title: Finding Dominators via Disjoint Set Union

abstract: The problem of finding dominators in a directed graph has many important applications, notably in global optimization of computer code. Although linear and near-linear-time algorithms exist, they use sophisticated data structures. We develop an algorithm for finding dominators that uses only a ''static tree'' disjoint set data structure in addition to simple lists and maps. The algorithm runs in near-linear or linear time, depending on the implementation of the disjoint set data structure. We give several versions of the algorithm, including one that computes loop nesting information (needed in many kinds of global code optimization) and that can be made self-certifying, so that the correctness of the computed dominators is very easy to verify.


title: Big Data Analytics for Emergency Communication Networks: A Survey

abstract: Disaster management is a crucial and urgent research issue. Emergency communication networks (ECNs) provide fundamental functions for disaster management, because communication service is generally unavailable due to large-scale damage and restrictions in communication services. Considering the features of a disaster (e.g., limited resources and dynamic changing of environment), it is always a key problem to use limited resources effectively to provide the best communication services. Big data analytics in the disaster area provides possible solutions to understand the situations happening in disaster areas, so that limited resources can be optimally deployed based on the analysis results. In this paper, we survey existing ECNs and big data analytics from both the content and the spatial points of view. From the content point of view, we survey existing data mining and analysis techniques, and further survey and analyze applications and the possibilities to enhance ECNs. From the spatial point of view, we survey and discuss the most popular methods and further discuss the possibility to enhance ECNs. Finally, we highlight the remaining challenging problems after a systematic survey and studies of the possibilities.


title: ROS-Based SLAM for a Gazebo-Simulated Mobile Robot in Image-Based 3D Model of Indoor Environment

abstract: Nowadays robot simulators have robust physics engines, high-quality graphics, and convenient interfaces, affording researchers to substitute physical systems with their simulation models in order to pre-estimate the performance of theoretical findings before applying them to real robots. This paper describes Gazebo simulation approach to simultaneous localization and mapping SLAM based on Robot Operating System ROS using PR2 robot. The ROS-based SLAM approach applies Rao-Blackwellized particle filters and laser data to locate the PR2 robot in unknown environment and build a map. The real room 3D model was obtained from camera shots and reconstructed with Autodesk 123D Catch and MeshLab software. The results demonstrate the fidelity of the simulated 3D room to the obtained from the robot laser system ROS-calculated map and the feasibility of ROS-based SLAM with a Gazebo-simulated mobile robot to its usage in camera-based 3D environment. This approach will be further extended to ROS-based robotic simulations in Gazebo with a Russian anthropomorphic robot AR-601M.


title: Developing a hybrid intelligent model for constructing a size recommendation expert system in textile industries

abstract: Purpose – In shopping, for selecting the appropriate garments, people have to try on multiple garments. This problem is due to lack of a sizing system based on updated anthropometric data and the classification system that introduces the appropriate size from the sizing chart to each person. To solve this problem, as a first study in the literature, a hybrid intelligent classification model as a size recommendation expert system is proposed. The paper aims to discuss these issues. Design/methodology/approach – Three stages for developing a hybrid intelligent classification system based on data clustering and probabilistic neural network (PNN) are proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference for developing a new intelligent classification system by using a PNN. At the last stage, the accuracy of the proposed model is evaluated by using the Iranian male's body type data set. Findings – E...


title: An Ensemble Multi-Label Feature Selection Algorithm Based on Information Entropy

abstract: In multilabel classification, feature selection is able to remove redundant and irrelevant features, which makes the classifiers faster and improves the prediction performance of the classifiers. Currently, most of feature selection algorithms in multilabel classification are dependent on the con crete classifier, which leads to high computation complexity. Hence this paper proposes an Ensemble Multilabel Feature Sele ction algorithm based on Information Entropy (EMFSIE), which is independent on any concrete classifiers. Its core idea consists of: Employing the information gain to evaluate the correlation between the feature and the label set, and filtering out useful features more effectively. We calculate the information gain in an ensemble framework and filter out useful features according to the threshold value determined by the effective factor. We validate EMFSIE on four datasets from two domains using four different multilabel classifiers. The ex perimental results and their analysis show preliminarily that EMFSIE can not only remove more than 70% of original features, which makes the classifiers faster, but also keep the prediction performance of the classifiers as good as before, even enhance the prediction performance on three datasets underthe twotailed pttests at 0.05 significance level.


title: Advanced Wireless LAN Technologies: IEEE 802.11AC and Beyond

abstract: In 2013, global mobile data traffic grew 81% and it is projected to increase 11-fold between 2013 and 2018. Further, it is predicted that by 2018, over two-thirds of the world's mobile traffic will be video and more than half of all traffic from wireless connected devices will be offloaded to Wi-Fi networks and femto-cells [1]. Consequently, wireless LANs need major upgrades to improve both throughput and efficiency. IEEE 802.11ac is an amendment to the 802.11 standard that was just ratified by IEEE 802.11. Promising up to gigabit data rates, many Wi-Fi products are being built based on this specification. In addition to technologies that improve throughput, IEEE 802.11ax is investigating and evaluating advanced wireless technologies that enable more efficient utilization of the existing spectrum.


title: Securing cloud infrastructure against co-resident DoS attacks using game theoretic defense mechanisms

abstract: Evolution in cloud services and infrastructure has been constantly reshaping the way we conduct business and provide services in our day to day lives. Tools and technologies created to improve such cloud services can also be used to impair them. By using generic tools like nmap, hping and wget, one can estimate the placement of virtual machines in a cloud infrastructure with a high likelihood. Moreover, such knowledge and tools can also be used by adversaries to further launch various kinds of attacks. In this paper we focus on one such specific kind of attack, namely a denial of service (DoS), where an attacker congests a bottleneck network channel shared among virtual machines (VMs) coresident on the same physical node in the cloud infrastructure. We evaluate the behavior of this shared network channel using Click modular router on DETER testbed. We illustrate that game theoretic concepts can be used to model this attack as a two-player game and recommend strategies for defending against such attacks.


title: Reinforcement learning based local search for grouping problems: A case study on graph coloring

abstract: Grouping problems naturally arise in numerous domains.This paper proposes a reinforcement learning based local search approach for grouping problems.The proposed approach is applied to the well-known graph coloring problem.Computational results are presented on a set of benchmark instances. Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of computational problems including clustering and classification that frequently appear in expert and intelligent systems as well as many real applications. This paper focuses on developing a general-purpose solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with local search. This paper makes the following contributions: we show that (1) reinforcement learning can help obtain useful information from discovered local optimum solutions; (2) the learned information can be advantageously used to guide the search algorithm towards promising regions. To the best of our knowledge, this is the first attempt to propose a formal model that combines reinforcement learning and local search for solving grouping problems. The proposed approach is verified on a well-known representative grouping problem (graph coloring). The generality of the approach makes it applicable to other grouping problems.


title: Spatiotemporal anomaly detection through visual analysis of geolocated Twitter messages

abstract: Analyzing message streams from social blogging services such as Twitter is a challenging task because of the vast number of documents that are produced daily. At the same time, the availability of geolocated, realtime, and manually created status updates are an invaluable data source for situational awareness scenarios. In this work we present an approach that allows for an interactive analysis of location-based microblog messages in realtime by means of scalable aggregation and geolocated text visualization. For this purpose, we use a novel cluster analysis approach and distinguish between local event reports and global media reaction to detect spatiotemporal anomalies automatically. A workbench allows the scalable visual examination and analysis of messages featuring perspective and semantic layers on a world map representation. Our novel techniques can be used by analysts to classify the presented event candidates and examine them on a global scale.


title: Business Intelligence and Business Process Management in Banking Operations

abstract: Success of banking operations is strongly correlated with the quality of customer relations and efficacy of banks processes. Banks seek means for efficient analysis of vast amount of gathered data from their IT systems. They are exploiting business intelligence (BI) technology to analyze every aspect of their data to understand behavior of their clients, striving to satisfy client's needs, in an endless race for a competitive advantage in the market. The intention of this paper is to review and discuss the most significant applications of business intelligence in banking as well as point to new technology trends that will affect bank's development.


title: Exploiting Semantic Information and Deep Matching for Optical Flow

abstract: We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving. We build on the observation that the scene is typically composed of a static background, as well as a relatively small number of traffic participants which move rigidly in 3D. We propose to estimate the traffic participants using instance-level segmentation. For each traffic participant, we use the epipolar constraints that govern each independent motion for faster and more accurate estimation. Our second contribution is a new convolutional net that learns to perform flow matching, and is able to estimate the uncertainty of its matches. This is a core element of our flow estimation pipeline. We demonstrate the effectiveness of our approach in the challenging KITTI 2015 flow benchmark, and show that our approach outperforms published approaches by a large margin.


