title: A Data-Centric Approach for Modeling and Estimating Efficiency of Dataflows for Accelerator Design

abstract: The mechanisms used by DNN accelerators to leverage data-reuse and perform data staging are known as \emph{dataflow}, and they directly impact the performance and energy efficiency of DNN accelerator designs. Co-optimizing the accelerator microarchitecture and its internal dataflow is crucial for accelerator designers, but there is a severe lack of tools and methodologies to help them explore the co-optimization design space. In this work, we first introduce a set of data-centric directives to concisely specify DNN dataflows in a compiler-friendly form. Next, we present an analytical model, MAESTRO, that estimates various cost-benefit tradeoffs of a dataflow including execution time and energy efficiency for a DNN model and hardware configuration. Finally, we demonstrate the use of MAESTRO to drive a hardware design space exploration (DSE) engine. The DSE engine searched 480M designs and identified 2.5M valid designs at an average rate of 0.17M designs per second, and also identified throughput- and energy-optimized designs among this set.


title: All-digital PLL and transmitter for mobile phones

abstract: We present the first all-digital PLL and polar transmitter for mobile phones. They are part of a single-chip GSM/EDGE transceiver SoC fabricated in a 90 nm digital CMOS process. The circuits are architectured from the ground up to be compatible with digital deep-submicron CMOS processes and be readily integrateable with a digital baseband and application processor. To achieve this, we exploit the new paradigm of a deep-submicron CMOS process environment by leveraging on the fast switching times of MOS transistors, the fine lithography and the precise device matching, while avoiding problems related to the limited voltage headroom. The transmitter architecture is fully digital and utilizes the wideband direct frequency modulation capability of the all-digital PLL. The amplitude modulation is realized digitally by regulating the number of active NMOS transistor switches in accordance with the instantaneous amplitude. The conventional RF frequency synthesizer architecture, based on a voltage-controlled oscillator and phase/frequency detector and charge-pump combination, has been replaced with a digitally controlled oscillator and a time-to-digital converter. The transmitter performs GMSK modulation with less than 0.5/spl deg/ rms phase error, -165 dBc/Hz phase noise at 20 MHz offset, and 10 /spl mu/s settling time. The 8-PSK EDGE spectral mask is met with 1.2% EVM. The transmitter occupies 1.5 mm/sup 2/ and consumes 42 mA at 1.2 V supply while producing 6 dBm RF output power.


title: Fog Computing: Helping the Internet of Things Realize Its Potential

abstract: The Internet of Things (IoT) could enable innovations that enhance the quality of life, but it generates unprecedented amounts of data that are difficult for traditional systems, the cloud, and even edge computing to handle. Fog computing is designed to overcome these limitations.


title: Matrix Factorization Techniques for Recommender Systems

abstract: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.


title: RESEARCH REPORT Spaced Retrieval: Absolute Spacing Enhances Learning Regardless of Relative Spacing

abstract: Repeated retrieval enhances long-term retention, and spaced repetition also enhances retention. A question with practical and theoretical significance is whether there are particular schedules of spaced retrieval (e.g., gradually expanding the interval between tests) that produce the best learning. In the present experiment, subjects studied and were tested on items until they could recall each one. They then practiced recalling the items on 3 repeated tests that were distributed according to one of several spacing schedules. Increasing the absolute (total) spacing of repeated tests produced large effects on long-term retention: Repeated retrieval with long intervals between each test produced a 200% improvement in long-term retention relative to repeated retrieval with no spacing between tests. However, there was no evidence that a particular relative spacing schedule (expanding, equal, or contracting) was inherently superior to another. Although expanding schedules afforded a pattern of increasing retrieval difficulty across repeated tests, this did not translate into gains in long-term retention. Repeated spaced retrieval had powerful effects on retention, but the relative schedule of repeated tests had no discernible impact.


title: Cloning, sequencing and production of the lantibiotic mersacidin.

abstract: Mersacidin is a lanthionine-containing peptide antibiotic that shows a good in vivo efficiency against methicillin-resistant Staphylococcus aureus. It is excreted during early stationary phase and could be purified from culture supernatant in a one-step procedure by reversed phase HPLC. Its structural gene was cloned from chromosomal DNA of the producer strain Bacillus subtilis HIL Y-85,54728. Sequencing revealed that pre-mersacidin consists of an unusually long 48 amino acid leader sequence and a 20 amino acid propeptide part which is modified during biosynthesis to the mature lantibiotic. The comparison of the mersacidin prepeptide with those of hitherto known lantibiotics demonstrates that mersacidin is more closely related to type B lantibiotic cinnamycin than to type A lantibiotics.


title: Image-based Calorie Content Estimation for Dietary Assessment

abstract: In this paper, we present an image-analysis based approach to calorie content estimation for dietary assessment. We make use of daily food images captured and stored by multiple users in a public Web service called Food Log. The images are taken without any control or markers. We build a dictionary dataset of 6512 images contained in Food Log the calorie content of which have been estimated by experts in nutrition. An image is compared to the ground truth data from the point of views of multiple image features such as color histograms, color correlograms and SURF fetures, and the ground truth images are ranked by similarities. Finally, calorie content of the input food image is computed by linear estimation using the top n ranked calories in multiple features. The distribution of the estimation shows that 79% of the estimations are correct within ±40% error and 35% correct within ±20% error.


title: A framework for next generation user authentication

abstract: The remarkable growth in digital data is changing what and how the defense against the unknown will take place. Big data is a technical term used today to represent this massive growth of digital data that's being created from many sources. Organizations have turned their attentions to the deployment of Big Data analytics to gain valuable insights that benefit their businesses within protected and secure environments. Hence, network security protocols, especially authentication protocols, are being re-designed to protect and to deliver the real benefits of this data growth. Contrary to the traditional perspective, in which researchers are focusing on identifying users' identity to protect Big Data-based environments, we have an opposite perspective that the Big Data itself would be the fuel for the next generation authentication. In other word, the main goal of this work is to propose a new framework for user authentication that leverages Big Data analytics. The core idea of this framework is to find out unique patterns of the users' dynamic behaviors. The proposed framework comprised of three main components. Data Security-based Analytics (DSA); describing the best utilization of the high velocity data streams, which is capable for distinguishing data that has security/identification potentials. Human dynamics measure engine; that develops an ambitious transformation from the Big Data characteristics into the relevant human dynamics measures. Big data-driven authentication service; describes the required engines to design software as a service-based authentication model. Our investigation shows that this new approach will help create a highly distributed authentication model, minimizing the storage of secrets, and lesser secret management overhead.


title: A compact printed UWB Pacman-shaped MIMO antenna with two frequency rejection bands

abstract: In this paper, the design and analysis of microstrip-fed low-profile, compact ultra-wideband (UWB) monopole antenna with its dual diversity multiple-input multiple-output (MIMO) configuration and two band-notches characteristics are carried out. The antenna is mounted on a low cost FR-4 substrate with dimensions of 38×60×1.6 mm3, and relative permittivity of 4.4. The original shape of the single antenna element is circular with radius of 11.5 mm, and then a sector is removed from the patch (making it a Pacman-shaped antenna) to improve the impedance bandwidth. The proposed antenna provides an impedance bandwidth between 2.9–15 GHz with better than 10 dB return loss and isolation less than −17 dB. Additionally, the antenna can reject the interferences from WiMAX (3.5 GHz center frequency) and WLAN (5.5 GHz center frequency).


