title: Design and sizing of short term energy storage for a PV system

abstract: Power generation in a PV system is defined by its V-I, and V-P characteristics. These curves are function of instantaneous solar irradiation and cell temperature; and outline the maximum power that the system can supply at any given moment. As a consequence of this PV systems lack overload capacity, which is common in traditional rotating generators; and therefore, PV systems cannot supply short term or transient load demands. To solve this issue two approaches can be taken; one is to constantly operate the PV system below its maximum power level, which has the drawback of non-optimal energy harvesting. The other is integration of energy storage elements to the power conditioning system, which allows optimizing power generation, while adding overload capacity. In addition, this approach also allows smoothing out short term irradiation variations allowing the system to improve energy harvesting. Focusing on the second approach analysis of short term energy storage requirements in PV systems is performed in this paper. As a result, sizing guidelines and a design procedure for a supercapacitor based energy storage system are developed. In addition, effectiveness of the proposed method on improving PV generator performance is tested and verified through computer simulations.


title: Depressing effect of sodium hexametaphosphate on apatite in flotation of rutile

abstract: Abstract The separation of rutile from apatite by flotation and the mechanism of depressing the apatite of sodium hexametaphosphate were studied. The results showed that rutile and apatite could be separated by using alkyl-mino-bismethylene phosphoric acid and sodium hexametaphosphate as a collector and a regulator, respectively. Sodium hexametaphosphate could selectively dissolve calcium ions on the apatite surface, and make calcium ions break away from lattice binding through combining.


title: Neural correlates of heart rate variability during emotion

abstract: Abstract The vagal (high frequency [HF]) component of heart rate variability (HRV) predicts survival in post-myocardial infarction patients and is considered to reflect vagal antagonism of sympathetic influences. Previous studies of the neural correlates of vagal tone involved mental stress tasks that included cognitive and emotional elements. To differentiate the neural substrates of vagal tone due to emotion, we correlated HF-HRV with measures of regional cerebral blood flow (rCBF) derived from positron emission tomography (PET) and 15 O-water in 12 healthy women during different emotional states. Happiness, sadness, disgust and three neutral conditions were each induced by film clips and recall of personal experiences (12 conditions). Inter-beat intervals derived from electrocardiographic recordings during the 60-second scans were spectrally-analyzed, generating 12 separate measures of HF-HRV in each subject. The six emotion and six neutral conditions were grouped together and contrasted. We observed substantial overlap between emotion-specific rCBF and the correlation between emotion-specific rCBF and HF-HRV, particularly in the medial prefrontal cortex. Emotion-specific rCBF also correlated with HF-HRV in the caudate nucleus, periacqueductal gray and left mid-insula. We also observed that the elements of cognitive control inherent in this experiment (that involved focusing on the target mental state) had definable neural substrates that correlated with HF-HRV and to a large extent differed from the emotion-specific correlates of HF-HRV. No statistically significant asymmetries were observed. Our findings are consistent with the view that the medial visceromotor network is a final common pathway by which emotional and cognitive functions recruit autonomic support.


title: Audio-visual speech recognition using deep bottleneck features and high-performance lipreading

abstract: This paper develops an Audio-Visual Speech Recognition (AVSR) method, by (1) exploring high-performance visual features, (2) applying audio and visual deep bottleneck features to improve AVSR performance, and (3) investigating effectiveness of voice activity detection in a visual modality. In our approach, many kinds of visual features are incorporated, subsequently converted into bottleneck features by deep learning technology. By using proposed features, we successfully achieved 73.66% lipreading accuracy in speaker-independent open condition, and about 90% AVSR accuracy on average in noisy environments. In addition, we extracted speech segments from visual features, resulting 77.80% lipreading accuracy. It is found VAD is useful in both audio and visual modalities, for better lipreading and AVSR.


title: iHadoop: Asynchronous Iterations for MapReduce

abstract: MapReduce is a distributed programming framework designed to ease the development of scalable data-intensive applications for large clusters of commodity machines. Most machine learning and data mining applications involve iterative computations over large datasets, such as the Web hyperlink structures and social network graphs. Yet, the MapReduce model does not efficiently support this important class of applications. The architecture of MapReduce, most critically its dataflow techniques and task scheduling, is completely unaware of the nature of iterative applications, tasks are scheduled according to a policy that optimizes the execution for a single iteration which wastes bandwidth, I/O, and CPU cycles when compared with an optimal execution for a consecutive set of iterations. This work presents iHadoop, a modified MapReduce model, and an associated implementation, optimized for iterative computations. The iHadoop model schedules iterations asynchronously. It connects the output of one iteration to the next, allowing both to process their data concurrently. iHadoop's task scheduler exploits inter-iteration data locality by scheduling tasks that exhibit a producer/consumer relation on the same physical machine allowing a fast local data transfer. For those iterative applications that require satisfying certain criteria before termination, iHadoop runs the check concurrently during the execution of the subsequent iteration to further reduce the application's latency. This paper also describes our implementation of the iHadoop model, and evaluates its performance against Hadoop, the widely used open source implementation of MapReduce. Experiments using different data analysis applications over real-world and synthetic datasets show that iHadoop performs better than Hadoop for iterative algorithms, reducing execution time of iterative applications by 25% on average. Furthermore, integrating iHadoop with HaLoop, a variant Hadoop implementation that caches invariant data between iterations, reduces execution time by 38% on average.


title: Cyber Threat Intelligence – Issue and Challenges

abstract: Today threat landscape evolving at the rapid rate with many organization continuously face complex and malicious cyber threats. Cybercriminal equipped by better skill, organized and well-funded than before. Cyber Threat Intelligence (CTI) has become a hot topic and being under consideration for many organization to counter the rise of cyber-attacks. The aim of this paper is to review the existing research related to CTI. Through the literature review process, the most basic question of what CTI is examines by comparing existing definitions to find common ground or disagreements. It is found that both organization and vendors lack a complete understanding of what information is considered to be CTI, hence more research is needed in order to define CTI. This paper also identified current CTI product and services that include threat intelligence data feeds, threat intelligence standards and tools that being used in CTI. There is an effort by specific industry to shared only relevance threat intelligence data feeds such as Financial Services Information Sharing and Analysis Center (FS-ISAC) that collaborate on critical security threats facing by global financial services sector only. While research and development center such as MITRE working in developing a standards format (e.g.; STIX, TAXII, CybOX) for threat intelligence sharing to solve interoperability issue between threat sharing peers. Based on the review for CTI definition, standards and tools, this paper identifies four research challenges in cyber threat intelligence and analyses contemporary work carried out in each. With an organization flooded with voluminous of threat data, the requirement for qualified threat data analyst to fully utilize CTI and turn the data into actionable intelligence become more important than ever. The data quality is not a new issue but with the growing adoption of CTI, further research in this area is needed.


title: The Synthetic Data Vault

abstract: The goal of this paper is to build a system that automatically creates synthetic data to enable data science endeavors. To achieve this, we present the Synthetic Data Vault (SDV), a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence the name SDV. When implementing the SDV, we also developed an algorithm that computes statistics at the intersection of related database tables. We then used a state-of-the-art multivariate modeling approach to model this data. The SDV iterates through all possible relations, ultimately creating a model for the entire database. Once this model is computed, the same relational information allows the SDV to synthesize data by sampling from any part of the database. After building the SDV, we used it to generate synthetic data for five different publicly available datasets. We then published these datasets, and asked data scientists to develop predictive models for them as part of a crowdsourced experiment. By analyzing the outcomes, we show that synthetic data can successfully replace original data for data science. Our analysis indicates that there is no significant difference in the work produced by data scientists who used synthetic data as opposed to real data. We conclude that the SDV is a viable solution for synthetic data generation.


title: A 2.5D Integrated Voltage Regulator Using Coupled-Magnetic-Core Inductors on Silicon Interposer

abstract: An integrated voltage regulator (IVR) is presented that uses custom fabricated thin-film magnetic power inductors. The inductors are fabricated on a silicon interposer and integrated with a multi-phase buck converter IC by 2.5D chip stacking. Several inductor design variations have been fabricated and tested. The best performance has been achieved with a set of eight coupled inductors that each occupies 0.245 mm2 and provides 12.5 nH with 270 mΩ DC. With early inductor prototypes, the IVR efficiency for a 1.8 V:1.0 V conversion ratio peaks at 71% with FEOL current density of 10.8 A/mm2 and inductor current density of 1.53 A/mm2. At maximum load current, 69% conversion efficiency and 1.8 V:1.2 V conversion ratio the FEOL current density reaches 22.6 A/mm2 and inductor current density reaches 3.21 A/mm2.


title: VADS: Visual attention detection with a smartphone

abstract: Identifying the object that attracts human visual attention is an essential function for automatic services in smart environments. However, existing solutions can compute the gaze direction without providing the distance to the target. In addition, most of them rely on special devices or infrastructure support. This paper explores the possibility of using a smartphone to detect the visual attention of a user. By applying the proposed VADS system, acquiring the location of the intended object only requires one simple action: gazing at the intended object and holding up the smartphone so that the object as well as user's face can be simultaneously captured by the front and rear cameras. We extend the current advances of computer vision to develop efficient algorithms to obtain the distance between the camera and user, the user's gaze direction, and the object's direction from camera. The object's location can then be computed by solving a trigonometric problem. VADS has been prototyped on commercial off-the-shelf (COTS) devices. Extensive evaluation results show that VADS achieves low error (about 1.5° in angle and 0.15m in distance for objects within 12m) as well as short latency. We believe that VADS enables a large variety of applications in smart environments.


