title: Molecular farming of pharmaceutical proteins using plant suspension cell and tissue cultures.

abstract: Plants have been used for more than 20 years to produce recombinant proteins but only recently has the focus shifted away from proof-of-principle studies (i.e. is my protein expressed and is it functional?) to a serious consideration of the requirements for sustainable productivity and the regulatory approval of pharmaceutical products (i.e. is my protein safe, is it efficacious, and does the product and process comply with regulatory guidelines?). In this context, plant tissue and cell suspension cultures are ideal production platforms whose potential has been demonstrated using diverse pharmaceutical proteins. Typically, cell/tissue cultures are grown in containment under defined conditions, allowing process controls to regulate growth and product formation, thus ensuring regulatory compliance. Recombinant proteins can also be secreted to the culture medium, facilitating recovery and subsequent purification because cells contain most of the contaminating proteins and can be removed from the culture broth. Downstream processing costs are therefore lower compared to whole plant systems, balancing the higher costs of the fermentation equipment. In this article, we compare different approaches for the production of valuable proteins in plant cell suspension and tissue cultures, describing the advantages and disadvantages as well as challenges that must be overcome to make this platform commercially viable. We also present novel strategies for system and process optimization, helping to increase yields and scalability.


title: A clustering-based approach for discovering interesting places in trajectories

abstract: Because of the large amount of trajectory data produced by mobile devices, there is an increasing need for mechanisms to extract knowledge from this data. Most existing works have focused on the geometric properties of trajectories, but recently emerged the concept of semantic trajectories, in which the background geographic information is integrated to trajectory sample points. In this new concept, trajectories are observed as a set of stops and moves, where stops are the most important parts of the trajectory. Stops and moves have been computed by testing the intersections of trajectories with a set of geographic objects given by the user. In this paper we present an alternative solution with the capability of finding interesting places that are not expected by the user. The proposed solution is a spatio-temporal clustering method, based on speed, to work with single trajectories. We compare the two different approaches with experiments on real data and show that the computation of stops using the concept of speed can be interesting for several applications.


title: Building RDF Content for Data-to-Text Generation

abstract: In Natural Language Generation (NLG), one important limitation is the lack of common benchmarks on which to train, evaluate and compare data-to-text generators. In this paper, we make one step in that direction and introduce a method for automatically creating an arbitrary large repertoire of data units that could serve as input for generation. Using both automated metrics and a human evaluation, we show that the data units produced by our method are both diverse and coherent.


title: High-level programming and control for industrial robotics: using a hand-held accelerometer-based input device for gesture and posture recognition

abstract: Purpose - Most industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. This is a tedious and time-consuming task that requires some technical expertise, and hence new approaches to robot programming are required. The purpose of this paper is to present a robotic system that allows users to instruct and program a robot with a high-level of abstraction from the robot language. Design/methodology/approach - The paper presents in detail a robotic system that allows users, especially non-expert programmers, to instruct and program a robot just showing it what it should do, in an intuitive way. This is done using the two most natural human interfaces (gestures and speech), a force control system and several code generation techniques. Special attention will be given to the recognition of gestures, where the data extracted from a motion sensor (three-axis accelerometer) embedded in the Wii remote controller was used to capture human hand behaviours. Gestures (dynamic hand positions) as well as manual postures (static hand positions) are recognized using a statistical approach and artificial neural networks. Practical implications - The key contribution of this paper is that it offers a practical method to program robots by means of gestures and speech, improving work efficiency and saving time. Originality/value - This paper presents an alternative to the typical robot teaching process, extending the concept of human-robot interaction and co-worker scenario. Since most companies do not have engineering resources to make changes or add new functionalities to their robotic manufacturing systems, this system constitutes a major advantage for small- to medium-sized enterprises.


title: A control theoretic analysis of RED

abstract: We use a previously developed nonlinear dynamic model of TCP to analyze and design active queue management (AQM) control systems using random early detection (RED). First, we linearize the interconnection of TCP and a bottlenecked queue and discuss its feedback properties in terms of network parameters such as link capacity, load and round-trip time. Using this model, we next design an AQM control system using the RED scheme by relating its free parameters such as the low-pass filter break point and loss probability profile to the network parameters. We present guidelines for designing linearly stable systems subject to network parameters like propagation delay and load level. Robustness to variations in system loads is a prime objective. We present no simulations to support our analysis.


title: Standing Out from the Crowd: Cancer Stem Cells in Hepatocellular Carcinoma

abstract: Cancer stem cells (CSCs) drive solid tumor formation. In this issue of Cancer Cell , Zhao and colleagues indentify the calcium channel α2δ1 subunit as a new functional hepatocellular carcinoma (HCC) CSC biomarker, which is vital for CSC biology as blocking α2δ1 in combination with doxorubicin treatment hinders HCC tumor formation.


title: Capturing Handwritten Ink Strokes with a Fast Video Camera

abstract: We present a system for capturing ink strokes written with ordinary pen and paper using a fast camera with a frame rate comparable to a stylus digitizer. From the video frames, ink strokes are extracted and used as input to an online handwriting recognition engine. A key component in our system is a pen up/down detection model for detecting the contact of the pen-tip with the paper in the video frames. The proposed model consists of feature representation with convolutional neural networks and classification with a recurrent neural network. We also use a high speed tracker with kernelized correlation filters to track the pen-tip. For training and evaluation, we collected labeled video data of users writing English and Japanese phrases from public datasets, and we report on character accuracy scores for different frame rates in the two languages.


title: A Level-Wise Load Balanced Scientific Workflow Execution Optimization Using NSGA-II

abstract: Over the past decade, cloud computing has grown in popularity for the processing of scientific applications as a result of the scalability of the cloud and the ready availability of on-demand computing and storage resources. It is also a cost-effective alternative for scientific workflow executions with a pay-per-use paradigm. However, providing services with optimal performance at the lowest financial resource deployment cost is still challenging. Several fine-grained tasks are included in scientific workflow applications, and efficient execution of these tasks according to their processing dependency to minimize the overall makespan during workflow execution is an important research area. In this paper, a system for level-wise workflow makespan optimization and virtual machine deployment cost minimization for overall workflow optimization in cloud infrastructure is proposed. Further, balanced task clustering, to ensure load balancing in different virtual machine instances at each workflow level during workflow execution, is also considered. The system retrieves the necessary workflow information from a directed acyclic graph and uses the non-dominated sorting genetic algorithm II (NSGA-II) to carry out multiobjective optimization. Pareto front solutions obtained for makespan time and instance resource deployment cost for several scientific workflow applications verify the efficacy of our system.


title: Stereo vision–based navigation for autonomous surface vessels

abstract: This paper describes a stereo vision–based system for autonomous navigation in maritime environments. The system consists of two key components. The Hammerhead vision system detects geometric hazards (i.e., objects above the waterline) and generates both grid-based hazard maps and discrete contact lists (objects with position and velocity). The R4SA (robust, real-time, reconfigurable, robotic system architecture) control system uses these inputs to implement sensor-based navigation behaviors, including static obstacle avoidance and dynamic target following. As far as the published literature is concerned, this stereo vision–based system is the first fielded system that is tailored for high-speed, autonomous maritime operation on smaller boats. In this paper, we present a description and experimental analysis of the Hammerhead vision system, along with key elements of the R4SA control system. We describe the integration of these systems onto a number of high-speed unmanned surface vessels and present experimental results for the combined vision-based navigation system. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.


title: Multimodal Classification for Analysing Social Media

abstract: Classification of social media data is an important approach in understanding user behavior on the Web. Although information on social media can be of different modalities such as texts, images, audio or videos, traditional approaches in classification usually leverage only one prominent modality. Techniques that are able to leverage multiple modalities are often complex and susceptible to the absence of some modalities. In this paper, we present simple models that combine information from different modalities to classify social media content and are able to handle the above problems with existing techniques. Our models combine information from different modalities using a pooling layer and an auxiliary learning task is used to learn a common feature space. We demonstrate the performance of our models and their robustness to the missing of some modalities in the emotion classification domain. Our approaches, although being simple, can not only achieve significantly higher accuracies than traditional fusion approaches but also have comparable results when only one modality is available.


title: A Novel Classifier Selection Approach for Adaptive Boosting Algorithms

abstract: Boosting is a general approach for improving classifier performances. In this research we investigated these issues with the latest Boosting algorithm AdaBoostMl. A trial and error classifier feeding with the AdaBoostMl algorithm is a regular practice for classification tasks in the research community. We provide a novel statistical information- based rule method for unique classifier selection with the AdaBoostMl algorithm. The solution also verified a wide range of benchmark classification problems.


title: Unlocking the black box: Exploring the link between high-performance work systems and performance.