title: SNR loss : A new objective measure for predicting the intelligibility of noise-suppressed speech

abstract: Most of the existing intelligibility measures do not account for the distortions present in processed speech, such as those introduced by speech-enhancement algorithms. In the present study, we propose three new objective measures that can be used for prediction of intelligibility of processed (e.g., via an enhancement algorithm) speech in noisy conditions. All three measures use a critical-band spectral representation of the clean and noise-suppressed signals and are based on the measurement of the SNR loss incurred in each critical band after the corrupted signal goes through a speech enhancement algorithm. The proposed measures are flexible in that they can provide different weights to the two types of spectral distortions introduced by enhancement algorithms, namely spectral attenuation and spectral amplification distortions. The proposed measures were evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech (consonants and sentences) corrupted by four different maskers (car, babble, train and street interferences). Highest correlation (r=-0.85) with sentence recognition scores was obtained using a variant of the SNR loss measure that only included vowel/consonant transitions and weak consonant information. High correlation was maintained for all noise types, with a maximum correlation (r=-0.88) achieved in street noise conditions.


title: Goodness: A Method for Measuring Machine Translation Confidence

abstract: State-of-the-art statistical machine translation (MT) systems have made significant progress towards producing user-acceptable translation output. However, there is still no efficient way for MT systems to inform users which words are likely translated correctly and how confident it is about the whole sentence. We propose a novel framework to predict word-level and sentence-level MT errors with a large number of novel features. Experimental results show that the MT error prediction accuracy is increased from 69.1 to 72.2 in F-score. The Pearson correlation between the proposed confidence measure and the human-targeted translation edit rate (HTER) is 0.6. Improvements between 0.4 and 0.9 TER reduction are obtained with the n-best list reranking task using the proposed confidence measure. Also, we present a visualization prototype of MT errors at the word and sentence levels with the objective to improve post-editor productivity.


title: A simulator for UAVs management in agriculture domain

abstract: Drones or Unmanned Aerial Vehicles (UAVs) receive a growing interest for agricultural purposes. The aim is to provide inspiring insights in this domain from a technological and computational point of view. In these last years, indeed, there is an enormous potential that UAV technologies presents to support the agricultural domain in monitoring the land for checking and countering the presence of parasites that can damage the crop. However, to properly manage a UAVs team, equipped with multiple sensors and actuators, it is necessary to test these technologies and design proper strategies and coordination techniques able to efficiently manage the team. At this purpose, the paper proposes a simulator suitable for the agriculture domain in order to design novel coordination and control techniques of a UAVs team. Moreover it is possible to define the main variables and parameters of this domain of interest. The work presented many coordination techniques both for monitoring the area and both for coordinating the actions of the drones in the presence of parasites in order to analyze how the performance can significantly change if more constraints, such as energy, communication range, resource capacities, are accounted.


title: Efficient safe corridor navigation with jerk limited trajectory for quadrotors

abstract: Quadrotors commonly operate in low attitude and obstacle-strewn environment. To guarantee its safety, it has to be operated inside the certain safe area. This paper presents an efficient technique to generated jerk limited trajectory that leads a quadrotor to travel inside such safe area. By limiting the trajectory's velocity, acceleration, and jerk, it is made feasible regarding the vehicle's physical limits. The underlying trajectory generation algorithm is extremely efficient and takes only several microseconds to produce the results. Finally, the algorithm is tested with real flight experiments.


title: TextDream: Conditional Text Generation by Searching in the Semantic Space

abstract: Conditional text generation is a fundamental task in natural language generation. Traditional conditional generative models build conditional probability distributions over the given labels. However, categorical label information is usually very abstract, e.g., sentiment, and it is difficult to be disentangled from the content. Therefore, instead of generating text by modeling conditional probability distribution, we propose a novel text generation method TextDream through searching in the semantic space. Specifically, in this method, a random text seed is initially given and the new text is generated by local search operation. The generation procedure is guided by a fitness function, typically a classification model. Text with higher fitness will be preserved. This procedure loops until the qualified solution is found. Experimental results show that our method is able to generate more diverse text compared with advanced conditional generative models.


title: Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends

abstract: The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called “big data” challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data.The MapReduce programming framework uses two tasks common in functional programming: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters. Compared with existing parallel processing paradigms (e.g. grid computing and graphical processing unit (GPU)), MapReduce and Hadoop have two advantages: 1) fault-tolerant storage resulting in reliable data processing by replicating the computing tasks, and cloning the data chunks on different computing nodes across the computing cluster; 2) high-throughput data processing via a batch processing framework and the Hadoop distributed file system (HDFS). Data are stored in the HDFS and made available to the slave nodes for computation.In this paper, we review the existing applications of the MapReduce programming framework and its implementation platform Hadoop in clinical big data and related medical health informatics fields. The usage of MapReduce and Hadoop on a distributed system represents a significant advance in clinical big data processing and utilization, and opens up new opportunities in the emerging era of big data analytics. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools. This paper is concluded by summarizing the potential usage of the MapReduce programming framework and Hadoop platform to process huge volumes of clinical data in medical health informatics related fields.


title: Human-Computer Interaction for BCI Games: Usability and User Experience

abstract: Brain-computer interfaces (BCI) come with a lot of issues, such as delays, bad recognition, long training times, and cumbersome hardware. Gamers are a large potential target group for this new interaction modality, but why would healthy subjects want to use it? BCI provides a combination of information and features that no other input modality can offer. But for general acceptance of this technology, usability and user experience will need to be taken into account when designing such systems. This paper discusses the consequences of applying knowledge from Human-Computer Interaction (HCI) to the design of BCI for games. The integration of HCI with BCI is illustrated by research examples and showcases, intended to take this promising technology out of the lab. Future research needs to move beyond feasibility tests, to prove that BCI is also applicable in realistic, real-world settings.


title: Hashing with Angular Reconstructive Embeddings

abstract: Large-scale search methods are increasingly critical for many content-based visual analysis applications, among which hashing-based approximate nearest neighbor search techniques have attracted broad interests due to their high efficiency in storage and retrieval. However, existing hashing works are commonly designed for measuring data similarity by the Euclidean distances. In this paper, we focus on the problem of learning compact binary codes using the cosine similarity. Specifically, we proposed novel angular reconstructive embeddings (ARE) method, which aims at learning binary codes by minimizing the reconstruction error between the cosine similarities computed by original features and the resulting binary embeddings. Furthermore, we devise two efficient algorithms for optimizing our ARE in continuous and discrete manners, respectively. We extensively evaluate the proposed ARE on several large-scale image benchmarks. The results demonstrate that ARE outperforms several state-of-the-art methods.


title: Graph-Based Multiprototype Competitive Learning and Its Applications

abstract: Partitioning nonlinearly separable datasets is a basic problem that is associated with data clustering. In this paper, a novel approach that is termed graph-based multiprototype competitive learning (GMPCL) is proposed to handle this problem. A graph-based method is employed to produce an initial, coarse clustering. After that, a multiprototype competitive learning is introduced to refine the coarse clustering and discover clusters of an arbitrary shape. The GMPCL algorithm is further extended to deal with high-dimensional data clustering, i.e., the fast graph-based multiprototype competitive learning (FGMPCL) algorithm. An experimental comparison has been performed by the exploitation of both synthetic and real-world datasets to validate the effectiveness of the proposed methods. Additionally, we apply our GMPCL/FGMPCL to two computer-vision tasks, namely, automatic color image segmentation and video clustering. Experimental results show that GMPCL/FGMPCL provide an effective and efficient tool with application to computer vision.


title: A Network-Centric Hardware/Algorithm Co-Design to Accelerate Distributed Training of Deep Neural Networks

abstract: Training real-world Deep Neural Networks (DNNs) can take an eon (i.e., weeks or months) without leveraging distributed systems. Even distributed training takes inordinate time, of which a large fraction is spent in communicating weights and gradients over the network. State-of-the-art distributed training algorithms use a hierarchy of worker-aggregator nodes. The aggregators repeatedly receive gradient updates from their allocated group of the workers, and send back the updated weights. This paper sets out to reduce this significant communication cost by embedding data compression accelerators in the Network Interface Cards (NICs). To maximize the benefits of in-network acceleration, the proposed solution, named INCEPTIONN (In-Network Computing to Exchange and Process Training Information Of Neural Networks), uniquely combines hardware and algorithmic innovations by exploiting the following three observations. (1) Gradients are significantly more tolerant to precision loss than weights and as such lend themselves better to aggressive compression without the need for the complex mechanisms to avert any loss. (2) The existing training algorithms only communicate gradients in one leg of the communication, which reduces the opportunities for in-network acceleration of compression. (3) The aggregators can become a bottleneck with compression as they need to compress/decompress multiple streams from their allocated worker group. To this end, we first propose a lightweight and hardware-friendly lossy-compression algorithm for floating-point gradients, which exploits their unique value characteristics. This compression not only enables significantly reducing the gradient communication with practically no loss of accuracy, but also comes with low complexity for direct implementation as a hardware block in the NIC. To maximize the opportunities for compression and avoid the bottleneck at aggregators, we also propose an aggregator-free training algorithm that exchanges gradients in both legs of communication in the group, while the workers collectively perform the aggregation in a distributed manner. Without changing the mathematics of training, this algorithm leverages the associative property of the aggregation operator and enables our in-network accelerators to (1) apply compression for all communications, and (2) prevent the aggregator nodes from becoming bottlenecks. Our experiments demonstrate that INCEPTIONN reduces the communication time by 70.9~80.7% and offers 2.2~3.1x speedup over the conventional training system, while achieving the same level of accuracy.