title: Photographic Text-to-Image Synthesis with a Hierarchically-Nested Adversarial Network

abstract: This paper presents a novel method to deal with the challenging task of generating photographic images conditioned on semantic image descriptions. Our method introduces accompanying hierarchical-nested adversarial objectives inside the network hierarchies, which regularize mid-level representations and assist generator training to capture the complex image statistics. We present an extensile single-stream generator architecture to better adapt the jointed discriminators and push generated images up to high resolutions. We adopt a multi-purpose adversarial loss to encourage more effective image and text information usage in order to improve the semantic consistency and image fidelity simultaneously. Furthermore, we introduce a new visual-semantic similarity measure to evaluate the semantic consistency of generated images. With extensive experimental validation on three public datasets, our method significantly improves previous state of the arts on all datasets over different evaluation metrics.


title: A Blockchain Based Liability Attribution Framework for Autonomous Vehicles

abstract: The advent of autonomous vehicles is envisaged to disrupt the auto insurance liability model.Compared to the the current model where liability is largely attributed to the driver,autonomous vehicles necessitate the consideration of other entities in the automotive ecosystem including the auto manufacturer,software provider,service technician and the vehicle owner.The proliferation of sensors and connecting technologies in autonomous vehicles enables an autonomous vehicle to gather sufficient data for liability attribution,yet increased connectivity exposes the vehicle to attacks from interacting entities.These possibilities motivate potential liable entities to repudiate their involvement in a collision event to evade liability. While the data collected from vehicular sensors and vehicular communications is an integral part of the evidence for arbitrating liability in the event of an accident,there is also a need to record all interactions between the aforementioned entities to identify potential instances of negligence that may have played a role in the accident.In this paper,we propose a BlockChain(BC) based framework that integrates the concerned entities in the liability model and provides untampered evidence for liability attribution and adjudication.We first describe the liability attribution model, identify key requirements and describe the adversarial capabilities of entities. Also,we present a detailed description of data contributing to evidence.Our framework uses permissioned BC and partitions the BC to tailor data access to relevant BC participants.Finally,we conduct a security analysis to verify that the identified requirements are met and resilience of our proposed framework to identified attacks.


title: Context Aware Recommender System for Large Scaled Flash Sale Sites

abstract: Flash Sale Sites popularize because they save great money for users. Good recommender systems can further save users’ time to improve their online shopping experiences. Although there exsit a lot of studies on recommender system, very few focus on flash sale sites. Big Data, Context Sensitivity, and Feature Engineering are three key challenges for one to build a good recommender system. This paper proposes two deep learning oriented models: Tensor-AutoRec and Hybrid-AutoRec to cope with the problems within an industrial context. First, these two models can handle storage and speed problem caused by big data. Second, both models incorporate context information, so they can generate more relevant recommendations by adapting to specific contextual situations. Third, our deep learning-based models can be trained end-to-end without tedious feature engineerings. Extensive experiments with a half year real transcation data demonstrate that our models can outperform classifcal ones in terms of different evaluation metrices. Finally, online A/B testing results showed that our model can improve our old recommendation system over various online performance indicators.


title: Applied attention-based LSTM neural networks in stock prediction

abstract: Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. This paper proposes an attention-based long short-term memory model to predict stock price movement and make trading strategies


title: High Efficiency Isolated DC/DC Converter Using Series Voltage Compensation

abstract: A new circuit topology is proposed for a high efficiency isolated buck-boost DC/DC converter. The proposed converter consists of a high efficiency resonance half-bridge converter and a series converter. The proposed circuit regulates the voltage output by the series converter, which provides only the differential voltage between the input voltage and output voltage. Therefore, the circuit achieves high efficiency when the input voltage is close to the output voltage, because only the resonance converter operates. The validity of the proposed circuit was confirmed by experiment and loss analysis, with a maximum efficiency of 95.8%.


title: Real-Time Posture Analysis in a Crowd using Thermal Imaging

abstract: This article describes a video-surveillance system developed within the ISCAPS project. Thermal imaging provides a robust solution to visibility change (illumination, smoke) and is a relevant technology for discriminating humans in complex scenes. In this article, we demonstrate its efficiency for posture analysis in dense groups of people. The objective is to automatically detect several persons lying down in a very crowded area. The presented method is based on the detection and segmentation of individuals within groups of people using a combination of several weak classifiers. The classification of extracted silhouettes enables to detect abnormal situations. This approach was successfully applied to the detection of terrorist gas attacks on railway platform and experimentally validated in the project. Some of the results are presented here.


title: A tale of two directories: implementing distributed shared objects in Java

abstract: We consider two alternative directory protocols. A directory service keep tracks of the location and status of mobile objects in a distributed system. This paper describes our experience implementing two distributed directory protocols as part of the Aleph tool& a distributed shared object system implement.ed in Java. One protocol is a conventional home-based protocol, in which a fixed node keeps track of the object’s location and status. The ot.her is a novel orrout protocol, based on a simple path-reversal algorithm. We were surprised to discover that the arrow protocol outperformed the home protocol, sometimes substantially, across a range of system sizes. This paper describes a series of experiments testing whether the discrepancy is due to an artifact of the Java run-time system (such as differences in thread management or object serialization costs), or whether it is something inherent in the protocols themselves. In the end, we use insights gained from these experimental results to design a new directory protocol that usually outperforms both.


Prosody-based automatic detection of annoyance and frustration in human-computer dialog


MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews


title: Determinants of Privatization Prices

abstract: Generating government revenue is a common objective in privatization. This paper asks: what determines privatization prices? Pursuing this query helps resolve the current controversies about the bearing of speed and the role for government actions prior to privatization. The data, gathered from primary sources, encompass 361 privatized Mexican companies in 49 four-digit industry codes. The determinants of auction privatization prices are divided into three groups: (1) company performance and industry parameters; (2) the auction process and its requirements; and (3) the prior restructuring actions taken by the government. Controlling for company and industry effects reveals the significant impact of the costs and characteristics of the labor force. Minority control packages carry large discounts. Auction requirements that allow foreign investors result in higher sale premia, while restrictions constraining participation or payment forms reduce net prices. The speed of privatization substantially influences net prices: the longer it takes to put the company on the block, the more severe the deterioration in performance, and the lower the premium obtained. Pre-sale reductions in labor force, and particularly the firing of CEOs, lead to significantly higher premiums. Debt absorption, investment, and performance improvement programs do not increase the net price, while de-investment measures prove more beneficial. Overall, the results show increased premia for government actions that stimulate bidder participation and expedite the privatization process.


title: Towards a new interpretation of separable convolutions

abstract: In recent times, the use of separable convolutions in deep convolutional neural network architectures has been explored. Several researchers, most notably (Chollet, 2016) and (Ghosh, 2017) have used separable convolutions in their deep architectures and have demonstrated state of the art or close to state of the art performance. However, the underlying mechanism of action of separable convolutions are still not fully understood. Although their mathematical definition is well understood as a depthwise convolution followed by a pointwise convolution, deeper interpretations such as the extreme Inception hypothesis (Chollet, 2016) have failed to provide a thorough explanation of their efficacy. In this paper, we propose a hybrid interpretation that we believe is a better model for explaining the efficacy of separable convolutions.