abstract: With a growing body of literature linking systems of high-performance work practices to organizational performance outcomes, recent research has pushed for examinations of the underlying mechanisms that enable this connection. In this study, based on a large sample of Welsh public-sector employees, we explored the role of several individual-level attitudinal factors--job satisfaction, organizational commitment, and psychological empowerment--as well as organizational citizenship behaviors that have the potential to provide insights into how human resource systems influence the performance of organizational units. The results support a unit-level path model, such that department-level, high-performance work system utilization is associated with enhanced levels of job satisfaction, organizational commitment, and psychological empowerment. In turn, these attitudinal variables were found to be positively linked to enhanced organizational citizenship behaviors, which are further related to a second-order construct measuring departmental performance.


title: Wound healing potential of lavender oil by acceleration of granulation and wound contraction through induction of TGF-β in a rat model

abstract: Although previous studies have suggested that lavender oil promote wound healing, no study has examined the molecular mechanisms of its effect. In this study, we investigated the effect of lavender oil on various steps of wound healing and its molecular mechanism, focusing on transforming growth factor-β (TGF-β). Circular full-thickness skin wounds were produced on rats. Control solution or lavender oil was topically applied to the wounds on alternating days for 14 days. The area of wounds topically treated with lavender oil was significantly decreased as compared to that of wounds of control rats at 4, 6, 8, and 10 days after wounding. Topical application of lavender oil induced expression of type I and III collagen at 4 days after wounding, accompanied by an increased number of fibroblasts, which synthesize collagen. Induced expression of type III collagen by topical application of lavender oil was reduced to control level at 7 days after wounding although increased expression of type I collagen still continued even at 7 days, suggesting rapid collagen replacement from type III to type I in wounds treated with lavender oil. Importantly, expression of TGF-β in wounds treated with lavender oil was significantly increased as compared to control. Moreover, an increased number of myofibroblasts was observed in wounds treated with lavender oil at 4 days after wounding, suggesting promotion of differentiation of fibroblasts through induction of TGF-β, which is needed for wound contraction. This study demonstrated that topical application of lavender oil promoted collagen synthesis and differentiation of fibroblasts, accompanied by up-regulation of TGF-β. These data suggest that lavender oil has the potential to promote wound healing in the early phase by acceleration of formation of granulation tissue, tissue remodeling by collagen replacement and wound contraction through up-regulation of TGF-β. The beneficial effect of lavender oil on wound healing may raise the possibility of new approaches as complementary treatment besides conventional therapy.


title: New “Smart Parking” System Based on Resource Allocation and Reservations

abstract: We propose a novel “smart parking” system for an urban environment. The system assigns and reserves an optimal parking space based on the driver's cost function that combines proximity to destination and parking cost. Our approach solves a mixed-integer linear programming (MILP) problem at each decision point defined in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and is updated at the next decision point with a guarantee that there is no resource reservation conflict and that no driver is ever assigned a resource with a cost function higher than this driver's current cost function value. Based on simulation results, compared with uncontrolled parking processes or state-of-the-art guidance-based systems, our system reduces the average time to find a parking space and the parking cost, whereas the overall parking capacity is more efficiently utilized. We also describe full implementation in a garage to test this system, where a new light system scheme is proposed to guarantee user reservations.


title: Maintaining population diversity in brain storm optimization algorithm

abstract: Swarm intelligence suffers the premature convergence, which happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm to measure the change of solutions' distribution is proposed in this paper. The algorithm's exploration and exploitation ability can be measured based on the change of population diversity. Two kinds of partial re-initialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by these two strategies.


title: Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping

abstract: We present a method to determine Fashion DNA, coordinate vectors locating fashion items in an abstract space. Our approach is based on a deep neural network architecture that ingests curated article information such as tags and images, and is trained to predict sales for a large set of frequent customers. In the process, a dual space of customer style preferences naturally arises. Interpretation of the metric of these spaces is straightforward: The product of Fashion DNA and customer style vectors yields the forecast purchase likelihood for the customer-item pair, while the angle between Fashion DNA vectors is a measure of item similarity. Importantly, our models are able to generate unbiased purchase probabilities for fashion items based solely on article information, even in absence of sales data, thus circumventing the "cold-start problem" of collaborative recommendation approaches. Likewise, it generalizes easily and reliably to customers outside the training set. We experiment with Fashion DNA models based on visual and/or tag item data, evaluate their recommendation power, and discuss the resulting article similarities.


title: The Semiotic Hierarchy : Life , Consciousness , Signs and Language

abstract: This article outlines a general theory of meaning, The Semiotic Hierarchy, which distinguishes between four major levels in the organization of meaning: life, consciousness, sign function and language, where each of these, in this order, both rests on the previous level, and makes possible the attainment of the next. This is shown to be one possible instantiation of the Cognitive Semiotics program, with influences from phenomenology, Popper’s tripartite ontology, semiotics, linguistics, enactive cognitive science and evolutionary biology. Key concepts such as “language” and “sign” are defined, as well as the four levels of The Semiotic Hierarchy, on the basis of the type of (a) subject, (b) value-system and (c) world in which the subject is embedded. Finally, it is suggested how the levels can be united in an evolutionary framework, assuming a strong form of emergence giving rise to “ontologically” new properties: consciousness, signs and languages, on the basis of a semiotic, though not standardly biosemiotic, understanding of life.


title: Friendbook: A Semantic-Based Friend Recommendation System for Social Networks

abstract: Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. In this paper, we present Friendbook, a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by text mining, we model a user’s daily life as life documents , from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate users’ impact in terms of life styles with a friend-matching graph . Upon receiving a request, Friendbook returns a list of people with highest recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on both small-scale experiments and large-scale simulations. The results show that the recommendations accurately reflect the preferences of users in choosing friends.


title: Combining Intra- and Multi-sentential Rhetorical Parsing for Document-level Discourse Analysis

abstract: We propose a novel approach for developing a two-stage document-level discourse parser. Our parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intrasentential parsing and the other for multisentential parsing. We present two approaches to combine these two stages of discourse parsing effectively. A set of empirical evaluations over two different datasets demonstrates that our discourse parser significantly outperforms the stateof-the-art, often by a wide margin.


Convolution kernels on discrete structures


title: A survey on reconfigurable accelerators for cloud computing

abstract: Data centers are experiencing an exponential increase in the amount of network traffic that they have to sustain due to cloud computing and several emerging web applications. To face this network load, large data centers are required with thousands of servers interconnected with high bandwidth switches. Current data center, based on general purpose processor, consume excessive power while their utilization is quite low. Hardware accelerators can provide high energy efficiency for many cloud applications but they lack the programming efficiency of processors. In the last few years, there several efforts for the efficient deployment of hardware accelerators in the data centers. This paper presents a thorough survey of the frameworks for the efficient utilization of the FPGAs in the data centers. Furthermore it presents the hardware accelerators that have been implemented for the most widely used cloud computing applications. Furthermore, the paper provides a qualitative categorization and comparison of the proposed schemes based on their main features such as speedup and energy efficiency.


Authoring Behaviors for Games using Learning from Demonstration


title: Gain enhancement of the Vivaldi antenna with band notch characteristics using zero-index metamaterial

abstract: In this letter, a tapered slot ultrawide band (UWB) Vivaldi antenna with enhanced gain having band notch characteristics in the WLAN/WiMAX band is presented. In this framework, a reference tapered slot Vivaldi antenna is first designed for UWB operation that is, 3.1–10.6 GHz using the standard procedure. The band-notch operation at 4.8 GHz is achieved with the help of especially designed complementary split ring resonator (CSRR) cell placed near the excitation point of the antenna. Further, the gain of the designed antenna is enhanced substantially with the help of anisotropic zero index metamaterial (AZIM) cells, which are optimized and positioned on the substrate in a particular fashion. In order to check the novelty of the design procedure, three distinct Vivaldi structures are fabricated and tested. Experimental data show quite good agreement with the simulated results. As the proposed antenna can minimize the electromagnetic interference (EMI) caused by the IEEE 802.11 WLAN/WiMAX standards, it can be used more efficiently in the UWB frequency band. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:233–238, 2016


title: Slanted-Edge MTF for Digital Camera and Scanner Analysis

abstract: The development and adoption of standards for the evaluation of digital camera resolution has helped foster the widespread use of slanted-edge-based analysis. In addition, the form of these evaluation methods suggests their use in imaging system analysis and design. The standards-specific methods and algorithms, however, are not intended for direct MTF evaluation, but if care is taken to avoid bias and minimize random error, the methods can successfully be used for this purpose. In this paper the influence of several variables are discussed. Specifically, the effect of color misregistration, edge location estimation, data-record length and image noise on the measured MTF are addressed.