title: Industrial Implementation of a Documentation Framework for Architectural Decisions

abstract: Architecture decisions are often not explicitly documented in practice but reside in the architect's mind as tacit knowledge, even though explicit capturing and documentation of architecture decisions has been associated with a multitude of benefits. As part of a research collaboration with ABB, we developed a tool to document architecture decisions. This tool is an add-in for Enterprise Architect and is an implementation of a viewpoint-based decision documentation framework. To validate the add-in, we conducted an exploratory case study with ABB architects. In the study, we assessed the status quo of architecture decision documentation, identified architects' expectations of the ideal decision documentation tool, and evaluated the new add-in. We found that although awareness of decision documentation is increasing at ABB, several barriers exist that limit the use of decisions in practice. Regarding their ideal tool, architects want a descriptive and efficient approach. Supplemental features like reporting or decision sharing are requested. The new add-in, was well-perceived by the architects. As a result of the study, we propose a clearer separation of problem, outcomes, and alternatives for the decision documentation framework.


title: Additive Manufacturing of Nickel Based Superalloy

abstract: This paper reviews the use of relatively new manufacturing method called as additive manufacturing, most often mentioned as 3D printing in fabrication of high performance superalloys. The overview of the article describes the structure, property, processing, performance relationship of the fabrication process and the superalloys. The manufacturing methods such as Electron Beam Melting, Laser Beam Melting and Direct Energy Deposition used to fabricate commercially available alloys are explained. The microstructure / grain structure resulting from directional building, complex thermal cycles is discussed. An overview of the properties of the superalloys and their performance as well as applications is presented.


title: Enhancing measurement quality through active sampling in mobile air quality monitoring sensor networks

abstract: In recent years, a growing number of research groups have targeted the development and deployment of networks using low-cost chemical sensors for monitoring air quality. Due to economical reasoning, most of these systems make use of some sort of mobility to increase spatial coverage. The effect of mobility on measurement quality has, however, been largely neglected. The long response time of the chemical sensors typically used for this type of application, in conjunction with platform mobility, leads to significant signal distortion. While this problem can be addressed through signal deconvolution techniques, their effectiveness is limited by the typical poor Signal-to-Noise Ratio (SNR) of the measured signal. In this paper, we study the possibility of enhancing the measurement quality of chemical sensors through the use of active sampling (or sniffing). We propose different sniffer designs, employing both fans and pumps as actuators. Using a rigorous experimental framework, inside a wind tunnel, we study the ability of active samplers to increase measurement SNR, and thus indirectly to improve sensor dynamic response. We obtain a significant and consistent improvement in SNR for one of our pump-based sniffer designs. Finally, we validate the robustness of this signal enhancement in real-world conditions through an outdoor car-based experiment.


title: Semi-invasive EM attack on FPGA RO PUFs and countermeasures

abstract: It is often argued that Physical Unclonable Functions (PUFs) are resistant against invasive and semi-invasive attacks since these attacks would damage the underlying PUF structure resulting in a different PUF response. In this paper, we demonstrate exemplarily that this assumption does not hold for a Ring Oscillator (RO) PUF implemented on a Xilinx Spartan 3 FPGA, where we were able to perform a semi-invasive attack. We present analysis methods to identify ring oscillator frequencies and to map them to their corresponding oscillators. We practically prove that it is possible to recover the generated RO PUF response bits with this approach. To harden RO PUFs against side-channel analysis, we also propose a RO PUF concept not leaking useful information through the side-channel of electro-magnetic radiation.


title: Design Support for Maintenance Tasks using TRIZ

abstract: Design for Maintenance is a method to optimize maintenance activities during the design process. To help maintenance engineers, the chair Maintenance Engineering at the University of Twente has set up a list of 37 guidelines. However, these guidelines only state the goal that should be achieved, but do not define the actions that should be taken to solve maintenance-related problems during the design phase. TRIZ is a product and process innovation method that uses various tools that propose paths to possible innovations and solutions. As such, TRIZ promises it could be a useful addition to the existing set of 37 maintenance guidelines. In this research the compatibility of TRIZ and maintenance guidelines is explored using a 3 stage research plan. Firstly, applicable guidelines are linked to TRIZ methods. In a second step these links are grouped and generalized so not every maintenance guideline also needs a unique TRIZ strategy. Finally a roadmap is presented that helps during Design for Maintenance activities in solving maintenance problems with the support of TRIZ tools.


title: Manufacturing Identity: Synchronicity and Correspondences in Kronos Capitalism

abstract: A sense of loss of identity exists in modern Kronos capitalism with its constant need to devour its own reproductions in order to survive. Discovery of identity comes through deconstruction and techniques of mysticism which are seen as similar processes. Identity is defined in terms of awareness of multiple levels of being. Understanding of identity lies in the successive abandonment of all kinds of schema or conditioning processes which we summarize by the term organizational grammar.


title: Stark effect for a rigid symmetric top molecule: exact solution

abstract: The authors present an exact expression for the energy levels of a rigid symmetric top molecule in an external electric field of arbitrary strength. The solution of this Stark splitting problem is presented in the form of a rapidly convergent continued fraction, well suited to numerical evaluation for any state.


title: BPP: Large Graph Storage for Efficient Disk Based Processing

abstract: Processing very large graphs like social networks, biological and chemical compounds is a challenging task. Distributed graph processing systems process the billion-scale graphs efficiently but incur overheads of efficient partitioning and distribution of the graph over a cluster of nodes. Distributed processing also requires cluster management and fault tolerance. In order to overcome these problems GraphChi was proposed recently. GraphChi significantly outperformed all the representative distributed processing frameworks. Still, we observe that GraphChi incurs some serious degradation in performance due to 1) high number of non-sequential I/Os for processing every chunk of graph; and 2) lack of true parallelism to process the graph. In this paper we propose a simple yet powerful engine BiShard Parallel Processor (BPP) to efficiently process billions-scale graphs on a single PC. We extend the storage structure proposed by GraphChi and introduce a new processing model called BiShard Parallel (BP). BP enables full CPU parallelism for processing the graph and significantly reduces the number of non-sequential I/Os required to process every chunk of the graph. Our experiments on real large graphs show that our solution significantly outperforms GraphChi.


title: A Comparative Study for Outlier Detection Techniques in Data Mining

abstract: Existing studies in data mining mostly focus on finding patterns in large datasets and further using it for organizational decision making. However, finding such exceptions and outliers has not yet received as much attention in the data mining field as some other topics have, such as association rules, classification and clustering. Thus, this paper describes the performance of control chart, linear regression, and Manhattan distance techniques for outlier detection in data mining. Experimental studies show that outlier detection technique using control chart is better than the technique modeled from linear regression because the number of outlier data detected by control chart is smaller than linear regression. Further, experimental studies shows that Manhattan distance technique outperformed compared with the other techniques when the threshold values increased.


title: A Detailed Algorithm for Vital Sign Monitoring of a Stationary/Non-Stationary Human through IR-UWB Radar

abstract: The vital sign monitoring through Impulse Radio Ultra-Wide Band (IR-UWB) radar provides continuous assessment of a patient’s respiration and heart rates in a non-invasive manner. In this paper, IR UWB radar is used for monitoring respiration and the human heart rate. The breathing and heart rate frequencies are extracted from the signal reflected from the human body. A Kalman filter is applied to reduce the measurement noise from the vital signal. An algorithm is presented to separate the heart rate signal from the breathing harmonics. An auto-correlation based technique is applied for detecting random body movements (RBM) during the measurement process. Experiments were performed in different scenarios in order to show the validity of the algorithm. The vital signs were estimated for the signal reflected from the chest, as well as from the back side of the body in different experiments. The results from both scenarios are compared for respiration and heartbeat estimation accuracy.