title: A Fast Optimization Method for General Binary Code Learning

abstract: Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision, and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this paper, we propose a novel binary code optimization method, dubbed discrete proximal linearized minimization (DPLM) , which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this paper by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised $\ell _{2}$ loss encodes the whole NUS-WIDE database into 64-b binary codes within 10 s on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale data sets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.


title: Interfacial electron accumulation for efficient homo-junction perovskite solar cells

abstract: Abstract Here we study perovskite solar cells based on mesoporous alumina scaffold infiltrated and capped with a perovskite absorber layer, which are devoid of a discrete n-type electron collection layer. We employ ethoxylated polyethylenimine (PEIE) to modify the interface between the perovskite absorber layer and the metallic transparent fluorine-doped SnO 2 (FTO) electrode. Surprisingly, the PEIE interlayer obviates the requirement for the conventional dense-TiO 2 (d-TiO 2 ) compact layer (or organic fullerene layer), usually required to selectively extract electrons from the perovskite film. The self-organized PEIE interlayer produced a strong induced dipole moment at the perovskite-FTO interface, with our results indicating that electrons accumulate within the perovskite film at this interface. The resultant “n-type” contact region within the perovskite absorber layer, progressing to an intrinsic (i) region within the bulk of the perovskite layer, represents an n-i homojunction and favorably enables selective electron extraction at the FTO electrode. Resulting solar cells deliver current-voltage measured power conversion efficiencies (η) of over 15.0% and a substantial stabilized efficiency (η) of 9.1%. Although our solar cell performance remains lower than the highest reported efficiencies for perovskite solar cells employing discrete charge selective extraction layers, it indicates significant potential for “homo-junction” perovskite solar cells, once the metallic-to-perovskite contact is fully controlled. Additionally, our work identifies the potential impact of modifying the interface between the perovskite absorber and the subsequent contact materials with dipolar organic compounds, which may be applicable to optimizing contact at perovskite-semiconductor heterojunctions.


title: Min Norm Point Algorithm for Higher Order MRF-MAP Inference

abstract: Many tasks in computer vision and machine learning can be modelled as the inference problems in an MRF-MAP formulation and can be reduced to minimizing a submodular function. Using higher order clique potentials to model complex dependencies between pixels improves the performance but the current state of the art inference algorithms fail to scale for larger clique sizes. We adapt a well known Min Norm Point algorithm from mathematical optimization literature to exploit the sum of submodular structure found in the MRF-MAP formulation. Unlike some contemporary methods, we do not make any assumptions (other than submodularity) on the type of the clique potentials. Current state of the art inference algorithms for general submodular function takes many hours for problems with clique size 16, and fail to scale beyond. On the other hand, our algorithm is highly efficient and can perform optimal inference in few seconds even on clique size an order of magnitude larger. The proposed algorithm can even scale to clique sizes of many hundreds, unlocking the usage of really large size cliques for MRF-MAP inference problems in computer vision. We demonstrate the efficacy of our approach by experimenting on synthetic as well as real datasets.


title: Recurrent Segmentation for Variable Computational Budgets

abstract: State-of-the-art systems for semantic image segmentation use feed-forward pipelines with fixed computational costs. Building an image segmentation system that works across a range of computational budgets is challenging and time-intensive as new architectures must be designed and trained for every computational setting. To address this problem we develop a recurrent neural network that successively improves prediction quality with each iteration. Importantly, the RNN may be deployed across a range of computational budgets by merely running the model for a variable number of iterations. We find that this architecture is uniquely suited for efficiently segmenting videos. By exploiting the segmentation of past frames, the RNN can perform video segmentation at similar quality but reduced computational cost compared to state-of-the-art image segmentation methods. When applied to static images in the PASCAL VOC 2012 and Cityscapes segmentation datasets, the RNN traces out a speed-accuracy curve that saturates near the performance of state-of-the-art segmentation methods.


title: A novel integrated dielectric-and-conductive ink 3D printing technique for fabrication of microwave devices

abstract: In this work a novel combination of printed electronics using conductive nanoparticle ink together with 3D printing of dielectric material is presented as one integrated process. This allows fabrication of complicated 3D electromagnetic (EM) structures such as wide range of different antennas and microwave devices to simultaneously include printing of conductive nanoparticle ink within 3D dielectric configurations. Characterization of conductive ink and polymer based substrate has been done to ensure proper RF, electric, thermal and mechanical performance of both substrate and the ink. A meander line dipole antenna on a V-shaped substrate is printed and tested to demonstrate the efficiency and accuracy of proposed technique. The goal in this paper is to provide a low cost, environmentally friendly integrated process for the fabrication of geometrically complicated 3D electromagnetic structures.


Things You May Not Know About Android (Un)Packers: A Systematic Study based on Whole-System Emulation


title: Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

abstract: Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates. This has been a notable problem in training deep RL agents to perform web-based tasks, such as booking flights or replying to emails, where a single mistake can ruin the entire sequence of actions. A common remedy is to "warm-start" the agent by pre-training it to mimic expert demonstrations, but this is prone to overfitting. Instead, we propose to constrain exploration using demonstrations. From each demonstration, we induce high-level "workflows" which constrain the allowable actions at each time step to be similar to those in the demonstration (e.g., "Step 1: click on a textbox; Step 2: enter some text"). Our exploration policy then learns to identify successful workflows and samples actions that satisfy these workflows. Workflows prune out bad exploration directions and accelerate the agent’s ability to discover rewards. We use our approach to train a novel neural policy designed to handle the semi-structured nature of websites, and evaluate on a suite of web tasks, including the recent World of Bits benchmark. We achieve new state-of-the-art results, and show that workflow-guided exploration improves sample efficiency over behavioral cloning by more than 100x.


title: Topological Analysis and Gaussian Decision Tree: Effective Representation and Classification of Biosignals of Small Sample Size

abstract: Goal: Bucking the trend of big data, in microdevice engineering, small sample size is common, especially when the device is still at the proof-of-concept stage. The small sample size, small interclass variation, and large intraclass variation, have brought biosignal analysis new challenges. Novel representation and classification approaches need to be developed to effectively recognize targets of interests with the absence of a large training set. Methods: Moving away from the traditional signal analysis in the spatiotemporal domain, we exploit the biosignal representation in the topological domain that would reveal the intrinsic structure of point clouds generated from the biosignal. Additionally, we propose a Gaussian-based decision tree (GDT), which can efficiently classify the biosignals even when the sample size is extremely small. Results: This study is motivated by the application of mastitis detection using low-voltage alternating current electrokinetics (ACEK) where five categories of bisignals need to be recognized with only two samples in each class. Experimental results demonstrate the robustness of the topological features as well as the advantage of GDT over some conventional classifiers in handling small dataset. Conclusion: Our method reduces the voltage of ACEK to a safe level and still yields high-fidelity results with a short assay time. Significance: This paper makes two distinctive contributions to the field of biosignal analysis, including performing signal processing in the topological domain and handling extremely small dataset. Currently, there have been no related works that can efficiently tackle the dilemma between avoiding electrochemical reaction and accelerating assay process using ACEK.


title: A Framework for Fast and Efficient Neural Network Compression

abstract: Network compression reduces the computational complexity and memory consumption of deep neural networks by reducing the number of parameters. In SVD-based network compression, the right rank needs to be decided for every layer of the network. In this paper, we propose an efficient method for obtaining the rank configuration of the whole network. Unlike previous methods which consider each layer separately, our method considers the whole network to choose the right rank configuration. We propose novel accuracy metrics to represent the accuracy and complexity relationship for a given neural network. We use these metrics in a non-iterative fashion to obtain the right rank configuration which satisfies the constraints on FLOPs and memory while maintaining sufficient accuracy. Experiments show that our method provides better compromise between accuracy and computational complexity/memory consumption while performing compression at much higher speed. For VGG-16 our network can reduce the FLOPs by 25% and improve accuracy by 0.7% compared to the baseline, while requiring only 3 minutes on a CPU to search for the right rank configuration. Previously, similar results were achieved in 4 hours with 8 GPUs. The proposed method can be used for lossless compression of neural network as well. The better accuracy and complexity compromise, as well as the extremely fast speed of our method makes it suitable for neural network compression.


title: Soft Sampling for Robust Object Detection

abstract: We study the robustness of object detection under the presence of missing annotations. In this setting, the unlabeled object instances will be treated as background, which will generate an incorrect training signal for the detector. Interestingly, we observe that after dropping 30% of the annotations (and labeling them as background), the performance of CNN-based object detectors like Faster-RCNN only drops by 5% on the PASCAL VOC dataset. We provide a detailed explanation for this result. To further bridge the performance gap, we propose a simple yet effective solution, called Soft Sampling. Soft Sampling re-weights the gradients of RoIs as a function of overlap with positive instances. This ensures that the uncertain background regions are given a smaller weight compared to the hardnegatives. Extensive experiments on curated PASCAL VOC datasets demonstrate the effectiveness of the proposed Soft Sampling method at different annotation drop rates. Finally, we show that on OpenImagesV3, which is a real-world dataset with missing annotations, Soft Sampling outperforms standard detection baselines by over 3%.


