title: FAST approaches to scalable similarity-based test case prioritization

abstract: Many test case prioritization criteria have been proposed for speeding up fault detection. Among them, similarity-based approaches give priority to the test cases that are the most dissimilar from those already selected. However, the proposed criteria do not scale up to handle the many thousands or even some millions test suite sizes of modern industrial systems and simple heuristics are used instead. We introduce the FAST family of test case prioritization techniques that radically changes this landscape by borrowing algorithms commonly exploited in the big data domain to find similar items. FAST techniques provide scalable similarity-based test case prioritization in both white-box and black-box fashion. The results from experimentation on real world C and Java subjects show that the fastest members of the family outperform other black-box approaches in efficiency with no significant impact on effectiveness, and also outperform white-box approaches, including greedy ones, if preparation time is not counted. A simulation study of scalability shows that one FAST technique can prioritize a million test cases in less than 20 minutes.


title: Event Detection and Retrieval on Social Media

abstract: In the recent years, we have witnessed the rapid adoption of social media platforms, such as Twitter, Facebook and YouTube, and their use as part of the everyday life of billions of people worldwide. Given the habit of people to use these platforms to share thoughts, daily activities and experiences it is not surprising that the amount of user generated content has reached unprecedented levels, with a substantial part of that content being related to real-world events, i.e. actions or occurrences taking place at a certain time and location. Given the key role of events in our life, the task of annotating and organizing social media content around them is of crucial importance for ensuring real-time and future access to multimedia content about an event of interest. In this chapter, we present several research efforts from recent years that tackle two main problems: a) event detection and b) event-based media retrieval and summarization. Given archived collections or live streams of social media items, the purpose of event detection methods is to identify previously unknown events in the form of sets of items that describe them. In general, the events could be of any type, but there are also approaches aiming at events of specific type. Given a target event the goal of event summarization is first to identify relevant content and then to represent it in a concise way, selecting the most appealing and representative content.


title: On the vulnerability of face verification systems to hill-climbing attacks

abstract: In this paper, we use a hill-climbing attack algorithm based on Bayesian adaption to test the vulnerability of two face recognition systems to indirect attacks. The attacking technique uses the scores provided by the matcher to adapt a global distribution computed from an independent set of users, to the local specificities of the client being attacked. The proposed attack is evaluated on an eigenface-based and a parts-based face verification system using the XM2VTS database. Experimental results demonstrate that the hill-climbing algorithm is very efficient and is able to bypass over 85% of the attacked accounts (for both face recognition systems). The security flaws of the analyzed systems are pointed out and possible countermeasures to avoid them are also proposed.


title: Caudal duplication (monocephalus tripus dibrachius) in a kid goat

abstract: Dipygus is a congenital duplication in the caudal region of the body. According to the extension of the anomaly, duplicated cases are classified as monoceophalus tripus dibrachius, monocephalus tetrapus dibrachius and cephalothoracopagus. A newborn five-legged male kid was referred to the veterinary teaching hospital of Shahid Bahonar University of Kerman, Iran. In ventrodorsal radiography, normal pelvic symphysis was not observed between middle and left or right limbs. Necropsy findings included duplication of the bony pelvic girdle, alimentary tract from the jejunum downward, bladder and urethra. We hereby describe the first report of monocephalus tripus dibrachius in a kid goat.


title: Breast cancer imaging at mm-waves: Feasibility study on the safety exposure limits

abstract: Breast cancer is a leading disease worldwide, and the success of medical therapies is heavily related to the availability of breast cancer imaging techniques. While current methods, mainly ultrasound, x-ray mammography, and magnetic resonance imaging, all exhibit some disadvantages, a possible alternative investigated in recent years is based on microwave and mm-wave imaging system. A key point for these systems is their reliability in terms of safety, in particular exposure limits. This paper presents a feasibility study for a mm-wave breast cancer imaging system, with the aim of ensuring safety and compliance with the widely adopted European ICNIRP recommendations. The study is based on finite element method models of human tissues, experimentally characterized by measures obtained at one of the most important European clinical center for cancer treatments. Results prove the feasibility of the system, which can meet the exposure limits while providing the required dynamic range to let the receiver detect the cancer anomaly. In addition, the dosimetric quantities used at the present and their maximum limits at mm-waves are taking into discussion and the possibility of needing moderns quantities and limitations is discussed.


title: Towards Blockchain-based Auditable Storage and Sharing of IoT Data

abstract: Today the cloud plays a central role in storing, processing, and distributing data. Despite contributing to the rapid development of IoT applications, the current IoT cloud-centric architecture has led into a myriad of isolated data silos that hinders the full potential of holistic data-driven analytics within the IoT. In this paper, we present a blockchain-based design for the IoT that brings a distributed access control and data management. We depart from the current trust model that delegates access control of our data to a centralized trusted authority and instead empower the users with data ownership. Our design is tailored for IoT data streams and enables secure data sharing. We enable a secure and resilient access control management, by utilizing the blockchain as an auditable and distributed access control layer to the storage layer. We facilitate the storage of time-series IoT data at the edge of the network via a locality-aware decentralized storage system that is managed with the blockchain technology. Our system is agnostic of the physical storage nodes and supports as well utilization of cloud storage resources as storage nodes.


title: Suicidal asphyxiation with helium: Report of three cases

abstract: Helium is an inert gas that among other things is used medically to alleviate the symptoms of airway obstruction, as part of a diving mix in deep-sea diving or as balloon gas. In recent years the so-called right-to-die literature has suggested suffocation with inhaled helium as an effective and peaceful means of self-deliverance for terminally ill patients. Helium displaces oxygen and carbon dioxide and can thus lead to asphyxia. We report three cases of suicidal asphyxiation with helium gas that were examined at the Department of Forensic Medicine Vienna within three months in 2006. In all three cases, autopsy was unrewarding from the point of view of gross pathology. Special autopsy techniques and devices are required for collection of the gas from the lungs. Gas-chromatography is used to examine the gas for helium; however, this requires replacement of the carrier gas, which is itself usually helium. The fact that three people in Vienna committed suicide using this method within a short period of time, together with the abundance of detailed how-to literature on the Internet, suggests a possible future increase in the number of deaths associated with the inhalation of inert gases, particularly helium. Because of the diagnostic obstacles involved, it is necessary to rely on good death-scene investigation for situational evidence when the body is discovered.


title: TruePIE: Discovering Reliable Patterns in Pattern-Based Information Extraction

abstract: Pattern-based methods have been successful in information extraction and NLP research. Previous approaches learn the quality of a textual pattern as relatedness to a certain task based on statistics of its individual content (e.g., length, frequency) and hundreds of carefully-annotated labels. However, patterns of good content-quality may generate heavily conflicting information due to the big gap between relatedness and correctness. Evaluating the correctness of information is critical in (entity, attribute, value)-tuple extraction. In this work, we propose a novel method, called TruePIE, that finds reliable patterns which can extract not only related but also correct information. TruePIE adopts the self-training framework and repeats the training-predicting-extracting process to gradually discover more and more reliable patterns. To better represent the textual patterns, pattern embeddings are formulated so that patterns with similar semantic meanings are embedded closely to each other. The embeddings jointly consider the local pattern information and the distributional information of the extractions. To conquer the challenge of lacking supervision on patterns' reliability, TruePIE can automatically generate high quality training patterns based on a couple of seed patterns by applying the arity-constraints to distinguish highly reliable patterns (i.e., positive patterns) and highly unreliable patterns (i.e., negative patterns). Experiments on a huge news dataset (over 25GB) demonstrate that the proposed TruePIE significantly outperforms baseline methods on each of the three tasks: reliable tuple extraction, reliable pattern extraction, and negative pattern extraction.


title: Spherical Convolutional Neural Network for 3D Point Clouds

abstract: We propose a neural network for 3D point cloud processing that exploits `spherical' convolution kernels and octree partitioning of space. The proposed metric-based spherical kernels systematically quantize point neighborhoods to identify local geometric structures in data, while maintaining the properties of translation-invariance and asymmetry. The network architecture itself is guided by octree data structuring that takes full advantage of the sparse nature of irregular point clouds. We specify spherical kernels with the help of neurons in each layer that in turn are associated with spatial locations. We exploit this association to avert dynamic kernel generation during network training, that enables efficient learning with high resolution point clouds. We demonstrate the utility of the spherical convolutional neural network for 3D object classification on standard benchmark datasets.