title: Causal embeddings for recommendation

abstract: Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website. This results in a gap between the final recommendation objective and the classical setup where recommendation candidates are evaluated by their coherence with past user behavior, by predicting either the missing entries in the user-item matrix, or the most likely next event. To bridge this gap, we optimize a recommendation policy for the task of increasing the desired outcome versus the organic user behavior. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy. To this end, we propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization methods, in addition to new approaches of causal recommendation and show significant improvements.


title: VNGuard: An NFV/SDN combination framework for provisioning and managing virtual firewalls

abstract: Network Function Virtualization (NFV) together with cloud technology enables users to request creating flexible virtual networks (VNs). Users also have specific security requirements to protect their VNs. Especially, due to changeable network perimeters, constant VM migrations, and user-centric security needs, VNs require new security features that traditional firewalls fail to provide, because traditional firewalls rely greatly on restricted network topology and entry points to provide effective security protection. To address this challenge, we propose VNGuard, a framework for effective provision and management of virtual firewalls to safeguard VNs, leveraging features provided by NFV and Software Defined Networking (SDN). VNGuard defines a high-level firewall policy language, finds optimal virtual firewall placement, and adapts virtual firewalls to VN changes. To demonstrate the feasibility of our approach, we have implemented core components of VNGuard on top of ClickOS. Our experimental results demonstrate the effectiveness and efficiency of virtual firewalls built on VNGuard.


title: Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases

abstract: MOTIVATION ::: It is popular to explore meaningful molecular targets and infer new functions of genes through gene functional similarity measuring and gene functional network construction. However, little work is available in this field for microRNA (miRNA) genes due to limited miRNA functional annotations. With the rapid accumulation of miRNAs, it is increasingly needed to uncover their functional relationships in a systems level. ::: ::: ::: RESULTS ::: It is known that genes with similar functions are often associated with similar diseases, and the relationship of different diseases can be represented by a structure of directed acyclic graph (DAG). This is also true for miRNA genes. Therefore, it is feasible to infer miRNA functional similarity by measuring the similarity of their associated disease DAG. Based on the above observations and the rapidly accumulated human miRNA-disease association data, we presented a method to infer the pairwise functional similarity and functional network for human miRNAs based on the structures of their disease relationships. Comparisons showed that the calculated miRNA functional similarity is well associated with prior knowledge of miRNA functional relationship. More importantly, this method can also be used to predict novel miRNA biomarkers and to infer novel potential functions or associated diseases for miRNAs. In addition, this method can be easily extended to other species when sufficient miRNA-associated disease data are available for specific species. ::: ::: ::: AVAILABILITY ::: The online tool is available at http://cmbi.bjmu.edu.cn/misim ::: ::: ::: CONTACT ::: cuiqinghua@hsc.pku.edu.cn ::: ::: ::: SUPPLEMENTARY INFORMATION ::: Supplementary data are available at Bioinformatics online.


title: Learning to Recommend Related Entities to Search Users

abstract: Over the past few years, major web search engines have introduced knowledge bases to offer popular facts about people, places, and things on the entity pane next to regular search results. In addition to information about the entity searched by the user, the entity pane often provides a ranked list of related entities. To keep users engaged, it is important to develop a recommendation model that tailors the related entities to individual user interests. We propose a probabilistic Three-way Entity Model (TEM) that provides personalized recommendation of related entities using three data sources: knowledge base, search click log, and entity pane log. Specifically, TEM is capable of extracting hidden structures and capturing underlying correlations among users, main entities, and related entities. Moreover, the TEM model can also exploit the click signals derived from the entity pane log. We further provide an inference technique to learn the parameters in TEM, and propose a principled preference learning method specifically designed for ranking related entities. Extensive experiments with two real-world datasets show that TEM with our probabilistic framework significantly outperforms a state of the art baseline, confirming the effectiveness of TEM and our probabilistic framework in related entity recommendation.


title: Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification

abstract: This paper explores the problem of breast tissue classification of microscopy images. Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. Given a suitable training dataset, we utilize deep learning techniques to address the classification problem. Due to the large size of each image in the training dataset, we propose a patch-based technique which consists of two consecutive convolutional neural networks. The first"patch-wise"network acts as an auto-encoder that extracts the most salient features of image patches while the second"image-wise"network performs classification of the whole image. The first network is pre-trained and aimed at extracting local information while the second network obtains global information of an input image. We trained the networks using the ICIAR 2018 grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method yields 95 % accuracy on the validation set compared to previously reported 77 % accuracy rates in the literature. Our code is publicly available at https://github.com/ImagingLab/ICIAR2018


title: WikiQA — A question answering system on Wikipedia using freebase, DBpedia and Infobox

abstract: Question Answering (QA) system is the task where arbitrary question IS posed in the form of natural language statements and a brief and concise text returned as an answer. Contrary to search engines where a long list of relevant documents returned as a result of a query, QA system aims at providing the direct answer or passage containing the answer. We propose a general purpose question answering system which can answer wh-interrogated questions. This system is using Wikipedia data as its knowledge source. We have implemented major components of a QA system which include challenging tasks of Named Entity Tagging, Question Classification, Information Retrieval and Answer Extraction. Implementation of state-of-the-art Entity Tagging mechanism has helped identify entities where systems like OpenEphyra or DBpedia spotlight have failed. The information retrieval task includes development of a framework to extract tabular information known as Infobox from Wikipedia pages which has ensured availability of latest updated information. Answer Extraction module has implemented an attributes mapping mechanism which is helpful to extract answer from data. The system is comparable in results with other available general purpose QA systems.


The effect of surroundings on gaming experience


Patterns in Scientific Abstracts


title: Extending Game User Experience - Exploring Player Feedback and Satisfaction : The Birth of the Playsona

abstract: Video games are experience-based products and user satisfaction is key for their popularity. To design for as strong an experience as possible, game developers incorporate evaluation methods that h ...


title: A Benchmark 3D Fingerprint Database (Invited Paper)

abstract: Fingerprint biometric is one of the most successful biometrics applied in both forensic law enforcement and security applications. Recent developments in fingerprint acquisition tech- nology have resulted in touchless live scan devices that generate 3D representation of fingerprints, and thus can overcome the deformation and smearing problems caused by conventional contact-based acquisition techniques. However, there are yet no 3D full fingerprint databases with their corresponding 2D prints for fingerprint biometric research. This paper presents a 3D fingerprint database we have established in order to investigate the 3D fingerprint biometric comprehensively. It consists of 3D fingerprints as well as their corresponding 2D fingerprints captured by two commercial fingerprint scanners from 150 subjects in Australia. Besides, we have tested the performance of 2D fingerprint verification, 3D fingerprint verification, and 2D to 3D fingerprint verification. The results show that more work is needed to improve the performance of 2D to 3D fingerprint verification. In addition, the database is expected to be released publicly in late 2014.


title: Predicting the Compositionality of Multiword Expressions Using Translations in Multiple Languages

abstract: In this paper, we propose a simple, languageindependent and highly effective method for predicting the degree of compositionality of multiword expressions (MWEs). We compare the translations of an MWE with the translations of its components, using a range of different languages and string similarity measures. We demonstrate the effectiveness of the method on two types of English MWEs: noun compounds and verb particle constructions. The results show that our approach is competitive with or superior to state-of-the-art methods over standard datasets.


title: Discernibility of Burial Mounds in High-Resolution X-Band SAR Images for Archaeological Prospections in the Altai Mountains

abstract: The Altai Mountains are a heritage-rich archaeological landscape with monuments in almost every valley. Modern nation state borders dissect the region and limit archaeological landscape analysis to intra-national areas of interest. Remote sensing can help to overcome these limitations. Due to its high precision, Synthetic Aperture Radar (SAR) data can be a very useful tool for supporting archaeological prospections, but compared to optical imagery, the detectability of sites of archaeological interest is limited. We analyzed the limitations of SAR using TerraSAR-X images in different modes. Based on ground truth, the discernibility of burial mounds was analyzed in different SAR acquisition modes. We show that very-high-resolution TerraSAR-X staring spotlight images are very well suited for the task, with >75% of the larger mounds being discernible, while in images with a lower spatial resolution only a few large sites can be detected, at rates below 50%.