title: Approximation Strategies for Incomplete MaxSAT

abstract: Incomplete MaxSAT solving aims to quickly find a solution that attempts to minimize the sum of the weights of the unsatisfied soft clauses without providing any optimality guarantees. In this paper, we propose two approximation strategies for improving incomplete MaxSAT solving. In one of the strategies, we cluster the weights and approximate them with a representative weight. In another strategy, we break up the problem of minimizing the sum of weights of unsatisfiable clauses into multiple minimization subproblems. Experimental results show that approximation strategies can be used to find better solutions than the best incomplete solvers in the MaxSAT Evaluation 2017.


title: Neural Code Comprehension: A Learnable Representation of Code Semantics

abstract: With the recent success of embeddings in natural language processing, research has been conducted into applying similar methods to code analysis. Most works attempt to process the code directly or use a syntactic tree representation, treating it like sentences written in a natural language. However, none of the existing methods are sufficient to comprehend program semantics robustly, due to structural features such as function calls, branching, and interchangeable order of statements. In this paper, we propose a novel processing technique to learn code semantics, and apply it to a variety of program analysis tasks. In particular, we stipulate that a robust distributional hypothesis of code applies to both human- and machine-generated programs. Following this hypothesis, we define an embedding space, inst2vec, based on an Intermediate Representation (IR) of the code that is independent of the source programming language. We provide a novel definition of contextual flow for this IR, leveraging both the underlying data- and control-flow of the program. We then analyze the embeddings qualitatively using analogies and clustering, and evaluate the learned representation on three different high-level tasks. We show that even without fine-tuning, a single RNN architecture and fixed inst2vec embeddings outperform specialized approaches for performance prediction (compute device mapping, optimal thread coarsening); and algorithm classification from raw code (104 classes), where we set a new state-of-the-art.


title: Using Neural Network for Identifying Clickbaits in Online News Media

abstract: Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called Clickbaits. Because of the importance of automatic clickbait detection in online medias, lots of machine learning methods were proposed and employed to find the clickbait headlines. In this research, a model using deep learning methods is proposed to find the clickbaits in Clickbait Challenge 2017's dataset. The proposed model gained the first rank in the Clickbait Challenge 2017 in terms of Mean Squared Error. Also, data analytics and visualization techniques are employed to explore and discover the provided dataset to get more insight from the data.


title: 5G cellular: key enabling technologies and research challenges

abstract: The evolving fifth generation (5G) cellular wireless networks are envisioned to provide higher data rates, enhanced end-user quality-of-experience (QoE), reduced end-to-end latency, and lower energy consumption. This article presents several emerging technologies, which will enable and define the 5G mobile communications standards. The major research problems, which these new technologies breed, as well as the measurement and test challenges for 5G systems are also highlighted.


title: A fault model and mutation testing of access control policies

abstract: To increase confidence in the correctness of specified policies, policy developers can conduct policy testing by supplying typical test inputs (requests) and subsequently checking test outputs (responses) against expected ones. Unfortunately, manual testing is tedious and few tools exist for automated testing of access control policies. We present a fault model for access control policies and a framework to explore it. The framework includes mutation operators used to implement the fault model, mutant generation, equivalent-mutant detection, and mutant-killing determination. This framework allows us to investigate our fault model, evaluate coverage criteria for test generation and selection, and determine a relationship between structural coverage and fault-detection effectiveness. We have implemented the framework and applied it to various policies written in XACML. Our experimental results offer valuable insights into choosing mutation operators in mutation testing and choosing coverage criteria in test generation and selection.


title: Meditation Awareness Training for the Treatment of Sex Addiction: A Case Study

abstract: BackgroundSex addiction is a disorder that can have serious adverse functional consequences. Treatment effectiveness research for sex addiction is currently underdeveloped, and interventions are generally based on the guidelines for treating other behavioral (as well as chemical) addictions. Consequently, there is a need to clinically evaluate tailored treatments that target the specific symptoms of sex addiction. It has been proposed that second-generation mindfulness-based interventions (SG-MBIs) may be an appropriate treatment for sex addiction because in addition to helping individuals increase perceptual distance from craving for desired objects and experiences, some SG-MBIs specifically contain meditations intended to undermine attachment to sex and/or the human body. The current study conducts the first clinical investigation into the utility of mindfulness for treating sex addiction.Case presentationAn in-depth clinical case study was conducted involving an adult male suffering from sex addiction ...


title: Merging datasets through deep learning

abstract: Merging datasets is a key operation for data analytics. A frequent requirement for merging is joining across columns that have different surface forms for the same entity (e.g., the name of a person might be represented as"Douglas Adams"or"Adams, Douglas"). Similarly, ontology alignment can require recognizing distinct surface forms of the same entity, especially when ontologies are independently developed. However, data management systems are currently limited to performing merges based on string equality, or at best using string similarity. We propose an approach to performing merges based on deep learning models. Our approach depends on (a) creating a deep learning model that maps surface forms of an entity into a set of vectors such that alternate forms for the same entity are closest in vector space, (b) indexing these vectors using a nearest neighbors algorithm to find the forms that can be potentially joined together. To build these models, we had to adapt techniques from metric learning due to the characteristics of the data; specifically we describe novel sample selection techniques and loss functions that work for this problem. To evaluate our approach, we used Wikidata as ground truth and built models from datasets with approximately 1.1M people's names (200K identities) and 130K company names (70K identities). We developed models that allow for joins with precision@1 of .75-.81 and recall of .74-.81. We make the models available for aligning people or companies across multiple datasets.


title: Searchbots: Using Chatbots in Collaborative Information-seeking Tasks

abstract: The popularity of messaging platforms such as Slack has given rise to thousands of different chatbots that users can engage with individually or as a group. The proposed dissertation research will investigate the use of searchbots (i.e., chatbots that perform specific search operations) during collaborative information-seeking tasks. Specifically, we will address the following research goals. RG1: Our first research goal will be to investigate the use of searchbots in a collaborative search scenario. The goal of collaborative search is to develop systems that help two or more people collaborate synchronously or asynchronously on information-seeking tasks. Collaborative search systems such as SearchTogether~\cite{Morris2007}, Coagmento~\cite{shah2010coagmento}, CollabSearch~\cite{Yue2012}, and ResultsSpace~\cite{Capra2012} allow users to share information, communicate asynchronously or in real-time, and provide interactive visualizations that raise awareness of each user's search activities, allowing users to learn from each other's search strategies and avoid duplicating work. Prior research shows that while people often search in pairs and in larger groups, they do so without the use of specialized search tools and instead coordinate via "out-of-channel" communication tools such as email, text messaging, phone, and social media~\cite{morris2008survey,Morris2013}. Our first goal will be to investigate the use of searchbots during real-time collaborative search tasks. Our interest in the use of searchbots for collaborative search echoes a suggestion made by Morris~\cite{Morris2013} to develop lightweight collaborative search tools over existing communication platforms.


title: Performance analysis of IEEE 802.11ac/ax WLAN technologies under the presence of CFO

abstract: In the recent twenty years, numerous IEEE 802.11 technologies have been developed for Wireless Local Area Network (WLAN) connectivity. The common point of these technologies is that the latest IEEE 802.11 version has always several advanced features compared to its predecessor. IEEE 802.11ax is one of the upcoming technologies, primarily developed for the Industrial, Security and Medical (ISM) band. IEEE 802.11ax is planned to be released in 2019 and extend the features of IEEE 802.11ac. This paper presents a simulation-based performance analysis of the IEEE 802.11ac/ax technologies on the physical layer (PHY) level. The transmitter and receiver models of both technologies have been implemented and tested in MATLAB. Furthermore, this work explores IEEE 802.11ac/ax performance degradation that may be caused by carrier frequency offset (CFO). Results show lower resistance of IEEE 802.11ax against CFO.


A retrospective case study to empirically validate 4-5 requirements management measures


title: One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning

abstract: Humans and animals are capable of learning a new behavior by observing others perform the skill just once. We consider the problem of allowing a robot to do the same -- learning from a raw video pixels of a human, even when there is substantial domain shift in the perspective, environment, and embodiment between the robot and the observed human. Prior approaches to this problem have hand-specified how human and robot actions correspond and often relied on explicit human pose detection systems. In this work, we present an approach for one-shot learning from a video of a human by using human and robot demonstration data from a variety of previous tasks to build up prior knowledge through meta-learning. Then, combining this prior knowledge and only a single video demonstration from a human, the robot can perform the task that the human demonstrated. We show experiments on both a PR2 arm and a Sawyer arm, demonstrating that after meta-learning, the robot can learn to place, push, and pick-and-place new objects using just one video of a human performing the manipulation.


