title: The stable partitioning triangle KBr-LiVO3-Li2MoO4 in the quaternary reciprocal system Li,K‖Br,VO3,MoO4

abstract: The composition and melting point of the eutectic of the stable partitioning triangle KBr-LiVO3-Li2MoO4 in the quaternary reciprocal system Li,K‖Br,VO3,MoO4 were determined by differential thermal analysis as (21% KBr, 61.2% LiVO3, 17.8% Li2MoO4, 440°C).


title: A 60-GHz Millimeter-wave CMOS Marchand Balun

abstract: A novel 60-GHz wideband Marchand balun fabricated with a standard 0.18 μm six-metal-layer CMOS process for millimeter-wave applications is presented. A technique for achieving good balance with the fourth metal layer microstrip conductor is used in the designed Marchand balun. The fabricated CMOS balun uses multilayer coupling with the top two layers. An output matching network is added to improve the output return loss. The measured amplitude imbalance was about ±1.5 dB from 25 to 65 GHz, over the 89% operating frequency band. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 766–770, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24176


title: On the use of reducible polynomials as random number generators

abstract: The randomness properties and the hierarchy of correlation coefficients are studied of approximate-maximum-length sequences, for which the characteristic polynomial is a product of several primitive polynomials. The randomness properties are almost the same as for maximum-length sequences characterized by a primitive polynomial with many terms and of the same degree. Reducible characteristic polynomials have acceptable figures of merit and can be of extremely high degree. Since they are also easily constructed and implemented, reducible polynomials are strong candidates for reliable random number generation, especially at the bit rates needed in large-scale Monte Carlo simulations.


title: Region-based Quality Estimation Network for Large-scale Person Re-identification

abstract: One of the major restrictions on the performance of video-based person re-id is partial noise caused by occlusion, blur and illumination. Since different spatial regions of a single frame have various quality, and the quality of the same region also varies across frames in a tracklet, a good way to address the problem is to effectively aggregate complementary information from all frames in a sequence, using better regions from other frames to compensate the influence of an image region with poor quality. To achieve this, we propose a novel Region-based Quality Estimation Network (RQEN), in which an ingenious training mechanism enables the effective learning to extract the complementary region-based information between different frames. Compared with other feature extraction methods, we achieved comparable results of 92.4%, 76.1% and 77.83% on the PRID 2011, iLIDS-VID and MARS, respectively. In addition, to alleviate the lack of clean large-scale person re-id datasets for the community, this paper also contributes a new high-quality dataset, named "Labeled Pedestrian in the Wild (LPW)" which contains 7,694 tracklets with over 590,000 images. Despite its relatively large scale, the annotations also possess high cleanliness. Moreover, it's more challenging in the following aspects: the age of characters varies from childhood to elderhood; the postures of people are diverse, including running and cycling in addition to the normal walking state.


title: Ultra-Wideband Phase Shifters

abstract: A method with clear guidelines is presented to design compact planar phase shifters with ultra-wideband (UWB) characteristics. The proposed method exploits broadside coupling between top and bottom elliptical microstrip patches via an elliptical slot located in the mid layer, which forms the ground plane. A theoretical model is used to analyze performance of the proposed devices. The model shows that it is possible to design high-performance UWB phase shifters for the 25deg-48deg range using the proposed structure. The method is used to design 30deg and 45deg phase shifters that have compact size, i.e., 2.5 cm times 2 cm. The simulated and measured results show that the designed phase shifters achieve better than plusmn3deg differential phase stability, less than 1-dB insertion loss, and better than 10-dB return loss across the UWB, i.e., 3.1-10.6 GHz.


title: Requirements Elicitation: A Survey of Techniques, Approaches, and Tools

abstract: Requirements elicitation is the process of seeking, uncovering, acquiring, and elaborating requirements for computer based systems. It is generally understood that requirements are elicited rather than just captured or collected. This implies there are discovery, emergence, and development elements in the elicitation process. Requirements elicitation is a complex process involving many activities with a variety of available techniques, approaches, and tools for performing them. The relative strengths and weaknesses of these determine when each is appropriate depending on the context and situation. The objectives of this chapter are to present a comprehensive survey of important aspects of the techniques, approaches, and tools for requirements elicitation, and examine the current issues, trends, and challenges faced by researchers and practitioners in this field.


title: Exploring Inter-feature and Inter-class Relationships with Deep Neural Networks for Video Classification

abstract: Videos contain very rich semantics and are intrinsically multimodal. In this paper, we study the challenging task of classifying videos according to their high-level semantics such as human actions or complex events. Although extensive efforts have been paid to study this problem, most existing works combined multiple features using simple fusion strategies and neglected the exploration of inter-class semantic relationships. In this paper, we propose a novel unified framework that jointly learns feature relationships and exploits the class relationships for improved video classification performance. Specifically, these two types of relationships are learned and utilized by rigorously imposing regularizations in a deep neural network (DNN). Such a regularized DNN can be efficiently launched using a GPU implementation with an affordable training cost. Through arming the DNN with better capability of exploring both the inter-feature and the inter-class relationships, the proposed regularized DNN is more suitable for identifying video semantics. With extensive experimental evaluations, we demonstrate that the proposed framework exhibits superior performance over several state-of-the-art approaches. On the well-known Hollywood2 and Columbia Consumer Video benchmarks, we obtain to-date the best reported results: 65.7% and 70.6% respectively in terms of mean average precision.


title: An improved Rogowski coil configuration for a high speed, compact current sensor with high immunity to voltage transients

abstract: Rogowski current transducers are often used to measure transient and pulsed currents in power electronic applications. They are small, clip-around and flexible making them easy to insert in circuits with a high power density, have a wide-bandwidth, and negligible insertion impedance. Recent advances in GaN and SiC semiconductor switches mean that the next generation of Rogowski transducers need to have: •Higher temperature operation. •Better high frequency bandwidth, enabling measurement of of rise times of 5–20ns with good fidelity and a predictable measurement delay for power measurements. •Improved rejection of common mode interference from close coupled capacitive interference due to voltage transients >20V/ns, whilst retaining high frequency capability and accuracy with conductor position in the sensor loop. •Size reduction as semiconductor package sizes shrink. The aim of this paper is to examine the relative advantages of screened and unscreened Rogowski coils and to show that screened coils are capable of providing the high bandwidths and noise immunity required for semiconductor switching current measurements.


title: Fatal and non-fatal repetition of self-harm: Systematic review

abstract: Background Non-fatal self-harm frequently leads to non-fatal repetition and sometimes to suicide. We need to quantify these two outcomes of self-harm to help us to develop and test effective interventions. ::: ::: Aims To estimate rates of fatal and non-fatal repetition of self-harm. ::: ::: Method A systematic review of published follow-up data, from observational and experimental studies. Four electronic databases were searched and 90 studies met the inclusion criteria. ::: ::: Results Eighty per cent of studies found were undertaken in Europe, over one-third in the UK. Median proportions for repetition 1 year later were: 16% non-fatal and 2% fatal; after more than 9 years, around 7% of patients had died by suicide. The UK studies found particularly low rates of subsequent suicide. ::: ::: Conclusions After 1 year, non-fatal repetition rates are around 15%. The strong connection between self-harm and later suicide lies somewhere between 0.5% and 2% after 1 year and above 5% after 9 years. Suicide risk among self-harm patients is hundreds of times higher than in the general population.


title: Compact Wide-Beam Circularly-Polarized Microstrip Antenna With a Parasitic Ring for CNSS Application

abstract: A compact single-feed circularly-polarized (CP) wide beam microstrip antenna is proposed for CNSS application. The antenna is designed with a double-layer structure, comprising a circular patch with two rectangular stubs along the diameter direction and a parasitic ring right above it. The resonance frequency and the CP characteristics are mainly controlled by the circular patch and the rectangular stubs, respectively. The vertical HPBW (half power beam width) could be widened by the parasitic ring. Experimental results show that the measured vertical HPBW is approximately 140° and the measured out-of-roundness for the horizontal radiation pattern is only 1.1 dB. Besides, it could maintain good low-profile characteristics.


