computers on the Internet. The first well-documented DDoS attack appears to have occurred in August 1999, when a DDoS tool called Trinoo was deployed in at least 227 systems, to flood a single University of Minnesota computer, which was knocked down for more than two days1. The first largescale DDoS attack took place on February 20001. On February 7, Yahoo! was the victim of a DDoS attack during which its Internet portal was inaccessible for three hours. On February 8, Amazon, Buy.com, CNN and eBay were all hit by DDoS attacks that caused them to either stop functioning completely or slowed them down significantly1. DDoS attack networks follow two types of architectures: the Agent-Handler architecture and the Internet Relay Chat (IRC)-based architecture as discussed by [7]. The Agent-Handler architecture for DDoS attacks is comprised of clients, handlers, and agents (see Figure 6). The attacker communicates with the rest of the DDoS attack system at the client systems. The handlers are often software packages located throughout the Internet that are used by the client to communicate with the agents. Instances of the agent software are placed in the compromised systems that finally carry out the attack. The owners and users of the agent systems are generally unaware of the situation. In the IRC-based DDoS attack architecture, an IRC communication channel is used to connect the client(s) to the agents. IRC


title: Electrospun poly(vinyl alcohol) nanofibrous membranes containing Coptidis Rhizoma extracts for potential biomedical applications

abstract: Coptidis Rhizoma is a medicinal plant that is well known for its high antibacterial activity against various pathogens and its anti-inflammatory, antioxidant, and hemostatic effects. Here, nanofibr...


title: Effect of Extraction Solvent/Technique on the Antioxidant Activity of Selected Medicinal Plant Extracts

abstract: Theeffects of four extracting solvents [absolute ethanol, absolute methanol, aqueous ethanol (ethanol: water, 80:20 v/v) and aqueous methanol (methanol: water, 80:20 v/v)] and two extraction techniques (shaking and reflux) on the antioxidant activity of extracts of barks of Azadirachta indica, Acacia nilotica, Eugenia jambolana, Terminalia arjuna, leaves and roots of Moringa oleifera, fruit of Ficus religiosa,and leaves of Aloe barbadensis were investigated. The tested plant materials contained appreciable amounts of total phenolic contents (0.31-16.5 g GAE /100g DW), total flavonoid (2.63-8.66 g CE/100g DW); reducing power at 10 mg/mL extract concentration (1.36-2.91), DPPH. scavenging capacity (37.2-86.6%), and percent inhibition of linoleic acid (66.0-90.6%). Generally higher extract yields, phenolic contents and plant material antioxidant activity were obtained using aqueous organic solvents, as compared to the respective absolute organic solvents. Although higher extract yields were obtained by the refluxing extraction technique, in general higher amounts of total phenolic contents and better antioxidant activity were found in the extracts prepared using a shaker.


title: Prosocial Behavior in Adolescence: Gender Differences in Development and Links with Empathy

abstract: Although adolescents’ prosocial behavior is related to various positive outcomes, longitudinal research on its development and predictors is still sparse. This 6-wave longitudinal study investigated the development of prosocial behavior across adolescence, and examined longitudinal associations with perspective taking and empathic concern. Participants were 497 adolescents (Mage t1 = 13.03 years, 43% girls) who reported on their prosocial behaviors, empathic concern, and perspective taking. The results revealed marked gender differences in the development of prosocial behavior. For boys, levels of prosocial behavior were stable until age 14, followed by an increase until age 17, and a slight decrease thereafter. For girls, prosocial behavior increased until age 16 years and then slightly decreased. Regarding longitudinal associations, empathic concern was consistently related to subsequent prosocial behavior. However, perspective taking was only indirectly related to prosocial behavior, via its effect on empathic concern. Tests of the direction of effects showed support for the notion that earlier prosocial behavior predicts subsequent empathy-related traits, but only for girls. The findings support cognitive-developmental and moral socialization theories of prosocial development and the primary role of moral emotions in predicting prosocial behaviors. Our findings inform strategies to foster prosocial behaviors by emphasizing moral emotions rather than moral cognitions during adolescence.


title: Coexistence of cervicogenic headache and migraine without aura (?).