Augmented Reality: A Review of Applications


title: A Novel Deep Learning Pipeline to Analyze Temporal Clinical Data

abstract: Analysis of clinical temporal data can be difficult due to natural properties that often characterize it. The large number of variables, missing values, and other characteristics lead to issues of sparsity and high dimensional complexity. We hypothesized that a pipeline application implementing relevant deep learning methods could sequentially address these difficulties, demonstrating their combined utility in a classification task. We implemented Word2Vec, t-distributed stochastic neighbor embedding, and a convolutional neural network in a pipeline application. To test the pipeline, we applied it to a simple, binary classification task to identify patient encounter care setting. In preliminary testing, the pipeline application achieved 92% accuracy. It also produced temporal data cubes indicative of clinical encounters in intensive care unit (ICU) and non-ICU care settings. A deep learning pipeline process combining multiple methods holds promise in improving analytical tasks of clinical temporal data.


title: An Aerial Image Recognition Framework using Discrimination and Redundancy Quality Measure

abstract: Aerial image categorization plays an indispensable role in remote sensing and artificial intelligence. In this paper, we propose a new aerial image categorization framework, focusing on organizing the local patches of each aerial image into multiple discriminative subgraphs. The subgraphs reflect both the geometric property and the color distribution of an aerial image. First, each aerial image is decomposed into a collection of regions in terms of their color intensities. Thereby region connected graph (RCG), which models the connection between the spatial neighboring regions, is constructed to encode the spatial context of an aerial image. Second, a subgraph mining technique is adopted to discover the frequent structures in the RCGs constructed from the training aerial images. Thereafter, a set of refined structures are selected among the frequent ones toward being highly discriminative and low redundant. Lastly, given a new aerial image, its sub-RCGs corresponding to the refined structures are extracted. They are further quantized into a discriminative vector for SVM classification. Thorough experimental results validate the e?ectiveness of the proposed method. In addition, the visualized mined subgraphs show that the discriminative topologies of each aerial image are discovered.


title: Context-Aware Activity Recognition and Anomaly Detection in Video

abstract: In this paper, we propose a mathematical framework to jointly model related activities with both motion and context information for activity recognition and anomaly detection. This is motivated from observations that activities related in space and time rarely occur independently and can serve as context for each other. The spatial and temporal distribution of different activities provides useful cues for the understanding of these activities. We denote the activities occurring with high frequencies in the database as normal activities. Given training data which contains labeled normal activities, our model aims to automatically capture frequent motion and context patterns for each activity class, as well as each pair of classes, from sets of predefined patterns during the learning process. Then, the learned model is used to generate globally optimum labels for activities in the testing videos. We show how to learn the model parameters via an unconstrained convex optimization problem and how to predict the correct labels for a testing instance consisting of multiple activities. The learned model and generated labels are used to detect anomalies whose motion and context patterns deviate from the learned patterns. We show promising results on the VIRAT Ground Dataset that demonstrates the benefit of joint modeling and recognition of activities in a wide-area scene and the effectiveness of the proposed method in anomaly detection.


title: Annotating Argument Components and Relations in Persuasive Essays

abstract: In this paper, we present a novel approach to model arguments, their components and relations in persuasive essays in English. We propose an annotation scheme that includes the annotation of claims and premises as well as support and attack relations for capturing the structure of argumentative discourse. We further conduct a manual annotation study with three annotators on 90 persuasive essays. The obtained inter-rater agreement of αU =0 .72 for argument components and α =0 .81 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to substantial agreement. The final corpus and the annotation guidelines are freely available to encourage future research in argument recognition.


Design of 1 X 2 Triangular Shaped Microstrip Patch Antenna Array for WLAN Applications with DGS Structures


title: Steering wheel behavior based estimation of fatigue

abstract: This paper examined a steering behavior based fatigue monitoring system. The advantages of using steering behavior for detecting fatigue are that these systems measure continuously, cheaply, non-intrusively, and robustly even under extremely demanding environmental conditions. The expected fatigue induced changes in steering behavior are a pattern of slow drifting and fast corrective counter steering. Using advanced signal processing procedures for feature extraction, we computed 3 feature set in the time, frequency and state space domain (a total number of 1251 features) to capture fatigue impaired steering patterns. Each feature set was separately fed into 5 machine learning methods (e.g. Support Vector Machine, K-Nearest Neighbor). The outputs of each single classifier were combined to an ensemble classification value. Finally we combined the ensemble values of 3 feature subsets to a of meta-ensemble classification value. To validate the steering behavior analysis, driving samples are taken from a driving simulator during a sleep deprivation study (N=12). We yielded a recognition rate of 86.1% in classifying slight from strong fatigue.


title: Leading change: why transformation efforts fail

abstract: This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.


title: Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce

abstract: Electronic commerce is revolutionizing the way we think about data modeling, by making it possible to integrate the processes of (costly) data acquisition and model induction. The opportunity for improving modeling through costly data acquisition presents itself for a diverse set of electronic commerce modeling tasks, from personalization to customer lifetime value modeling; we illustrate with the running example of choosing offers to display to web-site visitors, which captures important aspects in a familiar setting. Considering data acquisition costs explicitly can allow the building of predictive models at significantly lower costs, and a modeler may be able to improve performance via new sources of information that previously were too expensive to consider. However, existing techniques for integrating modeling and data acquisition cannot deal with the rich environment that electronic commerce presents. We discuss several possible data acquisition settings, the challenges involved in the integration with modeling, and various research areas that may supply parts of an ultimate solution. We also present and demonstrate briefly a unified framework within which one can integrate acquisitions of different types, with any cost structure and any predictive modeling objective.


title: Cognitive Architectures and Autonomy: A Comparative Review

abstract: One of the original goals of artificial intelligenc e (AI) research was to create machines with very general cognitive capabilities and a relatively hig h level of autonomy. It has taken the field longer than many had expected to achieve even a fraction o f this goal; the community has focused on building specific, targeted cognitive processes in isolation, and as of yet no system exists that integrates a broad range of capabilities or present s a general solution to autonomous acquisition of a large set of skills. Among the reasons for this a re the highly limited machine learning and adaptation techniques available, and the inherent c omplexity of integrating numerous cognitive and learning capabilities in a coherent architectur e. In this paper we review selected systems and architectures built expressly to address integrated skills. We highlight principles and features of these systems that seem promising for creating gene rally intelligent systems with some level of autonomy, and discuss them in the context of the de velopment of future cognitive architectures. Autonomy is a key property for any system to be considered generally intelligent, in our view; we use this concept as an organizing principle for com paring the reviewed systems. Features that remain largely unaddressed in present research, but seem nevertheless necessary for such efforts to succeed, are also discussed.


title: Rex: replication at the speed of multi-core

abstract: Standard state-machine replication involves consensus on a sequence of totally ordered requests through, for example, the Paxos protocol. Such a sequential execution model is becoming outdated on prevalent multi-core servers. Highly concurrent executions on multi-core architectures introduce non-determinism related to thread scheduling and lock contentions, and fundamentally break the assumption in state-machine replication. This tension between concurrency and consistency is not inherent because the total-ordering of requests is merely a simplifying convenience that is unnecessary for consistency. Concurrent executions of the application can be decoupled with a sequence of consensus decisions through consensus on partial-order traces, rather than on totally ordered requests, that capture the non-deterministic decisions in one replica execution and to be replayed with the same decisions on others. The result is a new multi-core friendly replicated state-machine framework that achieves strong consistency while preserving parallelism in multi-thread applications. On 12-core machines with hyper-threading, evaluations on typical applications show that we can scale with the number of cores, achieving up to 16 times the throughput of standard replicated state machines.


title: Controlling humanoid robots with human motion data: Experimental validation

abstract: Motion capture is a good source of data for programming humanoid robots because it contains the natural styles and synergies of human behaviors. However, it is difficult to directly use captured motion data because the kinematics and dynamics of humanoid robots differ significantly from those of humans. In our previous work, we developed a controller that allows a robot to maintain balance while tracking a given reference motion that does not include contact state changes. The controller consists of a balance controller based on a simplified robot model and a tracking controller that performs local joint feedback and an optimization process to obtain the joint torques to simultaneously realize balancing and tracking. In this paper, we improve the controller to address the issues related to root position/orientation estimation, model uncertainties, and the difference between expected and actual contact forces. We implement the controller on a full-body, force-controlled humanoid robot. Experimental results demonstrate that the controller can successfully make the robot track captured human motion sequences.