title: Duplex Generative Adversarial Network for Unsupervised Domain Adaptation

abstract: Domain adaptation attempts to transfer the knowledge obtained from the source domain to the target domain, i.e., the domain where the testing data are. The main challenge lies in the distribution discrepancy between source and target domain. Most existing works endeavor to learn domain invariant representation usually by minimizing a distribution distance, e.g., MMD and the discriminator in the recently proposed generative adversarial network (GAN). Following the similar idea of GAN, this work proposes a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation. Specifically, our proposed network consists of three parts, an encoder, a generator and two discriminators. The encoder embeds samples from both domains into the latent representation, and the generator decodes the latent representation to both source and target domains respectively conditioned on a domain code, i.e., achieves domain transformation. The generator is pitted against duplex discriminators, one for source domain and the other for target, to ensure the reality of domain transformation, the latent representation domain invariant and the category information of it preserved as well. Our proposed work achieves the state-of-the-art performance on unsupervised domain adaptation of digit classification and object recognition.


title: Database and Distributed Computing Fundamentals for Scalable, Fault-tolerant, and Consistent Maintenance of Blockchains

abstract: Bitcoin is a successful and interesting example of a global scale peer-to-peer cryptocurrency that integrates many techniques and protocols from cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the cryptocurrency system. In this tutorial, we discuss the basic protocols used in blockchain, and elaborate on its main advantages and limitations. To overcome these limitations, we provide the necessary distributed systems background in managing large scale fully replicated ledgers, using Byzantine Agreement protocols to solve the consensus problem. Finally, we expound on some of the most recent proposals to design scalable and efficient blockchains. The focus of the tutorial is on the distributed systems and database technical aspects of the recent innovations in blockchains.


title: Incentive Mechanism Design for Heterogeneous Peer-to-Peer Networks: A Stackelberg Game Approach

abstract: With high scalability, high video streaming quality, and low bandwidth requirement, peer-to-peer (P2P) systems have become a popular way to exchange files and deliver multimedia content over the internet. However, current P2P systems are suffering from “free-riding” due to the peers’ selfish nature. In this paper, we propose a credit-based incentive mechanism to encourage peers to cooperate with each other in a heterogeneous network consisting of wired and wireless peers. The proposed mechanism can provide differentiated service to peers with different credits through biased resource allocation. A Stackelberg game is formulated to obtain the optimal pricing and purchasing strategies, which can jointly maximize the revenue of the uploader and the utilities of the downloaders. In particular, peers’ heterogeneity and selfish nature are taken into consideration when designing the utility functions for theStackelberg game. It is shown that the proposed resource allocation scheme is effective in providing service differentiation for peers and stimulating them to make contribution to the P2P streaming system.


title: Automated Static Code Analysis for Classifying Android Applications Using Machine Learning

abstract: In this paper we apply Machine Learning (ML) techniques on static features that are extracted from Android's application files for the classification of the files. Features are extracted from Android’s Java byte-code (i.e.,.dex files) and other file types such as XML-files. Our evaluation focused on classifying two types of Android applications: tools and games. Successful differentiation between games and tools is expected to provide positive indication about the ability of such methods to learn and model Android benign applications and potentially detect malware files. The results of an evaluation, performed using a test collection comprising 2,285 Android. apk files, indicate that features, extracted statically from. apk files, coupled with ML classification algorithms can provide good indication about the nature of an Android application without running the application, and may assist in detecting malicious applications. This method can be used for rapid examination of Android. apks and informing of suspicious applications.


title: An 86% Efficiency 12 µW Self-Sustaining PV Energy Harvesting System With Hysteresis Regulation and Time-Domain MPPT for IOT Smart Nodes

abstract: This paper presents a fully-integrated µW-level photovoltaic (PV) self-sustaining energy harvesting system proposed for smart nodes of Internet of Things (IOT) networks. A hysteresis regulation is designed to provide a constant 3.3 V output voltage for a host of applications, including powering sensors, signal processors, and wireless transmitters. Due to the stringent power budget in IOT scenarios, the power consumption of the harvesting system is optimized by multiple system and circuit level techniques. Firstly, the hill-climbing MPPT mechanism reuses and processes the information of the hysteresis controller in the time-domain and is free of power hungry analog circuits. Secondly, the typical power-performance tradeoff of the hysteresis controller is solved by a self-triggered one-shot mechanism. Thus, the output regulation achieves high-performance and yet low-power operations. Thirdly, to execute the impedance tuning of MPPT, the capacitor value modulation (CVM) scheme is proposed instead of the conventional frequency modulation scheme, avoiding quiescent power consumption. Utilizing a commercial PV cell of 2.5 cm 2 , the proposed system provides 0–21 µW output power to the IOT smart nodes. Measured results showed that the PV harvesting system achieved both ultra-low power operation capability at 12 µW and a peak self-sustaining efficiency of 86%.


MULTI-VIEW CANONICAL CORRELATION ANALYSIS


title: Hybrid elevation maps: 3D surface models for segmentation

abstract: This paper presents an algorithm for segmenting 3D point clouds. It extends terrain elevation models by incorporating two types of representations: (1) ground representations based on averaging the height in the point cloud, (2) object models based on a voxelisation of the point cloud. The approach is deployed on Riegl data (dense 3D laser data) acquired in a campus type of environment and compared against six other terrain models. Amongst elevation models, it is shown to provide the best fit to the data as well as being unique in the sense that it jointly performs ground extraction, overhang representation and 3D segmentation. We experimentally demonstrate that the resulting model is also applicable to path planning.


title: Preventing Page Faults from Telling Your Secrets

abstract: New hardware primitives such as Intel SGX secure a user-level process in presence of an untrusted or compromised OS. Such "enclaved execution" systems are vulnerable to several side-channels, one of which is the page fault channel. In this paper, we show that the page fault side-channel has sufficient channel capacity to extract bits of encryption keys from commodity implementations of cryptographic routines in OpenSSL and Libgcrypt -- leaking 27% on average and up to 100% of the secret bits in many case-studies. To mitigate this, we propose a software-only defense that masks page fault patterns by determinising the program's memory access behavior. We show that such a technique can be built into a compiler, and implement it for a subset of C which is sufficient to handle the cryptographic routines we study. This defense when implemented generically can have significant overhead of up to 4000X, but with help of developer-assisted compiler optimizations, the overhead reduces to at most 29.22% in our case studies. Finally, we discuss scope for hardware-assisted defenses, and show one solution that can reduce overheads to 6.77% with support from hardware changes.


title: An automatic keyphrase extraction system for scientific documents

abstract: Automatic keyphrase extraction techniques play an important role for many tasks including indexing, categorizing, summarizing, and searching. In this paper, we develop and evaluate an automatic keyphrase extraction system for scientific documents. Compared with previous work, our system concentrates on two important issues: (1) more precise location for potential keyphrases: a new candidate phrase generation method is proposed based on the core word expansion algorithm, which can reduce the size of the candidate set by about 75% without increasing the computational complexity; (2) overlap elimination for the output list: when a phrase and its sub-phrases coexist as candidates, an inverse document frequency feature is introduced for selecting the proper granularity. Additional new features are added for phrase weighting. Experiments based on real-world datasets were carried out to evaluate the proposed system. The results show the efficiency and effectiveness of the refined candidate set and demonstrate that the new features improve the accuracy of the system. The overall performance of our system compares favorably with other state-of-the-art keyphrase extraction systems.