Exploring Entity-centric Networks in Entangled News Streams


title: Monocular Depth Prediction Using Generative Adversarial Networks

abstract: We present a technique for monocular reconstruction, i.e. depth map and pose prediction from input monocular video sequences, using adversarial learning. We extend current geometry-aware neural network architectures that learn from photoconsistency-based reconstruction loss functions defined over spatially and temporally adjacent images by leveraging recent advances in adversarial learning. We propose a generative adversarial network (GAN) that can learn improved reconstruction models, with flexible loss functions that are less susceptible to adversarial examples, using generic semi-supervised or unsupervised datasets. The generator function in the proposed GAN learns to synthesize neighbouring images to predict a depth map and relative object pose, while the discriminator function learns the distribution of monocular images to correctly classify the authenticity of the synthesized images. A typical photoconsistency-based reconstruction loss function is used to assist the generator function to train well and compete against the discriminator function. We demonstrate the performance of our method on the KITTI dataset in both, depth-supervised and unsupervised settings. The depth prediction results of the proposed GAN are shown to compare favorably with state-of-the-art techniques for monocular reconstruction.


title: Resolving constraint conflicts

abstract: In this paper, we define constraint conflicts and examine properties that may aid in guiding their resolution. A constraint conflict is an inconsistency between the access control policy and the constraints specified to limit that policy. For example, a policy that permits a high integrity subject to access low integrity data is in conflict with a Biba integrity constraint. Constraint conflicts differ from typical policy conflicts in that constraints are never supposed to be violated. That is, a conflict with a constraint results in a policy compilation error, whereas policy conflicts are resolved at runtime. As we have found in the past, when constraint conflicts occur in a specification a variety of resolutions are both possible and practical. In this paper, we detail some key formal properties of constraint conflicts and show how these are useful in guiding conflict resolution. We use the SELinux example policy for Linux 2.4.19 as the source of our constraint conflicts and resolution examples. The formal properties are used to guide the selection of resolutions and provide a basis for a resolution language that we apply to resolve conflicts in the SELinux example policy.


title: Validity of spatial covariance matrices over time and frequency

abstract: A MIMO channel measurement campaign with a moving mobile has been conducted in Vienna. The measured data will be used to investigate covariance matrices with respect to their dependence on time and frequency. This document focuses on the description of the evaluation techniques which will be applied to the measurement data in the future. The F-eigen-ratio is defined expressing the degradation due to out-dated covariance matrices. Illustrating the derived methods, first results based on the measured data are shown for a simple line-of-sight scenario.


title: An Automated Recommender System for Course Selection

abstract: Most of electronic commerce and knowledge management` systems use recommender systems as the underling tools for identifying a set of items that will be of interest to a certain user. Collaborative recommender systems recommend items based on similarities and dissimilarities among users’ preferences. This paper presents a collaborative recommender system that recommends university elective courses to students by exploiting courses that other similar students had taken. The proposed system employs an association rules mining algorithm as an underlying technique to discover patterns between courses. Experiments were conducted with real datasets to assess the overall performance of the proposed approach.


title: Joint training of speech separation, filterbank and acoustic model for robust automatic speech recognition.

abstract: Robustness is crucial for automatic speech recognition systems in real-world environments. Speech enhancement/separation algorithms are normally used to enhance noisy speech before recognition. However, such algorithms typically introduce distortions unseen by acoustic models. In this study, we propose a novel joint training approach to reduce this distortion problem. At the training stage, we first concatenate a speech separation DNN, a filterbank and an acoustic model DNN to form a deeper network, and then jointly train all of them. This way, the separation frontend and filterbank can provide enhanced speech desired by the acoustic model. In addition, the linguistic information contained in the acoustic model can have a positive effect on the frontend and filberbank. Besides the commonly used log mel-spectrogram feature, we also add more robust features for acoustic modeling. Our system obtains 14.1% average word error rate on the noisy and reverberant CHIME-2 corpus (track 2), which outperforms the previous best result by 8.4% relatively.


Jet clustering in particle physics , via a dynamic nearest neighbour graph implemented with CGAL


Instruction and practice in learning to use a device


title: Customer Experience Creation: Determinants, Dynamics and Management Strategies

abstract: Retailers, such as Starbucks and Victoria's Secret, aim to provide customers a great experience across channels. In this paper we provide an overview of the existing literature on customer experience and expand on it to examine the creation of a customer experience from a holistic perspective. We propose a conceptual model, in which we discuss the determinants of customer experience. We explicitly take a dynamic view, in which we argue that prior customer experiences will influence future customer experiences. We discuss the importance of the social environment, self-service technologies and the store brand. Customer experience management is also approached from a strategic perspective by focusing on issues such as how and to what extent an experience-based business can create growth. In each of these areas, we identify and discuss important issues worthy of further research.


title: Finding the most unusual time series subsequence: algorithms and applications

abstract: In this work we introduce the new problem of finding time seriesdiscords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While discords have many uses for data mining, they are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence) unlike most anomaly detection algorithms that typically require many parameters. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is three to four orders of magnitude faster than brute force, while guaranteed to produce identical results. We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms, space telemetry, respiration physiology, anthropological and video datasets.


title: IncQuery-D: A Distributed Incremental Model Query Framework in the Cloud

abstract: Queries are the foundations of data intensive applications. In model-driven software engineering (MDE), model queries are core technologies of tools and transformations. As software models are rapidly increasing in size and complexity, traditional tools exhibit scalability issues that decrease productivity and increase costs [17]. While scalability is a hot topic in the database community and recent NoSQL efforts have partially addressed many shortcomings, this happened at the cost of sacrificing the ad-hoc query capabilities of SQL. Unfortunately, this is a critical problem for MDE applications due to their inherent workload complexity. In this paper, we aim to address both the scalability and ad-hoc querying challenges by adapting incremental graph search techniques – known from the EMF-IncQuery framework – to a distributed cloud infrastructure. We propose a novel architecture for distributed and incremental queries, and conduct experiments to demonstrate that IncQuery-D, our prototype system, can scale up from a single workstation to a cluster that can handle very large models and complex incremental queries efficiently.


title: E-Government Challenges and the Role of Political Leadership in Indonesia: The Case of Sragen

abstract: In general, developing countries are lagging behind in e-government adoption compared to developed countries. Within Indonesia, there is a huge disparity in e- government implementation between districts. This study presents e-government challenges and the role of political leadership in the rural district of Sragen, one of the leading districts in implementing e-government in the country. The study focuses on the supply-side of e- government, and categorises the challenges in three main areas; management, infrastructure, and human factors. Initiatives taken to deal with these challenges are presented and strong political leadership is found to play an important role.


title: A new bidirectional DC-DC converter for fuel cell, solar cell and battery systems

abstract: This paper describes a new zero voltage switching (ZVS) bidirectional DC-DC converter (BDC) module. Compared to existing bidirectional DC-DC converter (BDC) modules for the same application, the new BDC module has the advantage of high efficiency, high power density and isolation. These advantages make the new BDC promising for medium and high power fuel cell, solar cell and battery applications where high power density, high efficiency, high reliability and lightweight power converters are needed. A real world implementation of 384 V to 48 V and 48 V to 384 V BDC is described, implemented and simulated using existing old components and proposed new power components for power level up to 1.65 kW. A new BDC provides a +13.5% increase in efficiency at 10% load, a +3.4% increase at 50% load and a +1.5% increase at 100% load in both forward and backward mode and more than double power density.


title: Hardware-Oriented Compression of Long Short-Term Memory for Efficient Inference

abstract: Long short-term memory (LSTM) and its variants have been widely adopted in processing sequential data. However, the intrinsic large memory requirement and high computational complexity make it hard to be employed in embedded systems. This incurs the need of model compression and dedicated hardware accelerator for LSTM. In this letter, efficient clipped gating and top- $k$ pruning schemes are introduced to convert the dense matrix computations in LSTM into structured sparse-matrix-sparse-vector multiplications. Then, mixed quantization schemes are developed to eliminate most of the multiplications in LSTM. The proposed compression scheme is well suited for efficient hardware implementations. Experimental results show that the model size and the number of matrix operations can be reduced by $32\times$ and $18.5\times$ , respectively, at a cost of less than $1\%$ accuracy loss on a word-level language modeling task.


title: Visual Interactive Creation, Customization, and Analysis of Data Quality Metrics

abstract: During data preprocessing, analysts spend a significant part of their time and effort profiling the quality of the data along with cleansing and transforming the data for further analysis. While quality metrics—ranging from general to domain-specific measures—support assessment of the quality of a dataset, there are hardly any approaches to visually support the analyst in customizing and applying such metrics. Yet, visual approaches could facilitate users’ involvement in data quality assessment. We present MetricDoc, an interactive environment for assessing data quality that provides customizable, reusable quality metrics in combination with immediate visual feedback. Moreover, we provide an overview visualization of these quality metrics along with error visualizations that facilitate interactive navigation of the data to determine the causes of quality issues present in the data. In this article, we describe the architecture, design, and evaluation of MetricDoc, which underwent several design cycles, including heuristic evaluation and expert reviews as well as a focus group with data quality, human-computer interaction, and visual analytics experts.


title: Data Fine-tuning

abstract: In real-world applications, commercial off-the-shelf systems are utilized for performing automated facial analysis including face recognition, emotion recognition, and attribute prediction. However, a majority of these commercial systems act as black boxes due to the inaccessibility of the model parameters which makes it challenging to fine-tune the models for specific applications. Stimulated by the advances in adversarial perturbations, this research proposes the concept of Data Fine-tuning to improve the classification accuracy of a given model without changing the parameters of the model. This is accomplished by modeling it as data (image) perturbation problem. A small amount of “noise” is added to the input with the objective of minimizing the classification loss without affecting the (visual) appearance. Experiments performed on three publicly available datasets LFW, CelebA, and MUCT, demonstrate the effectiveness of the proposed concept.