title: A Unified Personalized Video Recommendation via Dynamic Recurrent Neural Networks

abstract: Personalized video recommender systems play an essential role in bridging users and videos. However, most existing video recommendation methods assume that user profiles (interests) are static. In fact, the static assumption is inadequate to reflect users' dynamic interests as time goes by, especially in the online video recommendation scenarios with dramatic changes of video contents and frequent drift of users' interests over different topics. To overcome the above issue, we propose a dynamic recurrent neural network to model users' dynamic interests over time in a unified framework for personalized video recommendation. Furthermore, to build a much more comprehensive recommendation system, the proposed model is designed to exploit video semantic embedding, user interest modeling, and user relevance mining jointly to model users' preferences. By considering these three factors, the RNN model becomes an interest network which can capture users' high level interests effectively. Extensive experimental results on both single-network and cross-network video recommendation scenarios demonstrate the superior performance of the proposed model compared with other state-of-the-art algorithms.


title: A Stable Approach for Routing Queries in Unstructured P2P Networks

abstract: Finding a document or resource in an unstructured peer-to-peer network can be an exceedingly difficult problem. In this paper we propose a query routing approach that accounts for arbitrary overlay topologies, nodes with heterogeneous processing capacity, e.g., reflecting their degree of altruism, and heterogenous class-based likelihoods of query resolution at nodes which may reflect query loads and the manner in which files/resources are distributed across the network. The approach is shown to be stabilize the query load subject to a grade of service constraint, i.e., a guarantee that queries' routes meet pre-specified class-based bounds on their associated a priori probability of query resolution. An explicit characterization of the capacity region for such systems is given and numerically compared to that associated with random walk based searches. Simulation results further show the performance benefits, in terms of mean delay, of the proposed approach. Additional aspects associated with reducing complexity, estimating parameters, and adaptation to class-based query resolution probabilities and traffic loads are studied.


title: Users Profiling Using Clickstream Data Analysis and Classification

abstract: The nature of today's online communication and the emergence of online social networks have introduced a great challenge related to the identification and the verification of users in web environments. The assumption that every user interact uniquely with a web site provides a baseline for studying user identification based on historical records of user's interactions with specific web site. In this paper, we proposed an approach for using clickstream data to identify users based on their navigational behavior. The study investigates using server-side clickstream data collected from previous users' interaction to create a behavioral profile per user. User's profile can be used to identify possible future interactions, which can be associated with distinguishing authenticated user from malicious user. To accomplish this identification, the ability to gather a historical record for user's navigation in a specific web site, is investigated. The goal of the collected data is to use it for training a model that is able to identify if a future interaction can be associated with a certain user.


title: RESTful Industrial Communication With OPC UA

abstract: Representational state transfer (REST) is a wide-spread architecture style for decentralized applications. REST proposes the use of a fixed set of service interfaces to transfer heterogeneous resource representations instead of defining custom interfaces for individual applications. This paper explores the advantages of RESTful architectures, i.e., service-oriented software architectures comprised RESTful services, in industrial settings. These include communication advantages such as reduced communication overhead and the possibility to introduce caching layers, and system design advantages including stable service interfaces across applications and the use of resource-oriented information models in cyber–physical systems. Additionally, a RESTful extension to the open platform communications (OPC) unified architecture (OPC UA) binary protocol is proposed in order to leverage these advantages. It requires only minimal modifications to the existing OPC UA stacks and is fully backward compatible with the standard protocol. Performance benchmarks on industrial hardware show a throughput increase up to a factor of eight for short-lived interactions. This reduction of overhead is especially relevant for the use of OPC UA in the emerging Industrial Internet of Things.


title: Cost and Utilization Optimization of Amazon EC2 Instances

abstract: The monitoring and the analysis of public clouds gains momentum, due to their widespread exploitation by individual users, researchers and companies for their daily tasks. We propose an algorithm for optimizing the cost and the utilization of a set of running Amazon EC2 instances by resizing them appropriately. The algorithm, namely Cost and Utilization Optimization (CUO) algorithm, receives information regarding the current set of instances used (their number, type, utilization) and proposes a new set of instances for serving the same load, so as to minimize cost and maximize utilization, or increase performance efficiency. CUO is integrated in Smart cloud Monitoring (SuMo), an open-source tool we develop for collecting monitoring data from Amazon Web Services (AWS) and analyzing them. A number of experiments are performed, using input data that correspond to realist AWS configuration scenarios, which exhibit the benefits of the CUO algorithm.


title: Bayesian Estimation and Inference:

abstract: This paper introduces the “Bayesian revolution” that is sweeping across multiple disciplines but has yet to gain a foothold in organizational research. The foundations of Bayesian estimation and inference are first reviewed. Then, two empirical examples are provided to show how Bayesian methods can overcome limitations of frequentist methods: (a) a structural equation model of testosterone’s effect on status in teams, where a Bayesian approach allows directly testing a traditional null hypothesis as a research hypothesis and allows estimating all possible residual covariances in a measurement model, neither of which are possible with frequentist methods; and (b) an ANOVA-style model from a true experiment of ego depletion’s effects on performance, where Bayesian estimation with informative priors allows results from all previous research (via a meta-analysis and other previous studies) to be combined with estimates of study effects in a principled manner, yielding support for hypotheses that is not obtain...


title: Efficient waste collection system

abstract: Waste is an important issue, which needs to be tackled smartly. Wisely, we segregate the waste at our homes for ease at processing and recycling. We observed trash vans/trucks come irregular to homes creating a havoc of households. Due to this many civilians empty their overloaded dustbins in open spaces. This in turn increases environmental pollution. We got inspired from “Swachh Bharat Abhiyan” which is a national campaign by the Government of India, to clean the streets, roads and infrastructure of the country. The citizens want to have better service, lower cost and having easy accessible reports on what has been done and how much it cost. The proposed system aims to schedule trucks by finding shortest path between the almost filled waste bins and bins which have produced harmful gases and gives a route for collection. The system will consist of setting up smart waste bins/ trash cans per society, which will be IoT enabled. These smart bins will transmit information about its fill status and harmful gas levels. Proposed system provides efficient and optimized routes to collect maximum waste with less cost and fuel. The system provides estimated dates for collection of waste, real time bin status, expected fill up dates for the bins, and optimized shortest path for waste collection. The system will summarize the collected information and generate reports.


title: Lightweight and Serverless RFID Authentication and Search Protocol

abstract: We propose a new serverless RFID protocol for authentication. Our scheme not only retains the original security agreement but also reduce the operations and increase the efficiency of the implementation. We also propose a RFID tag search protocol which is a new concept that we believe this tag search will be the future trend. This protocol has a better execution efficiency in comparison with other protocols.


title: Semantic Part Segmentation with Deep Learning

abstract: In this work we address the task of segmenting an object into its parts, or semantic part segmentation. We start by adapting a state-of-the-art semantic segmentation system to this task, and show that a combination of a fully-convolutional Deep CNN system coupled with Dense CRF labelling provides excellent results for a broad range of object categories. Still, this approach remains agnostic to high-level constraints between object parts. We introduce such prior information by means of the Restricted Boltzmann Machine, adapted to our task and train our model in an entirely discriminative fashion, as a hidden CRF, demonstrating that prior information can yield additional improvements. We evaluate the performance of our approach on the Penn-Fudan and LFW datasets for the tasks of pedestrian parsing and face labelling respectively. We show superior performance with respect to competitive methods that have been extensively engineered on these benchmarks, as well as realistic qualitative results on part segmentation, even in challenging cases, such as occluded or deformable objects. We also provide quantitative and extensive qualitative results on three classes from the PASCAL Parts dataset.


title: Ideal Hash Trees

abstract: Hash Trees with nearly ideal characteristics are described. These Hash Trees require no initial root hash table yet are faster and use significantly less space than chained or double hash trees. Insert, search and delete times are small and constant, independent of key set size, operations are O(1). Small worst-case times for insert, search and removal operations can be guaranteed and misses cost less than successful searches. Array Mapped Tries(AMT), first described in Fast and Space Efficient Trie Searches, Bagwell [2000], form the underlying data structure. The concept is then applied to external disk or distributed storage to obtain an algorithm that achieves single access searches, close to single access inserts and greater than 80 percent disk block load factors. Comparisons are made with Linear Hashing, Litwin, Neimat, and Schneider [1993] and B-Trees, R.Bayer and E.M.McCreight [1972]. In addition two further applications of AMTs are briefly described, namely, Class/Selector dispatch tables and IP Routing tables. Each of the algorithms has a performance and space usage that is comparable to contemporary implementations but simpler.