title: Personality measurement, faking, and employment selection.

abstract: Real job applicants completed a 5-factor model personality measure as part of the job application process. They were rejected; 6 months later they (n 5,266) reapplied for the same job and completed the same personality measure. Results indicated that 5.2% or fewer improved their scores on any scale on the 2nd occasion; moreover, scale scores were as likely to change in the negative direction as the positive. Only 3 applicants changed scores on all 5 scales beyond a 95% confidence threshold. Construct validity of the personality scales remained intact across the 2 administrations, and the same structural model provided an acceptable fit to the scale score matrix on both occasions. For the small number of applicants whose scores changed beyond the standard error of measurement, the authors found the changes were systematic and predictable using measures of social skill, social desirability, and integrity. Results suggest that faking on personality measures is not a significant problem in real-world selection settings.


title: A Linux-based firewall for the DNP3 protocol

abstract: Firewall solutions, specifically designed for smart power grids and other industrial control systems, are quite limited, with only a few commercial offerings. This paper presents a novel methodology that extends existing Linux-based firewalls for use in systems that use DNP3 protocol for industrial control. The proposed solution uses the u32 byte-matching feature of the iptables firewall, a firewall solution available in most Linux distributions. To demonstrate the approach, filtering rules for common attacks on the DNP3 protocol were developed. DNP3 is an industrial control protocol typically used in the electric power sector. The main goal of our work is to leverage an openly available and robust firewall solution for use in protecting the U.S. smart grid. The prototype was tested on a scaled-down electric power substation which runs the DNP3 protocol for communication between the field devices and the SCADA master.


title: Challenges for Robust Trust and Reputation Systems

abstract: The purpose of trust and reputation systems is to strengthen the quality of markets and communities by providing an incentive for good behaviour and quality services, and by sanctioning bad behaviour and low quality services. However, trust and reputation systems will only be able to produce this effect when they are sufficiently robust against strategic manipulation or direct attacks. Currently, robustness analysis of TRSs is mostly done through simple simulated scenarios implemented by the TRS designers themselves, and this can not be considered as reliable evidence for how these systems would perform in a realistic environment. In order to set robustness requirements it is important to know how important robustness really is in a particular community or market. This paper discusses research challenges for trust and reputation systems, and proposes a research agenda for developing sound and reliable robustness principles and mechanisms for trust and reputation systems.


title: A new infrared image enhancement algorithm

abstract: In order to solve the problem of fuzzy details, low contrast and poor visual effect of infrared image. A novel scheme for infrared image enhancement is proposed in this paper, which solved drawbacks of the histogram equalization algorithm that loss detail information of image and some areas are bleached, greatly improved the visual effect of the infrared image. Firstly, bilateral filter is performed on the original infrared image, we can obtain a base layer and a detail layer. At base layer, most value normalizing and gray linear mapping is applied to enhance the global contrast. At detail layer, S-curve algorithm is applied to enhance detail information. Finally linear weighted synthesis is used to achieve the integration of base layer and detail layer, to improve the visual effect of the infrared image. This algorithm could also be applied to 14 bit infrared image, compressing 14bit image to 8 bit image and achieving good detail enhancement. Through simulation on MATLAB, the results show that the proposed scheme could noticeably enhance image contrast, and obtain a good enhanced infrared image, at end.


title: Organizational ambidexterity: Balancing exploitation and exploration for sustained performance

abstract: Organizational ambidexterity has emerged as a new research paradigm in organization theory, yet several issues fundamental to this debate remain controversial. We explore four central tensions here: Should organizations achieve ambidexterity through differentiation or through integration? Does ambidexterity occur at the individual or organizational level? Must organizations take a static or dynamic perspective on ambidexterity? Finally, can ambidexterity arise internally, or do firms have to externalize some processes? We provide an overview of the seven articles included in this special issue and suggest several avenues for future research.


title: Generalized Zero-Shot Recognition Based on Visually Semantic Embedding

abstract: We propose a novel Generalized Zero-Shot learning (GZSL) method that is agnostic to both unseen images and unseen semantic vectors during training. Prior works in this context propose to map high-dimensional visual features to the semantic domain, which we believe contributes to the semantic gap. To bridge the gap, we propose a novel low-dimensional embedding of visual instances that is “visually semantic.” Analogous to semantic data that quantifies the existence of an attribute in the presented instance, components of our visual embedding quantifies existence of a prototypical part-type in the presented instance. In parallel, as a thought experiment, we quantify the impact of noisy semantic data by utilizing a novel visual oracle to visually supervise a learner. These factors, namely semantic noise, visual-semantic gap and label noise lead us to propose a new graphical model for inference with pairwise interactions between label, semantic data, and inputs. We tabulate results on a number of benchmark datasets demonstrating significant improvement in accuracy over state-of-art under both semantic and visual supervision.


title: Energy Efficiency in Machine Learning : A position paper

abstract: Machine learning algorithms are usually evaluated and developed in terms of predictive performance. Since these types of algorithms often run on large-scale data centers, they account for a signifi ...


title: ROPNN: Detection of ROP Payloads Using Deep Neural Networks

abstract: Return-oriented programming (ROP) is a code reuse attack that chains short snippets of existing code (known as gadgets) to perform arbitrary operations on target machines. Existing detection mechanisms against ROP often rely on certain heuristic rules and/or require instrumentations to the program or the compiler. As a result, they exhibit low detection efficiency and/or have high runtime overhead. ::: In this paper, we present ROPNN, which innovatively combines address space layout guided disassembly and deep neural networks, to detect ROP payloads in HTTP requests, PDF files, and images, etc. The disassembler treats application input data as code pointers to potential gadgets and aims to find any potential gadget chains. The identified potential gadget chains are then classified by the deep neural network as benign or malicious. We propose novel methods to generate the two training datasets, respectively, and process huge amount (TB-level) of raw input data to obtain sufficient training data. Our experiments show that ROPNN has high detection rate (98.3%) while maintaining very low false positive rate (0.01%). To show ROPNN is usable in practical scenario, we also test it against ROP exploits that are collected in-the-wild, created manually or created by ROP exploit generation tools Ropper and ROPC. ROPNN successfully detects all of the 80 exploits. Meanwhile, ROPNN is completely non-intrusive and does not incur any runtime overhead to the protected program.


title: Linking Transformational Leadership and Knowledge Sharing: The Mediating Roles of Perceived Team Goal Commitment and Perceived Team Identification

abstract: It is widely assumed that transformational leadership can effectively facilitate the sharing of knowledge among followers, but most investigations of the underlying mechanisms were based on the social exchange perspective. Based on a sensegiving theory perspective, this article attempts to uncover the mechanisms by which transformational leadership have its impact on employee knowledge sharing behavior by proposing two team-directed mediating mechanisms: perceived team goal commitment and perceived team identification. Results of multi-source and time-lagged data from 186 leader-follower pairs supported the proposed mediating effects. Implications and limitations are discussed.