title: Attend More Times for Image Captioning

abstract: Most attention-based image captioning models attend to the image once per word. However, attending once per word is rigid and is easy to miss some information. Attending more times can adjust the attention position, find the missing information back and avoid generating the wrong word. In this paper, we show that attending more times per word can gain improvements in the image captioning task, without increasing the number of parameters. We propose a flexible two-LSTM merge model to make it convenient to encode more attentions than words. Our captioning model uses two LSTMs to encode the word sequence and the attention sequence respectively. The information of the two LSTMs and the image feature are combined to predict the next word. Experiments on the MSCOCO caption dataset show that our method outperforms the state-of-the-art. Using bottom up features and self-critical training method, our method gets BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE scores of 0.381, 0.283, 0.580, 1.261 and 0.220 on the Karpathy test split.


title: Deep Convolutional Traffic Light Recognition for Automated Driving

abstract: Robust traffic light detection and state recognition is of crucial importance on the path to automated vehicles. However, the mere classification of the signaled states does not suffice at complex multi-lane intersections. Rather, a complete understanding of the intersection, but at least the recognition of additional information (like arrows displayed on the traffic lights) is necessary. In this work, we developed a unified deep convolutional traffic light recognition system on the basis of the Faster R-CNN architecture, which is able to not only detect traffic lights and classify their state, but also distinguish their type (circle, straight, left, and right). An in-depth analysis of its performance on the large and diverse DriveU Traffic Light Dataset shows an overall detection performance of 0.92 Average Precision for traffic lights of width greater than 8 px. Additionally, other kinds of traffic lights, e.g. pedestrian lights, have been identified as main cause of false positives. Moreover, we evaluated the usefulness of the developed system to assess the traffic light states for all present driving directions revealing inconsistencies among multiple detections in single images.


title: An Initial Approach to Reuse Non-Functional Requirements Knowledge

abstract: Non-Functional Requirements (NFR) can be seen as qualities that software should deliver to cope with the stakeholders' demands. NFRs are fuzzy in nature and hence hard to identify. Despite the fact that both developers and users may value NFRs, they frequently do not identify the need for an NFR. Even when an NFR is identified as required, possible solutions to implement this NFR may be hard to fig- ure out. Furthermore, interdependencies among NFRs may implicate that a solution for one NFR may, at the same time, bring synergy to one NFR while conflicting with another. One approach to deal with that is to use Softgoal Interdependency Graphs (SIG) to capture knowledge describing alternatives to implement NFRs. We have ob- tained empirical evidence that using catalogues can help eliciting NFRs despite the fact that catalogues do not scale too well. To address this question, we have investi- gated the use of ontologies and semantic web techniques to represent SIGs in a ma- chine readable format. We have produced a tool (NDR) that starts to use these con- cepts. In its current form, the NDR tool only allows very basic queries done manual- ly. The NDR tool is part of the NDR framework which will facilitate the reuse of NFR knowledge on Alternatives to incorporate NFRs into the design of target sys-


Cultural Heritage Documentation


title: Item cold-start recommendations: learning local collective embeddings

abstract: Recommender systems suggest to users items that they might like (e.g., news articles, songs, movies) and, in doing so, they help users deal with information overload and enjoy a personalized experience. One of the main problems of these systems is the item cold-start, i.e., when a new item is introduced in the system and no past information is available, then no effective recommendations can be produced. The item cold-start is a very common problem in practice: modern online platforms have hundreds of new items published every day. To address this problem, we propose to learn Local Collective Embeddings: a matrix factorization that exploits items' properties and past user preferences while enforcing the manifold structure exhibited by the collective embeddings. We present a learning algorithm based on multiplicative update rules that are efficient and easy to implement. The experimental results on two item cold-start use cases: news recommendation and email recipient recommendation, demonstrate the effectiveness of this approach and show that it significantly outperforms six state-of-the-art methods for item cold-start.


title: H2RDF+: High-performance distributed joins over large-scale RDF graphs

abstract: The proliferation of data in RDF format calls for efficient and scalable solutions for their management. While scalability in the era of big data is a hard requirement, modern systems fail to adapt based on the complexity of the query. Current approaches do not scale well when faced with substantially complex, non-selective joins, resulting in exponential growth of execution times. In this work we present H2RDF+, an RDF store that efficiently performs distributed Merge and Sort-Merge joins over a multiple index scheme. H2RDF+ is highly scalable, utilizing distributed MapReduce processing and HBase indexes. Utilizing aggressive byte-level compression and result grouping over fast scans, it can process both complex and selective join queries in a highly efficient manner. Furthermore, it adaptively chooses for either single- or multi-machine execution based on join complexity estimated through index statistics. Our extensive evaluation demonstrates that H2RDF+ efficiently answers non-selective joins an order of magnitude faster than both current state-of-the-art distributed and centralized stores, while being only tenths of a second slower in simple queries, scaling linearly to the amount of available resources.


title: CID: an efficient complexity-invariant distance for time series

abstract: The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by "suggesting" to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.


title: Contribution to the Formal Specification and Verification of a Multi-Agent Robotic System

abstract: It is important to have multi-agent robotic system specifications that ensure correctness properties of safety and liveness. As these systems have concurrency, and often have dynamic environment, the formal specification and verification of these systems along with step-wise refinement from abstract to concrete concepts play a major role in system correctness. Formal verification is used for exhaustive investigation of the system space thus ensuring that undetected failures in the behavior are excluded. We construct the system incrementally from subcomponents, based on software architecture. The challenge is to develop a safe multi-agent robotic system, more specifically to ensure the correctness properties of safety and liveness. Formal specifications based on model-checking are flexible, have a concrete syntax, and play vital role in correctness of a multi-agent robotic system. To formally verify safety and liveness of such systems is important because they have high concurrency and in most of the cases have dynamic environment. We have considered a case-study of a multi-agent robotic system for the transport of stock between storehouses to exemplify our formal approach. Our proposed development approach allows for formal verification during specification definition. The development process has been classified in to four major phases of requirement specifications, verification specifications, architecture specifications and implementation.


title: Exploring hacker assets in underground forums

abstract: Many large companies today face the risk of data breaches via malicious software, compromising their business. These types of attacks are usually executed using hacker assets. Researching hacker assets within underground communities can help identify the tools which may be used in a cyberattack, provide knowledge on how to implement and use such assets and assist in organizing tools in a manner conducive to ethical reuse and education. This study aims to understand the functions and characteristics of assets in hacker forums by applying classification and topic modeling techniques. This research contributes to hacker literature by gaining a deeper understanding of hacker assets in well-known forums and organizing them in a fashion conducive to educational reuse. Additionally, companies can apply our framework to forums of their choosing to extract their assets and appropriate functions.


Botnet detection revisited: Theory and practice of finding malicious P2P networks via Internet connection graphs


title: Grant-free Rateless Multiple Access: A Novel Massive Access Scheme for Internet of Things

abstract: Rateless multiple access (RMA) is a novel non-orthogonal multiple access framework that is promising for massive access in Internet of Things due to its high efficiency and low complexity. In the framework, after certain registration, each active user, respectively, transmits to the access point randomly based on an assigned random access control function until receiving an acknowledgement. In this letter, by exploiting the intrinsic access pattern of each user, we propose a grant-free RMA scheme, which no longer needs the registration process as in the original RMA, thus greatly reduces the signaling overhead and system latency. Furthermore, we propose a low-complexity joint iterative detection and decoding algorithm in which the channel estimation, the active user detection, and the information decoding are done simultaneously. Finally, we propose a method based on density evolution to evaluate the system performance.


title: Cloud and traditional ERP systems in small and medium enterprises

abstract: Cloud computing is the new buzz word today attracting high interest among various domains like business enterprises, particularly in Small and Medium Enterprises. As it is a pay-per-use model, SMEs have high expectations that adapting this model will not only make them flexible, hassle free but also economic. In view of such expectations, this paper analyses the possibility of adapting Cloud computing technologies in SMEs in light of economic concerns. In this paper, two hypotheses are developed to compare the average annual peruser costs of using Enterprise Resource Planning systems in two ways, the traditional approach and the Cloud approach. A web based survey is conducted apart from the Interviews with the peers to collect the data across the selected SMEs and t-test is performed to compare both the technologies on the pro proposed hypothesis. Results achieved are produced and discussed.


title: The Million Song Dataset

abstract: We introduce the Million Song Dataset, a freely-available collection of audio features and metadata for a million contemporary popular music tracks. We describe its creation process, its content, and its possible uses. Attractive features of the Million Song Database include the range of existing resources to which it is linked, and the fact that it is the largest current research dataset in our field. As an illustration, we present year prediction as an example application, a task that has, until now, been difficult to study owing to the absence of a large set of suitable data. We show positive results on year prediction, and discuss more generally the future development of the dataset.