title: Energy efficient actuators with adjustable stiffness: a review on AwAS, AwAS-II and CompACT VSA changing stiffness based on lever mechanism

abstract: Purpose ::: ::: ::: ::: ::: This paper aims to discuss, analyze and compare members of a group of actuators with adjustable stiffness, namely: AwAS, AwAS-II and CompACT variable stiffness actuator (VSA) developed at Italian Institute of Technology (IIT). ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: These actuators are among series type of VSAs where one main motor is dedicated for link positioning and a secondary motor, in series with the first one, regulates the output link stiffness. Regulating the stiffness in this group of actuators is based on the lever concept. Initially, springs were moved along the lever to tune the stiffness while in the later versions stiffness was regulated through relocating pivot point along the lever. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: This paper discusses how different mechanisms have been employed in realization of the lever concept in these actuators and what are the advantages and disadvantages of each realization. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: Today’s robots are not supposed to be solid, isolated and rigid anymore but rather adaptive, cooperative and compliant entities in our daily life. The new attitudes demand for novel technologies substantially different from those developed for industrial domains both at the hardware and the software levels. This work presents latest three state-of-the-art actuators, developed at IIT, which are great answers to the needs of tomorrow’s robot. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: These novel actuators are really ready for commercial exploitation, as they are compact and reliable. The main novelty is based on employing concept of lever mechanism for stiffness regulation. They have been designed and manufactured in a very professional and optimized way.


title: Recent Advances in Wearable Sensors for Health Monitoring

abstract: Wearable sensor technology continues to advance and provide significant opportunities for improving personalized healthcare. In recent years, advances in flexible electronics, smart materials, and low-power computing and networking have reduced barriers to technology accessibility, integration, and cost, unleashing the potential for ubiquitous monitoring. This paper discusses recent advances in wearable sensors and systems that monitor movement, physiology, and environment, with a focus on applications for Parkinson’s disease, stroke, and head and neck injuries.


title: Anatomy of the visual word form area : Adjacent cortical circuits and long-range white matter connections

abstract: Circuitry in ventral occipital-temporal cortex is essential for seeing words. We analyze the circuitry within a specific ventral–occipital region, the visual word form area (VWFA). The VWFA is immediately adjacent to the retinotopically organized VO-1 and VO-2 visual field maps and lies medial and inferior to visual field maps within motion selective human cortex. Three distinct white matter fascicles pass within close proximity to the VWFA: (1) the inferior longitudinal fasciculus, (2) the inferior frontal occipital fasciculus, and (3) the vertical occipital fasciculus. The vertical occipital fasciculus terminates in or adjacent to the functionally defined VWFA voxels in every individual. The vertical occipital fasciculus projects dorsally to language and reading related cortex. The combination of functional responses from cortex and anatomical measures in the white matter provides an overview of how the written word is encoded and communicated along the ventral occipital-temporal circuitry for seeing words.


title: Evaluating the privacy properties of telephone metadata

abstract: Since 2013, a stream of disclosures has prompted reconsideration of surveillance law and policy. One of the most controversial principles, both in the United States and abroad, is that communications metadata receives substantially less protection than communications content. Several nations currently collect telephone metadata in bulk, including on their own citizens. In this paper, we attempt to shed light on the privacy properties of telephone metadata. Using a crowdsourcing methodology, we demonstrate that telephone metadata is densely interconnected, can trivially be reidentified, and can be used to draw sensitive inferences.


title: Blockchain Enabled Enhanced IoT Ecosystem Security

abstract: Blockchain (BC), the technology behind the Bitcoin cryptocurrency system, is starting to be adopted for ensuring enhanced security and privacy in the Internet of Things (IoT) ecosystem. Fervent research is currently being focused in both academia and industry in this domain. Proof of Work (PoW), a cryptographic puzzle, plays a vital role in ensuring BC security by maintaining a digital ledger of transactions, which are considered to be incorruptible. Furthermore, BC uses a changeable Public Key (PK) to record the identity of users, thus providing an extra layer of privacy. Not only in cryptocurrency has the successful adoption of the BC been implemented, but also in multifaceted non-monetary systems, such as in: distributed storage systems, proof of location and healthcare. Recent research articles and projects or applications were surveyed to assess the implementation of the BC for IoT Security and identify associated challenges and propose solutions for BC enabled enhanced security for the IoT ecosystem.


title: Development and Learning Control of a Human Limb With a Rehabilitation Exoskeleton

abstract: This paper describes a novel development of a lower limber exoskeleton for physical assistance and rehabilitation. The developed exoskeleton is a motorized leg device having a total of 4 DOF with hip, knee, and ankle actuated in the sagittal plane. The exoskeleton applies forces and learns the impedance parameters of both robot and human. An adaptive control scheme by incorporating learning control approaches into the exoskeleton system is developed to help the leg movement on a desired periodic trajectory and handle periodic uncertainties with known periods. The proposed control approach does not require a muscle model and can be proven to yield asymptotic stability for a nonlinear muscle model and an exoskeleton model in the presence of bounded nonlinear disturbances (e.g., spasticity and fatigue). The performance of the controller is demonstrated through closed-loop experiments on human subjects. The experiments illustrate the ability of the exoskeleton to enable the leg shank to track single and multiple period trajectories with different periods and ranges of motion.


title: Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation

abstract: Abstract The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP∖USD, EUR∖GBP, and EUR∖USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return.


title: Review of variable-flux permanent magnet machines

abstract: Variable-flux permanent-magnet machines (VFPM) are of great interest and many different machine topologies have been documented. This paper categorizes VFPM machine topologies with regard to the method of flux variation and further, in the case of hybrid excited machines with field coils, with regard to the location of the excitation sources. The different VFPM machines are reviewed and compared in terms of their torque density, complexity and their ability to vary the flux.


title: A review of metrics for knowledge management systems and knowledge management initiatives

abstract: Metrics are essential for the advancement of research and practice in an area. In knowledge management (KM), the process of measurement and development of metrics is made complex by the intangible nature of the knowledge asset. Further, the lack of standards for KM business metrics and the relative infancy of research on KM metrics points to a need for research in this area. This paper reviews KM metrics for research and practice and identifies areas where there is a gap in our understanding. It classifies existing research based on the units of evaluation such as user of KMS, KMS, project, KM process, KM initiative, and organization as a whole. The paper concludes by suggesting avenues for future research on KM and KMS metrics based on the gaps identified.


title: Perceptual Generative Adversarial Networks for Small Object Detection

abstract: Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to super-resolved ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K [45] and the Caltech [9] benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts.


title: Behavioral and cognitive effects of microwave exposure

abstract: This paper presents an overview of the recent behavioral literature concerning microwave exposure and discusses behavioral effects that have supported past exposure standards. Other effects, which are based on lower levels of exposure, are discussed as well, relative to setting exposure standards. The paper begins with a brief discussion of the ways in which behavioral end points are investigated in the laboratory, together with some of the methodological considerations pertinent to such studies when radio frequency (RF) exposure is involved. It has been pointed out by several sources that exposure to RF radiation can lead to changes in the behavior of humans and laboratory animals that can range from the perceptions of warmth and sound to lethal body temperatures. Behavior of laboratory animals can be perturbed and, under certain other conditions, animals will escape and subsequently avoid RF fields; but they will also work to obtain a burst of RF energy when they are cold. Reports of change of cognitive function (memory and learning) in humans and laboratory animals are in the scientific literature. Mostly, these are thermally mediated effects, but other low level effects are not so easily explained by thermal mechanisms. The phenomenon of behavioral disruption by microwave exposure, an operationally defined rate decrease (or rate increase), has served as the basis for human exposure guidelines since the early 1980s and still appears to be a very sensitive RF bioeffect. Nearly all evidence relates this phenomenon to the generation of heat in the tissues and reinforces the conclusion that behavioral changes observed in RF exposed animals are thermally mediated. Such behavioral alteration has been demonstrated in a variety of animal species and under several different conditions of RF exposure. Thermally based effects can clearly be hazardous to the organism and continue to be the best predictor of hazard for homosapiens. Nevertheless, similar research with man has not been conducted. Although some studies on human perception of RF exist, these should be expanded to include a variety of RF parameters.


title: Efficient Indexing of Billion-Scale Datasets of Deep Descriptors

abstract: Existing billion-scale nearest neighbor search systems have mostly been compared on a single dataset of a billion of SIFT vectors, where systems based on the Inverted Multi-Index (IMI) have been performing very well, achieving state-of-the-art recall in several milliseconds. SIFT-like descriptors, however, are quickly being replaced with descriptors based on deep neural networks (DNN) that provide better performance for many computer vision tasks. In this paper, we introduce a new dataset of one billion descriptors based on DNNs and reveal the relative inefficiency of IMI-based indexing for such descriptors compared to SIFT data. We then introduce two new indexing structures, the Non-Orthogonal Inverted Multi-Index (NO-IMI) and the Generalized Non-Orthogonal Inverted Multi-Index (GNO-IMI). We show that due to additional flexibility, the new structures are able to adapt to DNN descriptor distribution in a better way. In particular, extensive experiments on the new dataset demonstrate that these data structures provide considerably better trade-off between the speed of retrieval and recall, given similar amount of memory, as compared to the standard Inverted Multi-Index.


title: The role of ineffective erythropoiesis in non-transfusion-dependent thalassemia.