Developing Mathematical Thinking with Scratch An Experiment with 6 th Grade Students Luis


title: Chlorhexidine Gluconate Bathing to Reduce Methicillin-Resistant Staphylococcus aureus Acquisition

abstract: BACKGROUND ::: Methicillin-resistant Staphylococcus aureus (MRSA) is a virulent organism causing substantial morbidity and mortality in intensive care units. Chlorhexidine gluconate, a topical antiseptic solution, is effective against a wide spectrum of gram-positive and gram-negative bacteria, including MRSA. Objectives To examine the impact of a bathing protocol using chlorhexidine gluconate and bath basin management on MRSA acquisition in 5 adult intensive care units and to examine the cost differences between chlorhexidine bathing by using the bath-basin method versus using prepackaged chlorhexidine-impregnated washcloths. ::: ::: ::: METHODS ::: The protocol used a 4-oz bottle of 4% chlorhexidine gluconate soap in a bath basin of warm water. Patients in 3 intensive care units underwent active surveillance for MRSA acquisition; patients in 2 other units were monitored for a new positive culture for MRSA at any site 48 hours after admission. ::: ::: ::: RESULTS ::: Before the protocol, 132 patients acquired MRSA in 34333 patient days (rate ratio, 3.84). Afterwards, 109 patients acquired MRSA in 41376 patient days (rate ratio, 2.63). The rate ratio difference is 1.46 (95% CI, 1.12-1.90; P = .003). The chlorhexidine soap and bath basin method cost $3.18 as compared with $5.52 for chlorhexidine-impregnated wipes (74% higher). ::: ::: ::: CONCLUSIONS ::: The chlorhexidine bathing protocol is easy to implement, cost-effective, and led to decreased unit-acquired MRSA rates in a variety of adult intensive care units.


title: A Core Complex of BBS Proteins Cooperates with the GTPase Rab8 to Promote Ciliary Membrane Biogenesis

abstract: Primary cilium dysfunction underlies the pathogenesis of Bardet-Biedl syndrome (BBS), a genetic disorder whose symptoms include obesity, retinal degeneration, and nephropathy. However, despite the identification of 12 BBS genes, the molecular basis of BBS remains elusive. Here we identify a complex composed of seven highly conserved BBS proteins. This complex, the BBSome, localizes to nonmembranous centriolar satellites in the cytoplasm but also to the membrane of the cilium. Interestingly, the BBSome is required for ciliogenesis but is dispensable for centriolar satellite function. This ciliogenic function is mediated in part by the Rab8 GDP/GTP exchange factor, which localizes to the basal body and contacts the BBSome. Strikingly, Rab8(GTP) enters the primary cilium and promotes extension of the ciliary membrane. Conversely, preventing Rab8(GTP) production blocks ciliation in cells and yields characteristic BBS phenotypes in zebrafish. Our data reveal that BBS may be caused by defects in vesicular transport to the cilium.


title: A four-step camera calibration procedure with implicit image correction

abstract: In geometrical camera calibration the objective is to determine a set of camera parameters that describe the mapping between 3-D reference coordinates and 2-D image coordinates. Various methods for camera calibration can be found from the literature. However surprisingly little attention has been paid to the whole calibration procedure, i.e., control point extraction from images, model fitting, image correction, and errors originating in these stages. The main interest has been in model fitting, although the other stages are also important. In this paper we present a four-step calibration procedure that is an extension to the two-step method. There is an additional step to compensate for distortion caused by circular features, and a step for correcting the distorted image coordinates. The image correction is performed with an empirical inverse model that accurately compensates for radial and tangential distortions. Finally, a linear method for solving the parameters of the inverse model is presented.


title: On the assessment of usability testing methods for children

abstract: The paper motivates the need to acquire methodological knowledge for involving children as test users in usability testing. It introduces a methodological framework for delineating comparative assessments of usability testing methods for children participants. This framework consists in three dimensions: (1) assessment criteria for usability testing methods, (2) characteristics describing usability testing methods and, finally, (3) characteristics of children that may impact upon the process and the result of usability testing. Two comparative studies are discussed in the context of this framework along with implications for future research.


title: Barraquer-Simons syndrome: a rare clinical entity.

abstract: The Barraquer-Simons syndrome or acquired parital lipodystrophy is a rare form of partial lipodystrophy characterized by gradual onset of bilaterally symmetrical subcutaneous fat loss from the face, neck, upper extremities, thorax, and abdomen but sparing the lower extremities. The patients gradually loose their subcutaneous fat in clearly demarcated, generally symmetric areas of the body over several years. Nephropathy, myopathy, deafness, epilepsy, and intellectual disability have also been described. Although the etiology is unknown, heterozygous mutations in the gene encoding one of the nuclear lamina proteins, lamin B2, have been reported in several patients. We here report on a young female patient affected by Barraquer-Simons syndrome, without accompanying renal or central nervous system involvement in whom DNA sequencing did not reveal any mutations in the genes LMNB2, LMNA, PPARG, AGPAT2, BSCL2, CAV1, PTRF, PLIN1, and CIDEC.


title: Data transformation and migration in polystores

abstract: Ever increasing data size and new requirements in data processing has fostered the development of many new database systems. The result is that many data-intensive applications are underpinned by different engines. To enable data mobility there is a need to transfer data between systems easily and efficiently. We analyze the state-of-the-art of data migration and outline research opportunities for a rapid data transfer. Our experiments explore data migration between a diverse set of databases, including PostgreSQL, SciDB, S-Store and Accumulo. Each of the systems excels at specific application requirements, such as transactional processing, numerical computation, streaming data, and large scale text processing. Providing an efficient data migration tool is essential to take advantage of superior processing from that specialized databases. Our goal is to build such a data migration framework that will take advantage of recent advancement in hardware and software.


title: Community Detection with Prior Knowledge

abstract: The problem of community detection is a challenging one because of the presence of hubs and noisy links, which tend to create highly imbalanced graph clusters. Often, these resulting clusters are not very intuitive and difficult to interpret. With the growing availability of network information, there is a significant amount of prior knowledge available about the communities in social, communication and several other networks. These community labels may be noisy and very limited, though they do help in community detection. In this paper, we explore the use of such noisy labeled information for finding high quality communities. We will present an adaptive density-based clustering which allows flexible incorporation of prior knowledge in to the community detection process. We use a random walk framework to compute the node densities and the level of supervision regulates the node densities and the quality of resulting density based clusters. Our framework is general enough to produce both overlapping and non-overlapping clusters. We empirically show that even with a tiny amount of supervision, our approach can produce superior communities compared to popular baselines.


title: Hierarchical Dirichlet processes for tracking maneuvering targets

abstract: We consider the problem of state estimation for a dynamic system driven by unobserved, correlated inputs. We model these inputs via an uncertain set of temporally correlated dynamic models, where this uncertainty includes the number of modes, their associated statistics, and the rate of mode transitions. The dynamic system is formulated via two interacting graphs: a hidden Markov model (HMM) and a linear-Gaussian state space model. The HMM's state space indexes system modes, while its outputs are the unobserved inputs to the linear dynamical system. This Markovian structure accounts for temporal persistence of input regimes, but avoids rigid assumptions about their detailed dynamics. Via a hierarchical Dirichlet process (HDP) prior, the complexity of our infinite state space robustly adapts to new observations. We present a learning algorithm and computational results that demonstrate the utility of the HDP for tracking, and show that it efficiently learns typical dynamics from noisy data.


title: The UKIRT Infrared Deep Sky Survey (UKIDSS): Origins and Highlights

abstract: UKIDSS is now roughly half finished, covering several thousand square degrees, and expected to complete by the middle of 2012. So far 13.5 billion rows of data have been downloaded by several hundred users, resulting in 154 publications concerning high-z quasars, the nearest and coolest brown dwarfs, the evolution of galaxies, the sub-stellar mass function, and many other topics. I summarise the origins and status of UKIDSS, and briefly describe some scientific highlights.


title: A physical model of power amplifiers AM/AM and AM/PM distortions and their internal relationship

abstract: Signal-dependent phase variation, AM/PM, along with amplitude variation, AM/AM, are known to determine nonlinear distortion characteristics of current-mode PAs. However, these distortion effects have been treated separately, putting more weight on the amplitude distortion, while the AM/PM generation mechanisms are yet to be fully understood. Hence, the aim of this work is to present a large-signal physical model that can describe both the AM/AM and AM/PM PA nonlinear distortion characteristics and their internal relationship.


title: A Computer Vision-Inspired Deep Learning Architecture for Virtual Metrology Modeling With 2-Dimensional Data

abstract: The rise of industry 4.0 and data-intensive manufacturing makes advanced process control (APC) applications more relevant than ever for process/production optimization, related costs reduction, and increased efficiency. One of the most important APC technologies is virtual metrology (VM). VM aims at exploiting information already available in the process/system under exam, to estimate quantities that are costly or impossible to measure. Machine learning (ML) approaches are the foremost choice to design VM solutions. A serious drawback of traditional ML methodologies is that they require a features extraction phase that generally limits the scalability and performance of VM solutions. Particularly, in presence of multi-dimensional data, the feature extraction process is based on heuristic approaches that may capture features with poor predictive power. In this paper, we exploit modern deep learning (DL)-based technologies that are able to automatically extract highly informative features from the data, providing more accurate and scalable VM solutions. In particular, we exploit DL architectures developed in the realm of computer vision to model data that have both spatial and time evolution. The proposed methodology is tested on a real industrial dataset related to etching, one of the most important semiconductor manufacturing processes. The dataset at hand contains optical emission spectroscopy data and it is paradigmatic of the feature extraction problem in VM under examination.


title: Individual Swift Trust and Knowledge-Based Trust in Face-to-Face and Virtual Team Members

abstract: Traditionally, trust has been seen as a result of personal knowledge of an individual's past behavior. In this view, trust develops gradually over time based on an individual's cognitive assessment of the other person's behavior. However, high levels of trust have been observed among members of virtual teams, who often have little prior history of working together and may never meet each other in person. To integrate these two seemingly contradictory views of trust, this study manipulated team member characteristics and team member behavior to empirically test a two-stage theoretical model of trust formation and the influence of information and communication technologies (ICT) on trust formation. The results indicate that category-based processing of team member characteristics and an individual's own disposition to trust dominated the initial formation of swift trust. Once individuals accumulated sufficient information to assess a team member's trustworthiness, the effects of swift trust declined and knowledge-based trust formed using team members' behaviors (perceived ability, integrity, and benevolence) became dominant. The use of ICT increased perceived risk of team failure, which reduced the likelihood that team members would engage in future trusting behaviors.