title: Encrypted key exchange: password-based protocols secure against dictionary attacks

abstract: Classic cryptographic protocols based on user-chosen keys allow an attacker to mount password-guessing attacks. A combination of asymmetric (public-key) and symmetric (secret-key) cryptography that allow two parties sharing a common password to exchange confidential and authenticated information over an insecure network is introduced. In particular, a protocol relying on the counter-intuitive motion of using a secret key to encrypt a public key is presented. Such protocols are secure against active attacks, and have the property that the password is protected against offline dictionary attacks. >


title: Team vs. Team: Success Factors in a Multiplayer Online Battle Arena Game

abstract: There is a small but growing body of research investigating how teams form and how that affects how they perform. Much of that research focuses on teams that seek to accomplish certain tasks such as writing an article or perform a Broadway musical. There has been much less investigation of the relative performance of teams that form to directly compete against another team. In this study, we report on team-vs-team competitions in the multiplayer online game Dota 2. Here, the teams’ overall goal is to beat the opponent. We use this unique setting to observe multi-level factors influence the relative performance of the teams. Those factors include compositional factors or attributes of the individuals comprising a team, relational factors or prior relations among individuals within a team and ecosystem factors or overlapping prior membership of team members with others within the ecosystem of teams. We also study how these multilevel factors affect the duration of a match. Our results show that advantages a...


title: Mother-infant face-to-face interaction: Influence is bidirectional and unrelated to periodic cycles in either partner’s behavior

abstract: During mother-infant face-to-face interactions, bidirectional influence could be achieved through either the entraining of periodic cycles in the behavior of each partner or through the stochastic organization of behaviors. To determine whether and how bidirectional influence occurs, we used both timeand frequency-domain techniques to study the interactions of 54 mother-infant pairs, 18 each at 3, 6, and 9 months of age. Behavioral descriptors for each mother and infant were scaled to reflect levels of affective involvement during each second oftbe interaction. Periodic cycles were found in infants' expressive behavior only at 3 months and not in mothers' behavior. Nonperiodic cycles, which were found in some mothers' and infants' behavior at each age, were more common. At no age was the occurrence of cycles in mothers' or infants' behavior related to the achievement of bidirectional influence. Similar proportions of mothers and infants were responsive to momentto-moment changes in the other's behavior, except at 6 months when the proportion of mothers was higher. Bidirectional influence was brought about by the stochastic organization of behaviors rather than through the mutual entraining of periodic cycles.


title: Information Systems Development Methodologies : Are you being served ?

abstract: A steering and suspension mechanism for a wheeled vehicle incorporating bump steer control. The mechanism comprises a pair of spaced apart universal joints one of which is fixedly located and the other positionably supporting the wheel axle. A spacer member positions the universal joints at a selected spaced relationship. A rotatable steering shaft is connected to one side of the fixed in position universal joint. The center of the opposite side of the fixed in position universal joint is pivotally connected to one end of the spacer. The other end of the spacer is connected to the center of one side of the wheel axle supporting universal joint. The opposite side of the wheel supporting universal joint is connected to the wheel supporting axle. In one embodiment a tie rod is pivotally connected off center and adjacent to the spacer member on each universal joint through a ball connection. An attachment between the wheel axle and frame provides additional support thereto.


title: A Compiler for Deterministic, Decomposable Negation Normal Form

abstract: We present a compiler for converting CNF formulas into deterministic, decomposable negation normal form (d-DNNF). This is a logical form that has been identified recently and shown to support a number of operations in polynomial time, including clausal entailment; model counting, minimization and enumeration; and probabilistic equivalence testing, d-DNNFs are also known to be a superset of, and more succinct than, OBDDs. The polytime logical operations supported by d-DNNFs are a subset of those supported by OBDDs, yet are sufficient for model-based diagnosis and planning applications. We present experimental results on compiling a variety of CNF formulas, some generated randomly and others corresponding to digital circuits. A number of the formulas we were able to compile efficiently could not be similarly handled by some state-of-the-art model counters, nor by some state-of-the-art OBDD compilers.


title: Application of Linear-Frequency-Modulated Continuous-Wave (LFMCW) Radars for Tracking of Vital Signs

abstract: This paper focuses on the exploitation of linear-frequency-modulated continuous-wave (LFMCW) radars for noncontact range tracking of vital signs, e.g., respiration. Such short-range system combines hardware simplicity and tracking precision, thus outperforming other remote-sensing approaches in the addressed biomedical scenario. A rigorous mathematical analysis of the operating principle of the LFMCW radar in the context of vital-sign monitoring, which includes the explanation of key aspects for the maintenance of coherence, is detailed. A precise phase-based range-tracking algorithm is also presented. Exhaustive simulations are carried out to confirm the suitability and robustness against clutter, noise, and multiple scatterers of the proposed radar architecture, which is subsequently implemented at the prototype level. Moreover, live data from real experiments associated to a metal plate and breathing subjects are obtained and studied.


title: Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation

abstract: We tackle the problem of object detection and pose estimation in a shared space downtown environment. For perception multiple laser scanners with 360{\deg} coverage were fused in a dynamic occupancy grid map (DOGMa). A single-stage deep convolutional neural network is trained to provide object hypotheses comprising of shape, position, orientation and an existence score from a single input DOGMa. Furthermore, an algorithm for offline object extraction was developed to automatically label several hours of training data. The algorithm is based on a two-pass trajectory extraction, forward and backward in time. Typical for engineered algorithms, the automatic label generation suffers from misdetections, which makes hard negative mining impractical. Therefore, we propose a loss function counteracting the high imbalance between mostly static background and extremely rare dynamic grid cells. Experiments indicate, that the trained network has good generalization capabilities since it detects objects occasionally lost by the label algorithm. Evaluation reaches an average precision (AP) of 75.9%


title: Device Placement Optimization with Reinforcement Learning

abstract: The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these requirements is to use a heterogeneous distributed environment with a mixture of hardware devices such as CPUs and GPUs. Importantly, the decision of placing parts of the neural models on devices is often made by human experts based on simple heuristics and intuitions. In this paper, we propose a method which learns to optimize device placement for TensorFlow computational graphs. Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices. The execution time of the predicted placements is then used as the reward signal to optimize the parameters of the sequence-to-sequence model. Our main result is that on Inception-V3 for ImageNet classification, and on RNN LSTM, for language modeling and neural machine translation, our model finds non-trivial device placements that outperform hand-crafted heuristics and traditional algorithmic methods.


title: Neural Tensor Factorization

abstract: Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data. However, they are also limited in their assumption of static or sequential modeling of relational data as they do not account for evolving users' preference over time as well as changes in the underlying factors that drive the change in user-item relationship over time. We address these limitations by proposing a Neural Tensor Factorization (NTF) model for predictive tasks on dynamic relational data. The NTF model generalizes conventional tensor factorization from two perspectives: First, it leverages the long short-term memory architecture to characterize the multi-dimensional temporal interactions on relational data. Second, it incorporates the multi-layer perceptron structure for learning the non-linearities between different latent factors. Our extensive experiments demonstrate the significant improvement in rating prediction and link prediction on dynamic relational data by our NTF model over both neural network based factorization models and other traditional methods.


A High-Security Searchable Encryption Framework for Privacy-Critical Cloud Storage Services.


title: What drives consumers to shop online? A literature review

abstract: While a large number of consumers in the US and Europe frequently shop on the Internet, research on what drives consumers to shop online has typically been fragmented. This paper therefore proposes a framework to increase researchers’ understanding of consumers’ attitudes toward online shopping and their intention to shop on the Internet. The framework uses the constructs of the Technology Acceptance Model (TAM) as a basis, extended by exogenous factors and applies it to the online shopping context. The review shows that attitudes toward online shopping and intention to shop online are not only affected by ease of use, usefulness, and enjoyment, but also by exogenous factors like consumer traits, situational factors, product characteristics, previous online shopping experiences, and trust in online shopping.


title: Analysis of transmission lines matching using quarter-wave transformer

abstract: Due to conformal capability, research on transmission lines has received much attention lately. Many studies have been reported in the last decade in which transmission lines have been analyzed extensively using various techniques. It is well known that transmission lines are used for transmission of information, but in this case the main aim is to deliver information from generator to receiver with low attenuation. To achieve this, the load should be matched to the characteristic impedance of the line, meaning that the wave coefficient should be near 1 (one). One of the most important methods for line matching is through quarter-wavelength line (quarter-wave transformer). Analysis of transmission lines using numerical methods is difficult because of any possible error that can occur. Therefore, the best solution in this case would be the use of any software package which is designed for analysis of transmission lines. In this paper we will use Sonet software which is generally used for the analysis of planar lines.


title: Phighting the Phisher: Using Web Bugs and Honeytokens to Investigate the Source of Phishing Attacks

abstract: This paper presents a summary of research findings for a new reacitve phishing investigative technique using Web bugs and honeytokens. Phishing has become a rampant problem in today 's society and has cost financial institutions millions of dollars per year. Today's reactive techniques against phishing usually involve methods that simply minimize the damage rather than attempting to actually track down a phisher. Our research objective is to track down a phisher to the IP address of the phisher's workstation rather than innocent machines used as intermediaries. By using Web bugs and honeytokens on the fake Web site forms the phisher presents, one can log accesses to the honeytokens by the phisher when the attacker views the results of the forms. Research results to date are presented in this paper


title: Consensus and Cooperation in Networked Multi-Agent Systems

abstract: This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided. Our analysis frame- work is based on tools from matrix theory, algebraic graph theory, and control theory. We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in small- world networks, Markov processes and gossip-based algo- rithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms. A brief introduction is provided on networked systems with nonlocal information flow that are considerably faster than distributed systems with lattice-type nearest neighbor interactions. Simu- lation results are presented that demonstrate the role of small- world effects on the speed of consensus algorithms and cooperative control of multivehicle formations.