title: Analysis of Vector Space Model in Information Retrieval

abstract: Information retrieval is great technology behind web search services. In information retrieval, it is common to model index terms and documents as vectors in a suitably defined vector space. The vector space model is one of the classical and widely applied retrieval models to evaluate relevance of web page. The retrieval operation consists of computing the cosine similarity function between a given query vector and the set of documents vector and then ranking documents accordingly. In this paper, we present different approaches of vector space model to compute similarity score of hits from search engine and more importantly, it is felt that this investigation will lead to a clearer understanding of the issues and problems in using the vector space model in information retrieval and our work intends to discuss the main aspects of Vector space models and provide a comprehensive comparison for TermCount model, Tf-Idf model and Vector space model based on normalization.


title: Self processes in interdependent relationships: Partner affirmation and the Michelangelo phenomenon

abstract: This essay reviews theory and research regarding the “Michelangelo phenomenon,” which describes the manner in which close partners shape one another’s dispositions, values, and behavioral tendencies. Individuals are more likely to exhibit movement toward their ideal selves to the degree that their partners exhibit affirming perception and behavior; exhibiting confidence in the self’s capacity and enacting behaviors that elicit key features of the self’s ideal. In turn, movement towards the ideal self yields enhanced personal well-being and couple well-being. We review empirical evidence regarding this phenomenon and discuss self and partner variables that contribute to the process.


title: The Abandoned Side of the Internet: Hijacking Internet Resources When Domain Names Expire

abstract: The vulnerability of the Internet has been demonstrated by prominent IP prefix hijacking events. Major outages such as the China Telecom incident in 2010 stimulate speculations about malicious intentions behind such anomalies. Surprisingly, almost all discussions in the current literature assume that hijacking incidents are enabled by the lack of security mechanisms in the inter-domain routing protocol BGP. In this paper, we discuss an attacker model that accounts for the hijacking of network ownership information stored in Regional Internet Registry (RIR) databases. We show that such threats emerge from abandoned Internet resources (e.g., IP address blocks, AS numbers). When DNS names expire, attackers gain the opportunity to take resource ownership by re-registering domain names that are referenced by corresponding RIR database objects. We argue that this kind of attack is more attractive than conventional hijacking, since the attacker can act in full anonymity on behalf of a victim. Despite corresponding incidents have been observed in the past, current detection techniques are not qualified to deal with these attacks. We show that they are feasible with very little effort, and analyze the risk potential of abandoned Internet resources for the European service region: our findings reveal that currently 73 /24 IP prefixes and 7 ASes are vulnerable to be stealthily abused. We discuss countermeasures and outline research directions towards preventive solutions.


title: Echoes from the past: how technology mediated reflection improves well-being

abstract: As people document more of their lives online, some recent systems are encouraging people to later revisit those recordings, a practice we're calling technology-mediated reflection (TMR). Since we know that unmediated reflection benefits psychological well-being, we explored whether and how TMR affects well-being. We built Echo, a smartphone application for recording everyday experiences and reflecting on them later. We conducted three system deployments with 44 users who generated over 12,000 recordings and reflections. We found that TMR improves well-being as assessed by four psychological metrics. By analyzing the content of these entries we discovered two mechanisms that explain this improvement. We also report benefits of very long-term TMR.


title: Learning to learn with the informative vector machine

abstract: This paper describes an efficient method for learning the parameters of a Gaussian process (GP). The parameters are learned from multiple tasks which are assumed to have been drawn independently from the same GP prior. An efficient algorithm is obtained by extending the informative vector machine (IVM) algorithm to handle the multi-task learning case. The multi-task IVM (MTIVM) saves computation by greedily selecting the most informative examples from the separate tasks. The MT-IVM is also shown to be more efficient than random sub-sampling on an artificial data-set and more effective than the traditional IVM in a speaker dependent phoneme recognition task.


title: ERIT - A Collection of Efficient and Reliable Intersection Tests

abstract: We describe ERIT, a collection of C routines for efficiently and reliably handling intersection queries between pairs of primitive objects in three dimensions. ERIT supports intersection queries between the following pairs of primitives: triangle/line-segment, triangle/triangle, sphere/line-segment, sphere/triangle, cylinder/line-segment, cylinder/triangle, cylinder/sphere, cone/line-segment, cone/ triangle, toroid/line-segment, toroid/triangle, and sphere/sphere. All intersection routines are based on standard "epsilon-based" floating-point arithmetic. Practical tests have proved that ERIT's routines are efficient and reliable, and we provide performance statistics for three widely-used hardware platforms.


title: A Planar Magic-T Using Substrate Integrated Circuits Concept

abstract: In this letter, a slotline to substrate integrated waveguide transition is proposed for the development of substrate integrated circuits. The insertion loss of the back-to-back transition is less than 1 dB from 8.7 to 9.0 GHz. With this transition, a planar magic-T is studied and designed. Measured results indicate a very good performance of the fabricated magic-T is observed within the experimental frequency range of 8.4-9.4 GHz. The amplitude and phase imbalances are less than 0.2 dB and 1.5deg, respectively.


title: Intelligent RAM (IRAM): the industrial setting, applications, and architectures

abstract: The goal of intelligent RAM (IRAM) is to design a cost-effective computer by designing a processor in a memory fabrication process, instead of in a conventional logic fabrication process, and include memory on-chip. To design a processor in a DRAM process one must learn about the business and culture of the DRAMs, which is quite different from microprocessors. The authors describe some of those differences and their current vision of IRAM applications, architectures, and implementations.


title: Medical students’ attitudes to mental illnesses and to psychiatry before and after the psychiatric clerkship: Training in a specialty and a general hospital

abstract: Abstract Medical students’ attitudes to mental illnesses and psychiatry may be reshaped during the psychiatric training, with important implications in their future practice of the profession. Therefore, the present study set out to explore the impact of the psychiatric clerkship in students’ attitudes, while taking into consideration the site of their practical training. To this end, a total of 678 final-year medical students were recruited. Students completed a self-reported questionnaire entailing the Attitudes to Psychiatry scale, the Attitudes to Mental Illness scale and the Greek Social Distance scale before and after their placement. Findings indicate that the psychiatric clerkship had a positive effect in reducing stigma towards both psychiatry and mental illnesses, with the effect being more pronounced in the general hospital with respect to the former, while in the specialty hospital was more marked regarding the latter. A further exploration of the determinants of change revealed that the improvement discerned in the general hospital was only among those without professional experience of mental illnesses. Therefore, the psychiatric clerkship may exert a substantial influence on shaping favourable attitudes towards mental illnesses and psychiatry; however, other elements should also be taken into consideration, if the clerkship is to tackle stigma in healthcare.


title: Modeling and Reasoning with Bayesian Networks

abstract: This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.