title: A New Action Execution Module for the Learning Intelligent Distribution Agent (LIDA): The Sensory Motor System

abstract: This paper presents a cognitive model for an action execution process—the sensory motor system (SMS)—as a new module of the system-level cognitive model for “Learning Intelligent Distribution Agent” (LIDA). Action execution refers to a situation in which a software agent or robot transforms a selected goal-directed action into low-level executable actions and executes them in the real world. A sensorimotor system derived from the subsumption architecture has been implemented into the SMS; several cognitive neuroscience hypotheses have been incorporated as well, including the two visual systems. A computational SMS has been created inside a LIDA-based software agent in Webots to model the execution of a grip action; its simulated results have been verified against human performance. This computational verification by the comparison of model and human behaviors supports the SMS as a qualitatively reasonable cognitive model for action execution. Finally, the SMS has been compared with other alternative models of action execution; this supports the assertion that our new action execution module is applicable across a range of cognitive architectures.


title: Cellular senescence: when bad things happen to good cells

abstract: Cells continually experience stress and damage from exogenous and endogenous sources, and their responses range from complete recovery to cell death. Proliferating cells can initiate an additional response by adopting a state of permanent cell-cycle arrest that is termed cellular senescence. Understanding the causes and consequences of cellular senescence has provided novel insights into how cells react to stress, especially genotoxic stress, and how this cellular response can affect complex organismal processes such as the development of cancer and ageing.


title: Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction

abstract: We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints. Sufficient conditions are provided, under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. We further extend the proposed algorithm to an asynchronous parallel variant with a near linear speedup. Numerical experiments demonstrate the efficiency of our algorithm in terms of both parameter estimation and computational performance.


title: How do we learn models? Introducing the supposed range vs. real range hypothesis

abstract: We present a general learning model explaining in more depth how we learn (or fail to learn) models and theories. It specifically addresses the phenomenon of preconceptions introduced by the constructivist pedagogical approach. Nature of cognitive conflicts as well as existence, birth and resolution of preconceptions are clarified through a two-stage model based on the formalization of validity conditions of models and theories. Illustration of our hypotheses is provided by various examples, from limited-range models to wide-range scientific theories. Specific consequences on research are also discussed.


title: Dietary interventions for rheumatoid arthritis.

abstract: BACKGROUND ::: The question of what potential benefits and harms are associated with certain dietary regimes used in rheumatoid arthritis is an important one for many patients and health care providers. ::: ::: ::: OBJECTIVES ::: To assess the effectiveness and safety of dietary interventions in the treatment of rheumatoid arthritis. ::: ::: ::: SEARCH STRATEGY ::: We searched the Cochrane Central Register of Controlled Trials (CENTRAL)(The Cochrane Library, issue 1 2008), MEDLINE, EMBASE, AMED, CINAHL and reference lists of relevant articles (up to January 2008), and contacted authors of included articles. ::: ::: ::: SELECTION CRITERIA ::: Randomised controlled trials (RCTs) or controlled clinical trials (CCTs) where the effectiveness of dietary manipulation was evaluated. Dietary supplement studies (including fish oil supplements) were not included. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Two authors independently selected trials for inclusion, assessed the internal validity of included trials and extracted data. Investigators were contacted to obtain missing information. ::: ::: ::: MAIN RESULTS ::: Fourteen RCTs and one CCT, with a total of 837 patients, were included. Due to heterogeneity of interventions and outcomes, baseline imbalance and inadequate data reporting, no overall effects were calculated. A single trial with a moderate risk of bias found that fasting, followed by 13 months on a vegetarian diet, may reduce pain (mean difference (MD) on a 0 to 10 scale -1.89, 95% confidence interval (CI) -3.62 to -0.16), but not physical function or morning stiffness immediately after intervention. Another single trial with a moderate risk of bias found that a 12-week Cretan Mediterranean diet may reduce pain (MD on a 0 to 100 scale -14.00, 95% CI -23.6 to -4.37), but not physical function or morning stiffness immediately after intervention. Two trials compared a 4-week elemental diet with an ordinary diet and reported no significant differences in pain, function or stiffness. Due to inadequate data reporting, the effects of vegan and elimination diets are uncertain. When comparing any dietary manipulation with an ordinary diet we found a significantly higher total drop-out of 10% (risk difference (RD) 0.10, 95% CI 0.02 to 0.18), higher treatment-related drop-out of 5% (RD 0.05, 95% CI -0.03 to 0.14) and a significantly higher weight loss (weighted mean difference -3.23, 95% CI -4.79 to -1.67 kg) in the diet groups compared to the control groups. ::: ::: ::: AUTHORS' CONCLUSIONS ::: The effects of dietary manipulation, including vegetarian, Mediterranean, elemental and elimination diets, on rheumatoid arthritis are still uncertain due to the included studies being small, single trials with moderate to high risk of bias. Higher drop-out rates and weight loss in the groups with dietary manipulation indicate that potential adverse effects should not be ignored.


title: SMSAssassin: crowdsourcing driven mobile-based system for SMS spam filtering

abstract: Due to increase in use of Short Message Service (SMS) over mobile phones in developing countries, there has been a burst of spam SMSes. Content-based machine learning approaches were effective in filtering email spams. Researchers have used topical and stylistic features of the SMS to classify spam and ham. SMS spam filtering can be largely influenced by the presence of regional words, abbreviations and idioms. We have tested the feasibility of applying Bayesian learning and Support Vector Machine(SVM) based machine learning techniques which were reported to be most effective in email spam filtering on a India centric dataset. In our ongoing research, as an exploratory step, we have developed a mobile-based system SMSAssassin that can filter SMS spam messages based on bayesian learning and sender blacklisting mechanism. Since the spam SMS keywords and patterns keep on changing, SMSAssassin uses crowd sourcing to keep itself updated. Using a dataset that we are collecting from users in the real-world, we evaluated our approaches and found some interesting results.


title: An implementation of the 802.1AE MAC Security Standard for in-car networks

abstract: The continuous increase in complexity in automotive electronics has led to cars that include up to 80 Electronic Control Units (ECUs). As a consequence, in-car networks are currently up to their limit in terms of data load, flexibility and bandwidth. The Ethernet backbone is thus considered as the best performing solution. On the other hand, the growing interconnection of cars with the external world requires high security standards in order to prevent safety risks for car passengers. The IEEE 802.1AE MAC Security Standard (MACSec) solves the security issues of Ethernet networks by providing confidentiality, authenticity and integrity of data. This paper presents an efficient hardware implementation of the MACSec standard for the automotive world. The system was synthesized on a Stratix V FPGA and on a 28nm standard-cell CMOS technology. In terms of maximum throughput, the FPGA results in 1.1 Gbps while the standard-cell technology reaches 3.9 Gbps. The FPGA implementation occupies 4.5% of the Adaptive Logic Modules (ALMs) while the standard-cell one gives a 285 kgate size. The proposed architecture represents a suitable implementation for a low area and high-performance solution as usually required by in-car network controllers.


title: Topological Representations of Vector Fields

abstract: This chapter gives an overview on topological methods for vector field processing. After introducing topological features for 2D and 3D vector fields, we discuss how to extract and use them as visualization tools for complex flow phenomena. We do so both for static and dynamic fields. Finally, we introduce further applications of topological methods for compressing, simplifying, comparing, and constructing vector fields.


title: The Effects of Social Media on E-Commerce: A Perspective of Social Impact Theory

abstract: In recent years social media has become more and more popular all around the world. This study aims to examine the influence of social media in the e-commerce context and to find how it impacts users' visit intention and purchase intention. Through a questionnaire survey, we test and analyze the research model and its related hypotheses by making use of structural equation modeling. The results indicate that social media interaction ties and social media commitment positively affect normative social influence and informational social influence. The last one in turn influences visit intention and purchase intention in e-commerce. In conclusion, we discuss the research findings and suggest some implications for researchers and practitioners.


title: Predicting postpartum changes in emotion and behavior via social media

abstract: We consider social media as a promising tool for public health, focusing on the use of Twitter posts to build predictive models about the forthcoming influence of childbirth on the behavior and mood of new mothers. Using Twitter posts, we quantify postpartum changes in 376 mothers along dimensions of social engagement, emotion, social network, and linguistic style. We then construct statistical models from a training set of observations of these measures before and after the reported childbirth, to forecast significant postpartum changes in mothers. The predictive models can classify mothers who will change significantly following childbirth with an accuracy of 71%, using observations about their prenatal behavior, and as accurately as 80-83% when additionally leveraging the initial 2-3 weeks of postnatal data. The study is motivated by the opportunity to use social media to identify mothers at risk of postpartum depression, an underreported health concern among large populations, and to inform the design of low-cost, privacy-sensitive early-warning systems and intervention programs aimed at promoting wellness postpartum.


title: Embedded Systems Architecture for SLAM Applications

abstract: In recent years, we have observed a clear trend in the rapid rise of autonomous vehicles, robotics, virtual reality, and augmented reality. The core technology enabling these applications, Simultaneous Localization And Mapping (SLAM), imposes two main challenges: first, these workloads are computationally intensive and they often have real-time requirements; second, these workloads run on battery-powered mobile devices with limited energy budget. In short, the essence of these challenges is that performance should be improved while simultaneously reducing energy consumption, two rather contradicting goals by conventional wisdom. In this paper, we take a close look at state-of-the-art Simultaneous Localization And Mapping (SLAM) workloads, especially how these workloads behave on mobile devices. Based on the results, we propose a mobile architecture to improve SLAM performance on mobile devices.