title: Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

abstract: Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.


title: Automatic calibration of RGBD and thermal cameras

abstract: Reasoning about a scene's thermal signature, in addition to its visual appearance and spatial configuration, would facilitate significant advances in perceptual systems. Applications involving the segmentation and tracking of persons, vehicles, and other heat-emitting objects, for example, could benefit tremendously from even coarsely accurate relative temperatures. With the increasing affordability of commercially available thermal cameras, as well as the imminent introduction of new, mobile form factors, such data will be readily and widely accessible. However, in order for thermal processing to complement existing methods in RGBD, there must be an effective procedure for calibrating RGBD and thermal cameras to create RGBDT (red, green, blue, depth, and thermal) data. In this paper, we present an automatic method for the synchronization and calibration of RGBD and thermal cameras in arbitrary environments. While traditional calibration methods fail in our multimodal setting, we leverage invariant features visible by both camera types. We first synchronize the streams with a simple optimization procedure that aligns their motion statistic time series. We then find the relative poses of the cameras by minimizing an objective that measures the alignment between edge maps from the two streams. In contrast to existing methods that use special calibration targets with key points visible to both cameras, our method requires nothing more than some edges visible to both cameras, such as those arising from humans. We evaluate our method and demonstrate that it consistently converges to the correct transform and that it results in high-quality RGBDT data.


title: Fast Point Feature Histograms (FPFH) for 3D registration

abstract: In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multi-dimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views. More concretely, we present several optimizations that reduce their computation times drastically by either caching previously computed values or by revising their theoretical formulations. The latter results in a new type of local features, called Fast Point Feature Histograms (FPFH), which retain most of the discriminative power of the PFH. Moreover, we propose an algorithm for the online computation of FPFH features for realtime applications. To validate our results we demonstrate their efficiency for 3D registration and propose a new sample consensus based method for bringing two datasets into the convergence basin of a local non-linear optimizer: SAC-IA (SAmple Consensus Initial Alignment).


title: Journal of Medical Virology 72:533–537 (2004) Hepatitis in Albanian Children: Molecular Analysis of Hepatitis A Virus Isolates

abstract: Hepatitis A is a common disease in developing countries and Albania has a high prevalence of this disease associated to young age. In spite of the occurrence of a unique serotype there are different genotypes classified from I to VII. Genotype characterisation of HAV isolates circulating in Albania has been undertaken, as well as the study of the occurrence of antigenic variants in the proteins VP3 and VP1. To evaluate the genetic variability of the Albanian hepatitis A virus (HAV) isolates, samples were collected from 12 different cities, and the VP1/2A junction amplified and sequenced. These sequences were aligned and a phylogenetic analysis performed. Additionally, the amino half sequence of the protein VP3 and the complete sequence of the VP1 was determined. Anti-HAV IgM were present in 66.2% of all the sera. Fifty HAV isolates were amplified and the analysis revealed that all the isolates were sub-genotype IA with only limited mutations. When the deduced amino acid sequences were obtained, the alignment showed only two amino acids substitutions at positions 22 and 34 of the 2A protein. A higher genomic stability of the VP1/2A region, in contrast with what occurs in other parts of the world could be observed, indicating high endemicity of HAV in Albania. In addition, two potential antigenic variants were detected. The first at position 46 of VP3 in seven isolates and the second at position 23 of VP1 in six isolates.


title: Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

abstract: We propose a randomized block-coordinate variant of the classic Frank-Wolfe algorithm for convex optimization with block-separable constraints. Despite its lower iteration cost, we show that it achieves a similar convergence rate in duality gap as the full Frank-Wolfe algorithm. We also show that, when applied to the dual structural support vector machine (SVM) objective, this yields an online algorithm that has the same low iteration complexity as primal stochastic subgradient methods. However, unlike stochastic subgradient methods, the block-coordinate Frank-Wolfe algorithm allows us to compute the optimal step-size and yields a computable duality gap guarantee. Our experiments indicate that this simple algorithm outperforms competing structural SVM solvers.


title: Multi-domain dialogue success classifiers for policy training

abstract: We propose a method for constructing dialogue success classifiers that are capable of making accurate predictions in domains unseen during training. Pooling and adaptation are also investigated for constructing multi-domain models when data is available in the new domain. This is achieved by reformulating the features input to the recurrent neural network models introduced in [1]. Importantly, on our task of main interest, this enables policy training in a new domain without the dialogue success classifier (which forms the reinforcement learning reward function) ever having seen data from that domain before. This occurs whilst incurring only a small reduction in performance relative to developing and using an in-domain dialogue success classifier. Finally, given the motivation with these dialogue success classifiers is to enable policy training with real users, we demonstrate that these initial policy training results obtained with a simulated user carry over to learning from paid human users.


title: An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark

abstract: Question answering (QA) over an existing knowledge base (KB) such as Microsoft Satori or open Freebase is one of the most important natural language processing applications. There are approaches based on web-search motivated statistic techniques as well as linguistically oriented knowledge engineering. Both methods face the key challenge on how to handle diverse ways of naturally expressing predicates and entities existing in the KB. The domain independent web information extracted from the massive amount of web usage data can be used with traditional semantic parsing through a unified framework. We provide such a unified framework utilizing both statistically motivated information-theoretic embeddings and logically driven proof-theoretic decoding to significantly improve Stanford’s WebQuestions QA benchmark. In comparison to Stanford’s state of the art ParaSempre 39.9 (F1-score), our Deep QA system achieves 45.3 on the same test data and protocol.


title: A Critical Success Factors Model for Enterprise Resource Planning Implementation

abstract: Enterprise Resource Planning (ERP) systems are integrated enterprise-wide standard information systems that automate all aspects of an organisations’ business processes. The ERP philosophy is that business systems incorporating sales, marketing, manufacturing, distribution, personnel and finance modules can be supported by a single integrated system with all of the company’s data captured in a ::: central database. The ERP packages of vendors such as SAP, Baan, J.D. Edwards and Intentia represent more than a common systems platform for a business. They prescribe information blueprints of how organisation’s business processes should operate. In this paper, the scale and strategic importance of ERP systems is identified and the problem of ERP implementation is defined. Five company examples are analysed using a Critical Success Factors (CSFs) theoretical framework. The paper offers a framework ::: for managers which provides the basis for developing an ERP implementation strategy. The case analysis identifies different approaches to ERP implementation, highlights the critical role of legacy systems in influencing the implementation process, and identifies the importance of business process change and software configuration in addition to factors already cited in the literature such as top management support and communication. The implications of the results and future research opportunities are ::: outlined.


title: FAST: an efficient flash translation layer for flash memory

abstract: Flash memory is used at high speed as storage of personal information utilities, ubiquitous computing environments, mobile phones, electronic goods, etc. This is because flash memory has the characteristics of low electronic power, non-volatile storage, high performance, physical stability, portability, and so on. However, differently from hard disks, it has a weak point that overwrites on already written block of flash memory is impossible to be done. In order to make it possible, an erase operation on the written block should be performed before the overwrite, which lowers the performance of flash memory highly. In order to solve this problem, the flash memory controller maintains a system software module called the flash translation layer(FTL). In this paper, we propose an enhanced log block buffer FTL scheme, FAST(Fully Associative Sector Translation), which improves the page usability of each log block by fully associating sectors to be written by overwrites to the entire log blocks. We also show that our FAST scheme outperforms the previous log block buffer scheme.


title: The 10-ps wave union TDC: Improving FPGA TDC resolution beyond its cell delay

abstract: There are two major issues in the delay chain based FPGA TDC due to uneven internal delay in the carry chain. (1) The bin widths are uneven and depend on temperature and power supply voltage, which must be calibrated as frequently as possible. The auto-calibration functional block developed in this work provides semi-continuous calibration that converts the TDC measurements from bins to picoseconds. (2) In many applications, the TDC resolution is limited by the “ultra-wide bins”, corresponding to the carry chain crossing at the boundaries of the logic array blocks. The apparent widths of these ultra-wide bins can be several times bigger than the average bin width. The “wave union launchers” described in this paper are designed to make multiple measurements with a single delay chain structure, effectively to sub-divide the ultra-wide bins in each raw measurement. Several TDC schemes with resolutions in 20 to10 picoseconds range implemented in today’s low cost FPGA have been tested.