title: A comparative study of the magnetic field homogeneity for circular, square and equilateral triangular helmholtz coils

abstract: This paper presents a comparative study of the magnetic field homogeneity for circular, square and equilateral triangular Helmholtz coils that includes a power consumption analysis. The magnetic field distribution is obtained using the Biot-Savart law, furthermore a Taylor series approximation is performed in order to obtain simple mathematical expressions that relate the area, with uniform magnetic field, to the length of the different coils. For the analysis of magnetic field homogeneity and power consumption, two scenarios were considered: by taking the radius of the circular coil equal to half of the side length of the square and equilateral triangular coils and then equating the perimeters of all of them. Therefore, computational models are constructed and simulated using finite element software in order to validate the calculated results of the magnetic field distribution and the experimental tests of each geometry considering equal perimeters. The results show that the circular geometry has the highest magnetic field homogeneity compared to other Helmholtz coil geometries with the same perimeter.


title: Relative age, talent identification and youth skill development: Do relatively younger athletes have superior technical skills?

abstract: Relative age effects (RAEs) refer to differences among individuals in age-based cohorts typically used in sport. These effects usually favour relatively older members of the cohort and are thought to result from differences in maturation and experience among athletes of different chronological age. Recently, researchers suggested that relatively younger participants may not be as disadvantaged as previously thought. In two studies, we examined whether relatively younger athletes who were able to survive in a system that advantages their relatively older counterparts would develop superior technical skills. In study one, participants aged 13-15 years (n=140) drawn from a regional handball talent selection camp in Germany demonstrated a general relative age effect but no differences between relatively older and relatively younger athletes in physical body size (i.e., height/weight) or technical skills. In study two, similar tests were considered with a larger sample (n=478) and revealed similar results. Furthermore, there were no differences between those selected for the national youth team and those not selected. Differences in RAEs do not seem to be due to technical skills or body size variables. Moreover, the homogeneity of these results suggests causes of the relative age effect occur early in development.


title: A novel adaptable approach for sentiment analysis on big social data

abstract: Gathering public opinion by analyzing big social data has attracted wide attention due to its interactive and real time nature. For this, recent studies have relied on both social media and sentiment analysis in order to accompany big events by tracking people’s behavior. In this paper, we propose an adaptable sentiment analysis approach that analyzes social media posts and extracts user’s opinion in real-time. The proposed approach consists of first constructing a dynamic dictionary of words’ polarity based on a selected set of hashtags related to a given topic, then, classifying the tweets under several classes by introducing new features that strongly fine-tune the polarity degree of a post. To validate our approach, we classified the tweets related to the 2016 US election. The results of prototype tests have performed a good accuracy in detecting positive and negative classes and their sub-classes.


The Role of IS Capabilities in the Development of Multi-Sided Platforms: The Digital Ecosystem Strategy of Alibaba.com


title: Arguing Machines: Perception-Control System Redundancy and Edge Case Discovery in Real-World Autonomous Driving

abstract: Safe autonomous driving may be one of the most difficult engineering challenges that any artificial intelligence system has been asked to do since the birth of AI over sixty years ago. The difficulty is not within the task itself, but rather in the extremely small margin of allowable error given the human life at stake and the extremely large number of edge cases that have to be accounted for. In other words, we task these systems to expect the unexpected with near 100% accuracy, which is a technical challenge for machine learning methods that to date have generally been better at memorizing the expected than predicting the unexpected. In fact, the process of efficiently and automatically discovering the edge cases of driving may be the key to solving this engineering challenge. In this work, we propose and evaluate a method for discovering edge cases by monitoring the disagreement between two monocular-vision-based automated steering systems. The first is a proprietary Tesla Autopilot system equipped in the first generation of Autopilot-capable vehicles. The second is a end-to-end neural network trained on a large-scale naturalistic dataset of 420 hours or 45 million frames of autonomous driving in Tesla vehicles.


title: A Generative Model of 3D Object Layouts in Apartments.

abstract: Understanding indoor scenes is an important task in computer vision. This task is typically ambiguous, so we require a strong prior, that captures the regularity of indoor environments. This is naturally expressed by a probabilistic model over 3D room layouts and geometry, reasoning over complex layouts in 3D space, including high-order spatial relations among objects. In this work, we construct such a model, trained on over 250000 human-designed rooms with 170 object classes. We conduct extensive experiments to show the quality of our model. First, we show that it generates samples that are plausible, by an extensive user study involving human comparisons of sampled layouts to ground-truth. Second, we demonstrate the value of incorporating spatial relationships between objects, by showing that this increases the plausibility of samples. Third, we show that our model generalises, rather than simply memorising its training set. Finally, we provide many examples of knowledge learnt by our model, such as support relationships, and common spatial relations between object classes.


title: On the Spectral Bias of Deep Neural Networks.

abstract: It is well known that over-parametrized deep neural networks (DNNs) are an overly expressive class of functions that can memorize even random data with $100\%$ training accuracy. This raises the question why they do not easily overfit real data. To answer this question, we study deep networks using Fourier analysis. We show that deep networks with finite weights (or trained for finite number of steps) are inherently biased towards representing smooth functions over the input space. Specifically, the magnitude of a particular frequency component ($k$) of deep ReLU network function decays at least as fast as $\mathcal{O}(k^{-2})$, with width and depth helping polynomially and exponentially (respectively) in modeling higher frequencies. This shows for instance why DNNs cannot perfectly \textit{memorize} peaky delta-like functions. We also show that DNNs can exploit the geometry of low dimensional data manifolds to approximate complex functions that exist along the manifold with simple functions when seen with respect to the input space. As a consequence, we find that all samples (including adversarial samples) classified by a network to belong to a certain class are connected by a path such that the prediction of the network along that path does not change. Finally we find that DNN parameters corresponding to functions with higher frequency components occupy a smaller volume in the parameter.


title: Struggling and Success in Web Search

abstract: Web searchers sometimes struggle to find relevant information. Struggling leads to frustrating and dissatisfying search experiences, even if searchers ultimately meet their search objectives. Better understanding of search tasks where people struggle is important in improving search systems. We address this important issue using a mixed methods study using large-scale logs, crowd-sourced labeling, and predictive modeling. We analyze anonymized search logs from the Microsoft Bing Web search engine to characterize aspects of struggling searches and better explain the relationship between struggling and search success. To broaden our understanding of the struggling process beyond the behavioral signals in log data, we develop and utilize a crowd-sourced labeling methodology. We collect third-party judgments about why searchers appear to struggle and, if appropriate, where in the search task it became clear to the judges that searches would succeed (i.e., the pivotal query). We use our findings to propose ways in which systems can help searchers reduce struggling. Key components of such support are algorithms that accurately predict the nature of future actions and their anticipated impact on search outcomes. Our findings have implications for the design of search systems that help searchers struggle less and succeed more.


title: Spatial Transformer Networks

abstract: Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations.


title: Guardians Final Report Part 1 (draft): A Robot Swarm assisting a Human Fire Fighter

abstract: Emergencies in industrial warehouses are a major concern for re ghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist re ghters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting re ghters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus the robot swarm is able to provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human re ghter. We have designed and experimented


A rapid and systematic review of the clinical effectiveness and cost-effectiveness of glycoprotein IIb/IIIa antagonists in the medical management of unstable angina.


title: Interlocking obfuscation for anti-tamper hardware

abstract: Tampering and Reverse Engineering of a chip to extract the hardware Intellectual Property (IP) core or to inject malicious alterations is a major concern. Digital systems susceptible to tampering are of immense concern to defense organizations. First, offshore chip manufacturing allows the design secrets of the IP cores to be transparent to the foundry and other entities along the production chain. Second, small malicious modifications to the design may not be detectable after fabrication without anti-tamper mechanisms. Some techniques have been developed in the past to improve the defense against such attacks but they tend to fall prey to the increasing power of the attacker. We present a new way to protect against tampering by a clever obfuscation of the design, which can be unlocked with a specific, dynamic path traversal. Hence, the functional mode of the controller is hidden with the help of obfuscated states, and the functional mode is made operational only on the formation of a specific interlocked Code-Word during state transition. No comparator is needed as the obfuscation is embedded within the transition function of the state machine itself. A side benefit is that any small alteration will be magnified via the obfuscated design. In other words, an alteration to the design will manifest itself as a large difference in the circuit's functionality. Experimental results on an Advanced Encryption Standard (AES) circuit from the open-source IP-cores suite suggest that the proposed method provides better active defense mechanisms against attacks with nominal (7.8%) area overhead.


title: New dimensions of temporal serendipity and temporal novelty in recommender system

abstract: Recommender system focuses on techniques that could predict user interest and give assistance while th e user interacts with the Web in finding relevant informat ion. It attempt to make sense of the data generate d by his past interaction and predict in future choices. The focu s of research in the area of recommender system has been on accuracy in the past decade, but the trend is chang ing with an increasing interest in this area of res earch. This paper is an attempt to provide an overview of the sof the art in new dimension of recommender sys tem research. Novelty and serendipity refers to the search of fin ding something new by a user while browsing world w ide web. Traditional recommender system algorithm focuses on accuracy that tries to compare accuracy with past data which limits the scope of novelty and serendipity tgreat extent. Novelty pertains to giving someth ing new which the user have not accesses before but similar in ta ste while serendipity is a chance discovery that co uld be really beneficial for a user at certain times. This paper will present an outlook on the existing research ca rried out in this area, their specialized focus with respect to an ap plicative objectives and the need for a more compre hensive new entrant in this sphere in the light of the current scenario. The paper will also present a novel metho dology based on temporal parameters to include the novelty and sere ndipity in recommender system. In the end, the pape r will be concluded by listing some challenges and future tre nds in this research area.