abstract: It is well known that migraine with aura may coexist with various unilateral headaches, like cluster headache and chronic paroxysmal hemicrania. It may also coexist with cervicogenic headache. The diagnosis of migraine without aura ("common migraine") poses greater problems than the diagnosis of migraine with aura. Cervicogenic headache diagnosis also poses problems when these two headaches coexist, since they have symptoms in common. Therefore, the scientific demonstration of coexistence of migraine without aura and cervicogenic headache is bound to be a difficult task. In the present study, migraine without aura and cervicogenic headache seemed to coexist in 4 patients (3 F and 1 M, mean age 50). Attacks with migraine characteristics fulfilled the IHS and IASP migraine criteria. Out of a maximum of 13 migraine characteristics based on the IHS/IASP migraine criteria, such as unilaterality, aggravation on minor physical activity, etc., none of the patients presented less than 11, as opposed to a mean of < or = 4 of these criteria in the cervicogenic type attacks. A similar system, based on criteria such as: reduction of range of motion in the neck, mechanical precipitation of attacks, etc., was also developed for cervicogenic headache. The mean number of cervicogenic headache criteria was 4.3 (out of a total of 5) in the "cervicogenic part of the picture", as opposed to 1.5 (1.8 if laterality is considered, see text) in the "migraine part of the picture". Drug regimens and anaesthetic blocks also showed different results in the two different headaches in the same patient. All in all, this study seems to support a coexistence of the two headache types.


title: Frequency Principle in Deep Learning with General Loss Functions and Its Potential Application

abstract: Previous studies have shown that deep neural networks (DNNs) with common settings often capture target functions from low to high frequency, which is called Frequency Principle (F-Principle). It has also been shown that F-Principle can provide an understanding to the often observed good generalization ability of DNNs. However, previous studies focused on the loss function of mean square error, while various loss functions are used in practice. In this work, we show that the F-Principle holds for a general loss function (e.g., mean square error, cross entropy, etc.). In addition, DNN's F-Principle may be applied to develop numerical schemes for solving various problems which would benefit from a fast converging of low frequency. As an example of the potential usage of F-Principle, we apply DNN in solving differential equations, in which conventional methods (e.g., Jacobi method) is usually slow in solving problems due to the convergence from high to low frequency.


title: Open-Finger: Mobile Application Platform Enhanced by Physical Finger

abstract: We introduce our Open-Finger that integrates the smartphone with a physical finger. Smartphones are widely used for communication and entertainment, and have characteristic features such an even surface and a few buttons. Our interaction with them is quite simple and really limited. In contrast, we have found a way to use a physical finger attached to a smartphone. A real finger has many capabilities such as pointing and touching. For example, we use our finger to point at something or someone, to move something, or to count a number. We can also use such features for interactions between us and our smartphones. Thus, the finger approach makes smartphone more intuitive and familiar for novice and elderly users who are not good at manipulating smartphone. In this paper, we describe our design concepts, prototype implementation and application possibilities.


title: Integration of Robotic Technologies for Rapidly Deployable Robots

abstract: The automation of production lines in industrial scenarios implies solving different problems, such as the flexibility to deploy robotic solutions to different production lines, usability to allow non-robotics expert users to teach robots different tasks, and safety to enable operators to physically interact with robots without the need of fences. In this paper, we present a system that integrates three novel technologies to address the above mentioned problems. We use an autocalibrated multi-modal robot skin , a general robot control framework to generate dynamic behaviors fusing multiple sensor signals, and an intuitive and fast teaching by demonstration method based on semantic reasoning. We validate the proposed technologies with a wheeled humanoid robot in an industrial set-up. The benefits of our system are the transferability of the learned tasks to different robots, the reusability of the models when new objects are introduced in the production line, the capability of detecting and recovering from errors, and the reliable detection of collisions and pre-collisions to provide a fast reactive robot that improves the physical human-robot interaction.