DeepEye: Visualizing Your Data by Keyword Search.


title: Fault-tolerant quantum computation

abstract: It has recently been realized that use of the properties of quantum mechanics might speed up certain computations dramatically. Interest in quantum computation has since been growing. One of the main difficulties in realizing quantum computation is that decoherence tends to destroy the information in a superposition of states in a quantum computer making long computations impossible. A further difficulty is that inaccuracies in quantum state transformations throughout the computation accumulate, rendering long computations unreliable. However, these obstacles may not be as formidable as originally believed. For any quantum computation with t gates, we show how to build a polynomial size quantum circuit that tolerates O(1/log/sup c/t) amounts of inaccuracy and decoherence per gate, for some constant c; the previous bound was O(1/t). We do this by showing that operations can be performed on quantum data encoded by quantum error-correcting codes without decoding this data.


title: Semi-Supervised Ensemble Ranking

abstract: Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance by combining the outputs from multiple ranking algorithms. Many ensemble ranking approaches employ supervised learning techniques to learn appropriate weights for combining multiple rankers. The main shortcoming with these approaches is that the learned weights for ranking algorithms are query independent. This is suboptimal since a ranking algorithm could perform well for certain queries but poorly for others. In this paper, we propose a novel semi-supervised ensemble ranking (SSER) algorithm that learns query-dependent weights when combining multiple rankers in document retrieval. The proposed SSER algorithm is formulated as an SVM-like quadratic program (QP), and therefore can be solved efficiently by taking advantage of optimization techniques that were widely used in existing SVM solvers. We evaluated the proposed technique on a standard document retrieval testbed and observed encouraging results by comparing to a number of state-of-the-art techniques.


title: Four Models of the Physician-Patient Relationship

abstract: During the last two decades or so, there has been a struggle over the patient's role in medical decision-making that is often characterized as a conflict between autonomy and health, between the values of the patient and the values of the physician. Seeking to curtail physician dominance, many have advocated an ideal of greater patient control.',' Others question this ideal because it fails to acknowledge the potentially imbalanced nature of this interaction when one party is sick and searching for security, and when judgments entail the interpretation of technical information.14 Still others are trying to delineate a more mutual relationship.5,6 This struggle shapes the expectations of physicians and patients as well as the ethical and legal standards for the physician's duties, informed consent, and medical malpractice. This struggle forces us to ask, What should be the ideal physician-patient relationship? We shall outline four models of the physicianpatient interaction, emphasizing the different understandings of (1) the goals of the physicianpatient interaction, (2) the physician's obligations, (3) the role of patient values, and (4) the conception of patient autonomy. To elaborate the abstract description of these four models, we shall indicate the types of response the models might suggest in a clinical situation. Third, we shall also indicate how these models inform the current debate about the ideal physician-patient relationship. Finally, we shall evaluate these models and recommend one as the preferred model. As outlined, the models are Weberian ideal types. They may not describe any particular physician-patient interactions but highlight, free from complicating details, different visions of the essential characteristics of the physician-patient interaction. 7 Consequently, they do not embody minimum ethical or legal standards, but rather constitute regulative ideals that are "higher than the law" but not "above the law."' THE PATERNALISTIC MODEL First is the paternalistic model, sometimes called the parental 9 or priestly model. In this model, the physician-patient interaction ensures that patients receive the interventions that best promote their health and well-being. To this end, physicians use their skills to determine the patient's medical condition and his or her stage in the disease process and to identify the medical tests and treatments most likely to restore the patient's health or ameliorate pain. Then the physician presents the patient with selected information that will encourage the patient to consent to the intervention the physician considers best. At the extreme, the physician authoritatively informs the patient when the intervention will be initiated. The paternalistic model assumes that there are shared objective criteria for determining what is best. Hence the physician can discern what is in the patient's best interest with limited patient participation. Ultimately, it is assumed that the patient will be thankful for decisions made by the physician even if he or she would not agree to them at the time." In the tension


title: code_swarm: A Design Study in Organic Software Visualization

abstract: In May of 2008, we published online a series of software visualization videos using a method called code_swarm. Shortly thereafter, we made the code open source and its popularity took off. This paper is a study of our code swarm application, comprising its design, results and public response. We share our design methodology, including why we chose the organic information visualization technique, how we designed for both developers and a casual audience, and what lessons we learned from our experiment. We validate the results produced by code_swarm through a qualitative analysis and by gathering online user comments. Furthermore, we successfully released the code as open source, and the software community used it to visualize their own projects and shared their results as well. In the end, we believe code_swarm has positive implications for the future of organic information design and open source information visualization practice.


title: We Need Numbers!: Heuristic Evaluation during Demonstrations (HED) for Measuring Usability in IT System Procurement

abstract: We introduce a new usability inspection method called HED (heuristic evaluation during demonstrations) for measuring and comparing usability of competing complex IT systems in public procurement. The method presented enhances traditional heuristic evaluation to include the use context, comprehensive view of the system, and reveals missing functionality by using user scenarios and demonstrations. HED also quantifies the results in a comparable way. We present findings from a real-life validation of the method in a large-scale procurement project of a healthcare and social welfare information system. We analyze and compare the performance of HED to other usability evaluation methods used in procurement. Based on the analysis HED can be used to evaluate the level of usability of an IT system during procurement correctly, comprehensively and efficiently.


title: Episodic Curiosity through Reachability

abstract: Rewards are sparse in the real world and most today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, observing something novel could be rewarded with a bonus. Such bonus is summed up with the real task reward - making it possible for RL algorithms to learn from the combined reward. We propose a new curiosity method which uses episodic memory to form the novelty bonus. To determine the bonus, the current observation is compared with the observations in memory. Crucially, the comparison is done based on how many environment steps it takes to reach the current observation from those in memory - which incorporates rich information about environment dynamics. This allows us to overcome the known"couch-potato"issues of prior work - when the agent finds a way to instantly gratify itself by exploiting actions which lead to hardly predictable consequences. We test our approach in visually rich 3D environments in ViZDoom, DMLab and MuJoCo. In navigational tasks from ViZDoom and DMLab, our agent outperforms the state-of-the-art curiosity method ICM. In MuJoCo, an ant equipped with our curiosity module learns locomotion out of the first-person-view curiosity only.


title: FashionNet: Personalized Outfit Recommendation with Deep Neural Network

abstract: With the rapid growth of fashion-focused social networks and online shopping, intelligent fashion recommendation is now in great need. We design algorithms which automatically suggest users outfits (e.g. a shirt, together with a skirt and a pair of high-heel shoes), that fit their personal fashion preferences. Recommending sets, each of which is composed of multiple interacted items, is relatively new to recommender systems, which usually recommend individual items to users. We explore the use of deep networks for this challenging task. Our system, dubbed FashionNet, consists of two components, a feature network for feature extraction and a matching network for compatibility computation. The former is achieved through a deep convolutional network. And for the latter, we adopt a multi-layer fully-connected network structure. We design and compare three alternative architectures for FashionNet. To achieve personalized recommendation, we develop a two-stage training strategy, which uses the fine-tuning technique to transfer a general compatibility model to a model that embeds personal preference. Experiments on a large scale data set collected from a popular fashion-focused social network validate the effectiveness of the proposed networks.


title: Mobile Robot Path Planning by Improved Brain Storm Optimization Algorithm

abstract: Robots have found their purpose in various situations, from speeding the manufacturing processes to performing complicated tasks in dangerous and hostile environments. One of the important problems in robotics is mobile robot path planning. Robot path planning represents a hard optimization problem that needs to be solved in numerous applications. In this paper we propose path planning method in environments with static obstacles based on the recent swarm intelligence algorithm, brain storm optimization. The brain storm optimization algorithm was improved by local search procedure that each new candidate solution moves to the local best position thus reducing computational time. We tested the proposed method on several benchmark examples from the literature and it has been shown that our approach finds better and more consistent paths using less computational time.


title: Pulling Things out of Perspective

abstract: The limitations of current state-of-the-art methods for single-view depth estimation and semantic segmentations are closely tied to the property of perspective geometry, that the perceived size of the objects scales inversely with the distance. In this paper, we show that we can use this property to reduce the learning of a pixel-wise depth classifier to a much simpler classifier predicting only the likelihood of a pixel being at an arbitrarily fixed canonical depth. The likelihoods for any other depths can be obtained by applying the same classifier after appropriate image manipulations. Such transformation of the problem to the canonical depth removes the training data bias towards certain depths and the effect of perspective. The approach can be straight-forwardly generalized to multiple semantic classes, improving both depth estimation and semantic segmentation performance by directly targeting the weaknesses of independent approaches. Conditioning the semantic label on the depth provides a way to align the data to their physical scale, allowing to learn a more discriminative classifier. Conditioning depth on the semantic class helps the classifier to distinguish between ambiguities of the otherwise ill-posed problem. We tested our algorithm on the KITTI road scene dataset and NYU2 indoor dataset and obtained obtained results that significantly outperform current state-of-the-art in both single-view depth and semantic segmentation domain.