The medical teaching of demonology in the 17th and 18th centuries


title: Measuring prevalence of other-oriented transactive contributions using an automated measure of speech style accommodation

abstract: This paper contributes to a theory-grounded methodological foundation for automatic collaborative learning process analysis. It does this by illustrating how insights from the social psychology and sociolinguistics of speech style provide a theoretical framework to inform the design of a computational model. The purpose of that model is to detect prevalence of an important group knowledge integration process in raw speech data. Specifically, this paper focuses on assessment of transactivity in dyadic discussions, where a transactive contribution is operationalized as one where reasoning is made explicit, and where that reasoning builds on a prior reasoning statement within the discussion. Transactive contributions can be either self-oriented, where the contribution builds on the speaker’s own prior contribution, or other-oriented, where the contribution builds on a prior contribution of a conversational partner. Other-oriented transacts are particularly central to group knowledge integration processes. An unsupervised Dynamic Bayesian Network model motivated by concepts from Speech Accommodation Theory is presented and then evaluated on the task of estimating prevalence of other-oriented transacts in dyadic discussions. The evaluation demonstrates a significant positive correlation between an automatic measure of speech style accommodation and prevalence of other-oriented transacts (R = .36, p < .05).


title: Analysis and performance comparison of different power conditioning systems for SMES-based energy systems in wind turbines

abstract: Suitability of energy systems based on Superconducting Magnetic Energy Storage (SMES) has been widely tested in the field of wind energy, being able to supply power in cases such as low wind speeds or voltage dips, and to store energy when there are surpluses. This article analyzes and compares the performance of three SMES-based systems that differ in the topology of power converter: a two-level Voltage Source Converter (VSC), a three-level VSC and a two-level Current Source Converter (CSC). Their performance has been improved by means of an appropriate modulation strategy. To obtain a high reliability and accuracy, a co-simulation between MATLAB/Simulink ® (running the control system) and PSIM ® (running the power system) has been executed.


title: Design of low-cost, wearable remote health monitoring and alert system for elderly heart patients

abstract: Most senior citizens are often neglected and are helpless in times of medical emergencies, as they are alone in their twilight years. To tackle this, we have come up with a prospective Remote Health Monitoring and Alert System (RHMAS). In elderly people heart attacks are associated with symptoms like, body temperature fluctuations, high BP, profuse sweating, improper cardiac rhythm etc. Our solution is to integrate robust sensors capable of sensing and monitoring these symptoms with a microcontroller to alert the next of kin and health services during emergencies. Since most devices for this purpose are wired, a wireless device wouldn't interfere with the movement of the user The proposed system consists of a Photo-Plethysmography (PPG) based pulse sensor, to detect arrhythmia, a temperature sensor to constantly monitor their body temperature and a sweat sensor to monitor the Galvanic Skin Response (GSR). A GSM module will send an alert to the next of kin if the monitored data deviates from the nominal body condition. If the monitored data reaches emergency levels, health services would also be alerted.


Development and implementation using Arduino and Raspberry Pi based Ignition control system


title: Do CIFAR-10 Classifiers Generalize to CIFAR-10?

abstract: Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing models are questionable because the same test sets have been used to select these models for multiple years now. To understand the danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by creating a new test set of truly unseen images. Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models. Yet more recent models with higher original accuracy show a smaller drop and better overall performance, indicating that this drop is likely not due to overfitting based on adaptivity. Instead, we view our results as evidence that current accuracy numbers are brittle and susceptible to even minute natural variations in the data distribution.


title: Research Article Analysis of Human Electrocardiogram for Biometric Recognition

abstract: Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact localization of wave boundaries. This paper presents a systematic analysis for human identification from ECG data. A fiducial-detection-based framework that incorporates analytic and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point only. Further, to completely relax the detection of fiducial points, a new approach based on autocorrelation (AC) in conjunction with discrete cosine transform (DCT) is proposed. Experimentation demonstrates that the AC/DCT method produces comparable recognition accuracy with the fiducial-detection-based approach.


title: Esophageal Involvement in Multiple System Atrophy

abstract: The prevalence of esophageal involvement and its impact on clinical manifestations in patients with multiple system atrophy (MSA) remains unknown. We recruited 16 consecutive patients with dysphagia associated with MSA (MSA group) and 16 consecutive patients with dysphagia associated with amyotrophic lateral sclerosis (ALS group). We assessed the presence or absence of food stagnation within the esophagus using videofluorography. Food stagnation within the esophagus was observed in 16 patients (100 %; 7 severe, 9 mild) in the MSA group and in 4 patients (25 %; 4 mild) in the ALS group (P < 0.001). Follow-up videofluorography revealed that food stagnation in patients with MSA could exacerbate during the disease course. Patients with MSA and severe food stagnation showed a wide range of intraesophageal stasis by videofluorography. Among the 16 patients in the MSA group, 4 developed aspiration pneumonia and 1 died of suffocation associated with food regurgitation during continuous positive airway pressure therapy. In conclusion, food stagnation within the esophagus occurs more frequently in MSA patients with dysphagia than in ALS patients with dysphagia. Because food stagnation can cause serious complications such as aspiration pneumonia and suffocation, patients with MSA should be evaluated by videofluorography, especially those with stagnation in the esophageal phase.


title: Mean Field Multi-Agent Reinforcement Learning

abstract: Existing multi-agent reinforcement learning methods are limited typically to a small number of agents. When the agent number increases largely, the learning becomes intractable due to the curse of the dimensionality and the exponential growth of agent interactions. In this paper, we present Mean Field Reinforcement Learning where the interactions within the population of agents are approximated by those between a single agent and the average effect from the overall population or neighboring agents; the interplay between the two entities is mutually reinforced: the learning of the individual agent's optimal policy depends on the dynamics of the population, while the dynamics of the population change according to the collective patterns of the individual policies. We develop practical mean field Q-learning and mean field Actor-Critic algorithms and analyze the convergence of the solution to Nash equilibrium. Experiments on Gaussian squeeze, Ising model, and battle games justify the learning effectiveness of our mean field approaches. In addition, we report the first result to solve the Ising model via model-free reinforcement learning methods.


title: High-Efficiency Class-E Power Amplifier With Shunt Capacitance and Shunt Filter

abstract: An analysis of a novel single-ended Class-E mode with shunt capacitance and shunt filter with explicit derivation of the idealized optimum voltage and current waveforms and load-network parameters with their verification by frequency domain simulations with 50% duty ratio is presented. The ideal collector voltage and current waveforms demonstrate a possibility of 100% efficiency. The circuit design with transmission lines at 2.14 GHz is discussed and analyzed. In order to reduce the voltage peak factor, the load network parameters can be rearranged to correspond to Class-E/F 3 mode by providing a short-circuit condition at the third harmonic when the second-harmonic tank is connected in series to the shunt filter. Broadband capability of a Class-E mode with shunt filter using reactance compensation technique has been demonstrated by two examples, one with lumped elements and the other with transmission-line elements. The test board of a transmission-line broadband Class-E GaN HEMT power amplifier with shunt filter was measured and high-performance results with the output power of around 41 dBm, average drain efficiency of 68%, and power gain of about 9 dB were achieved across the frequency band from 1.4 to 2.7 GHz.


title: Meta-learning for time series forecasting and forecast combination

abstract: In research of time series forecasting, a lot of uncertainty is still related to the task of selecting an appropriate forecasting method for a problem. It is not only the individual algorithms that are available in great quantities; combination approaches have been equally popular in the last decades. Alone the question of whether to choose the most promising individual method or a combination is not straightforward to answer. Usually, expert knowledge is needed to make an informed decision, however, in many cases this is not feasible due to lack of resources like time, money and manpower. This work identifies an extensive feature set describing both the time series and the pool of individual forecasting methods. The applicability of different meta-learning approaches are investigated, first to gain knowledge on which model works best in which situation, later to improve forecasting performance. Results show the superiority of a ranking-based combination of methods over simple model selection approaches.


title: Amulet: An Energy-Efficient, Multi-Application Wearable Platform

abstract: Wearable technology enables a range of exciting new applications in health, commerce, and beyond. For many important applications, wearables must have battery life measured in weeks or months, not hours and days as in most current devices. Our vision of wearable platforms aims for long battery life but with the flexibility and security to support multiple applications. To achieve long battery life with a workload comprising apps from multiple developers, these platforms must have robust mechanisms for app isolation and developer tools for optimizing resource usage. We introduce the Amulet Platform for constrained wearable devices, which includes an ultra-low-power hardware architecture and a companion software framework, including a highly efficient event-driven programming model, low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. We present the design and evaluation of our prototype Amulet hardware and software, and show how the framework enables developers to write energy-efficient applications. Our prototype has battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicts battery lifetime within 6-10% of the measured lifetime.