abstract: Ineffective erythropoiesis is the hallmark of beta-thalassemia that triggers a cascade of compensatory mechanisms resulting in clinical sequelae such as erythroid marrow expansion, extramedullary hematopoiesis, splenomegaly, and increased gastrointestinal iron absorption. Recent studies have begun to shed light on the complex molecular mechanisms underlying ineffective erythropoiesis and the associated compensatory pathways; this new understanding may lead to the development of novel therapies. Increased or excessive activation of the Jak2/STAT5 pathway promotes unnecessary disproportionate proliferation of erythroid progenitors, while other factors suppress serum hepcidin levels leading to dysregulation of iron metabolism. Preclinical studies suggest that Jak inhibitors, hepcidin agonists, and exogenous transferrin may help to restore normal erythropoiesis and iron metabolism and reduce splenomegaly; however, further research is needed.


title: Rethinking individualism and collectivism: Evaluation of theoretical assumptions and meta-analyses

abstract: Are Americans more individualistic and less collectivistic than members of other groups? The authors summarize plausible psychological implications of individualism– collectivism (IND-COL), metaanalyze cross-national and within-United States IND-COL differences, and review evidence for effects of IND-COL on self-concept, well-being, cognition, and relationality. European Americans were found to be both more individualistic—valuing personal independence more—and less collectivistic—feeling duty to in-groups less—than others. However, European Americans were not more individualistic than African Americans, or Latinos, and not less collectivistic than Japanese or Koreans. Among Asians, only Chinese showed large effects, being both less individualistic and more collectivistic. Moderate IND-COL effects were found on self-concept and relationality, and large effects were found on attribution and cognitive style.


title: MySong: automatic accompaniment generation for vocal melodies

abstract: We introduce MySong, a system that automatically chooses chords to accompany a vocal melody. A user with no musical experience can create a song with instrumental accompaniment just by singing into a microphone, and can experiment with different styles and chord patterns using interactions designed to be intuitive to non-musicians. We describe the implementation of MySong, which trains a Hidden Markov Model using a music database and uses that model to select chords for new melodies. Model parameters are intuitively exposed to the user. We present results from a study demonstrating that chords assigned to melodies using MySong and chords assigned manually by musicians receive similar subjective ratings. We then present results from a second study showing that thirteen users with no background in music theory are able to rapidly create musical accompaniments using MySong, and that these accompaniments are rated positively by evaluators.


title: Co‐citation in the scientific literature: A new measure of the relationship between two documents

abstract: A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles.


title: Preimplantation genetic screening: does it help or hinder IVF treatment and what is the role of the embryo?

abstract: Despite an ongoing debate over its efficacy, preimplantation genetic screening (PGS) is increasingly being used to detect numerical chromosomal abnormalities in embryos to improve implantation rates after IVF. The main indications for the use of PGS in IVF treatments include advanced maternal age, repeated implantation failure, and recurrent pregnancy loss. The success of PGS is highly dependent on technical competence, embryo culture quality, and the presence of mosaicism in preimplantation embryos. Today, cleavage stage biopsy is the most commonly used method for screening preimplantation embryos for aneuploidy. However, blastocyst biopsy is rapidly becoming the more preferred method due to a decreased likelihood of mosaicism and an increase in the amount of DNA available for testing. Instead of using 9 to 12 chromosome FISH, a 24 chromosome detection by aCGH or SNP microarray will be used. Thus, it is advised that before attempting to perform PGS and expecting any benefit, extended embryo culture towards day 5/6 should be established and proven and the clinical staff should demonstrate competence with routine competency assessments. A properly designed randomized control trial is needed to test the potential benefits of these new developments.


title: Studies with psychedelic drugs in human volunteers

abstract: Abstract Scientific curiosity and fascination have played a key role in human research with psychedelics along with the hope that perceptual alterations and heightened insight could benefit well-being and play a role in the treatment of various neuropsychiatric disorders. These motivations need to be tempered by a realistic assessment of the hurdles to be cleared for therapeutic use. Development of a psychedelic drug for treatment of a serious psychiatric disorder presents substantial although not insurmountable challenges. While the varied psychedelic agents described in this chapter share some properties, they have a range of pharmacologic effects that are reflected in the gradation in intensity of hallucinogenic effects from the classical agents to DMT, MDMA, ketamine, dextromethorphan and new drugs with activity in the serotonergic system. The common link seems to be serotonergic effects modulated by NMDA and other neurotransmitter effects. The range of hallucinogens suggest that they are distinct pharmacologic agents and will not be equally safe or effective in therapeutic targets. Newly synthesized specific and selective agents modeled on the legacy agents may be worth considering. Defining therapeutic targets that represent unmet medical need, addressing market and commercial issues, and finding treatment settings to safely test and use such drugs make the human testing of psychedelics not only interesting but also very challenging. This article is part of the Special Issue entitled ‘Psychedelics: New Doors, Altered Perceptions’.


abstract: The design and implementation of Web-based Medical Image Archiving and Communication System for Teleimaging which was developed using software such as Borland C++ Builder 5.0, MyDAC, PHP, Apache and MySQL for image acquisition system, image viewer and web-based information system. The system provides the following facilities: (1) Image acquisition system can connect and display medical image signal from endoscope in real-time and it can save single frame in .bmp, .jpg, .jpg2, .avi and .dcm file format into database system. (2) Image viewer is developed for DICOM viewing and image processing based on local contrast enhancement, adaptive interpolation technique, colour transformation and cine loop. (3) Web-based information system developed under a cooperation effort between Rangsit University and Hospital. It presents a set of tools that allow physician to manipulate and annotate images. The results shown that the system enables physician to store, display, retrieve and transmit medical images throughout interfaces that are used to present data to the user via a choice of viewing tools.


title: Role of local surface plasmon modes in the extraordinary optical transmission of sub-wavelength apertures

abstract: The extraordinary optical transmission through single sub-wavelength apertures is investigated in this paper. SPP modes on the upper and lower in-plane surfaces and Local Surface Plasmon (LSP) modes propagating along the vertical hole wall are clearly distinguished. We have numerically studied the procedure of wave propagating through a single sub-wavelength hole with a 3D finite-difference time-domain (FDTD) method and traced the wave vectors during the enhanced transmission using full wave analysis. It shows clear evidence that the LSP modes propagating along the hole wall is the origin of the extraordinary optical transmission of single sub-wavelength apertures, as a strong support to the previous experimental results. The role of these modes is identified by comparison of transmission properties of the sandwich-like structures made of different metallic materials. Transmission at inclined incidence for p-polarization and s-polarization are also calculated and the results can be properly explained with our view.


title: AFNI: software for analysis and visualization of functional magnetic resonance neuroimages

abstract: Abstract A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described. The software can color overlay neural activation maps onto higher resolution anatomical scans. Slices in each cardinal plane can be viewed simultaneously. Manual placement of markers on anatomical landmarks allows transformation of anatomical and functional scans into stereotaxic (Talairach–Tournoux) coordinates. The techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described. Facilities are provided for several types of statistical analyses of multiple 3D functional data sets. The programs are written in ANSI C and Motif 1.2 to run on Unix workstations.


title: Do the rich get richer? An empirical analysis of the BitCoin transaction network

abstract: The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only some macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze Bitcoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling law between the degree and wealth associated to individual nodes.


title: Delineation the anatomy of posterior tibial artery perforator flaps using human cadavers with a modified technique

abstract: BACKGROUND ::: To delineate the distribution and course layer of the perforator vessels using a modified technique. ::: ::: ::: METHODS ::: Twelve perforator flaps were obtained from the crura of six fresh adult cadavers. The flaps were randomized into three groups (n = 4 per group): the full-thickness flap group; the deep fascia-free flap group, and the subcutaneous adipose layer-free group. The flaps were smoothened on a silk screen on a batten frame and the isolated flaps were perfused at a perfusion pressure of 140 mmHg for 10 min via the trunk of the posterior tibial artery. Perforator flaps were photographed using a digital camera and radiographed using a mammography device. The imaging data were processed by digital software system. ::: ::: ::: RESULTS ::: The mean number of the posterior tibial artery perforator was 4.17 ± 0.94. The three relatively constant perforators varied in the projection points as well as the diameter and the length. The vascular branches and courses of the perforators were clearly visible on the mammograms. Elimination of all the deep fascia or the subcutaneous adipose tissues in the distal portion had no significant impact on the blood supply of posterior tibial artery perforator flaps while the vascular areas of the artery perforators were significantly reduced after the subcutaneous adipose tissue was eliminated in the proximal portion. ::: ::: ::: CONCLUSIONS ::: We developed an effective modified technique for delineating the vascular territory on perforator flaps of different thicknesses. Our results provide significant guidance for clinical surgeons by providing them with more detailed anatomical knowledge of perforator flaps.