title: The unscented Kalman filter for nonlinear estimation

abstract: This paper points out the flaws in using the extended Kalman filter (EKE) and introduces an improvement, the unscented Kalman filter (UKF), proposed by Julier and Uhlman (1997). A central and vital operation performed in the Kalman filter is the propagation of a Gaussian random variable (GRV) through the system dynamics. In the EKF the state distribution is approximated by a GRV, which is then propagated analytically through the first-order linearization of the nonlinear system. This can introduce large errors in the true posterior mean and covariance of the transformed GRV, which may lead to sub-optimal performance and sometimes divergence of the filter. The UKF addresses this problem by using a deterministic sampling approach. The state distribution is again approximated by a GRV, but is now represented using a minimal set of carefully chosen sample points. These sample points completely capture the true mean and covariance of the GRV, and when propagated through the true nonlinear system, captures the posterior mean and covariance accurately to the 3rd order (Taylor series expansion) for any nonlinearity. The EKF in contrast, only achieves first-order accuracy. Remarkably, the computational complexity of the UKF is the same order as that of the EKF. Julier and Uhlman demonstrated the substantial performance gains of the UKF in the context of state-estimation for nonlinear control. Machine learning problems were not considered. We extend the use of the UKF to a broader class of nonlinear estimation problems, including nonlinear system identification, training of neural networks, and dual estimation problems. In this paper, the algorithms are further developed and illustrated with a number of additional examples.


title: Machine learning algorithms for document clustering and fraud detection

abstract: Machine Learning plays very important role in processing of large amounts of structured and unstructured data. A set of algorithms can be used to get meaningful insights into the data that are helpful in making effective business decisions. Document clustering is one of the popular machine learning technique used to group unstructured data (text documents) based on its content and further analyze the data to understand the patterns in it. The unstructured data gets transformed into semi-structured data and structured data in stages by using text mining and clustering (k-means) techniques. Classification is another machine learning technique that can be implemented for use cases like "fraud detection and cross-sell & up-sell opportunity identification" in banking, financial services and insurance industry. This paper focuses on the implementation of both document clustering algorithm and a set of classification algorithms (Decision Tree, Random Forest and Naive Bayes), along with appropriate industry use cases. Also, the performance of three classification algorithms will be compared by calculation of "Confusion Matrix" which in turn helps us to calculate performance measures such as, "accuracy", "precision", and "recall".


title: A Study on Consumers' Attitude towards Online Shopping in China

abstract: Online shopping provides a good example of the business revolution. In China, e-commerce is currently experiencing a period of rapid development; the large number of Internet users provides a good foundation for the expansion of the online shopping market. In this study, perceived usability, perceived security, perceived privacy, perceived after-sales service, perceived marketing mix, and perceived reputation were used for analysis. This research was conducted by using the primary data source, and the survey method was employed in the research. This research found that there were relationships between the perceived usability, perceived security, perceived privacy, perceived after-sales service, perceived marketing mix, perceived reputation and consumers’ attitude to adopting online shopping in China. However, only marketing mix and reputation were found to significantly influence consumers’ attitude to adopt online shopping. The findings help us in understanding consumers’ online purchase behaviour.


title: Building linked ontologies with high precision using subclass mapping discovery

abstract: The creation of links between schemas of published datasets is a key part of the Linked Open Data (LOD) paradigm. The ability to discover these links "on the go" requires that ontology matching techniques achieve good precision and recall within acceptable execution times. In this paper, we add similarity-based and mediator-based ontology matching methods to the Agreementmaker ontology matching system, which aim to efficiently discover high precision subclass mappings between LOD ontologies. Similarity-based matching methods discover subclass mappings by extrapolating them from a set of high quality equivalence mappings and from the interpretation of compound concept names. Mediator-based matching methods discover subclass mappings by comparing polysemic lexical annotations of ontology concepts and by considering external web ontologies. Experiments show that when compared with a leading LOD approach, Agreementmaker achieves considerably higher precision and F-measure, at the cost of a slight decrease in recall.


Question Answering Using Match-LSTM and Answer Pointer


title: Query-by-example: the invocation and definition of tables and forms

abstract: Query-by-Example is a query language for use by non-programmers querying a relational data base. In an earlier paper, the features of this language were introduced; however, it was assumed that the data base was already defined and available to the user. In the first part of this paper we demonstrate that the operations of Query-by-Example can be used not only to query the data base but also to define it, including data descriptions and declarations of various integrity constraints. This is an attempt to provide the user with a simplified, unified interface for a variety of functions. In the second part of this paper it is shown how one can make preliminary interrogations to retrieve a subset of the data base needed for the formulation of a specific query. This becomes very useful in cases where the data-base is very large and the user's initial task of scanning the tables for the ones relevant to his specific query becomes by itself horrendous.


Incidence of Bronchiolitis-Associated Hospitalization Among Children in Olmsted County, Minnesota


title: Decomposition of Organometal Halide Perovskite Films on Zinc Oxide Nanoparticles

abstract: Solution processed zinc oxide (ZnO) nanoparticles (NPs) with excellent electron transport properties and a low-temperature process is a viable candidate to replace titanium dioxide (TiO2) as electron transport layer to develop high-efficiency perovskite solar cells on flexible substrates. However, the number of reported high-performance perovskite solar cells using ZnO-NPs is still limited. Here we report a detailed investigation on the chemistry and crystal growth of CH3NH3PbI3 perovskite on ZnO-NP thin films. We find that the perovskite films would severely decompose into PbI2 upon thermal annealing on the bare ZnO-NP surface. X-ray photoelectron spectroscopy (XPS) results show that the hydroxide groups on the ZnO-NP surface accelerate the decomposition of the perovskite films. To reduce the decomposition, we introduce a buffer layer in between the ZnO-NPs and perovskite layers. We find that a commonly used buffer layer with small molecule [6,6]-phenyl-C61-butyric acid methyl ester (PC61BM) can slow down but cannot completely avoid the decomposition. On the other hand, a polymeric buffer layer using poly(ethylenimine) (PEI) can effectively separate the ZnO-NPs and perovskite, which allows larger crystal formation with thermal annealing. The power conversion efficiencies of perovskite photovoltaic cells are significantly increased from 6.4% to 10.2% by replacing PC61BM with PEI as the buffer layer.


title: Design of a capacitance sensor in 0.18um CMOS technology for biomedical application

abstract: To determine the existence of a certain target molecules, antibody molecules can be placed on a set of parallel electrodes for binding the target molecules. Detection of target molecules bound to the antibody can be achieved by measuring changes in capacitance between the electrodes. A capacitive sensor used for this purpose must have high accuracy and preferably be low power. This paper describes the design of a capacitive sensor which is capable of detecting as small as 10fF change in capacitance. The design was implemented in a 0.18μm CMOS process.


title: Grover’s Quantum Searching Algorithm is Optimal” Phys

abstract: I show that for any number of oracle lookups up to about {pi}/4thinsp{radical} (N) , Grover{close_quote}s quantum searching algorithm gives the maximal possible probability of finding the desired element. I explain why this is also true for quantum algorithms which use measurements during the computation. I also show that unfortunately quantum searching cannot be parallelized better than by assigning different parts of the search space to independent quantum computers. {copyright} {ital 1999} {ital The American Physical Society}


title: MeDShare: Trust-Less Medical Data Sharing Among Cloud Service Providers via Blockchain

abstract: The dissemination of patients’ medical records results in diverse risks to patients’ privacy as malicious activities on these records cause severe damage to the reputation, finances, and so on of all parties related directly or indirectly to the data. Current methods to effectively manage and protect medical records have been proved to be insufficient. In this paper, we propose MeDShare, a system that addresses the issue of medical data sharing among medical big data custodians in a trust-less environment. The system is blockchain-based and provides data provenance, auditing, and control for shared medical data in cloud repositories among big data entities. MeDShare monitors entities that access data for malicious use from a data custodian system. In MeDShare, data transitions and sharing from one entity to the other, along with all actions performed on the MeDShare system, are recorded in a tamper-proof manner. The design employs smart contracts and an access control mechanism to effectively track the behavior of the data and revoke access to offending entities on detection of violation of permissions on data. The performance of MeDShare is comparable to current cutting edge solutions to data sharing among cloud service providers. By implementing MeDShare, cloud service providers and other data guardians will be able to achieve data provenance and auditing while sharing medical data with entities such as research and medical institutions with minimal risk to data privacy.


title: A Coverage Analysis of Java Benchmark Suites

abstract: The Java programming language provides an almost ideal ::: environment for both static and dynamic analysis, being ::: easy to parse, and supporting a standardised, easily-profiled ::: virtual environment. ::: In this paper we study the relationship between results ::: obtainable from static and dynamic analysis of Java programs, ::: and in particular the difficulties of correlating static ::: and dynamic results. As a foundation for this study, we focus ::: on various criteria related to run-time code coverage, ::: as commonly used in test suite analysis. ::: We have implemented a dynamic coverage analysis ::: tool for Java programs, and we use it to evaluate several ::: standard Java benchmark suites using line, instruction and ::: branch coverage criteria. We present data indicating a considerable ::: variance in static and dynamic analysis results ::: between these suites, and even between programs in these ::: suites.