title: Strawman: an Ensemble of Deep Bag-of-Ngrams for Sentiment Analysis

abstract: This paper describes a builder entry, named "strawman", to the sentence-level sentiment analysis task of the "Build It, Break It" shared task of the First Workshop on Building Linguistically Generalizable NLP Systems. The goal of a builder is to provide an automated sentiment analyzer that would serve as a target for breakers whose goal is to find pairs of minimally-differing sentences that break the analyzer.


title: Systems for Safety and Autonomous Behavior in Cars : The DARPA Grand Challenge Experience Car safety systems are helping drivers to stay in their lanes , improving their night vision and are making driving decisions where millisecond response is needed

abstract: In this paper, we review technologies for auto- nomous ground vehicles and their present capabilities in research and in the automotive market. We outline technology requirements for enhanced functions and for infrastructure development. Since the recent Grand Challenge competition is a major force to advance technology in this field, we specifically refer to our experiences in developing a partici- pating vehicle. We present a multisensor platform that has been proven in an off-road environment. It combines different sensing modalities that inherently yield uncertain information. Finite-state machines are formulated to generate rule-based autonomous behavior that enables fully autonomous off-road driving. Overall, the intent of the paper is to evaluate approaches and technologies used in the two Grand Chal- lenges as they contribute to the needs of autonomous cars on the road.


title: Associating sentimental orientation of Chinese neologism in social media data

abstract: Sentiment analysis has always found its practical use in collecting people's preferences towards any subject in the context of social media. Unlike normal words available in dictionaries, neologisms are not easy to be labeled with a sentimental orientation while they have been widely used in conveying people's feelings and opinions. In order to conduct a reliable sentiment analysis for neologisms, a neologism discovery method is first required. Next, a sentimental analysis based on the discovery results can be performed. This paper proposes a 2-step novel solution by having a Chinese neologism discovery method and then a sentimental orientation determination algorithm based on varied TF-IDF. For neologism discovery, statistical data include frequency, duration of appearance and the number of users using a neologism. For sentimental orientation determination, we consider keyword term frequency, and document frequency together and use a varied TF-IDF algorithm. The preliminary experimental results show good precision rate and recall rates for a collection of social media data in both neologism discovery and sentimental analysis.


title: Power harvesting for smart sensor networks in monitoring water distribution system

abstract: Recently, there has been a growing interest in using wireless sensor networks for monitoring water distribution infrastructure to help drinking water utilities to have better understanding of hydraulic and water quality statement of their underground assets. One of the challenges is limited power resources for operating the smart sensors and sensor networks. Current common used power supplies for sensor node are batteries. Batteries have many drawbacks such as short life time and need to be replaced on regular basis which is uneconomical and unmanageable in hard access environment such as buried underground water pipelines. Energy harvesting of ambient energy in the water pipeline and powering wireless sensor node including sensing, processing, and communications would be particularly attractive option because the life time of the node will be potentially infinite for supporting wireless sensor networks. The paper will review and discuss the potential of using power harvesting techniques for monitoring water distribution networks and the work done in the area of monitoring water distribution systems using smart sensor networks.


title: A Target-Guided Neural Memory Model for Stance Detection in Twitter

abstract: Exploring user stances and attitudes is beneficial to a number of Web related research and applications, especially in social media platforms such as Twitter. Stance detection in Twitter aims at identifying the stance expressed in a tweet towards a given target (e.g., a government policy). A key challenge of this task is that a tweet may not explicitly express opinion about the target. To effectively detect user stances implied in tweets, target content information plays an important role. In previous studies, conventional feature-based methods often ignore target content. Although more recent neural network-based methods attempt to integrate target information using attention mechanism, the performance improvement is rather limited due to the underuse of this information. To address this issue, we propose an endto- end neural model, TGMN-CR, which makes better use of target content information. Specifically, our model first learns conditional tweet representation with respect to specific target. It then employs a target-guided iterative process to extract crucial stance-indicative clues via multiple interactions between target and tweet words. Experimental results on SemEval-2016 Task 6.A Twitter Stance Detection dataset show that our proposed method outperforms the state-of-the-art alternative methods, and substantially outperforms the comparative methods when a tweet does not explicitly express opinion about the given target.


title: Human-Like Playtesting with Deep Learning

abstract: We present an approach to learn and deploy human-like playtesting in computer games based on deep learning from player data. We are able to learn and predict the most "human" action in a given position through supervised learning on a convolutional neural network. Furthermore, we show how we can use the learned network to predict key metrics of new content — most notably the difficulty of levels. Our player data and empirical data come from Candy Crush Saga (CCS) and Candy Crush Soda Saga (CCSS). However, the method is general and well suited for many games, in particular where content creation is sequential. CCS and CCSS are non-deterministic match-3 puzzle games with multiple game modes spread over a few thousand levels, providing a diverse testbed for this technique. Compared to Monte Carlo Tree Search (MCTS) we show that this approach increases correlation with average level difficulty, giving more accurate predictions as well as requiring only a fraction of the computation time.


title: Building Evaluation Functions for Chess and Shogi with Uniformity Regularization Networks

abstract: Building evaluation functions for chess variants is a challenging goal. At this time, only AlphaZero succeeded with millions of self-play records produced by using thousands of tensor processing units (TPUs), which are not available for most researchers. This paper presents the challenge of training evaluation functions on the basis of deep convolutional neural networks using decent data and computing resources, where regularization is crucial as complex models trained with limited data are more prone to overfitting. We present a novel training scheme by introducing a uniformity regularization (UR) network. In the proposed approach, a value network and a discriminator network share common convolutional layers and both networks are trained simultaneously. Loss functions for them are based on the difference between the score of a random move and that of an experts’ move as a comparison training method. The value network is expected to give precise scores for all positions, while the discriminator makes qualitative evaluations for move pairs, and acts as a regularizer that penalizes differences in evaluation results to ensure all samples are uniformly discriminated. Due to the existence of shared layers, such regularization is beneficial for improving the overall accuracy of the value network. Experimental results for chess and shogi demonstrate the proposed method surpassed the standard L 2 regularization method, and successfully helped obtain decently accurate value networks.


An Intuitionistic Fuzzy-Based DEMATEL to Rank Risks of Construction Projects


Laparoscopic Management of a Canal of Nuck Cyst


title: Graphic Depictions: Portrayals of Mental Illness in Video Games

abstract: Although studies have examined portrayals of mental illness in the mass media, little attention has been paid to such portrayals in video games. In this descriptive study, the fifty highest-selling video games in each year from 2011 to 2013 were surveyed through application of search terms to the Wikia search engine, with subsequent review of relevant footage on YouTube. Depiction categories were then assigned based on the extent of portrayal and qualitative characteristics compared against mental illness stereotypes in cinema. Twenty-three of the 96 surveyed games depicted at least one character with mental illness. Forty-two characters were identified as portraying mental illness, with most characters classified under a “homicidal maniac” stereotype, although many characters did not clearly reflect cinema stereotypes and were subcategorized based on the shared traits. Video games contain frequent and varied portrayals of mental illness, with depictions most commonly linking mental illness to dangerous and violent behaviors.


title: Nanocrystalline-to-amorphous transition in nanolaminates grown by low temperature atomic layer deposition and related mechanical properties

abstract: We report on a comprehensive structural and nanoindentation study of nanolaminates of Al2O3 and ZnO synthesized by atomic layer deposition (ALD). By reducing the bilayer thickness from 50 nm to below 1 nm, the nanocrystal size could be controlled in the nanolaminate structure. The softer and more compliant response of the multilayers as compared to the single layers of Al2O3 and ZnO is attributed to the structural change from nanocrystalline to amorphous at smaller bilayer thicknesses. It is also shown that ALD is a unique technique for studying the inverse Hall-Petch softening mechanism (E. Voce and D. Tabor, J. Inst. Metals 79(12), 465 (1951)) related to grain size effects in nanomaterials.


title: Learning to Discriminate Noises for Incorporating External Information in Neural Machine Translation

abstract: Previous studies show that incorporating external information could improve the translation quality of Neural Machine Translation (NMT) systems. However, there are inevitably noises in the external information, severely reducing the benefit that the existing methods could receive from the incorporation. To tackle the problem, this study pays special attention to the discrimination of the noises during the incorporation. We argue that there exist two kinds of noise in this external information, i.e. global noise and local noise, which affect the translations for the whole sentence and for some specific words, respectively. Accordingly, we propose a general framework that learns to jointly discriminate both the global and local noises, so that the external information could be better leveraged. Our model is trained on the dataset derived from the original parallel corpus without any external labeled data or annotation. Experimental results in various real-world scenarios, language pairs, and neural architectures indicate that discriminating noises contributes to significant improvements in translation quality by being able to better incorporate the external information, even in very noisy conditions.