title: Cell Detection with Deep Convolutional Neural Network and Compressed Sensing

abstract: The ability to automatically detect certain types of cells or cellular subunits in microscopy images is of significant interest to a wide range of biomedical research and clinical practices. Cell detection methods have evolved from employing hand-crafted features to deep learning-based techniques to locate target cells. The essential idea of these methods is that their cell classifiers or detectors are trained in the pixel space, where the locations of target cells are labeled. In this paper, we seek a different route and propose a convolutional neural network (CNN)-based cell detection method that uses encoding of the output pixel space. For the cell detection problem, the output space is the sparsely labeled pixel locations indicating cell centers. Consequently, we employ random projections to encode the output space to a compressed vector of fixed dimension. Then, CNN regresses this compressed vector from the input pixels. Using $L_1$-norm optimization, we recover sparse cell locations on the output pixel space from the predicted compressed vector. In the past, output space encoding using compressed sensing (CS) has been used in conjunction with linear and non-linear predictors. To the best of our knowledge, this is the first successful use of CNN with CS-based output space encoding. We experimentally demonstrate that proposed CNN + CS framework (referred to as CNNCS) exceeds the accuracy of the state-of-the-art methods on many benchmark datasets for microscopy cell detection. Additionally, we show that CNNCS can exploit ensemble average by using more than one random encodings of the output space. In the AMIDA13 MICCAI grand competition, we achieve the 3rd highest F1-score in all the 17 participated teams. More ranking details are available at this http URL Implementation of CNNCS is available at this https URL


title: A Study on the Retweeting Behaviour of Marketing Microblogs with High Retweets in Sina Weibo

abstract: This research studies marketing microblogs with high retweets in Sina Weibo. The content features of marketing microblogs, the structural and temporal characteristics of retweet networks, the major features of retweeters, the incentive mechanisms for retweeting, and the influential retweeters are investigated. A microblog dataset including 19,889 microblogs from 20 enterprise accounts and a retweet network dataset including 207,649 retweets and 113,755 retweeters are analysed. This analysis shows that the marketing microblogs with high retweets are normally the results of some incentive mechanisms, high percentage of their retweets are retweeted from the original microblog directly, the frequencies of retweets vary over the activity periods, incentive mechanisms can significantly affect the behaviour of retweeters, and influential retweeters have significant impact on arousing subsequent retweets. These results could be utilized by enterprises for improving their marketing activities.


SELECTION OF CANDIDATES IN THE PROCESS OF RECRUITMENT AND SELECTION OF PERSONNEL BASED ON THE SWARA AND ARAS METHODS


title: VisTrails: visualization meets data management

abstract: Scientists are now faced with an incredible volume of data to analyze. To successfully analyze and validate various hypothesis, it is necessary to pose several queries, correlate disparate data, and create insightful visualizations of both the simulated processes and observed phenomena. Often, insight comes from comparing the results of multiple visualizations. Unfortunately, today this process is far from interactive and contains many error-prone and time-consuming tasks. As a result, the generation and maintenance of visualizations is a major bottleneck in the scientific process, hindering both the ability to mine scientific data and the actual use of the data. The VisTrails system represents our initial attempt to improve the scientific discovery process and reduce the time to insight. In VisTrails, we address the problem of visualization from a data management perspective: VisTrails manages the data and metadata of a visualization product. In this demonstration, we show the power and flexibility of our system by presenting actual scenarios in which scientific visualization is used and showing how our system improves usability, enables reproducibility, and greatly reduces the time required to create scientific visualizations.


title: Spatial panorama of malaria prevalence in Africa under climate change and interventions scenarios

abstract: Malaria is highly sensitive to climatic variables and is strongly influenced by the presence of vectors in a region that further contribute to parasite development and sustained disease transmission. Mathematical analysis of malaria transmission through the use and application of the value of the basic reproduction number (R0) threshold is an important and useful tool for the understanding of disease patterns. Temperature dependence aspect of R0 obtained from dynamical mathematical network model was used to derive the spatial distribution maps for malaria transmission under different climatic and intervention scenarios. Model validation was conducted using MARA map and the Annual Plasmodium falciparum Entomological Inoculation Rates for Africa. The inclusion of the coupling between patches in dynamical model seems to have no effects on the estimate of the optimal temperature (about 25 °C) for malaria transmission. In patches environment, we were able to establish a threshold value (about α = 5) representing the ratio between the migration rates from one patch to another that has no effect on the magnitude of R0. Such findings allow us to limit the production of the spatial distribution map of R0 to a single patch model. Future projections using temperature changes indicated a shift in malaria transmission areas towards the southern and northern areas of Africa and the application of the interventions scenario yielded a considerable reduction in transmission within malaria endemic areas of the continent. The approach employed here is a sole study that defined the limits of contemporary malaria transmission, using R0 derived from a dynamical mathematical model. It has offered a unique prospect for measuring the impacts of interventions through simple manipulation of model parameters. Projections at scale provide options to visualize and query the results, when linked to the human population could potentially deliver adequate highlight on the number of individuals at risk of malaria infection across Africa. The findings provide a reasonable basis for understanding the fundamental effects of malaria control and could contribute towards disease elimination, which is considered as a challenge especially in the context of climate change.


title: Using spin images for efficient object recognition in cluttered 3D scenes

abstract: We present a 3D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin image representation. The spin image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes.


title: Shuffler: fast and deployable continuous code re-randomization

abstract: While code injection attacks have been virtually eliminated on modern systems, programs today remain vulnerable to code reuse attacks. Particularly pernicious are Just-In-Time ROP (JIT-ROP) techniques, where an attacker uses a memory disclosure vulnerability to discover code gadgets at runtime. We designed a code-reuse defense, called Shuffler, which continuously re-randomizes code locations on the order of milliseconds, introducing a real-time deadline on the attacker. This deadline makes it extremely difficult to form a complete exploit, particularly against server programs that often sit tens of milliseconds away from attacker machines. ::: ::: Shuffler focuses on being fast, self-hosting, and nonintrusive to the end user. Specifically, for speed, Shuffler randomizes code asynchronously in a separate thread and atomically switches from one code copy to the next. For security, Shuffler adopts an "egalitarian" principle and randomizes itself the same way it does the target. Lastly, to deploy Shuffler, no source, kernel, compiler, or hardware modifications are necessary. ::: ::: Evaluation shows that Shuffler defends against all known forms of code reuse, including ROP, direct JIT-ROP, indirect JIT-ROP, and Blind ROP. We observed 14.9% overhead on SPEC CPU when shuffling every 50 ms, and ran Shuffler on real-world applications such as Nginx. We showed that the shuffled Nginx scales up to 24 worker processes on 12 cores.


title: Predictive missile guidance for agile maneuvering targets with stochastic hybrid dynamics

abstract: In this work we consider the problem of designing predictive guidance and control laws for missiles targeting agile aircraft. The target and missile dynamics are modeled as stochastic hybrid systems, which enables expression of complex maneuvers as sequences of simpler maneuver modes. Target's missile evasion strategy is modeled based on domain expertise and the interception problem is formulated as a Partially Observable Markov Decision Process, which is approximately solved online. The results show that the developed guidance method is superior to methods that do not utilize prediction of target's maneuvers or ignores the uncertainty in target's evasion tactics.