A Quantitative Analysis of the Use of Microdata for Semantic Annotations on Educational Resources


title: Inference Compilation and Universal Probabilistic Programming

abstract: We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do "compilation of inference" because our method transforms a denotational specification of an inference problem in the form of a probabilistic program written in a universal programming language into a trained neural network denoted in a neural network specification language. When at test time this neural network is fed observational data and executed, it performs approximate inference in the original model specified by the probabilistic program. Our training objective and learning procedure are designed to allow the trained neural network to be used as a proposal distribution in a sequential importance sampling inference engine. We illustrate our method on mixture models and Captcha solving and show significant speedups in the efficiency of inference.


title: Foundations of attack-defense trees

abstract: We introduce and give formal definitions of attack-defense trees. We argue that these trees are a simple, yet powerful tool to analyze complex security and privacy problems. Our formalization is generic in the sense that it supports different semantical approaches. We present several semantics for attack-defense trees along with usage scenarios, and we show how to evaluate attributes.


title: NUMERICAL FRAMEWORK FOR SEISMIC COLLAPSE ASSESSMENT OF RIGID WALL-FLEXIBLE DIAPHRAGM STRUCTURES

abstract: This study focuses on the development of a two dimensional (2D) simplified numerical framework of rigid wall-flexible diaphragm (RWFD) structures that can be used to validate seismic design approaches. This type of low-rise industrial buildings, which is widely used in North America, incorporates rigid inplane concrete or masonry walls and flexible in-plane wood, steel or “hybrid” roof diaphragms. The numerical modeling is detailed enough to capture the nonlinear seismic response of RWFD buildings, but simplified enough to efficiently conduct a large number of nonlinear time-history dynamic analyses. The 2D numerical modeling framework is based on a three step sub-structuring approach including: (1) a hysteretic response database for diaphragm connectors, (2) a 2D inelastic roof diaphragm model incorporating hysteretic connector response and (3) a simplified 2D building model incorporating hysteretic diaphragm model response. The diaphragm connector database (step 1) was developed for both wood and steel deck connectors using cyclic test data available in the literature. Two well-known hysteretic models (Wayne-Stewart and CUREE-SAWS) were used for estimating/fitting hysteretic parameters of each connector type. The analytical model of the inelastic roof diaphragm (step 2) was generated to account for the elastic shear deformation of deck panels, elastic flexural deformations of chord members as well as inelastic deformations of deck-to-frame connectors (from the connector database-step 1). This model includes monotonic and cyclic analysis capabilities. The last step of the proposed analytical framework is a simplified two dimensional model of a RWFD building developed in RUAUMOKO2D to account for the inelastic response of roof diaphragms (based on the analytical roof diaphragm model-step 2) and the out-of-plane walls as well as second order (P-Δ) effects. Both the proposed analytical model of the roof diaphragm and the proposed simplified building model were validated with experimental and analytical studies available in the literature. Furthermore, a sensitivity study was conducted to examine the effect of: (i) analysis time step, (ii) different base fixity of the out-of plane walls, (iii) P-Δ effects, (iv) inherent viscous damping and (v) direction of shaking on the collapse assessment of RWFD structures.


title: A relay placement mechanism based on UAV mobility for satisfactory video transmissions

abstract: The user experience on watching live videos transmitted by Unmanned Aerial Vehicles (UAVs) must be satisfactory even under the influence of topology changes caused by UAVs mobility on a Flying Ad-Hoc Networks (FANETs). However, one of the main challenges to route packets in FANETs is how to mitigate the effects of UAV mobility to preclude communication flaws, delays, and void area, which increase the packet loss during video transmissions. In this way, routing protocols should consider a relay placement service to find the ideal location for UAVs that act as relay nodes, and thus avoiding the effects of UAV movements on the video dissemination. In this paper, we introduce a relay placement mechanism, called MobiFANET, to support live video transmissions with satisfactory Quality of Experience (QoE) to the users. MobiFANET considers both node location and UAV mobility to establish the ideal relay location, precluding route failures and void areas due to UAV movements. Simulation results show how MobiFANET works jointly with a routing protocol for satisfactory multimedia transmission, as required for many safety FANET scenarios.


title: A fuzzy association rule mining approach using movie lens dataset

abstract: In the modern era of e-commerce and social networking, people wants to get everything best and better from every other person in their group. There is always an urge within people to find products, services and opportunities better than everyone else. Nowadays, business grows by advertising it online because people are always online and they find it more interesting to review products on a social platform. Mining of association rules is an advanced data mining research area. Currently many works are undergoing in this domain. Completeness is one of the key strengths of fuzzy association rule mining. Fuzzy cannot work with large datasets. The massive amount of candidate item sets sometimes makes it unproductive for a data mining system to analyze them. At the end, it produces a significant number of fuzzy associations. The research focuses on helping the needful to find the best suited movies depending on the previous ratings. Rules have been generated by using fuzzy association rule mining.


title: Nonparametric Spherical Topic Modeling with Word Embeddings

abstract: Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropriate to leverage such correlations. In this paper, we propose to use the von Mises-Fisher distribution to model the density of words over a unit sphere. Such a representation is well-suited for directional data. We use a Hierarchical Dirichlet Process for our base topic model and propose an efficient inference algorithm based on Stochastic Variational Inference. This model enables us to naturally exploit the semantic structures of word embeddings while flexibly discovering the number of topics. Experiments demonstrate that our method outperforms competitive approaches in terms of topic coherence on two different text corpora while offering efficient inference.1


title: An Experimental Study on the Effectiveness of Multimedia in College English Teaching

abstract: Based on empirical research and qualitative analysis, this paper aims to explore the effectiveness of multimedia assisted methods in college English teaching. It seems and has been proved by some studies that multimedia assisted methods can effectively promote students’ English learning. But the results of this study do not positively contribute to the previous hypothesis. Is multimedia a definite facilitator in college English teaching? The paper is trying to draw attention and offer insights into this problem.


Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images


title: StepDeep: A Novel Spatial-temporal Mobility Event Prediction Framework based on Deep Neural Network

abstract: A mobility event occurs when a passenger moves out or takes off from a particular location. Mobility event prediction is of utmost importance in the field of intelligent transportation systems. It has a huge potential in solving important problems such as minimizing passenger waiting time and maximizing the utilization of the transportation resources by planning vehicle routes and dispatching transportation resources. Recently, numerous mobility pattern mining methods have been proposed to predict the transportation supply and demand in different locations. Those methods first reveal the event patterns of each Place of Interests (POI) independently and then employ a separate region function as a post-processing step. This separate process, that disregards the intrinsic spatial and temporal pattern correlations between POI, is sub-optimal and complex, resulting in a poor generalization in different scenarios. In this work, we propose a Spatial-Temporal mobility Event Prediction framework based on Deep neural network (StepDeep) for simultaneously taking into account all correlated spatial and temporal mobility patterns. StepDeep not only simplifies the prediction process but also enhances the prediction accuracy. Our StepDeep proposes a novel problem formulation towards an end-to-end mobility prediction framework, that is, switching mobility events over time in an area into an event video and then posing the mobility prediction problem as a video prediction task. Such a novel formulation can naturally encode spatial and temporal dependencies for each POI. StepDeep thus predicts the spatial-temporal events by incorporating the new time sensitive convolution filters, spatial sensitive convolution filters, and spatial-temporal sensitive convolution filters into a single network. We conduct experimental evaluations on a real-world 547-day New York City taxi trajectory dataset, which show that StepDeep provides higher prediction accuracy than five existing baselines. Moreover, StepDeep is generalizable and can be applied to numerous spatial-temporal event prediction scenarios.


title: Transform Blockchain into Distributed Parallel Computing Architecture for Precision Medicine

abstract: This paper provides a vision and proposes mechanisms to transform the blockchain duplicated computing into distributed parallel computing architecture by transforming smart contract which features data driven from the ground up to support moving computing to native data strategy. This new distributed parallel computing architecture can be employed to build a large size of data set from various distributed hosted medical data sets which might consist of personal electronic medical record (EMR) and various medical data. This large medical data set will enable researchers to jump start the deep learning research for medical domain. Distributed data management, distributed data sharing, and distributed learning are the core mechanisms in the new architecture. The required new researches and developments to employ Google federated learning and transfer learning algorithms in this new architecture are discussed. The approach and mechanism enabled by the new architecture is illustrated to build a real world evidence of clinical trial toward personal and precision medicine. Research issues and technical challenges are provided.