title: Segmental Convolutional Neural Networks for Detection of Cardiac Abnormality With Noisy Heart Sound Recordings

abstract: Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classification of heart sound recordings but rely on expert features that have not been thoroughly evaluated on noisy recordings. Here we propose a segmental convolutional neural network architecture that achieves automatic feature learning from noisy heart sound recordings. Our experiments show that our best model, trained on noisy recording segments acquired with an existing hidden semi-markov model-based approach, attains a classification accuracy of 87.5% on the 2016 PhysioNet/CinC Challenge dataset, compared to the 84.6% accuracy of the state-of-the-art statistical classifier trained and evaluated on the same dataset. Our results indicate the potential of using neural network-based methods to increase the accuracy of automated classification of heart sound recordings for improved screening of heart diseases.


title: The global brain as a new utopia

abstract: Francis HeylighenCenter “Leo Apostel”, Free University of Brusselshttp://pcp.vub.ac.be/HEYL.htmlThe role of utopiasUtopias seem to be out of fashion these days. The old ideologies, such as communism,have shown their inadequacies, and the “laissez-faire” liberalism that has replaced themis coming under more and more criticism. Instead, the intellectual climate has turned toeither gloom and doom, or an “anything goes” postmodernist relativism. This generalpessimism and apathy is fed by an increasing amount of bad news about wars,terrorism, ecological catastrophes, global warming, rising xenophobia and inequalities.Yet, objective statistics show that humanity is doing rather well on a global scale:life expectancy, wealth, education level, democracy, safety, and even average IQ havebeen consistently rising for the world as a whole, and this for at least the past century[Heylighen & Bernheim, 2000a; Simon, 1995]. Still, it cannot be denied that acceleratingchanges in all segments of science, technology and society and growing complexityproduce a lot of stress and uncertainty. While, on average, developments seem to be forthe better, overall social evolution has become much more unstable, unpredictable, anduncontrollable [Heylighen, submitted], raising the risks of major catastrophes. Theanxiety, distrust and hopelessnes that this engenders is made worse by the lack of ashared


title: Emerging non-volatile memories: opportunities and challenges

abstract: In recent years, non-volatile memory (NVM) technologies have emerged as candidates for future universal memory. N-VMs generally have advantages such as low leakage power, high density, and fast read spead. At the same time, NVM-s also have disadvantages. For example, NVMs often have asymetric read and write speed and energy cost, which poses new challenges when applying NVMs. This paper contains a collection of four contributions, presenting basic introduction on three emerging NVM technologies, their unique characteristics, potential challenges, and new opportunities that they may bring forward in memory systems.


title: DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds

abstract: We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame. We use DNNs to model the highly non-convex mapping process that traditionally involves hand-crafted data association, sensor pose initialization, and global refinement. Our key novelty is that “training” these DNNs with properly defined unsupervised losses is equivalent to solving the underlying registration problem, but less sensitive to good initialization than ICP. Our framework contains two DNNs: a localization network that estimates the poses for input point clouds, and a map network that models the scene structure by estimating the occupancy status of global coordinates. This allows us to convert the registration problem to a binary occupancy classification, which can be solved efficiently using gradient-based optimization. We further show that DeepMapping can be readily extended to address the problem of Lidar SLAM by imposing geometric constraints between consecutive point clouds. Experiments are conducted on both simulated and real datasets. Qualitative and quantitative comparisons demonstrate that DeepMapping often enables more robust and accurate global registration of multiple point clouds than existing techniques. Our code is available at https://ai4ce.github.io/DeepMapping/.


title: LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

abstract: Although weight and activation quantization is an effective approach for Deep Neural Network (DNN) compression and has a lot of potentials to increase inference speed leveraging bit-operations, there is still a noticeable gap in terms of prediction accuracy between the quantized model and the full-precision model. To address this gap, we propose to jointly train a quantized, bit-operation-compatible DNN and its associated quantizers, as opposed to using fixed, handcrafted quantization schemes such as uniform or logarithmic quantization. Our method for learning the quantizers applies to both network weights and activations with arbitrary-bit precision, and our quantizers are easy to train. The comprehensive experiments on CIFAR-10 and ImageNet datasets show that our method works consistently well for various network structures such as AlexNet, VGG-Net, GoogLeNet, ResNet, and DenseNet, surpassing previous quantization methods in terms of accuracy by an appreciable margin. Code available at this https URL


title: A Multilayer Ontology-based Hybrid Recommendation Model

abstract: We propose a novel hybrid recommendation model in which user preferences and item features are described in terms of semantic concepts defined in domain ontologies. The concept, item and user spaces are clustered in a coordinated way, and the resulting clusters are used to find similarities among individuals at multiple semantic layers. Such layers correspond to implicit Communities of Interest and enable enhanced recommendations.


Modulation formats for 100 G and beyond


title: Is meta-analysis the platinum standard of evidence?

abstract: An astonishing volume and diversity of evidence is available for many hypotheses in the biomedical and social sciences. Some of this evidence—usually from randomized controlled trials (RCTs)—is amalgamated by meta-analysis. Despite the ongoing debate regarding whether or not RCTs are the ‘gold-standard’ of evidence, it is usually meta-analysis which is considered the best source of evidence: meta-analysis is thought by many to be the platinum standard of evidence. However, I argue that meta-analysis falls far short of that standard. Different meta-analyses of the same evidence can reach contradictory conclusions. Meta-analysis fails to provide objective grounds for intersubjective assessments of hypotheses because numerous decisions must be made when performing a meta-analysis which allow wide latitude for subjective idiosyncrasies to influence its outcome. I end by suggesting that an older tradition of evidence in medicine—the plurality of reasoning strategies appealed to by the epidemiologist Sir Bradford Hill—is a superior strategy for assessing a large volume and diversity of evidence.


title: The effect of mesoporous silica nanoparticles loaded with epirubicin on drug-resistant cancer cells

abstract: Objective (s): In chemotherapy for cancer treatment, the cell resistance to multiple anticancer drugs is the major clinical problem. In the present study, mesoporous silica nanoparticles (MSNs) were used as a carrier for epirubicin (EPI) in order to improve the cytotoxic efficacy of this drug against the P-glycoprotein (P-gp) overexpressing cell line. Materials and Methods: MSNs with phosphonate groups were synthesized and characterized. The cytotoxicity of the prepared nanoparticles on drug-sensitive human breast cancer cell line (MCF-7) and drug-resistant cancer cells (MCF-7/ADR) was evaluated. Results: The hydrodynamic size of nanoparticles was 98 nm and surface charge was negative. The viability of sensitive MCF-7 and resistant MCF-7/ADR cells after incubation with MSNs containing EPI at concentration of 5 I¼g/ml was about 75 and 44. On the other hand, the viability of sensitive and resistant cells after incubation with free EPI at this concentration was about 48 and 60, respectively. Conclusion: These nanoparticles exhibited suitable drug efficiencies against drug-resistant MCF-7/ADR cells in vitro experiments.


title: Millimeter Wave MIMO channel estimation based on adaptive compressed sensing

abstract: Multiple-input multiple-output (MIMO) systems are well suited for millimeter-wave (mmWave) wireless communications where large antenna arrays can be integrated in small form factors due to tiny wavelengths, thereby providing high array gains while supporting spatial multiplexing, beamforming, or antenna diversity. It has been shown that mmWave channels exhibit sparsity due to the limited number of dominant propagation paths, thus compressed sensing techniques can be leveraged to conduct channel estimation at mmWave frequencies. This paper presents a novel approach of constructing beamforming dictionary matrices for sparse channel estimation using the continuous basis pursuit (CBP) concept, and proposes two novel low-complexity algorithms to exploit channel sparsity for adaptively estimating multipath channel parameters in mmWave channels. We verify the performance of the proposed CBP-based beamforming dictionary and the two algorithms using a simulator built upon a three-dimensional mmWave statistical spatial channel model, NYUSIM, that is based on real-world propagation measurements. Simulation results show that the CBP-based dictionary offers substantially higher estimation accuracy and greater spectral efficiency than the grid-based counterpart introduced by previous researchers, and the algorithms proposed here render better performance but require less computational effort compared with existing algorithms.


title: Machine Learning Model Interpretability for Precision Medicine

abstract: Interpretability of machine learning models is critical for data-driven precision medicine efforts. However, highly predictive models are generally complex and are difficult to interpret. Here using Model-Agnostic Explanations algorithm, we show that complex models such as random forest can be made interpretable. Using MIMIC-II dataset, we successfully predicted ICU mortality with 80% balanced accuracy and were also were able to interpret the relative effect of the features on prediction at individual level.


title: Fuzzy Joins Using MapReduce

abstract: Fuzzy/similarity joins have been widely studied in the research community and extensively used in real-world applications. This paper proposes and evaluates several algorithms for finding all pairs of elements from an input set that meet a similarity threshold. The computation model is a single MapReduce job. Because we allow only one MapReduce round, the Reduce function must be designed so a given output pair is produced by only one task, for many algorithms, satisfying this condition is one of the biggest challenges. We break the cost of an algorithm into three components: the execution cost of the mappers, the execution cost of the reducers, and the communication cost from the mappers to reducers. The algorithms are presented first in terms of Hamming distance, but extensions to edit distance and Jaccard distance are shown as well. We find that there are many different approaches to the similarity-join problem using MapReduce, and none dominates the others when both communication and reducer costs are considered. Our cost analyses enable applications to pick the optimal algorithm based on their communication, memory, and cluster requirements.