title: Using EEG to Decode Subjective Levels of Emotional Arousal During an Immersive VR Roller Coaster Ride

abstract: Emotional arousal is a key component of a user's experience in immersive virtual reality (VR). Subjective and highly dynamic in nature, emotional arousal involves the whole body and particularly the brain. However, it has been difficult to relate subjective emotional arousal to an objective, neurophysiological marker-especially in naturalistic settings. We tested the association between continuously changing states of emotional arousal and oscillatory power in the brain during a VR roller coaster experience. We used novel spatial filtering approaches to predict self-reported emotional arousal from the electroencephalogram (EEG) signal of 38 participants. Periods of high vs. low emotional arousal could be classified with accuracies significantly above chance level. Our results are consistent with prior findings regarding emotional arousal in less naturalistic settings. We demonstrate a new approach to decode states of subjective emotional arousal from continuous EEG data in an immersive VR experience.


title: Learning Low Precision Deep Neural Networks through Regularization

abstract: We consider the quantization of deep neural networks (DNNs) to produce low-precision models for efficient inference of fixed-point operations. Compared to previous approaches to training quantized DNNs directly under the constraints of low-precision weights and activations, we learn the quantization of DNNs with minimal quantization loss through regularization. In particular, we introduce the learnable regularization coefficient to find accurate low-precision models efficiently in training. In our experiments, the proposed scheme yields the state-of-the-art low-precision models of AlexNet and ResNet-18, which have better accuracy than their previously available low-precision models. We also examine our quantization method to produce low-precision DNNs for image super resolution. We observe only $0.5$~dB peak signal-to-noise ratio (PSNR) loss when using binary weights and 8-bit activations. The proposed scheme can be used to train low-precision models from scratch or to fine-tune a well-trained high-precision model to converge to a low-precision model. Finally, we discuss how a similar regularization method can be adopted in DNN weight pruning and compression, and show that $401\times$ compression is achieved for LeNet-5.


title: Antibiotics Use and Misuse in Children: A Knowledge, Attitude and Practice Survey of Parents in India.

abstract: INTRODUCTION ::: Antibiotic resistance is a topic of global concern these days. Irrational, excessive use of antibiotics by the general public is one of the key factors responsible for this. ::: ::: ::: AIM ::: Through this study, we aim to analyse the knowledge, attitude and practices of antibiotics use among parents of children presenting to a tertiary care hospital in India. Also, correlate it with the gender, education level and previous use of antibiotics by the parents. ::: ::: ::: MATERIALS AND METHODS ::: A cross sectional study was conducted at a tertiary care hospital in Navi Mumbai, India from September to November 2014 and a total of 1000 parents were interviewed using a questionnaire designed by the authors. Descriptive statistics were used for the analysis of data. ::: ::: ::: RESULTS ::: A total of 872 parents were included in the study. Around one in every four (28%) parents correctly identified that antibiotics are used against bacterial infections while only 15.5% parents knew the meaning of the term antibiotic resistance. Majority of the respondents appreciated that unnecessary use of antibiotics could harm the child (73.6%). It was noteworthy that 85.2% parents stated that they don't use leftover antibiotics from the previous prescription for the next time without doctor's consult. Males, parents with higher level of formal education and use of antibiotics previously were found to have more knowledge regarding antibiotics and lesser misconceptions (p<0.05). ::: ::: ::: CONCLUSION ::: Overall, in this study it was found that misconceptions exist about the use and indications of antibiotics. Lack of knowledge regarding antibiotic resistance was prevalent. But participants were aware of the risks associated with use of excessive antibiotics. More interaction with paediatricians and involvement of mass media may help to improve the antibiotics knowledge and practices among parents and consequently, control the problem of antibiotic resistance.


title: True IOMMU Protection from DMA Attacks: When Copy is Faster than Zero Copy

abstract: Malicious I/O devices might compromise the OS using DMAs. The OS therefore utilizes the IOMMU to map and unmap every target buffer right before and after its DMA is processed, thereby restricting DMAs to their designated locations. This usage model, however, is not truly secure for two reasons: (1) it provides protection at page granularity only, whereas DMA buffers can reside on the same page as other data; and (2) it delays DMA buffer unmaps due to performance considerations, creating a vulnerability window in which devices can access in-use memory. We propose that OSes utilize the IOMMU differently, in a manner that eliminates these two flaws. Our new usage model restricts device access to a set of shadow DMA buffers that are never unmapped, and it copies DMAed data to/from these buffers, thus providing sub-page protection while eliminating the aforementioned vulnerability window. Our key insight is that the cost of interacting with, and synchronizing access to the slow IOMMU hardware---required for zero-copy protection against devices---make copying preferable to zero-copying. We implement our model in Linux and evaluate it with standard networking benchmarks utilizing a 40,Gb/s NIC. We demonstrate that despite being more secure than the safest preexisting usage model, our approach provides up to 5x higher throughput. Additionally, whereas it is inherently less scalable than an IOMMU-less (unprotected) system, our approach incurs only 0%--25% performance degradation in comparison.


title: Nuclear numbers in the stipe cells of Coprinus cinereus

abstract: Changes in nuclear number in early stages of stipe development of Coprinus cinereus are described. There was no indication that the progressive increase and variability in numbers of nuclei per cell resulted from their synchronous division to give 2 n nuclei per cell.


title: Common Mode Modeling and Reduction of GaN-based Full Bridge Inverters

abstract: Although GaN HEMTs show impressive improvements in characteristics compared with Si MOSFETs, the control circuits suffer from high common-mode (CM) noise caused by the high-speed switching actions. CM transient current will lead to failure of controllers and driver ICs if not adequately reduced. Lots of research have been conducted on the generation and propagation of the CM current, but mainly focus on a single phase-leg. This paper investigates the CM current of a full-bridge inverter. A CM propagation model is proposed, in which both phase-legs and all the signal and power paths are considered. The connection between the control ground and the power ground will lead to inequality of the phase-legs, which is also discussed in this paper. To reduce CM current, CM chokes and power supply with low isolation capacitance are employed. In addition, differential voltage sensing method that has high CM transient immunity is adopted. Experimental results on a 500W full-bridge inverter verify the effectiveness of the proposed analysis and methods.


title: TTP - A time-triggered protocol for fault-tolerant real-time systems

abstract: The Time-Triggered Protocol (TTP), which is intended for use in distributed real-time control applications that require a high dependability and guaranteed timeliness, is discussed. It integrates all services that are required in the design of a fault-tolerant real-time system, such as predictable message transmission, message acknowledgment in group communication, clock synchronization, membership, rapid mode changes, redundancy management, and temporary blackout handling. It supports fault-tolerant configurations with replicated nodes and replicated communication channels. TTP provides these services with a small overhead so it can be used efficiently on twisted pair channels as well as on fiber optic networks.


title: Preference ranking of screen layout principles

abstract: This paper presents the results of a study on the preference ranking of six layout principles (Cohesion, Economy, Regularity, Sequence, Symmetry, and Unity). Preference judgments were conducted using a forced-choice paired comparisons method. The findings of the present study indicate that, contrary to suggestions in previous literature, an interface was most preferred when it found that the layout principles of Symmetry and Cohesion were more influential than the other layout principles. Further research is needed to identify other aesthetics factors which might influence preferences, and establish appropriate design guidelines


title: Mobile Health (m-Health) System in the Context of IoT

abstract: In this paper we describe the mobile health (m-health) system in the context of Internet of Things (IoT). We describe the fundamental characteristics of m-health devices such as compactness, IP connectivity, low-power consumption and security. We discuss acquisition of mobile health data via medical gadgets and wearables and application of this data in monitoring various health conditions such as blood sugar level, ECG, blood-pressure, asthma, etc. Security is very critical for IoT based m-health system. We address the issues of confidentiality, privacy, and security in the context of secure m-health system. We listed several measures to protect the information of patients and m-health system. The m-health system will benefit the patients in many ways such as quick diagnosis, remote monitoring and home rehabilitation. Overall m-health system will significantly reduces healthcare cost and unnecessary hospitalizations.