title: Semantics of Ranking Queries for Probabilistic Data and Expected Ranks

abstract: When dealing with massive quantities of data, top-k queries are a powerful technique for returning only the k most relevant tuples for inspection, based on a scoring function. The problem of efficiently answering such ranking queries has been studied and analyzed extensively within traditional database settings. The importance of the top-k is perhaps even greater in probabilistic databases, where a relation can encode exponentially many possible worlds. There have been several recent attempts to propose definitions and algorithms for ranking queries over probabilistic data. However, these all lack many of the intuitive properties of a top-k over deterministic data. Specifically, we define a number of fundamental properties, including exact-k, containment, unique-rank, value-invariance, and stability, which are all satisfied by ranking queries on certain data. We argue that all these conditions should also be fulfilled by any reasonable definition for ranking uncertain data. Unfortunately, none of the existing definitions is able to achieve this. To remedy this shortcoming, this work proposes an intuitive new approach of expected rank. This uses the well-founded notion of the expected rank of each tuple across all possible worlds as the basis of the ranking. We are able to prove that, in contrast to all existing approaches, the expected rank satisfies all the required properties for a ranking query. We provide efficient solutions to compute this ranking across the major models of uncertain data, such as attribute-level and tuple-level uncertainty. For an uncertain relation of N tuples, the processing cost is O(N logN)—no worse than simply sorting the relation. In settings where there is a high cost for generating each tuple in turn, we provide pruning techniques based on probabilistic tail bounds that can terminate the search early and guarantee that the top-k has been found. Finally, a comprehensive experimental study confirms the effectiveness of our approach.


title: 2007 Special Issue A computational neuroscience approach to consciousness

abstract: Simultaneous recordings from populations of neurons in the inferior temporal visual cortex show that most of the information about which stimulus was shown is available in the number of spikes (or firing rate) of each neuron, and not from stimulus-dependent synchrony, so that it is unlikely that stimulus-dependent synchrony (or indeed oscillations) is an essential aspect of visual object perception. Neurophysiological investigations of backward masking show that the threshold for conscious visual perception may be set to be higher than the level at which small but significant information is present in neuronal firing and which allows humans to guess which stimulus was shown without conscious awareness. The adaptive value of this may be that the systems in the brain that implement the type of information processing involved in conscious thoughts are not interrupted by small signals that could be noise in sensory pathways. I then consider what computational processes are closely related to conscious processing, and describe a higher order syntactic thought (HOST) computational theory of consciousness. It is argued that the adaptive value of higher order thoughts is to solve the credit assignment problem that arises if a multistep syntactic plan needs to be corrected. It is then suggested that it feels like something to be an organism that can think about its own linguistic, and semantically-based thoughts. It is suggested that qualia, raw sensory and emotional feels, arise secondarily to having evolved such a higher order thought system, and that sensory and emotional processing feels like something because it would be unparsimonious for it to enter the planning, higher order thought, system and not feel like something.


title: Learning model trees from evolving data streams

abstract: The problem of real-time extraction of meaningful patterns from time-changing data streams is of increasing importance for the machine learning and data mining communities. Regression in time-changing data streams is a relatively unexplored topic, despite the apparent applications. This paper proposes an efficient and incremental stream mining algorithm which is able to learn regression and model trees from possibly unbounded, high-speed and time-changing data streams. The algorithm is evaluated extensively in a variety of settings involving artificial and real data. To the best of our knowledge there is no other general purpose algorithm for incremental learning regression/model trees able to perform explicit change detection and informed adaptation. The algorithm performs online and in real-time, observes each example only once at the speed of arrival, and maintains at any-time a ready-to-use model tree. The tree leaves contain linear models induced online from the examples assigned to them, a process with low complexity. The algorithm has mechanisms for drift detection and model adaptation, which enable it to maintain accurate and updated regression models at any time. The drift detection mechanism exploits the structure of the tree in the process of local change detection. As a response to local drift, the algorithm is able to update the tree structure only locally. This approach improves the any-time performance and greatly reduces the costs of adaptation.


title: Automating formal proofs for reactive systems

abstract: Implementing systems in proof assistants like Coq and proving their correctness in full formal detail has consistently demonstrated promise for making extremely strong guarantees about critical software, ranging from compilers and operating systems to databases and web browsers. Unfortunately, these verifications demand such heroic manual proof effort, even for a single system, that the approach has not been widely adopted. We demonstrate a technique to eliminate the manual proof burden for verifying many properties within an entire class of applications, in our case reactive systems, while only expending effort comparable to the manual verification of a single system. A crucial insight of our approach is simultaneously designing both (1) a domain-specific language (DSL) for expressing reactive systems and their correctness properties and (2) proof automation which exploits the constrained language of both programs and properties to enable fully automatic, pushbutton verification. We apply this insight in a deeply embedded Coq DSL, dubbed Reflex, and illustrate Reflex's expressiveness by implementing and automatically verifying realistic systems including a modern web browser, an SSH server, and a web server. Using Reflex radically reduced the proof burden: in previous, similar versions of our benchmarks written in Coq by experts, proofs accounted for over 80% of the code base; our versions require no manual proofs.


title: Degradation of floating-gate memory reliability by few electron phenomena

abstract: The purpose of this paper is to give a quantitative evaluation of the intrinsic reliability limits of floating-gate (FG) memories in the decananometer range due to the reduction of collective phenomena and to the dominance of single-electron stochastic behaviors. To this end, first, a model that quantitatively predicts the intrinsic dispersions of the memory retention time and programming window is proposed. Second, experimental results obtained on ultrascaled memory devices (with an active area as small as 30 nm times 30 nm) with either a continuous poly-Si FG or silicon nanocrystals will be shown and used to validate this model. Finally, extrapolations on the intrinsic reliability limits of future generations of Flash memories will be done


title: Combining Caption and Visual Features for Semantic Event Classification of Baseball Video

abstract: In baseball game, an event is defined as the portion of video clip between two pitches, and a play is defined as a batter finishing his plate appearance. A play is a concatenation of many events, and a baseball game is formed by a series of plays. In this paper, only the event happened in the last pitch of a plate appearance is detected. It is then semantically classified to represent the corresponding play by using an algorithm integrating caption rule-inference and visual feature analysis. Our proposed system is capable of classifying each baseball play into eleven semantic categories, which are popular and familiar to most of the audiences. In an experiment of 260 testing plays, the classification rate achieves up to 87%


title: Formal concept analysis as mathematical theory of concepts and concept hierarchies

abstract: Formal Concept Analysis has been originally developed as a subfield of Applied Mathematics based on the mathematization of concept and concept hierarchy. Only after more than a decade of development, the connections to the philosophical logic of human thought became clearer and even later the connections to Piaget's cognitive structuralism which Thomas Bernhard Seiler convincingly elaborated to a comprehensive theory of concepts in his recent book [Se01]. It is the main concern of this paper to show the surprisingly rich correspondences between Seiler's multifarious aspects of concepts in the human mind and the structural properties and relationships of formal concepts in Formal Concept Analysis. These correspondences make understandable, what has been experienced in a great multitude of applications, that Formal Concept Analysis may function in the sense of transdisciplinary mathematics, i.e., it allows mathematical thought to aggregate with other ways of thinking and thereby to support human thought and action.


title: CoFiSet: Collaborative Filtering via Learning Pairwise Preferences over Item-sets

abstract: Collaborative filtering aims to make use of users’ feedbacks to improve the recommendation performance, which has been deployed in various industry recommender systems. Some recent works have switched from exploiting explicit feedbacks of numerical ratings to implicit feedbacks like browsing and shopping records, since such data are more abundant and easier to collect. One fundamental challenge of leveraging implicit feedbacks is the lack of negative feedbacks, because there are only some observed relatively “positive” feedbacks, making it difficult to learn a prediction model. Previous works address this challenge via proposing some pointwise or pairwise preference assumptions on items. However, such assumptions with respect to items may not always hold, for example, a user may dislike a bought item or like an item not bought yet. In this paper, we propose a new and relaxed assumption of pairwise preferences over item-sets, which defines a user’s preference on a set of items (item-set) instead of on a single item. The relaxed assumption can give us more accurate pairwise preference relationships. With this assumption, we further develop a general algorithm called CoFiSet (collaborative filtering via learning pairwise preferences over item-sets). Experimental results show that CoFiSet performs better than several stateof-the-art methods on various ranking-oriented evaluation metrics on two real-world data sets. Furthermore, CoFiSet is very efficient as shown by both the time complexity and CPU time.


title: Accelerated ordered-subsets algorithm based on separable quadratic surrogates for regularized image reconstruction in X-ray CT

abstract: Iterative algorithms for X-ray CT image reconstruction offer the possibility of reduced dose and/or improved image quality, but require substantial compute time. Reducing the time will likely require algorithms that can be massively parallelized. Ordered subsets (OS) algorithms update all voxels simultaneously and thus are amenable to such parallelization. We present an new monotonic algorithm for regularized image reconstruction that is derived using optimization transfer with separable quadratic surrogates (SQS). The new algorithm accelerates the convergence rate by adapting reduced curvature values for the regularizer that were proposed by Yu et al. [1] for coordinate descent algorithms. We further accelerate the algorithm using ordered subsets. Simulation results show that the proposed OS algorithm converges faster than the traditionalOS algorithmforX-ray CT reconstruction from a limited number of projection views.


title: BYE BYE LITTLE BOY.

abstract: The article reviews the art exhibition "Bye Bye Kitty!!! Between Heaven and Hell in Contemporary Japanese Art" at the Japan Society in New York City.