title: Bayesian, maneuver-based, long-term trajectory prediction and criticality assessment for driver assistance systems

abstract: The authors propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in advance. The authors first infer a distribution of high-level, abstract driving maneuvers such as lane changes, turns, road followings, etc. of all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic evidences. This is followed by maneuver-based, long-term trajectory predictions, which themselves contain random components due to the immanent uncertainty of how drivers execute specific maneuvers. Taking all uncertain predictions of all maneuvers of every vehicle into account, the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction horizon. This serves as the basis for calculating a novel criticality measure, the Time-To-Critical-Collision-Probability (TTCCP) - a generalization of the common Time-To-Collision (TTC) in arbitrary, uncertain, multi-object driving environments and valid for longer prediction horizons. The system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class.


title: The direction change concept for reticulospinal control of goldfish escape

abstract: This is an analysis of whether biomechanical or kinematic variables are controlled by descending reticulospinal commands to the spinal cord during escape responses (C-starts) in the goldfish. We studied how the animal contracted its trunk musculature to orient an escape trajectory. We used trunk EMG recordings as a measure of the reticulospinal output to the musculature and we simultaneously gathered high-speed cinematic records of the resulting movements. We found that the escape trajectory is controlled by (1) the relative size of the agonist versus the antagonist muscle contractions on two sides of the body and (2) the timing between these contractions. We found no separate signal for forward propulsion (or force) apart from the initial stage 1 bending of the body. Rather, the neural specification of force is embedded in the commands to bend the body. Thus, our findings demonstrate the importance of the angular kinematic components, or direction changes, caused by the descending reticulospinal command. This new direction change concept is important for two reasons. First, it unifies the diversity of C-start movement patterns into a single and rather simple quantitative model. Second, the model is analogous to the systematic EMG and kinematic changes observed by others to underlie single joint movements of limbs in other vertebrates such as primates. As in these cases, the fish capitalizes on the mechanical properties of the muscle by setting the extent and timing of agonist and antagonist contractions. This, plus the fact that sensory feedback is likely to be minimal, may enable the animal to reduce the number of computational steps in its motor commands used to produce the escape response. Because horizontal body movements in fish are a fundamental vertebrate movement pattern produced by a highly conserved brainstem movement system, our findings may have general implications for understanding the neural basis of rapid movements of diverse body parts.


title: Design of PCB leaky-wave antennas for Wide angle beam steering

abstract: A PCB based leaky-wave antenna (LWA) design for wide angle beam steering is presented. This design employs longitudinal slots inserted into a substrate-integrated waveguide for operation in the mm-wave V-band. It is optimized for a reduced open-stopband and linear through broadside beam steering. The base design is outlined and adapted for three high frequency laminates with different dielectric constants. It is further described how the dielectric constant directly affects the beam steering properties of the LWA and how to scale the achieved beam angle for the given design by only changing the PCB material. The three LWA designs achieve beam steering from about 45° at Sr =2.2, over 60° at Sr =6.15 to 75° at Sr =10.2. The simulated radiation patterns show a flat gain of up to 18.0 dBi, 17.85 dBi and 15.27 dBi respectively. Furthermore, the effect of the modified PCB permittivity on the radiation efficiency as well as multiuser support with a beam per user is investigated.


title: Automatic rumors identification on Sina Weibo

abstract: In this paper, we study the problem of detecting rumors spreading in the social networks. Different from the most of the previous works on identifying rumors in Twitter, we select Sina Weibo, the China's major microblog system, as our target. We use two interfaces named “@Weibopiyao” and “Weibo Misinformation-Declaration” from Sina Weibo to help us construct high accuracy training dataset. We analyze data types of microblogs based on their content and the role and possible social impacts of different types of microblogs in rumors spreading. Leveraging our findings, we then focus on detecting social news rumors on Weibo. A new method is proposed to annotate the collected data from Weibo automatically, and three new features for identifying social news rumors are proposed. Experimental results illustrate the efficacy and efficiency of the methods and features proposed in this paper.


title: Service Level Agreement in Cloud Computing

abstract: Cloud computing that provides cheap and pay-as-you-go computing resources is rapidly gaining momentum as an alternative to traditional IT Infrastructure. As more and more consumers delegate their tasks to cloud providers, Service Level Agreements(SLA) between consumers and providers emerge as a key aspect. Due to the dynamic nature of the cloud, continuous monitoring on Quality of Service (QoS) attributes is necessary to enforce SLAs. Also numerous other factors such as trust (on the cloud provider) come into consideration, particularly for enterprise customers that may outsource its critical data. This complex nature of the cloud landscape warrants a sophisticated means of managing SLAs. This paper proposes a mechanism for managing SLAs in a cloud computing environment using the Web Service Level Agreement(WSLA) framework, developed for SLA monitoring and SLA enforcement in a Service Oriented Architecture (SOA). We use the third party support feature of WSLA to delegate monitoring and enforcement tasks to other entities in order to solve the trust issues. We also present a real world use case to validate our proposal.


title: An affordable, robust mining inspection robot

abstract: Mining accidents claim hundreds and perhaps thousands of lives throughout the world every year. Despite rapid progress in the development of robotic systems, a robot has never been successful in saving a human life in a disaster situation. Factors that have contributed to the failure of deployed robotic systems include reliance on a tether, reliance on expert operators, significant cost and failure due to moisture ingress. We have designed a low cost, robust mine inspection robot with a novel system of mobile communication and sensing nodes. Locomotion is designed to be non-complex and resistant to snagging. Equipped with a range of sensors, this system is easy to use and at can be deployed at an order of magnitude lower cost than current systems.


title: Effects of reuse on quality, productivity, and economics

abstract: This article presents metrics from two Hewlett-Packard (HP) reuse programs that document the improved quality, increased productivity, shortened time-to-market, and enhanced economics resulting from reuse. Work products are the products or by-products of the software-development process: for example, code, design, and test plans. Reuse is the use of these work products without modification in the development of other software. Leveraged reuse is modifying existing work products to meet specific system requirements. A producer is a creator of reusable work products, and the consumer is someone who uses them to create other software. Time-to-market is the time it takes to deliver a product from the time it is conceived. Experience with reuse has been largely positive. Because work products are used multiple times, the accumulated defect fixes result in a higher quality work product. Because the work products have already been created, tested, and documented, productivity increases because consumers of reusable work products need to do less work. However, increased productivity from reuse does not necessarily shorten time-to-market. To reduce time-to-market, reuse must be used effectively on the critical path of a development project. Finally, we have found that reuse allows an organization to use personnel more effectively because it leverages expertise. However, software reuse is not free. It requires resources to create and maintain reusable work products, a reuse library, and reuse tools. To help evaluate the costs and benefits of reuse, we have developed an economic analysis method, which we have applied to multiple reuse programs at HP. >


title: A Trusted Lightweight Communication Strategy for Flying Named Data Networking

abstract: Flying Ad hoc Network (FANET) is a new resource-constrained breed and instantiation of Mobile Ad hoc Network (MANET) employing Unmanned Aerial Vehicles (UAVs) as communicating nodes. These latter follow a predefined path called 'mission' to provide a wide range of applications/services. Without loss of generality, the services and applications offered by the FANET are based on data/content delivery in various forms such as, but not limited to, pictures, video, status, warnings, and so on. Therefore, a content-centric communication mechanism such as Information Centric Networking (ICN) is essential for FANET. ICN addresses the problems of classical TCP/IP-based Internet. To this end, Content-centric networking (CCN), and Named Data Networking (NDN) are two of the most famous and widely-adapted implementations of ICN due to their intrinsic security mechanism and Interest/Data-based communication. To ensure data security, a signature on the contents is appended to each response/data packet in transit. However, trusted communication is of paramount importance and currently lacks in NDN-driven communication. To fill the gaps, in this paper, we propose a novel trust-aware Monitor-based communication architecture for Flying Named Data Networking (FNDN). We first select the monitors based on their trust and stability, which then become responsible for the interest packets dissemination to avoid broadcast storm problem. Once the interest reaches data producer, the data comes back to the requester through the shortest and most trusted path (which is also the same path through which the interest packet arrived at the producer). Simultaneously, the intermediate UAVs choose whether to check the data authenticity or not, following their subjective belief on its producer's behavior and thus-forth reducing the computation complexity and delay. Simulation results show that our proposal can sustain the vanilla NDN security levels exceeding the 80% dishonesty detection ratio while reducing the generated end-to-end delay to less than 1 s in the worst case and reducing the average consumed energy by more than two times.