title: End-to-end attention-based large vocabulary speech recognition

abstract: Many of the current state-of-the-art Large Vocabulary Continuous Speech Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov Models (HMMs). Most of these systems contain separate components that deal with the acoustic modelling, language modelling and sequence decoding. We investigate a more direct approach in which the HMM is replaced with a Recurrent Neural Network (RNN) that performs sequence prediction directly at the character level. Alignment between the input features and the desired character sequence is learned automatically by an attention mechanism built into the RNN. For each predicted character, the attention mechanism scans the input sequence and chooses relevant frames. We propose two methods to speed up this operation: limiting the scan to a subset of most promising frames and pooling over time the information contained in neighboring frames, thereby reducing source sequence length. Integrating an n-gram language model into the decoding process yields recognition accuracies similar to other HMM-free RNN-based approaches.


title: The perception of phantom limbs. The D. O. Hebb lecture.

abstract: Summary Almost everyone who has a limb amputated will experience a phantom limb—the vivid impression that the limb is not only still present, but in some cases, painful. There is now a wealth of empirical evidence demonstrating changes in cortical topography in primates following deafferentation or amputation, and this review will attempt to relate these in a systematic way to the clinical phenomenology of phantom limbs. With the advent of non-invasive imaging techniques such as MEG (magnetoencephalogram) and functional MRI, topographical reorganization can also be demonstrated in


title: Social ties and health: a social neuroscience perspective

abstract: Research over the last several decades has shown that the health of the body is intimately tied to the strength of our social connections, but why? This article reviews evidence from affective and social neuroscience suggesting that, because of the importance of social ties for mammalian survival, threats to social connection are processed by some of the same neural regions that process basic threats to survival and consequently trigger physiological threat responses that have negative health implications. Likewise, social support is processed by some of the same neural regions that process safety or protection from basic threats and inhibit these same health-relevant physiological threat responses.


title: Cloud Computing Architecture for Social Computing - A Comparison Study of Facebook and Google

abstract: As far as we know, cloud computing is a new business model and the cloud computing architecture is the famous topic recently. Today, everyone enjoy the innovative search engine or social network application for new Internet services no longer require the large capital outlays in hardware to access those service or the human expense to operate it. Currently, Google is the largest search engine and Face book is the largest social network in the Software as a Service (SaaS). But how them can support the huge requests from world thought each personal computer, mobile device, and smart phone. In this paper, we will try to analysis their backend cloud computing architecture to support future SaaS especially in large social network.


title: Facial expression recognition and histograms of oriented gradients: a comprehensive study

abstract: Automatic facial expression recognition (FER) is a topic of growing interest mainly due to the rapid spread of assistive technology applications, as human–robot interaction, where a robust emotional awareness is a key point to best accomplish the assistive task. This paper proposes a comprehensive study on the application of histogram of oriented gradients (HOG) descriptor in the FER problem, highlighting as this powerful technique could be effectively exploited for this purpose. In particular, this paper highlights that a proper set of the HOG parameters can make this descriptor one of the most suitable to characterize facial expression peculiarities. A large experimental session, that can be divided into three different phases, was carried out exploiting a consolidated algorithmic pipeline. The first experimental phase was aimed at proving the suitability of the HOG descriptor to characterize facial expression traits and, to do this, a successful comparison with most commonly used FER frameworks was carried out. In the second experimental phase, different publicly available facial datasets were used to test the system on images acquired in different conditions (e.g. image resolution, lighting conditions, etc.). As a final phase, a test on continuous data streams was carried out on-line in order to validate the system in real-world operating conditions that simulated a real-time human–machine interaction.


title: Extending asset management system functionality in bitcoin platform

abstract: Bitcoin is a digital payment system empowered by a distributed database called blockchain. The blockchain is an open ledger containing records of every transaction within Bitcoin system maintained by Bitcoin nodes all over the world. Because of its availability and robustness, the blockchain could be utilized as a record-keeping tool for information not related to any Bitcoin transactions. We propose a method of utilizing the blockchain of Bitcoin system to publish information by embedding an arbitrary size of data into Bitcoin transactions. By publishing information using the blockchain, the information also carries the characteristics of Bitcoin transaction: anonymous, decentralized, and permanent. The proposed protocol could be used to extend the functionality of asset management systems which are limited to a maximum of 80 bytes data. The proposed method also offers an efficiency of the transaction fee by 18 percent compared to Bitcoin Messaging protocol.


title: The Foundations of Cost-Sensitive Learning

abstract: This paper revisits the problem of optimal learning and decision-making when different misclassification errors incur different penalties. We characterize precisely but intuitively when a cost matrix is reasonable, and we show how to avoid the mistake of defining a cost matrix that is economically incoherent. For the two-class case, we prove a theorem that shows how to change the proportion of negative examples in a training set in order to make optimal cost-sensitive classification decisions using a classifier learned by a standard non-costsensitive learning method. However, we then argue that changing the balance of negative and positive training examples has little effect on the classifiers produced by standard Bayesian and decision tree learning methods. Accordingly, the recommended way of applying one of these methods in a domain with differing misclassification costs is to learn a classifier from the training set as given, and then to compute optimal decisions explicitly using the probability estimates given by the classifier. 1 Making decisions based on a cost matrix Given a specification of costs for correct and incorrect predictions, an example should be predicted to have the class that leads to the lowest expected cost, where the expectation is computed using the conditional probability of each class given the example. Mathematically, let the entry in a cost matrix be the cost of predicting class when the true class is . If then the prediction is correct, while if the prediction is incorrect. The optimal prediction for an example is the class that minimizes ! (1) Costs are not necessarily monetary. A cost can also be a waste of time, or the severity of an illness, for example. For each , is a sum over the alternative possibilities for the true class of . In this framework, the role of a learning algorithm is to produce a classifier that for any example can estimate the probability " # of each class being the true class of . For an example , making the prediction means acting as if is the true class of . The essence of cost-sensitive decision-making is that it can be optimal to act as if one class is true even when some other class is more probable. For example, it can be rational not to approve a large credit card transaction even if the transaction is most likely legitimate. 1.1 Cost matrix properties A cost matrix always has the following structure when there are only two classes: actual negative actual positive predict negative $% $& ' )(!*+* $% -,. /(!*10 predict positive 2,& $& ' )(302* 2,& -,. /(30+0 Recent papers have followed the convention that cost matrix rows correspond to alternative predicted classes, while columns correspond to actual classes, i.e. row/column = / = predicted/actual. In our notation, the cost of a false positive is (302* while the cost of a false negative is (!*!0 . Conceptually, the cost of labeling an example incorrectly should always be greater than the cost of labeling it correctly. Mathematically, it should always be the case that ( 0 *54 ( *+* and ( *!064 ( 0 0 . We call these conditions the “reasonableness” conditions. Suppose that the first reasonableness condition is violated, so (!*+*879(302* but still (!*!0 4 (30+0 . In this case the optimal policy is to label all examples positive. Similarly, if (:0 * 4 (!*+* but (30 0;7 in a cost matrix if for all , = ?7 > @ A . In this case the cost of predicting > is no greater than the cost of predicting = , regardless of what the true class is. So it is optimal never to predict = . As a special case, the optimal prediction is always > if row > is dominated by all other rows in a cost matrix. The two reasonableness conditions for a two-class cost matrix imply that neither row in the matrix dominates the other. Given a cost matrix, the decisions that are optimal are unchanged if each entry in the matrix is multiplied by a positive constant. This scaling corresponds to changing the unit of account for costs. Similarly, the decisions that are optimal are unchanged B if a constant is added to each entry in the matrix. This shifting corresponds to changing the baseline away from which costs are measured. By scaling and shifting entries, any two-class cost matrix that satisfies the reasonableness conditions can be transformed into a simpler matrix that always leads to the same decisions:


title: >1.3-Tb/s VCSEL-Based On-Board Parallel-Optical Transceiver Module for High-Density Optical Interconnects

abstract: This paper gives a detailed description of a >1.3-Tb/s VCSEL-based on-board parallel-optical transceiver module for high-density optical interconnects. The optical module integrates a 28-Gb/s × 24-channel transmitter and receiver into one package with a 1-in2 footprint, thereby yielding a data rate density as high as 1.34 Tb/s/in2. A unique module design is developed to utilize the whole top surface for thermal dissipation and the bottom surface for optical and electrical interfaces. The heat dissipation characteristics are studied in simulations and experiments. A test system of 24-channel optical loop-back link is built to evaluate the performance when operating all 24 channels by 28.05-Gb/s 231–1 PRBS bit streams simultaneously. With a total power consumption of 9.1 W, the optical module can be operated within the operating case temperature under a practical air cooling condition. When operating all 24 channels simultaneously, a total jitter margin at BER of 10–12 is larger than 0.4 U.I. at a monitor channel. The measured jitter margin is consistent regardless of any channel operation.