title: Integrating emotional cues into a framework for dialogue management

abstract: Emotions are very important in human-human communication but are usually ignored in human-computer interaction. Recent work focuses on recognition and generation of emotions as well as emotion driven behavior. Our work focuses on the use of emotions in dialogue systems that can be used with speech input or as well in multi-modal environments.This paper describes a framework for using emotional cues in a dialogue system and their informational characterization. We describe emotion models that can be integrated into the dialogue system and can be used in different domains and tasks. Our application of the dialogue system is planned to model multi-modal human-computer-interaction with a humanoid robotic system.


title: Event-Based Multi-level Service Monitoring

abstract: Due to the growing pervasiveness of the service paradigm, modern systems are now often built as software as a service, and tend to exploit underlying platforms and virtualized resources also provided as services. Managing such systems requires that we be aware of the behaviors of all the different layers, and of the strong dependencies that exist between them. In this paper we present the Multi-layer Collection and Constraint Language (mlCCL). It allows us to define how to collect, aggregate, and analyze runtime data in a multi-layered system. We also present ECoWare, a framework for event correlation and aggregation that supports mlCCL, and provides a dashboard for on-line and off-line drill-down analyses of collected data. Empirical assessment shows that the impact of the approach on runtime performance is negligible.


title: User Experience of On-Screen Interaction Techniques: An Experimental Investigation of Clicking, Sliding, Zooming, Hovering, Dragging, and Flipping

abstract: From scrolling and clicking to dragging, flipping, sliding, hovering, and zooming, the wide array of interaction techniques has vastly expanded the range of user actions on an interface. Each of these interaction techniques affords a distinct action. But do these techniques differ in their ability to engage users and contribute to their user experience? Furthermore, do they affect how users view the content and how much they learn from it? We address these questions via two between-subjects laboratory experiments. Study 1 N = 128 investigated the relative effects of six on-screen interaction techniques click-to-download, drag, mouseover, slide, zoom, and 3D carousel on users' assessment of—as well as their engagement with—an informational website. The site for each condition was identical in content and design, except for the interaction technique used, so that we could isolate the effects of each technique on various cognitive, attitudinal and behavioral outcomes. Study 2 N = 127 examined the relative effects of four combinations of interaction techniques slide+click, slide+mouseover, drag+mouseover, and drag+zoom on the same dependent variables. Data from Study 1 suggest that although the 3D carousel generates more user action, the slide is better at aiding memory. The zoom-in/out tool was the least favored, whereas the mouseover feature fostered greater engagement among power users. Findings from Study 2, which was conducted with a different content domain, replicated the positive effects of slide and negative effects of drag in influencing user experience. Path analyses, using structural equation modeling, revealed the importance of users' assessment of the interface perceived levels of natural mapping, intuitiveness, and ease of use, which can have significant consequences for user engagement as well as resulting attitudes and behavioral outcomes. Design insights, theories, and techniques to test and capture user experience are discussed.


title: DRINet for Medical Image Segmentation

abstract: Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The U-Net architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers. These convolution layers learn representative features of input images and construct segmentations based on the features. However, the features learned by standard convolution layers are not distinctive when the differences among different categories are subtle in terms of intensity, location, shape, and size. In this paper, we propose a novel CNN architecture, called Dense-Res-Inception Net (DRINet), which addresses this challenging problem. The proposed DRINet consists of three blocks, namely a convolutional block with dense connections, a deconvolutional block with residual inception modules, and an unpooling block. Our proposed architecture outperforms the U-Net in three different challenging applications, namely multi-class segmentation of cerebrospinal fluid on brain CT images, multi-organ segmentation on abdominal CT images, and multi-class brain tumor segmentation on MR images.


title: Multi-Material Volume Rendering with a Physically-Based Surface Reflection Model

abstract: Rendering techniques that increase realism in volume visualization help enhance perception of the 3D features in the volume data. While techniques focusing on high-quality global illumination have been extensively studied, few works handle the interaction of light with materials in the volume. Existing techniques for light-material interaction are limited in their ability to handle high-frequency real-world material data, and the current treatment of volume data poorly supports the correct integration of surface materials. In this paper, we introduce an alternative definition for the transfer function which supports surface-like behavior at the boundaries between volume components and volume-like behavior within. We show that this definition enables multi-material rendering with high-quality, real-world material data. We also show that this approach offers an efficient alternative to pre-integrated rendering through isosurface techniques. We introduce arbitrary spatially-varying materials to achieve better multi-material support for scanned volume data. Finally, we show that it is possible to map an arbitrary set of parameters directly to a material representation for the more intuitive creation of novel materials.


title: Minocycline ameliorates depressive behaviors and neuro‐immune dysfunction induced by chronic unpredictable mild stress in the rat

abstract: Abstract Activated microglia-induced neuroinflammation can stimulate the hypothalamic- pituitary-adrenal (HPA) axis to release glucocorticoids and suppress astrocyte functions, such as reducing neurotrophin production, which occur in depression. However, the balance between M1 (pro-inflammation) and M2 (anti-inflammation) microglial phenotypes and the interaction between these two glial cells are unclear in the depression. Hence, the chronic unpredictable mild stress (CUMS)-induced depression model was chosen to study depression- and anxiety-like behaviors, the concentration of corticosterone and relevant hippocampal cytokines, mRNA and protein expressions of microglial and astrocyte markers. To demonstrate the role of M1 phenotype activation in depression, the effect of microglial inhibitor minocycline on these aspects was also evaluated. Six weeks after CUMS exposure, behaviors were tested. Compared to the control group, CUMS increased serum corticosterone concentration and depression-like behaviors, like anhedonia, helplessness and anxiety. Moreover, CUMS increased microglia M1 marker CD11b expression and tumor necrosis factor (TNF)-α, interferon (INF)-γ, interleukin (IL)-1β and IL-17 concentrations, but decreased the concentration of M2 cytokines, IL-4, IL-10 and IL-13. Meanwhile, CUMS inhibited the expressions of astrocyte marker glial fibrillary acidic protein (GFAP), brain-derived neurotrophic factor (BDNF) and TrKB. Minocycline (40 mg/kg, 45 days) treatment significantly attenuated CUMS-induced behavioral abnormalities, which were associated with the suppressed M1 response, restored GFAP, BDNF and its receptor expression. In conclusion, CUMS-induced depression- and anxiety-like behavior may result from an imbalance between M1 and M2 and suppressed astrocyte function. Minocycline treatment reversed M1 response, which was associated with behavioral normalization.


title: Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation

abstract: In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically for mobile devices with limited power capacity and computation resources. By assuming the same architecture to full-precision networks, previous works on quantizing CNNs seek to preserve the floating-point information using a set of discrete values, which we call value approximation. However, we take a novel ``structure approximation'' view for quantization--- it is very likely that a different architecture may be better for best performance. In particular, we propose a ``network decomposition'' strategy, named Group-Net, in which we divide the network into groups. In this way, each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches. In addition, we learn effect connections among groups to improve the representational capability. Moreover, the proposed Group-Net shows strong generalization to other tasks. For instance, we extend Group-Net for highly accurate semantic segmentation by embedding rich context into the binary structure. Experiments on both classification and semantic segmentation tasks demonstrate the superior performance of the proposed methods over various popular architectures. In particular, we outperform the previous best binary neural networks in terms of accuracy and huge computation saving.


CRITICAL SUCCESS FACTORS TO IMPROVE KNOWLEDGE SHARING IN SOUTH AFRICAN LOCAL GOVERNMENT


title: Complex Activity Recognition Using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video

abstract: Modeling interactions of multiple co-occurring objects in a complex activity is becoming increasingly popular in the video domain. The Dynamic Bayesian Network (DBN) has been applied to this problem in the past due to its natural ability to statistically capture complex temporal dependencies. However, standard DBN structure learning algorithms are generatively learned, require manual structure definitions, and/or are computationally complex or restrictive. We propose a novel structure learning solution that fuses the Granger Causality statistic, a direct measure of temporal dependence, with the Adaboost feature selection algorithm to automatically constrain the temporal links of a DBN in a discriminative manner. This approach enables us to completely define the DBN structure prior to parameter learning, which reduces computational complexity in addition to providing a more descriptive structure. We refer to this modeling approach as the Granger Constraints DBN (GCDBN). Our experiments show how the GCDBN outperforms two of the most relevant state-of-the-art graphical models in complex activity classification on handball video data, surveillance data, and synthetic data.