Gaussian mixture models and the EM algorithm


title: Recent progress in flexible and stretchable piezoelectric devices for mechanical energy harvesting, sensing and actuation

abstract: Abstract Recent advances in materials science and mechanical engineering enable the realization of high performance piezoelectric systems in soft, flexible/stretchable formats, with unique opportunities for use in bio-integrated applications, from mechanical energy harvesting to sensing and actuation. This article highlights the essential mechanical to electrical conversion processes in devices and systems of this type, along with key considerations in their designs. Quantitative, experimentally validated mechanics models provide guidelines in the selection of optimized configurations and materials choices. The former focuses on thin geometries, neutral mechanical plane construction and controlled buckling. The latter includes options such as organic polymers, inorganic nanomaterials and various types of composites. Concluding sections summarize representative applications in biomedicine, ranging from devices for mechanical energy harvesting from natural motions of internal organs to sensors and actuators for the skin.


title: DCT statistics model-based blind image quality assessment

abstract: We propose an efficient, general-purpose, distortion-agnostic, blind/no-reference image quality assessment (NR-IQA) algorithm based on a natural scene statistics model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. We propose a generalized parametric model of the extracted DCT coefficients. The parameters of the model are utilized to predict image quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human visual perception of quality, at a level that is even competitive with the powerful full-reference SSIM index.


title: Thru-Reflect-Line: An Improved Technique for Calibrating the Dual Six-Port Automatic Network Analyzer

abstract: In an earlier paper, the use of a "thru-short-delay" (TSD) technique for calibrating the dual six-port automatic network analyzer was described. Another scheme required only a length of precision transmission line and a "calibration circuit." The better features of these two somewhat different approaches have now been combined and the requirement for either a known short, or a "calibration circuit" eliminated. This paper will develop the theory for this new procedure.


title: Compensation algorithms for sliding mode observers in sensorless control of IPMSMs

abstract: Electromechanical sensors are commonly used to obtain rotor position/speed for high-performance control of interior permanent magnet synchronous machines (IPMSMs) in vehicle systems. However, the use of these sensors increases the cost, size, weight, wiring complexity and reduces the mechanical robustness of IPMSM drive systems. These issues, together with some practical requirements, e.g., wide speed range, extreme environment temperature, and adverse loading conditions, make a sensorless control scheme desirable. This paper proposes an extended back electromotive force (EMF)-based sliding mode rotor position observer for sensorless vector control of IPMSMs. Based on filter characteristics, a robust compensation algorithm is developed to improve the performance of the sliding-mode observer (SMO). Multistage-filter and dual-filter schemes are designed to further improve the steady-state and transient performance, respectively, of the compensation algorithms. The proposed SMO and compensation algorithms are validated by simulations in MATLAB Simulink as well as experiments on a practical IPMSM drive system.


title: The Security Development Lifecycle in the Context of Accreditation Policies and Standards

abstract: The proposed security development lifecycle (SecDLC) model delivers a perpetual cycle of information security management and refinement. Using real-world examples, the authors show how SecDLC ensures the goals of preserving, monitoring, and improving security practices, policies, and standards in private and public sectors. The authors describe the four phases of SecDLC, comparing and contrasting them to existing security development models.


title: Class Proportion Estimation with Application to Multiclass Anomaly Rejection

abstract: This work addresses two classification problems that fall under the heading of domain adaptation, wherein the distributions of training and testing examples differ. The first problem studied is that of class proportion estimation, which is the problem of estimating the class proportions in an unlabeled testing data set given labeled examples of each class. Compared to previous work on this problem, our approach has the novel feature that it does not require labeled training data from one of the classes. This property allows us to address the second domain adaptation problem, namely, multiclass anomaly rejection. Here, the goal is to design a classifier that has the option of assigning a "reject" label, indicating that the instance did not arise from a class present in the training data. We establish consistent learning strategies for both of these domain adaptation problems, which to our knowledge are the first of their kind. We also implement the class proportion estimation technique and demonstrate its performance on several benchmark data sets.


title: Review and Analysis of Pothole Detection Methods

abstract: As one type of pavement distresses, potholes are important clues indicating structural defects of the asphalt road, and accurately detecting these potholes is one of important tasks for determining proper strategies of asphalt-surfaced pavement maintenance and rehabilitation. However, manually detecting and evaluating methods are expensive and timeconsuming. Thus, several efforts have been made for developing a technology which can automatically detect and recognize potholes, which may contribute to improvement of survey efficiency and pavement quality through prior investigation and immediate action. In this study, we investigate and analyze pothole detection methods which have developed and propose a potential direction of developing a pothole detection method to accurately and efficiently detect potholes.


title: A wideband LNA with an excellent gain flatness for 60 GHz 16QAM modulation in 65 nm CMOS

abstract: In 60 GHz radios, at least 9 GHz flat gain is needed for the whole band for 16-QAM modulation. A 23 GHz 3 dB bandwidth, 6.3 dB to 17.5 dB variable gain, less than 4.3 dB NF, −7.7 dBm IIP3, four-stage common source LNA is proposed in this paper. The LNA uses guided micro-strip transmission line to realize simple input and inter-stage impedance matching networks. The prototype LNA is implemented in 65 nm CMOS with a chip area of 0.354 mm2 including RF pad.


title: A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity

abstract: The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves. Disparate streams of research have approached non-stationarity from several angles, which make a variety of implicit assumptions that make it hard to keep an overview of the state of the art and to validate the innovation and significance of new works. This survey presents a coherent overview of work that addresses opponent-induced non-stationarity with tools from game theory, reinforcement learning and multi-armed bandits. Further, we reflect on the principle approaches how algorithms model and cope with this non-stationarity, arriving at a new framework and five categories (in increasing order of sophistication): ignore, forget, respond to target models, learn models, and theory of mind. A wide range of state-of-the-art algorithms is classified into a taxonomy, using these categories and key characteristics of the environment (e.g., observability) and adaptation behaviour of the opponents (e.g., smooth, abrupt). To clarify even further we present illustrative variations of one domain, contrasting the strengths and limitations of each category. Finally, we discuss in which environments the different approaches yield most merit, and point to promising avenues of future research.


title: Refining the Construct of Narcissistic Personality Disorder: Diagnostic Criteria and Subtypes

abstract: Objective: Narcissistic personality disorder has received relatively little empirical attention. This study was designed to provide an empirically valid and clinically rich portrait of narcissistic personality disorder and to identify subtypes of the disorder. Method: A random national sample of psychiatrists and clinical psychologists (N=1,201) described a randomly selected current patient with personality pathology. Clinicians provided detailed psychological descriptions of the patients using the Shedler-Westen Assessment Procedure-II (SWAP-II), completed a checklist of axis II diagnostic criteria, and provided construct ratings for each axis II personality disorder. Descriptions of narcissistic patients based on both raw and standardized SWAP-II item scores were aggregated to identify, respectively, the most characteristic and the most distinctive features of narcissistic personality disorder. Results: A total of 255 patients met DSMIV criteria for narcissistic personality disor


Visual Saliency Based on Conditional Entropy


title: A study on receiver circuit topology of a cordless battery charger for electric vehicles

abstract: A cordless battery charger will greatly improve the user friendliness of electric vehicles (EVs), accelerating the replacement of traditional internal combustion engine (ICE) vehicles with EVs and improving energy sustainability as a result. Resonant circuits are used for both the power transmitter and receiver of a cordless charger to compensate their coils and improve power transfer efficiency. However, conventional compensation circuit topology is not suitable for application to an EV, which involves very large power, a wide gap between the transmitter and receiver coils, and large horizontal misalignment. This paper proposes a novel compensation circuit topology that has a carefully designed series capacitor added to the parallel resonant circuit of the receiver. The proposed circuit has been implemented and tested on an EV. The simulation and experimental results are presented to show that the circuit can improve the power factor and power transfer efficiency, and as a result, allow a larger gap between the transmitter and receiver coils.