title: Substrate Integrated Waveguide (SIW) Leaky-Wave Antenna With Transverse Slots

abstract: A novel slotted substrate integrated waveguide (SIW) leaky-wave antenna is proposed. This antenna works in the TE10 mode of the SIW. Leakage is obtained by introducing a periodic set of transverse slots on the top of the SIW, which interrupt the current flow on the top wall. It is seen that three modes (a leaky mode, a proper waveguide mode, and a surface-wave-like mode) can all propagate on this structure. The wavenumbers of the modes are calculated theoretically and are numerically evaluated by HFSS simulation. The leakage loss, dielectric loss, and conductor loss are also analyzed. A uniform slotted SIW leaky-wave antenna is designed that has good beam scanning from near broadside (though not exactly at broadside) to forward endfire. This type of SIW leaky-wave antenna has a wide impedance bandwidth and a narrow beam that scans with frequency. Measured results are consistent with the simulation and the theoretical analysis.


title: Factorized Geometrical Autofocus for Synthetic Aperture Radar Processing

abstract: This paper describes a factorized geometrical autofocus (FGA) algorithm, specifically suitable for ultrawideband synthetic aperture radar. The strategy is integrated in a fast factorized back-projection chain and relies on varying track parameters step by step to obtain a sharp image; focus measures are provided by an object function (intensity correlation). The FGA algorithm has been successfully applied on synthetic and real (Coherent All RAdio BAnd System II) data sets, i.e., with false track parameters introduced prior to processing, to set up constrained problems involving one geometrical quantity. Resolution (3 dB in azimuth and slant range) and peak-to-sidelobe ratio measurements in FGA images are comparable with reference results (within a few percent and tenths of a decibel), demonstrating the capacity to compensate for residual space variant range cell migration. The FGA algorithm is finally also benchmarked (visually) against the phase gradient algorithm to emphasize the advantage of a geometrical autofocus approach.


title: Credit Risk Management and Financial Performance of Microfinance Institutions in Kampala, Uganda

abstract: The objective of this study was to evaluate whether relationship exist between credit risk management techniques and financial performance of microfinance institutions in Kampala, Uganda. Specifically, the study examined whether there is a relationship between credit risk identification, credit risk appraisal, credit risk monitoring, credit risk mitigation and financial performance of microfinance institutions in Kampala using sample of 60 members of staff in finance and credit departments of three licensed microfinance institutions in Kampala, Uganda namely Finca Uganda Ltd, Pride Microfinance Ltd, UGAFODE Microfinance Ltd. Primary data was collected using questionnaires and it comprised of closed ended questions. Secondary data was collected from the microfinance institutions (MDI’s) annual reports (2011 - 2015). Frequencies and descriptive statistics were used to analyse the population. Pearson linear correlation coefficient was adopted to examine relationship between credit risk management techniques and financial performance. The findings indicate that credit risk identification and credit risk appraisal has a strong positive relationship on financial performance of MDIs, while credit risk monitoring and credit risk mitigation have moderate significant positive relationship on financial performance of MDIs. The study recommends, among others, that the credit risk appraisal process should identify and analyse all loss exposures, and measure such loss exposures. This should guide in selection of technique or combination of techniques to handle each exposure. The study concludes that MDIs should continually emphasise effective credit risk identification, credit risk appraisal, credit risk monitoring, and credit risk mitigation techniques to enhance maximum financial performance.


title: An UNet-Based Head Shoulder Segmentation Network

abstract: Within the rapidly developing field of computer vision, pedestrian detection is a fundamental and challenging task for both industry and academia. However, object segmentation information can help the network to capture the attention of the model during training. In this paper, we propose a head-shoulder segmentation network based on modified U-Net network. The architecture consists of a contracting path to capture information from a lower layer and a symmetric expanding path to enable precise localization. The proposed model aims to effectively segment the head-shoulder portion of pedestrian without a huge annotated training sample. Segmentation of a random image takes less than a second on NVIDIA GTX 1070. This paper will show the mean IOU and some segmentation results to prove effectiveness of this model.


title: YouTube-VOS: Sequence-to-Sequence Video Object Segmentation

abstract: Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentation techniques, and methods capturing temporal dependency for segmentation have to depend on pretrained optical flow models, leading to suboptimal solutions for the problem. End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i.e., even the largest video segmentation dataset only contains 90 short video clips. To solve this problem, we build a new large-scale video object segmentation dataset called YouTube Video Object Segmentation dataset (YouTube-VOS). Our dataset contains 3,252 YouTube video clips and 78 categories including common objects and human activities. This is by far the largest video object segmentation dataset to our knowledge and we have released it at https://youtube-vos.org. Based on this dataset, we propose a novel sequence-to-sequence network to fully exploit long-term spatial-temporal information in videos for segmentation. We demonstrate that our method is able to achieve the best results on our YouTube-VOS test set and comparable results on DAVIS 2016 compared to the current state-of-the-art methods. Experiments show that the large scale dataset is indeed a key factor to the success of our model.


title: Multi-Modal Coreference Resolution with the Correlation between Space Structures

abstract: Multi-modal data is becoming more common in big data background. Finding the semantically similar objects from different modality is one of the heart problems of multi-modal learning. Most of the current methods try to learn the inter-modal correlation with extrinsic supervised information, while intrinsic structural information of each modality is neglected. The performance of these methods heavily depends on the richness of training samples. However, obtaining the multi-modal training samples is still a labor and cost intensive work. In this paper, we bring a extrinsic correlation between the space structures of each modalities in coreference resolution. With this correlation, a semi-supervised learning model for multi-modal coreference resolution is proposed. We firstly extract high-level features of images and text, then compute the distances of each object from some reference points to build the space structure of each modality. With a shared reference point set, the space structures of each modality are correlated. We employ the correlation to build a commonly shared space that the semantic distance between multi-modal objects can be computed directly. The experiments on two multi-modal datasets show that our model performs better than the existing methods with insufficient training data.


title: The Anti-vaccination Movement: A Regression in Modern Medicine

abstract: There have been recent trends of parents in Western countries refusing to vaccinate their children due to numerous reasons and perceived fears. While opposition to vaccines is as old as the vaccines themselves, there has been a recent surge in the opposition to vaccines in general, specifically against the MMR (measles, mumps, and rubella) vaccine, most notably since the rise in prominence of the notorious British ex-physician, Andrew Wakefield, and his works. This has caused multiple measles outbreaks in Western countries where the measles virus was previously considered eliminated. This paper evaluates and reviews the origins of the anti-vaccination movement, the reasons behind the recent strengthening of the movement, role of the internet in the spread of anti-vaccination ideas, and the repercussions in terms of public health and safety.


title: Developing Socially-Constructed Quality Metrics in Agile: A Multi-Faceted Perspective

abstract: This research proposes development of socially-constructed metrics for quality ::: assessment and improvement in Agile Software Development (ASD) projects. The first ::: phase of our research includes an extensive literature review, which indicates that ::: traditional (outcome-focused) metrics that evaluate quality are not directly transferable ::: to adaptive, ASD projects. We then conduct semi-structured interviews confirming the ::: necessity of considering people and process aspects for quality considerations in agile. ::: We propose three dimensions for composite metrics in ASD, namely, (1) evidence (2) ::: expectation and (3) critical evaluation. This combines quantitative and qualitative ::: information drawn from people, process, and outcome-related factors. The proposed ::: model allows ASD teams to concurrently conduct quality assessment and improvement ::: during their projects, producing innovative metrics, adhering to the core principles of ::: the agile manifesto. In our next research stage, this reference model will be tested and ::: validated in practice.


title: Data-driven biological network alignment that uses topological, sequence, and functional information

abstract: Many proteins remain functionally unannotated. Sequence alignment (SA) uncovers missing annotations by transferring functional knowledge between species' sequence-conserved regions. Because SA is imperfect, network alignment (NA) complements SA by transferring functional knowledge between conserved biological network, rather than just sequence, regions of different species. Existing NA assumes that it is topological similarity (isomorphic-like matching) between network regions that corresponds to the regions' functional relatedness. However, we recently found that functionally unrelated proteins are almost as topologically similar as functionally related proteins. So, we redefined NA as a data-driven framework, TARA, which learns from network and protein functional data what kind of topological relatedness (rather than similarity) between proteins corresponds to the proteins' functional relatedness. TARA used topological information (within each network) but not sequence information (between proteins across networks). Yet, its alignments yielded higher protein functional prediction accuracy than alignments of existing NA methods, even those that used both topological and sequence information. Here, we propose TARA++ that is also data-driven, like TARA and unlike other existing methods, but that uses across-network sequence information on top of within-network topological information, unlike TARA. To deal with the within-and-across-network analysis, we adapt social network embedding to the problem of biological NA. TARA++ outperforms protein functional prediction accuracy of existing methods.