Multi-source Neural Automatic Post-Editing: FBK's participation in the WMT 2017 APE shared task


title: Hardware-software integrated approaches to defend against software cache-based side channel attacks

abstract: Software cache-based side channel attacks present serious threats to modern computer systems. Using caches as a side channel, these attacks are able to derive secret keys used in cryptographic operations through legitimate activities. Among existing countermeasures, software solutions are typically application specific and incur substantial performance overhead. Recent hardware proposals including the Partition-Locked cache (PLcache) and Random-Permutation cache (RPcache) [23], although very effective in reducing performance overhead while enhancing the security level, may still be vulnerable to advanced cache attacks. In this paper, we propose three hardware-software approaches to defend against software cache-based attacks - they present different tradeoffs between hardware complexity and performance overhead. First, we propose to use preloading to secure the PLcache. Second, we leverage informing loads, which is a lightweight architectural support originally proposed to improve memory performance, to protect the RPcache. Third, we propose novel software permutation to replace the random permutation hardware in the RPcache. This way, regular caches can be protected with hardware support for informing loads. In our experiments, we analyze various processor models for their vulnerability to cache attacks and demonstrate that even to the processor model that is most vulnerable to cache attacks, our proposed software-hardware integrated schemes provide strong security protection.


title: Can emotions be truly group level? Evidence regarding four conceptual criteria.

abstract: Recent advances in understanding prejudice and intergroup behavior have made clear that emotions help explain people’s reactions to social groups and their members. Intergroup emotions theory (D. M. Mackie, T. Devos, & E. R. Smith, 2000; E. R. Smith, 1993) holds that intergroup emotions are experienced by individuals when they identify with a social group, making the group part of the psychological self. What differentiates such group-level emotions from emotions that occur purely at the individual level? The authors argue that 4 key criteria define group-level emotions: Group emotions are distinct from the same person’s individual-level emotions, depend on the person’s degree of group identification, are socially shared within a group, and contribute to regulating intragroup and intergroup attitudes and behavior. Evidence from 2 studies supports all 4 of these predictions and thus points to the meaningfulness, coherence, and functionality of group-level emotions.


title: LRRK2 secretion in exosomes is regulated by 14-3-3

abstract: Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene cause late-onset Parkinson's disease (PD). Emerging evidence suggests a role for LRRK2 in the endocytic pathway. Here, we show that LRRK2 is released in extracellular microvesicles (i.e. exosomes) from cells that natively express LRRK2. LRRK2 localizes to collecting duct epithelial cells in the kidney that actively secrete exosomes into urine. Purified urinary exosomes contain LRRK2 protein that is both dimerized and phosphorylated. We provide a quantitative proteomic profile of 1673 proteins in urinary exosomes and find that known LRRK2 interactors including 14-3-3 are some of the most abundant exosome proteins. Disruption of the 14-3-3 LRRK2 interaction with a 14-3-3 inhibitor or through acute LRRK2 kinase inhibition potently blocks LRRK2 release in exosomes, but familial mutations in LRRK2 had no effect on secretion. LRRK2 levels were overall comparable but highly variable in urinary exosomes derived from PD cases and age-matched controls, although very high LRRK2 levels were detected in some PD affected cases. We further characterized LRRK2 exosome release in neurons and macrophages in culture, and found that LRRK2-positive exosomes circulate in cerebral spinal fluid (CSF). Together, these results define a pathway for LRRK2 extracellular release, clarify one function of the LRRK2 14-3-3 interaction and provide a foundation for utilization of LRRK2 as a biomarker in clinical trials.


title: Social Commerce: A Contingency Framework for Assessing Marketing Potential

abstract: A key issue for marketers resulting from the dramatic rise of social media is how it can be leveraged to generate value for firms. Whereas the importance of social media for brand management and customer relationship management is widely recognized, it is unclear whether social media can also help companies market and sell products. Extant discussions of social commerce present a variety of perspectives, but the core issue remains unresolved. This paper aims to make two contributions. First, to address the lack of clarity in the literature regarding the meaning and domain of social commerce, the paper offers a definition stemming from important research streams in marketing. This definition allows for both a broad (covering all steps of the consumer decision process) and a narrow (focusing on the purchase act itself) construal of social commerce. Second, we build on this definition and develop a contingency framework for assessing the marketing potential that social commerce has to offer to firms. Implications for researchers and managers, based on the proposed definition and framework, are also discussed.


title: On Analyzing Hashtags in Twitter

abstract: Hashtags, originally introduced in Twitter, are now becoming the most used way to tag short messages in social networks since this facilitates subsequent search, classification and clustering over those messages. However, extracting information from hashtags is difficult because their composition is not constrained by any (linguistic) rule and they usually appear in short and poorly written messages which are difficult to analyze with classic IR techniques. In this paper we address two challenging problems regarding the meaning of hashtags — namely, hashtag relatedness and hashtag classification - and we provide two main contributions. First we build a novel graph upon hashtags and (Wikipedia) entities drawn from the tweets by means of topic annotators (such as TagME); this graph will allow us to model in an efficacious way not only classic co-occurrences but also semantic relatedness among hashtags and entities, or between entities themselves. Based on this graph, we design algorithms that significantly improve state-of-the-art results upon known publicly available datasets. The second contribution is the construction and the public release to the research community of two new datasets: the former is a new dataset for hashtag relatedness, the latter is a dataset for hashtag classification that is up to two orders of magnitude larger than the existing ones. These datasets will be used to show the robustness and efficacy of our approaches, showing improvements in F1 up to two-digits in percentage (absolute).


title: Review of state-of-the-arts in artificial intelligence with application to AI safety problem

abstract: Here, I review current state-of-the-arts in many areas of AI to estimate when it's reasonable to expect human level AI development. Predictions of prominent AI researchers vary broadly from very pessimistic predictions of Andrew Ng to much more moderate predictions of Geoffrey Hinton and optimistic predictions of Shane Legg, DeepMind cofounder. Given huge rate of progress in recent years and this broad range of predictions of AI experts, AI safety questions are also discussed.


title: Active Literature Discovery for Scoping Evidence Reviews How Many Needles are There

abstract: Scoping reviews of the biomedical literature are very commonly used in health technology assessments to inform the planning of more detailed and resource-intensive evaluations. A typical task is to ‘map’ the literature addressing a specific clinical question, i.e., (i) identify as many relevant articles of interest as feasible under a constrained budget, and (ii) estimate how many such articles likely exist. These are competing objectives. Using active retrieval strategies (e.g., active learning) to realize the former aim immediately hinders our ability to achieve the latter: ‘naive’ estimates of the amount of relevant articles taken over an enriched sampled acquired through selective sampling will be inflated. We propose a novel method for correcting such estimates. We demonstrate the e!cacy of our approach on three systematic review datasets, showing that we can achieve both aims: rapid evidence discovery and acceptably accurate estimation of the number of relevant articles.


title: Collaborative Learning in Geographically Distributed and In-person Groups

abstract: Open online courses attract a diverse global audience of learners, many of whom might not be self-directed autodidacts with the necessary web competencies to reap the full benefits of such courses. Most of these learners would benefit from increased guidance on how to use MOOCs to enhance their learning. One potential area for guidance is in group collaboration where learners form teams to collaboratively work on assignments. Despite the global scope of these courses, a large proportion of learners live within relatively close proximity of each other, such that in-person collaboration is a feasible option. However, geographically distributed groups of learners are more likely to bring diverse viewpoints to the discussion than learners who live close to each other. Research suggests that the diversity of viewpoints in a group positively affects the quality of collaboration and outcomes. This paper reviews the literature on the feasibility of assigning local groups for collaboration and proposes concrete research directions.


title: Omnidirectional Mobile Robot - Design and Implementation

abstract: Industrial and technical applications of mobile robots are continuously gaining in importance. They are already widely used for surveillance, inspection and transportation tasks. A further emerging market is that of mobile entertainment robots. One of the main requirements of an autonomous mobile robot is its ability to move through the operational space, avoiding obstacles and finding its way to the next location, in order to perform its task, capabilities known as localization and navigation. In order to know where to go, the robot must have accurate knowledge of its current location. It means, it should use a great variety of sensors, external references and algorithms. In order to move in tight areas and to avoid obstacles mobile robots should have good mobility and maneuverability. These capabilities mainly depend on the wheels design. Research is continuously going on in this field, to improve the autonomous navigation capability of mobile robotic systems. This chapter introduces an omnidirectional mobile robot for educational purposes. The robot has full omnidirectional motion capabilities, thanks to its special Mecanum wheels. The present chapter provides some information about conventional and special wheels designs, mechanical design aspects of the Mecanum wheel and also of the robot, kinematic models, as well as electronics and control strategies: remote control, line follow, autonomous strategy. Thanks to its motion capabilities and to its different control possibilities, the robot discussed in this chapter could be used as an interesting educational platform. This report is the result of a research conducted at the Robotics Laboratory of the Mechanical Engineering Faculty, “Gh. Asachi” Technical University of Iasi, Romania.