title: Exploring information technology outsourcing relationships: theory and practice

abstract: Abstract A growing concern among the organisations who are actively involved in Information Technology outsourcing is post-contract management and the ensuing development of what many practitioners and scholars have coined the ‘outsourcing partnership’. This paper integrates theoretical concepts from organisation theory, social exchange theory, and relational contract theory with existing research on IT outsourcing, to develop a conceptual model for understanding the relationship. In particular, we conceptually elaborate and then address the relationship's properties — identified as interactions, contract, context, structure, and behavioural dimensions. Preliminary exploratory research into relationship practice in twelve organisations involved in outsourcing presents some interesting findings that advance the thinking about the outsourcing relationship. We found the conceptual model useful in elucidating important relationship management areas, highlighting not only the outsourcing relationship's contractual, social, and economic characteristics, but also many additional elements found to have relevance in practice.


title: Propagation and establishment of rupestrian grassland grasses for restoration of degraded areas by mining

abstract: Investigations on the propagation and establishment of native grasses of rupestrian grassland on degraded substrates are essential to enable their use in the restoration of degraded areas, thus reducing the use of exotic species. This study aimed to evaluate the sexual and asexual propagation, establishment and growth of Axonopus laxiflorus (Trin.) Chase and Sporobolus metallicolus Longhi-Wagner & Boechat, two native grasses from rupestrian grassland developed on laterite substrate of an area degraded by bauxite mining. In greenhouse experiments, A. laxiflorus was propagated by tillers and S. metallicolus by seeds and tillers. Both species were also evaluated for germination in a germination chamber on the treatments: control, addition of potassium nitrate and heating at 80 °C for 2 min. In these evaluations, success of S. metallicolus germination was between 77 and 90%, without significative difference among treatments, whereas A. laxiflorus did not germinate. In the laterite substrate, S. metallicolous, propagated by seeds, showed 29% of survival, whereas in the vegetative propagation all plants of the two species survived. Eight months after starting the tiller experiment, S. metallicolus and A. laxiflorus presented a biomass increase of 239 and 75%, respectively. Although A. laxiflorus showed root biomass approximately seven times higher than S. metallicolus, the two species presented similar root length, that is, S. metallicolus contained more fine roots. The results show that the species can efficiently propagate vegetatively and, in the case of S. metallicolus, sexually. The success of the species in a substrate poor in nutrients and rich in Al, Fe and Mn indicates that they can be used in the restoration of areas degraded by bauxite and iron mining, which are important mining activities in Brazil.


title: A flexible vehicle surround view camera system by central-around coordinate mapping model

abstract: The surround view camera system is an emerging driving assistant technology that can assist drivers in parking by providing top-down view of surrounding situations. Such a system usually consists of four wide-angle or fish-eye cameras that mounted around the vehicle, and a bird-eye view is synthesized from images of these cameras. Commonly there are two fundamental problems for the surround view synthesis, geometric alignment and image synthesis. Geometric alignment performs fish-eye calibration and computes the image perspective transformation between the bird-eye view and images from the surrounding cameras. Image synthesis technique dedicates to seamless stitch between adjacent views and color balancing. In this paper, we propose a flexible central-around coordinate mapping (CACM) model for vehicle surround view synthesis. The CACM model calculates perspective transformation between a top-view central camera coordinate and the around camera coordinates by a marker point based method. With the transformation matrices, we could generate the pixel point mapping relationship between the bird-eye view and images of the surrounding cameras. After geometric alignment, an image fusion method based on distance weighting is adopted for seamless stitch, and an effective overlapping region brightness optimization method is proposed for color balancing. Both the seamless stitch and color balancing can be easily operated by using two types of weight coefficient under the framework of the CACM model. Experimental results show that the proposed approaches could provide a high-performance surround view camera system.


title: MORFACE: FACE MORPHING

abstract: The purpose of this project was to produce a real-time installation which uses two approaches: a close approach dealing with user’s face and head position and a far approach where the installation reacts to user’s motion differently if it is repetitive or not. The installation was personalized by a Mona Lisa.


title: Considerations for ceramic inlays in posterior teeth: a review

abstract: This review of ceramic inlays in posterior teeth includes a review of the history of ceramic restorations, followed by common indications and contraindications for their use. A discussion on the potential for tooth wear is followed by a review of recommended preparation design considerations, fabrication methods, and material choices. Despite the improved materials available for fabrication of porcelain inlays, fracture remains a primary mode of inlay failure. Therefore, a brief discussion on strengthening methods for ceramics is included. The review concludes with a section on luting considerations, and offers the clinician specific recommendations for luting procedures. In conclusion, inlay success rates and longevity, as reported in the literature, are summarized.


title: The many faces of publish/subscribe

abstract: Well adapted to the loosely coupled nature of distributed interaction in large-scale applications, the publish/subscribe communication paradigm has recently received increasing attention. With systems based on the publish/subscribe interaction scheme, subscribers register their interest in an event, or a pattern of events, and are subsequently asynchronously notified of events generated by publishers. Many variants of the paradigm have recently been proposed, each variant being specifically adapted to some given application or network model. This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations.


title: Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base

abstract: We present an approach to map utterances in conversation to logical forms, which will be executed on a large-scale knowledge base. To handle enormous ellipsis phenomena in conversation, we introduce dialog memory management to manipulate historical entities, predicates, and logical forms when inferring the logical form of current utterances. Dialog memory management is embodied in a generative model, in which a logical form is interpreted in a top-down manner following a small and flexible grammar. We learn the model from denotations without explicit annotation of logical forms, and evaluate it on a large-scale dataset consisting of 200K dialogs over 12.8M entities. Results verify the benefits of modeling dialog memory, and show that our semantic parsing-based approach outperforms a memory network based encoder-decoder model by a huge margin.


title: Reflections on SOA and Microservices

abstract: Today's Enterprises are facing many challenges in the service oriented, customer experience centric and customer demand driven global environment where ICT is becoming the leading enabler and partner of the modern enterprise. In the last decade, many enterprises have invested heavily in SOA-aligned IT transformations, but not harvested what SOA promised to provide. Now the API and Microservice paradigm has emerged as the "next big thing" for delivering IT outcomes to support the modern enterprise, with many technology vendors and service jumping on the bandwagon. This paper undertakes a critical investigation of the key concepts around SOA, API and Microservices, identifying similarities and differences between them and dispelling the confusion and hype around them. Based on our discussion and analysis, this paper presents a set of recommendations and best practices on the effective use and management of enterprise software components, drawing upon the best of SOA, API and Microservice concepts and practice.


title: Integrative model of the response of yeast to osmotic shock

abstract: Integration of experimental studies with mathematical modeling allows insight into systems properties, prediction of perturbation effects and generation of hypotheses for further research. We present a comprehensive mathematical description of the cellular response of yeast to hyperosmotic shock. The model integrates a biochemical reaction network comprising receptor stimulation, mitogen-activated protein kinase cascade dynamics, activation of gene expression and adaptation of cellular metabolism with a thermodynamic description of volume regulation and osmotic pressure. Simulations agree well with experimental results obtained under different stress conditions or with specific mutants. The model is predictive since it suggests previously unrecognized features of the system with respect to osmolyte accumulation and feedback control, as confirmed with experiments. The mathematical description presented is a valuable tool for future studies on osmoregulation in yeast and—with appropriate modifications—other organisms. It also serves as a starting point for a comprehensive description of cellular signaling.


title: Collocated learning experience within collaborative augmented environment (anatomy course)

abstract: Nowadays, collaborative systems and applications are widely used to allow multiple users to work together in order to achieve one same goal. In the field of virtual and augmented realities, such collaboration is often sought to effectuate complex activities in some relevant experiences. Users can share virtual objects, but most importantly, they use collaborative 3D interaction, visualize their objects manipulation in real time and communicate with each other to coordinate their actions efficiently. These users will, therefore, be able to work together in a shared virtual environment to carry out different tasks. In this paper, we present our work related to the design and implementation of a collaborative augmented system, our application introduces a multi-users experience in a face-to-face configuration and can be used by the students in anatomy course.


title: FMCW Based MIMO Imaging Radar for Maritime Navigation

abstract: The berthing of large ships in inclement weather with frequently poor visibility presents a challenge. To assist with this application, it may be beneficial to utilise standard radar imaging. Whilst this may be achieved using a mechanically-scanned system, reliability, cost and weight issues, coupled with the need to primarily image only a 1200 sector on the port and starboard of the ship, make phased array radar an attractive possibility. Multiple-Input Multiple-Output (MIMO) radar, with its ability to enhance the resolution available from a given number of elements, is particularly suited to a short-range application such as this in which there is sufficient time to switch between antenna elements as an alternative to more complex implementations. This paper describes a system of this nature from its basic architecture to development and validation, including some artefacts of the particular topology employed.