title: Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics

abstract: While significant progress has been made separately on analytics systems for scalable stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics frameworks have incorporated differentially private SGD. There are two inter-related issues for this disconnect between research and practice: (1) low model accuracy due to added noise to guarantee privacy, and (2) high development and runtime overhead of the private algorithms. This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address \emph{both} issues in an integrated manner. In contrast to the white-box approach adopted by previous work, we revisit and use the classical technique of {\em output perturbation} to devise a novel "bolt-on" approach to private SGD. While our approach trivially addresses (2), it makes (1) even more challenging. We address this challenge by providing a novel analysis of the $L_2$-sensitivity of SGD, which allows, under the same privacy guarantees, better convergence of SGD when only a constant number of passes can be made over the data. We integrate our algorithm, as well as other state-of-the-art differentially private SGD, into Bismarck, a popular scalable SGD-based analytics system on top of an RDBMS. Extensive experiments show that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms on many real datasets.


title: Innovation of Neuropsychopharmacological Experimental Methods to Investigate Attention in Human Participants and It’s Validation by Randomized Double Blind Placebo Controlled Clinical Trial

abstract: In terms of advancement of life to meet the challenges new method for neuropsychopharmacological research are continually developing to ensure superior safety, accuracy, efficacy, precision etc. In this experiment, we suggested and validated two neuropsychopharmacological testing methods, these are Numeral Finding (NF) and Typo Revealing (TR) tests for the determination of attention in healthy aged human participants. The objective of this experiment was to generate more effective testing methods for the assessment of attention in human. The rationality of the aforementioned methods was checked by a randomized double blind placebo controlled clinical trial for the period of 21 days among healthy aged human participants. The experiment was conducted at Gazipur Sadar, Gazipur-1703, Dhaka, Bangladesh between the period of December 2015 and March 2016. The experiment center was the Department of Pharmacy, Southeast University, Dhaka-1213, Bangladesh. In this experiment, 60 healthy male participants between the age ranges of 40 to 50 years old were selected and randomly divided by simple randomization process into 3 groups with 20 participants in each. Ginkgo biloba standardized extract’s capsule (Giloba? and Kobi?) was used as a standard and as a placebo control psyllium seed husk (i.e., seed of the Plantago ovata) was used after filling in the hard gelatin capsule shell (size 0). Group 1 was treated with Giloba? in which each capsule contained 120 mg Ginkgo biloba standardized extract (GBG) and group 2 was treated with Kobi? in which each capsule contained 60 mg Ginkgo biloba standardized extract (GBK). The treatment procedure for group 3 was psyllium seed husk’s capsule that served as placebo control (PC). Throughout the experiment each capsule was administered 3 times daily. Treatment with Ginkgo biloba standardized extracts for successive days markedly increased the attention of the participants with respect to PC group. In the NF test, administration of GBG and GBK extracts significantly (P < 0.05, P < 0.01; P < 0.01) increased the attention of the participants on 14th and 21st day as compared to that of PC group. For TR test treatment with GBG and GBK extracts markedly (P < 0.01; P < 0.001, P < 0.01) increased the attention of the participants on 14th and 21st day with respect to PC group. The existing experiment proved that NF and TR tests undoubtedly revealed the attention enhancing activity of the Ginkgo biloba standardized extracts in healthy aged human participants. Consequently, the recommended newly emerging methods can be used for the assessment of attention in human and will create a miracle in the domain of neuroscience.


Comparative Study of Classification Algorithms used in Sentiment Analysis


title: Inserting needles into the body: a meta-analysis of brain activity associated with acupuncture needle stimulation.

abstract: Abstract Acupuncture is a therapeutic treatment that is defined as the insertion of needles into the body at specific points (ie, acupoints). Advances in functional neuroimaging have made it possible to study brain responses to acupuncture; however, previous studies have mainly concentrated on acupoint specificity. We wanted to focus on the functional brain responses that occur because of needle insertion into the body. An activation likelihood estimation meta-analysis was carried out to investigate common characteristics of brain responses to acupuncture needle stimulation compared to tactile stimulation. A total of 28 functional magnetic resonance imaging studies, which consisted of 51 acupuncture and 10 tactile stimulation experiments, were selected for the meta-analysis. Following acupuncture needle stimulation, activation in the sensorimotor cortical network, including the insula, thalamus, anterior cingulate cortex, and primary and secondary somatosensory cortices, and deactivation in the limbic-paralimbic neocortical network, including the medial prefrontal cortex, caudate, amygdala, posterior cingulate cortex, and parahippocampus, were detected and assessed. Following control tactile stimulation, weaker patterns of brain responses were detected in areas similar to those stated above. The activation and deactivation patterns following acupuncture stimulation suggest that the hemodynamic responses in the brain simultaneously reflect the sensory, cognitive, and affective dimensions of pain. Perspective This article facilitates a better understanding of acupuncture needle stimulation and its effects on specific activity changes in different brain regions as well as its relationship to the multiple dimensions of pain. Future studies can build on this meta-analysis and will help to elucidate the clinically relevant therapeutic effects of acupuncture.


title: A current-mode non-inverting CMOS buck-boost DC-DC converter

abstract: This paper presents a current-mode control non-inverting buck-boost converter. The proposed circuit is controlled by the current mode and operated in three operation modes which are buck, buck-boost, and boost mode. The operation mode is automatically determined by the ratio between the input and output voltages. The proposed circuit is simulated by HSPICE with 0.5 um standard CMOS parameters. Its input voltage range is 2.5–5 V, and the output voltage range is 1.5–5 V. The maximum efficiency is 92% when it operates in buck mode.


title: A Study of Personal Cloud Computing: Compatibility, Social Influence, and Moderating Role of Perceived Familiarity

abstract: Building on a research framework based on the Theory of Reasoned Action (TRA), Innovation Diffusion Technology (IDT), and the Technology Adoption Model (TAM), we propose a model integrating compatibility, social influence, and perceived familiarity given the implicit uncertainty of personal cloud. Our model emphasizes the moderating effect of perceived familiarity on the relationships between both perceived compatibility and social influence on behavioral intention. PLS-based structural equation modeling is employed to test the related propositions empirically. Results from a survey, involving 265 university students, reveal that perceived compatibility explains a larger proportion of the variance in behavioral intention; perceived familiarity plays a significant role in moderating the impact of perceived compatibility and social influence on intention to adopt personal cloud. Managerial and theoretical implications are discussed.


title: The Epoch Interpretation of Learning

abstract: In this paper we propose a simple, alternative interpretation of backpropagation learning. We call this the “epoch interpretation of learning” and show how it can be used to improve the performance of earlystopping based techniques used for improving generalization performance in neural networks. Experiments performed on noisy, non-linear foreign exchange rate data demonstrate that networks built using an early-stopping technique that uses the epoch interpretation of learning on average out-perform networks built using a conventional early-stopping technique by 11%.


title: A 2.2GHz PLL using a phase-frequency detector with an auxiliary sub-sampling phase detector for in-band noise suppression

abstract: Tri-state digital phase-frequency detectors (PFDs) are widely used for the large capture and locking range that they enable, but suffer from relatively large in-band phase noise. Sub-sampling phase detectors have recently been demonstrated to offer very low in-band noise but with only a very small capture range. We show how a PFD and a sub-sampling phase detector can be combined to maintain the phase-frequency detection capabilities while simultaneously obtaining in-band noise suppression. A 2.2GHz PLL is demonstrated in a 65nm CMOS process with an on-chip loop filter area of 0.04mm2. The measured in-band phase noise improves from −110dBc/Hz to −122dBc/Hz when the auxiliary sub-sampling phase detector is active.


title: Links: Web programming without tiers

abstract: Links is a programming language for web applications that generates code for all three tiers of a web application from a single source, compiling into JavaScript to run on the client and into SQL to run on the database. Links supports rich clients running in what has been dubbed 'Ajax' style, and supports concurrent processes with statically-typed message passing. Links is scalable in the sense that session state is preserved in the client rather than the server, in contrast to other approaches such as Java Servlets or PLT Scheme. Client-side concurrency in JavaScript and transfer of computation between client and server are both supported by translation into continuation-passing style.


title: A synchronized self oscillating Class-D amplifier for mobile application

abstract: An integrated Class-D amplifier using a synchronized self-oscillating modulator based on a hysteresis control is presented. This new feedback topology can achieve a higher gain bandwidth product to increase linearity and power supply rejection than the fixed switching frequency solutions. The switching frequency spread can also reduce the external EMI filtering constraints. To reduce the idle consumption and radiated emissions at zero output level, a synchronization technique is presented to provide truly differential outputs. The proposed amplifier, realized in CMOS 130nm process, drives up to 1W load with 0.02% THD+N, 90% efficiency with power supply range from 2.3 to 5V. The synchronization technique reduces shows up to 75% of idle consumption.


title: Scleral cross-linking by riboflavin and blue light application in young rabbits: damage threshold and eye growth inhibition

abstract: Background ::: Scleral cross-linking (SXL) by riboflavin and light application has been introduced as a possible treatment to increase scleral tissue stiffness and to inhibit excessive axial elongation of highly myopic eyes. We evaluated an ocular tissue damage threshold for blue light irradiation, and used SXL treatment to induce eye growth inhibition.