title: A large-scale car dataset for fine-grained categorization and verification

abstract: This paper aims to highlight vision related tasks centered around “car”, which has been largely neglected by vision community in comparison to other objects. We show that there are still many interesting car-related problems and applications, which are not yet well explored and researched. To facilitate future car-related research, in this paper we present our on-going effort in collecting a large-scale dataset, “CompCars”, that covers not only different car views, but also their different internal and external parts, and rich attributes. Importantly, the dataset is constructed with a cross-modality nature, containing a surveillance-nature set and a web-nature set. We further demonstrate a few important applications exploiting the dataset, namely car model classification, car model verification, and attribute prediction. We also discuss specific challenges of the car-related problems and other potential applications that worth further investigations. The latest dataset can be downloaded at http://mmlab.ie.cuhk.edu.hk/ datasets/comp_cars/index.html


title: HEVC Complexity and Implementation Analysis

abstract: Advances in video compression technology have been driven by ever-increasing processing power available in software and hardware. The emerging High Efficiency Video Coding (HEVC) standard aims to provide a doubling in coding efficiency with respect to the H.264/AVC high profile, delivering the same video quality at half the bit rate. In this paper, complexity-related aspects that were considered in the standardization process are described. Furthermore, profiling of reference software and optimized software gives an indication of where HEVC may be more complex than its predecessors and where it may be simpler. Overall, the complexity of HEVC decoders does not appear to be significantly different from that of H.264/AVC decoders; this makes HEVC decoding in software very practical on current hardware. HEVC encoders are expected to be several times more complex than H.264/AVC encoders and will be a subject of research in years to come.


title: Assessment and Treatment of Suicidal Patients

abstract: The assessment of suicidal thoughts or behavior and its management are topics relevant to all clinicians, not just mental health professionals. Up to two thirds of patients who commit suicide have seen a physician in the month before their death.1–5 Many patients who kill themselves do so by taking an overdose of prescribed medications, and physicians sometimes unwittingly provide the means for suicide in a single prescription.3 Few patients spontaneously report their suicidal thoughts and intentions to their physicians,4 so the clinician must be alert to signals that a patient may be at risk for suicide. Although the patient . . .


title: Interpolation/Extrapolation of Radar Cross-Section (RCS) Data in the Frequency Domain Using the Cauchy Method

abstract: We apply the Cauchy method to interpolate/extrapolate the radar cross-section (RCS) data which is amplitude-only data over a given frequency band. This is accomplished by approximating the amplitude-only data by a ratio of two polynomials, the coefficients of which are calculated by using the total least squares (TLS) implementation of a singular value decomposition (SVD) technique so as to properly estimate the dimension of the null space. By applying the Cauchy method, the power spectrum of an electromagnetic system is represented by a set of symmetric pole and zero pairs in the -plane. Once these coefficients in the numerator and the denominator polynomials in the Cauchy method are computed using the amplitude-only data, the response can be interpolated/extrapolated over other frequencies of interest. Numerical examples are presented to illustrate the applicability of the Cauchy method in interpolating/extrapolating RCS data over a frequency band, including a method of generating the phase response from the amplitude-only data.


title: Hidden Talents of the Variational Autoencoder.

abstract: Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information-theoretic variational bound on the true underlying distribution. Once so-obtained, the model can be putatively used to generate new samples from this distribution, or to provide a low-dimensional latent representation of existing samples. While quite effective in numerous application domains, certain important mechanisms which govern the behavior of the VAE are obfuscated by the intractable integrals and resulting stochastic approximations involved. Moreover, as a highly non-convex model, it remains unclear exactly how minima of the underlying energy relate to original design purposes. We attempt to better quantify these issues by analyzing a series of tractable special cases of increasing complexity. In doing so, we unveil interesting connections with more traditional dimensionality reduction models, as well as an intrinsic yet underappreciated propensity for robustly dismissing sparse outliers when estimating latent manifolds. With respect to the latter, we demonstrate that the VAE can be viewed as the natural evolution of recent robust PCA models, capable of learning nonlinear manifolds of unknown dimension obscured by gross corruptions.


title: Bulk current injection test modeling using an equivalent circuit for 1.8V mobile ICs

abstract: This paper shows a novel simulation method for bulk current injection (BCI) tests of I/O buffer circuits of mobile system memory. The simulation model consists of BCI probe, directional coupler, PCB, PKG, and IC. The proposed method is based on a behavioural I/O buffer model using a pulse generator as an input. A detailed simulation flow is introduced and validated through simulations performed on several injection probe loading conditions using a power decoupling capacitor and an on-chip decoupling capacitor.


Flexible Primitives for Distributed Deep Learning in Ray


title: Message passing algorithms for compressed sensing: I. motivation and construction

abstract: In a recent paper, the authors proposed a new class of low-complexity iterative thresholding algorithms for reconstructing sparse signals from a small set of linear measurements \cite{DMM}. The new algorithms are broadly referred to as AMP, for approximate message passing. This is the first of two conference papers describing the derivation of these algorithms, connection with the related literature, extensions of the original framework, and new empirical evidence. ::: In particular, the present paper outlines the derivation of AMP from standard sum-product belief propagation, and its extension in several directions. We also discuss relations with formal calculations based on statistical mechanics methods.


Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics


title: Seizure impact on the school attendance in children with epilepsy

abstract: Summary Rationale Epilepsy has a significant influence on the patient's quality of life. The objective of this study was to evaluate the impact of ongoing seizures on school attendance amongst children with epilepsy. Methods This was a prospective study, conducted at the University of Campinas, in the pediatric epilepsy clinic, from January 2005 to August 2006. We enrolled 50 consecutive children who had a diagnosis of epilepsy. Parents were interviewed by one of the authors using a semi-structured questionnaire that included questions specifically addressing the impact of epilepsy on the child's academic life. Results Fifty patients were evaluated, 34 boys and 16 girls; ages ranged from 6 to 18 years old (mean=11). Eighty-eight percent of patients in the study missed at least 1 day of school due to seizures. The reason given by parents for a school absence was seizure in 75%, medical appointment in 79.5%, epilepsy related tests (EEG, MRI, etc.) in 68.2%, and other in 0.03%. Almost half of the parents (46%) believed that if the child had a seizure at school he/she should leave school immediately. Sixty percent of families allowed the child to skip a school day even if there was no illness and the child had no seizures on that day. Among those patients with siblings, 12.5% had a brother or sister who was absent from school for at least 1 day due to his/her sibling's epilepsy. Conclusion Seizures have a significant impact on school attendance, and as a result may increase the academic difficulties faced by children with epilepsy. This effect may be especially pronounced in children with symptomatic epilepsy and medically intractable seizures.


title: Secure Scrum: Development of Secure Software with Scrum

abstract: Nowadays, the use of agile software development methods like Scrum is common in industry and academia. Considering the current attacking landscape, it is clear that developing secure software should be a main concern in all software development projects. In traditional software projects, security issues require detailed planning in an initial planning phase, typically resulting in a detailed security analysis (e.g., threat and risk analysis), a security architecture, and instructions for security implementation (e.g., specification of key sizes and cryptographic algorithms to use). Agile software development methods like Scrum are known for reducing the initial planning phases (e.g., sprint 0 in Scrum) and for focusing more on producing running code. Scrum is also known for allowing fast adaption of the emerging software to changes of customer wishes. For security, this means that it is likely that there are no detailed security architecture or security implementation instructions from the start of the project. It also means that a lot of design decisions will be made during the runtime of the project. Hence, to address security in Scrum, it is necessary to consider security issues throughout the whole software development process. Secure Scrum is a variation of the Scrum framework with special focus on the development of secure software throughout the whole software development process. It puts emphasis on implementation of security related issues without the need of changing the underlying Scrum process or influencing team dynamics. Secure Scrum allows even non- security experts to spot security issues, to implement security features, and to verify implementations. A field test of Secure Scrum shows that the security level of software developed using Secure Scrum is higher then the security level of software developed using standard Scrum.


title: Towards an Automatic PL Requirements Configuration through Constraints Reasoning

abstract: Production of a longitudinally striated coating on a strip article by advancing the article past an open side of an otherwise-enclosed trench so that the article surface to be coated closes the open trench side, while delivering concurrent laminar flows of two different liquid coating materials to the trench at least at one locality spaced from the trench open side, to keep the trench completely filled and to deposit a layer of coating material on the moving article surface. Liquid circulation in the trench caused by the motion of the article surface so distributes the two coating materials along the trench that the applied coating layer is constituted of alternating longitudinal striations of the two materials. With delivery of concurrent flows of the two coating materials at each of plural localities spaced along the trench, positional and other variations of the striations can be achieved by varying the relative total flows delivered at different ones of these localities, for example to create a simulated wood-grain pattern.