title: Learning and Recognition of Clothing Genres From Full-Body Images

abstract: According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory. In addition, the corresponding salient visual features of each style element were identified and formulated with variables that can be computationally derived with various computer vision algorithms. To evaluate the performance of our algorithm, a dataset containing 3250 full-body shots crawled from popular online stores was built. Recognition results show that our proposed algorithms achieved promising overall precision, recall, and -score of 88.76%, 88.53%, and 88.64% for recognizing upperwear genres, and 88.21%, 88.17%, and 88.19% for recognizing lowerwear genres, respectively. The effectiveness of each style element and its visual features on recognizing clothing genres was demonstrated through a set of experiments involving different sets of style elements or features. In summary, our experimental results demonstrate the effectiveness of the proposed method in clothing genre recognition.


title: Augmented Reality and Gamification in Heritage Museums

abstract: Augmented Reality (AR) technology is one of the fastest growing areas in the computing field and it has pervaded many applications in the market including museums. However, there is a need for a survey exploring the effectiveness of augmented reality as a communication medium in museums. This paper reviews the development of Augmented Reality as a mass communication [1] tool in museums. We introduce a communication model which would work as a roadmap building AR guidance system with ensuring this system will be a successful method of communication with users. Besides, we propose a novel way to enhance the visitors’ experience and learning by combining AR with games in museums.


title: An Analysis of Scale Invariance in Object Detection - SNIP

abstract: An analysis of different techniques for recognizing and detecting objects under extreme scale variation is presented. Scale specific and scale invariant design of detectors are compared by training them with different configurations of input data. By evaluating the performance of different network architectures for classifying small objects on ImageNet, we show that CNNs are not robust to changes in scale. Based on this analysis, we propose to train and test detectors on the same scales of an image-pyramid. Since small and large objects are difficult to recognize at smaller and larger scales respectively, we present a novel training scheme called Scale Normalization for Image Pyramids (SNIP) which selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. On the COCO dataset, our single model performance is 45.7% and an ensemble of 3 networks obtains an mAP of 48.3%. We use off-the-shelf ImageNet-1000 pre-trained models and only train with bounding box supervision. Our submission won the Best Student Entry in the COCO 2017 challenge. Code will be made available at http://bit.ly/2yXVg4c.


title: SENSORIMOTOR CONVERGENCE IN VISUAL NAVIGATION AND FLIGHT CONTROL SYSTEMS

abstract: Insects exhibit unparalleled and incredibly robust flight dynamics in the face of uncertainties. A fundamental principle contributing to this amazing behavior is rapid processing and convergence of visual sensory information to flight motor commands via spatial wide-field integration, accomplished by motion pattern sensitive interneurons in the lobula plate portion of the visual ganglia. Within a control-theoretic framework, a model for wide-field integration of retinal image flow is developed, establishing the connection between image flow kernels (retinal motion pattern sensitivities) and the feedback terms they represent. It is demonstrated that the proposed output feedback methodology is sufficient to give rise to experimentally observed navigational heuristics as the centering and forward speed regulation responses exhibited by honeybees.


title: Analysis of ReGEN as a Graph-Rewriting System for Quest Generation

abstract: Using procedural narrative generation in video games provides a flexible way to extend game play and provide more depth to the game world at low cost to the developers. Current examples of narrative generation in commercial games, however, tend to be simplistic, resulting in repetitive and uninteresting stories. In this paper, we develop a system for narrative generation using a context-aware graph-rewriting framework. We use a graph representation of the game world to create narratives which reflect and modify the current world state. Using a novel set of metrics to evaluate narrative quality, we validate our approach by comparing our generated narratives to other procedurally generated stories, as well as to authored narratives from commercially successful and critically praised games. The results show that our narratives compare favorably to the authored narratives. Our metrics provide a new approach to narrative analysis, and our system provides a unique and practical approach to story generation.


title: Model-Based Quantitative Network Security Metrics: A Survey

abstract: Network security metrics (NSMs) based on models allow to quantitatively evaluate the overall resilience of networked systems against attacks. For that reason, such metrics are of great importance to the security-related decision-making process of organizations. Considering that over the past two decades several model-based quantitative NSMs have been proposed, this paper presents a deep survey of the state-of-the-art of these proposals. First, to distinguish the security metrics described in this survey from other types of security metrics, an overview of security metrics, in general, and their classifications is presented. Then, a detailed review of the main existing model-based quantitative NSMs is provided, along with their advantages and disadvantages. Finally, this survey is concluded with an in-depth discussion on relevant characteristics of the surveyed proposals and open research issues of the topic.