title: How Symbiosis Can Guide Evolution

abstract: Hinton and Nowlan have demonstrated a model of how lifetime plasticity can guide evolution. They show how acquired traits change the shape of the reward landscape in which subsequent genetic variation takes place, and in so doing encourage the discovery of equivalent heritable traits. This enables the seemingly Lamarkian inheritance of acquired characteristics without the direct transfer of information from the phenotype to the genotype. This paper draws direct inspiration from their work to illustrate a different phenomenon. We demonstrate how the formation of symbiotic relationships in an ecosystem can guide the course of subsequent genetic variation. This phenomenon can be described as two phases: First, symbiotic groups find solutions where individual organisms cannot, simply because lifetime interaction produces new combinations of abilities more rapidly than the relatively slow genetic variation of individuals. Second, these symbiotic groups subsequently change the shape of the reward landscape for evolution, providing a gradient that guides genetic variation to the same solution. Ultimately, an individual organism exhibits the capabilities formerly exhibited by the group. This process enables the combination of characteristics from organisms of distinct species without direct transfer of genetic information.


title: High-Performance and Lightweight Transaction Support in Flash-Based SSDs

abstract: Flash memory has accelerated the architectural evolution of storage systems with its unique characteristics compared to magnetic disks. The no-overwrite property of flash memory naturally supports transactions, a commonly used mechanism in systems to provide consistency. However, existing embedded transaction designs in flash-based Solid State Drives (SSDs) either limit the transaction concurrency or introduce high overhead in tracking transaction states. This leads to low or unstable SSD performance. In this paper, we propose a transactional SSD (TxSSD) architecture, LightTx, to enable better concurrency and low overhead. First, LightTx improves transaction concurrency arbitrarily by using a page-independent commit protocol. Second, LightTx tracks the recent updates by leveraging the near-log-structured update property of SSDs and periodically retires dead transactions to reduce the transaction state tracking cost. Experiments show that LightTx achieves nearly the lowest overhead in garbage collection, memory consumption and mapping persistence compared to existing embedded transaction designs. LightTx also provides up to 20.6 percent performance improvement due to improved transaction concurrency.


title: Evaluation of MPEG-7 shape descriptors against other shape descriptors

abstract: Shape is an important image feature-it is one of the primary low level image features exploited in content-based image retrieval (CBIR). There are generally two types of shape descriptors in the literature: contour-based and region-based. In MPEG-7, the curvature scale space descriptor (CSSD) and Zernike moment descriptor (ZMD) have been adopted as the contour-based shape descriptor and region-based shape descriptor, respectively. In this paper, the two shape descriptors are evaluated against other shape descriptors, and the two shape descriptors are also evaluated against each other. Standard methodology is used in the evaluation. Specifically, we use standard databases, large data sets and query sets, commonly used performance measurement and guided principles. A Java-based client-server retrieval framework has been implemented to facilitate the evaluation. Results show that Fourier descriptor (FD) outperforms CSSD, and that CSSD can be replaced by either FD or ZMD.


title: A Review and Proof of Concept for Phishing Scam Detection and Response using Apoptosis

abstract: Phishing scam is a well-known fraudulent activity in which victims are tricked to reveal their confidential information especially those related to financial information. There are various phishing schemes such as deceptive phishing, malware based phishing, DNS-based phishing and many more. Therefore in this paper, a systematic review analysis on existing works related with the phishing detection and response techniques together with apoptosis have been further investigated and evaluated. Furthermore, one case study to show the proof of concept how the phishing works is also discussed in this paper. This paper also discusses the challenges and the potential research for future work related with the integration of phishing detection model and response with apoptosis. This research paper also can be used as a reference and guidance for further study on phishing detection and response.


title: Speech enhancement with missing data techniques using recurrent neural networks

abstract: This paper presents an application of missing data techniques in speech enhancement. The enhancement system consists of two stages: the first stage uses a recurrent neural network, which is supplied with noisy speech and produces enhanced speech; whereas the second stage uses missing data techniques to further improve the quality of enhanced speech. The results suggest that combining missing data technique with RNN enhancement is an effective enhancement scheme resulting in a 16 dB background noise reduction for all input signal to noise ratio (SNR) conditions from -5 to 20 dB, improved spectral quality and robust automatic speech recognition performance.


title: Automated concolic testing of smartphone apps

abstract: We present an algorithm and a system for generating input events to exercise smartphone apps. Our approach is based on concolic testing and generates sequences of events automatically and systematically. It alleviates the path-explosion problem by checking a condition on program executions that identifies subsumption between different event sequences. We also describe our implementation of the approach for Android, the most popular smartphone app platform, and the results of an evaluation that demonstrates its effectiveness on five Android apps.


title: Online Resource Allocation for Arbitrary User Mobility in Distributed Edge Clouds

abstract: As clouds move to the network edge to facilitate mobile applications, edge cloud providers are facing new challenges on resource allocation. As users may move and resource prices may vary arbitrarily, %and service delays are heterogeneous, resources in edge clouds must be allocated and adapted continuously in order to accommodate such dynamics. In this paper, we first formulate this problem with a comprehensive model that captures the key challenges, then introduce a gap-preserving transformation of the problem, and propose a novel online algorithm that optimally solves a series of subproblems with a carefully designed logarithmic objective, finally producing feasible solutions for edge cloud resource allocation over time. We further prove via rigorous analysis that our online algorithm can provide a parameterized competitive ratio, without requiring any a priori knowledge on either the resource price or the user mobility. Through extensive experiments with both real-world and synthetic data, we further confirm the effectiveness of the proposed algorithm. We show that the proposed algorithm achieves near-optimal results with an empirical competitive ratio of about 1.1, reduces the total cost by up to 4x compared to static approaches, and outperforms the online greedy one-shot optimizations by up to 70%.


title: A Characterization of Monotone Influence Measures for Data Classification

abstract: In this work we focus on the following question: how important was the i-th feature in determining the outcome for a given datapoint? We identify a family of influence measures; functions that, given a datapoint x, assign a value phi_i(x) to every feature i, which roughly corresponds to that i's importance in determining the outcome for x. This family is uniquely derived from a set of axioms: desirable properties that any reasonable influence measure should satisfy. Departing from prior work on influence measures, we assume no knowledge of - or access to - the underlying classifier labelling the dataset. In other words, our influence measures are based on the dataset alone, and do not make any queries to the classifier. While this requirement naturally limits the scope of explanations we provide, we show that it is effective on real datasets.


title: Agile Modeling with the UML

abstract: This paper discusses a model-based approach to software development. It argues that an approach using models as central development artifact needs to be added to the portfolio of software engineering techniques, to further increase efficiency and flexibility of the development as well as quality and reusability of the results. Two major and strongly related techniques are identified and discussed: Test case modeling and an evolutionary approach to model transformation.


title: Omni-directional robot with spherical orthogonal wheels: concepts and analyses

abstract: This article addresses the omni directional mechanical architectures problematic for mobile robots using 3 motorized axles with 2 spherical orthogonal wheels. These wheels transmit a power torque according to a direction and are completely free with respect to the two others. They are used on 2 mechanical structures: SM1 and SM2. The complete kinematic study emphasizes the facility of controlling such omni-directional robots, and examines the problems involved in the transition of the wheel-to-floor contact on each axle. From this double study emerges an optimal theoretical architecture, but difficult to realize mechanically. We then present a controller which compensates for the errors related to the simplest mechanical structure; and it is validated on a mobile robot ROMNI using mechanical structure SM1


title: Computational Aesthetic Evaluation of Logos

abstract: Computational aesthetics has become an active research field in recent years, but there have been few attempts in computational aesthetic evaluation of logos. In this article, we restrict our study on black-and-white logos, which are professionally designed for name-brand companies with similar properties, and apply perceptual models of standard design principles in computational aesthetic evaluation of logos. We define a group of metrics to evaluate some aspects in design principles such as balance, contrast, and harmony of logos. We also collect human ratings of balance, contrast, harmony, and aesthetics of 60 logos from 60 volunteers. Statistical linear regression models are trained on this database using a supervised machine-learning method. Experimental results show that our model-evaluated balance, contrast, and harmony have highly significant correlation of over 0.87 with human evaluations on the same dimensions. Finally, we regress human-evaluated aesthetics scores on model-evaluated balance, contrast, and harmony. The resulted regression model of aesthetics can predict human judgments on perceived aesthetics with a high correlation of 0.85. Our work provides a machine-learning-based reference framework for quantitative aesthetic evaluation of graphic design patterns and also the research of exploring the relationship between aesthetic perceptions of human and computational evaluation of design principles extracted from graphic designs.


title: Detecting bad smells in source code using change history information

abstract: Code smells represent symptoms of poor implementation choices. Previous studies found that these smells make source code more difficult to maintain, possibly also increasing its fault-proneness. There are several approaches that identify smells based on code analysis techniques. However, we observe that many code smells are intrinsically characterized by how code elements change over time. Thus, relying solely on structural information may not be sufficient to detect all the smells accurately. We propose an approach to detect five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy, by exploiting change history information mined from versioning systems. We applied approach, coined as HIST (Historical Information for Smell deTection), to eight software projects written in Java, and wherever possible compared with existing state-of-the-art smell detectors based on source code analysis. The results indicate that HIST's precision ranges between 61% and 80%, and its recall ranges between 61% and 100%. More importantly, the results confirm that HIST is able to identify code smells that cannot be identified through approaches solely based on code analysis.