title: Multiagent Reinforcement Learning for Multi-Robot Systems : A Survey

abstract: Multiagent reinforcement learning for multirobot systems is a challenging issue in both robotics and artificial intelligence. With the ever increasing interests in theoretical researches and practical applications, currently there have been a lot of efforts towards providing some solutions to this challenge. However, there are still many difficulties in scaling up the multiagent reinforcement learning to multi-robot systems. The main objective of this paper is to provide a survey, though not completely on the multiagent reinforcement learning in multi-robot systems. After reviewing important advances in this field, some challenging problems and promising research directions are analyzed. A concluding remark is made from the perspectives of the authors.


title: The Emergence of Crowdinvesting in Europe: With an In-Depth Analysis of the German Market

abstract: This paper presents the development of the Internet-based crowdinvesting market in Europe since its start in 2007. It evidences a great variety in portal design and contract forms used by crowdinvesting portals. By analyzing more detailed, hand-collected data on the complete set of successful and unsuccessful crowdinvesting campaigns run in Germany, the paper further tests whether different portal and contractual mechanisms affect crowd participation. The latter is a necessary, though not sufficient, precondition for achieving “wisdom of the crowd”. Consistent with predictions on the different mechanisms used, the results show that crowd participation is largest when the minimum ticket size is small, the crowd is pooled in a financial vehicle, and the crowd is offered investments in the form of profit-particiapting loans (so-called partiarische Darlehen). Moreover, the very same mechanisms increase the chance of achieving successful campaigns and of raising larger amounts. These findings are useful for entrepreneurs who need to choose among a large range of crowdinvesting portals and other forms of entrepreneurial finance.


title: Design and development of hand gesture recognition system for speech impaired people

abstract: All over world, deaf and dumb people face struggle in expressing their feelings to other people. There are various challenges experienced by speech and hearing impaired people at public places in expressing themselves to normal people. The solution to this problem is determined in this paper, by the usage of the Indian sign language symbols which are generic to all deaf and dumb people in India. The gestures illustrated by the Indian sign language symbols will be conquered with the support of the flex sensors and accelerometer. The movements included during gesture representation are rotation, angle tilt, and direction changes. The flex sensor and the accelerometer are incorporated over fingers and wrist respectively to acquire their dynamics, a these sensors are fitted over the data glove. These voltage signals will then be processed by microcontroller and sent to voice module, where the words voice outputs are stored and play backed equivalent to each word values to produce the appropriate voice words with the help of the speaker.


title: Text Generation From Tables

abstract: This paper proposes a neural generative model, namely Table2Seq, to generate a natural language sentence based on a table. Specifically, the model maps a table to continuous vectors and then generates a natural language sentence by leveraging the semantics of a table. Since rare words, e.g., entities and values, usually appear in a table, we develop a flexible copying mechanism that selectively replicates contents from the table to the output sequence. We conduct extensive experiments to demonstrate the effectiveness of our Table2Seq model and the utility of the designed copying mechanism. On the WIKIBIO and SIMPLEQUESTIONS datasets, the Table2Seq model improves the state-of-the-art results from 34.70 to 40.26 and from 33.32 to 39.12 in terms of BLEU-4 scores, respectively. Moreover, we construct an open-domain dataset WIKITABLETEXT that includes 13 318 descriptive sentences for 4962 tables. Our Table2Seq model achieves a BLEU-4 score of 38.23 on WIKITABLETEXT outperforming template-based and language model based approaches. Furthermore, through experiments on 1 M table-query pairs from a search engine, our Table2Seq model considering the structured part of a table, i.e., table attributes and table cells, as additional information outperforms a sequence-to-sequence model considering only the sequential part of a table, i.e., table caption.


title: The chaining approach for software test data generation

abstract: Software testing is very labor intensive and expensive and accounts for a significant portion of software system development cost. If the testing process could be automated, the cost of developing software could be significantly reduced. Test data generation in program testing is the process of identifying a set of test data that satisfies a selected testing criterion, such as statement coverage and branch coverage. In this article we present a chaining approach for automated software test data generation which builds on the current theory of execution-oriented test data generation. In the chaining approach, test data are derived based on the actual execution of the program under test. For many programs, the execution of the selected statement may require prior execution of some other statements. The existing methods of test data generation may not efficiently generate test data for these types of programs because they only use control flow information of a program during the search process. The chaining approach uses data dependence analysis to guide the search process, i.e., data dependence analysis automatically identifies statements that affect the execution of the selected statement. The chaining approach uses these statements to form a sequence of statements that is to be executed prior to the execution of the selected statement. The experiments have shown that the chaining approach may significantly improve the chances of finding test data as compared to the existing methods of automated test data generation.


title: Substantial contribution of extrinsic risk factors to cancer development

abstract: Recent research has highlighted a strong correlation between tissue-specific cancer risk and the lifetime number of tissue-specific stem-cell divisions. Whether such correlation implies a high unavoidable intrinsic cancer risk has become a key public health debate with the dissemination of the 'bad luck' hypothesis. Here we provide evidence that intrinsic risk factors contribute only modestly (less than ~10-30% of lifetime risk) to cancer development. First, we demonstrate that the correlation between stem-cell division and cancer risk does not distinguish between the effects of intrinsic and extrinsic factors. We then show that intrinsic risk is better estimated by the lower bound risk controlling for total stem-cell divisions. Finally, we show that the rates of endogenous mutation accumulation by intrinsic processes are not sufficient to account for the observed cancer risks. Collectively, we conclude that cancer risk is heavily influenced by extrinsic factors. These results are important for strategizing cancer prevention, research and public health.


title: Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations

abstract: We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this second part of our two-part treatise, we focus on the problem of data-driven discovery of partial differential equations. Depending on whether the available data is scattered in space-time or arranged in fixed temporal snapshots, we introduce two main classes of algorithms, namely continuous time and discrete time models. The effectiveness of our approach is demonstrated using a wide range of benchmark problems in mathematical physics, including conservation laws, incompressible fluid flow, and the propagation of nonlinear shallow-water waves.


title: Effects of dopamine receptor antagonism and amphetamine sensitization on sign- and goal-tracking after extended training

abstract: Rationale The dopamine system is thought to be important in incentive salience attribution, where motivational value is assigned to a cue that predicts an appetitive reinforcer (sign-tracking), however, dopamine’s role may change with extended training. Objectives We tested the effects of selective dopamine D1-like and D2-like receptor antagonism on the expression of Pavlovian conditioned approach after extended Pavlovian conditioned approach (PCA) training. We also tested the hypothesis that locomotor sensitization would accelerate the phenotypic shift to sign-tracking. Methods 24 male Long-Evans rats were subjected to 20 PCA sessions in which one lever (CS+, 10 s) predicted 0.2 mL sucrose delivery and the other lever (CS–) did not. SCH-23390 or eticlopride were administered prior to behavioral tests at doses of 0, 0.01, and 0.1 mg/kg (s.c.). In a subsequent experiment, rats were exposed to vehicle or 2 mg/kg amphetamine (i.p.) for 7 days (n = 12/group). After a 10-day incubation period, they were subjected to PCA training for 16 sessions. Results The D1 antagonist SCH-23390 reduced locomotor activity and port entries during inter-trial intervals, but the D2 antagonist eticlopride selectively reduced CS+ port entries in goal-trackers, i.e. animals motivated towards the reinforcer. Locomotor sensitization had no effect on the acquisition of sign-tracking. Conclusions A commonly used dose of SCH-23390 exhibited off-target locomotor effects and D2 receptors were not required for expression of sign-tracking after extended training. Amphetamine-induced locomotor sensitization did not enhance acquisition of sign-tracking behavior, suggesting that the sensitivity of the dopamine system does not drive acquisition of sign-tracking behavior.


A Review on Energy Efficiency Protocols in WSNs


title: MIMO radar: an idea whose time has come

abstract: It has recently been shown that multiple-input multiple-output (MIMO) antenna systems have the potential to improve dramatically the performance of communication systems over single antenna systems. Unlike beamforming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO concept exploits the independence between signals at the array elements. In conventional radar, target scintillations are regarded as a nuisance parameter that degrades radar performance. The novelty of MIMO radar is that it takes the opposite view; namely, it capitalizes on target scintillations to improve the radar's performance. We introduce the MIMO concept for radar. The MIMO radar system under consideration consists of a transmit array with widely-spaced elements such that each views a different aspect of the target. The array at the receiver is a conventional array used for direction finding (DF). The system performance analysis is carried out in terms of the Cramer-Rao bound of the mean-square error in estimating the target direction. It is shown that MIMO radar leads to significant performance improvement in DF accuracy.


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