title: A measurement device for motion analysis of patients with Parkinson's disease using sensor based smart clothes

abstract: Physical examination, interviews with patients and the results of rating scales for movement disturbances are the basis for the assessment of Parkinson's disease (PD). However, subjectivity in the assessment of the symptoms and the short period of observation are disadvantageous. This paper presents the results of a study to assess the feasibility of using accelerometer data, acquired from smart clothes, to estimate the severity of tremor in patients with PD. Algorithms were implemented to estimate the severity of rest and postural tremor of hands from accelerometer data features. The system developed consists of a pullover with eight integrated accelerometers and a computer. The newly developed system for the detection and assessment of tremor was tested with PD patients. System-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. A quantifiable objective data acquisition by means of a portable wireless system could evaluate the motor disorders better.


title: Stereo matching using belief propagation

abstract: In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF's). These three MRF's model a smooth field for depth/disparity, a line process for depth discontinuity and a binary process for occlusion, respectively. After eliminating the line process and the binary process by introducing two robust functions, we obtain the maximum a posteriori (MAP) estimation in the Markov network by applying a Bayesian belief propagation (BP) algorithm. Furthermore, we extend our basic stereo model to incorporate other visual cues (e.g., image segmentation) that are not modeled in the three MRF's, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.


title: Nitrogen Distribution Model: A Farmer and Farm-Centred Model to Monitor N Movement in the Soil

abstract: There are ::: several models that monitor movement of nitrogen in the soil. Most of these ::: models have not been widely used in southern Africa because of sophisticated ::: equipment required to collect data and the data needed to run the model are ::: intensive. Nitrogen Distribution Model (NDM) has been developed to ensure that ::: it responds to increasing need of managing nitrogen in agricultural systems ::: characterized by smallholder farmers who do not have adequate resources to ::: collect intensive data for modeling. NDM has parameters that are explicit and ::: mostly intuitive and maintains good balance of simplicity and robustness. With ::: the nature of smallholder farming in Malawi where over 85% of population are ::: rural-based smallholder farmers, the model has also be designed so that it can ::: acts as database to keep track of farmers and farms so that were given farm-specific ::: nitrogen and water management advice.


World Cup Web Site Access Logs


CMOS Low-Power Analog Circuit Design


title: Geo-located Twitter as proxy for global mobility patterns

abstract: Pervasive presence of location-sharing services made it possible for researchers to gain an unprecedented access to the direct records of human activity in space and time. This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012, we estimate the volume of international travelers by country of residence. Mobility profiles of different nations were examined based on such characteristics as mobility rate, radius of gyration, diversity of destinations, and inflow–outflow balance. Temporal patterns disclose the universally valid seasons of increased international mobility and the particular character of international travels of different nations. Our analysis of the community structure of the Twitter mobility network reveals spatially cohesive regions that follow the regional division of the world. We validate our result using global tourism statistics and mobility models provided by other authors and argue that Twitter is exceptionally useful for understanding and quantifying global mobility patterns.


title: Supervised Classification Performance of Multispectral Images

abstract: Nowadays government and private agencies use remote sensing imagery for a wide range of applications from military applications to farm development. The images may be a panchromatic, multispectral, hyperspectral or even ultraspectral of terra bytes. Remote sensing image classification is one amongst the most significant application worlds for remote sensing. A few number of image classification algorithms have proved good precision in classifying remote sensing data. But, of late, due to the increasing spatiotemporal dimensions of the remote sensing data, traditional classification algorithms have exposed weaknesses necessitating further research in the field of remote sensing image classification. So an efficient classifier is needed to classify the remote sensing images to extract information. We are experimenting with both supervised and unsupervised classification. Here we compare the different classification methods and their performances. It is found that Mahalanobis classifier performed the best in our classification.


title: EXIT Charts for System Design and Analysis

abstract: Near-capacity performance may be achieved with the aid of iterative decoding, where extrinsic soft information is exchanged between the constituent decoders in order to improve the attainable system performance. Extrinsic Information Transfer (EXIT) charts constitute a powerful semi-analytical tool used for analysing and designing iteratively decoded systems. In this tutorial, we commence by providing a rudimentary overview of the iterative decoding principle and the concept of soft information exchange. We then elaborate on the concept of EXIT charts using three iteratively decoded prototype systems as design examples. We conclude by illustrating further applications of EXIT charts, including near-capacity designs, the concept of irregular codes and the design of modulation schemes.


title: The influence of repressive legislation on the structure of a social media network

abstract: Social media have been widely used to organise citizen movements. In 2012, 75% of university and college students in Quebec, Canada, participated in mass protests against an announced increase in tuition fees. These protests were primarily organised using social media. To reduce public disruption, the government passed a special legislation designed to impede protest organisation. Here, we show that the legislation changed the behaviour of Twitter users but not the overall structure of the microblogging site social network. After the passage of the legislation, the rate of increase in tweets posted per day dropped. In addition, tweet exchanges became more clustered once the legislation was in place. However, the social network kept its scale-free hierarchical structure. This natural experiment shows the power of social media in political mobilization, as well as behavioural flexibility in information flow over a large number of individuals.


title: A new technique for the evaluation of the adrenal gland with gray scale ultrasonography.

abstract: Recent improvements in ultrasound instrumentation have provided the potential resolution to visualize the normal adrenal gland. However, conventional scanning approaches lack precise anatomic alignment. A new technique for scanning the adrenal gland and the results of a prospective evaluation of normal adrenal glands are presented.


title: Eyes-Free Art : Exploring Proxemic Audio Interfaces For Blind and Low Vision Art Engagement

abstract: Engagement in the arts1 is an important component of participation in cultural activities, but remains a largely unaddressed challenge for people with sensory disabilities. Visual arts are generally inaccessible to people with visual impairments due to their inherently visual nature. To address this, we present Eyes-Free Art, a design probe to explore the use of proxemic audio for interactive sonic experiences with 2D art work. The proxemic audio interface allows a user to move closer and further away from a painting to experience background music, a novel sonification, sound effects, and a detailed verbal description. We conducted a lab study by creating interpretations of five paintings with 13 people with visual impairments and found that participants enjoyed interacting with the artwork. We then created a live installation with a visually impaired artist to iterate on this concept to account for multiple users and paintings. We learned that a proxemic audio interface allows for people to feel immersed in the artwork. Proxemic audio interfaces are similar to visual because they increase in detail with closer proximity, but are different because they need a descriptive verbal overview to give context. We present future research directions in the space of proxemic audio interactions.


A HYBRID TEXT STEGANOGRAPHY APPROACH UTILIZING UNICODE SPACE CHARACTERS AND ZERO-WIDTH CHARACTER


title: Energy Shaping Control of an Inverted Flexible Pendulum Fixed to a Cart

abstract: Control of compliant mechanical systems is increasingly being researched for several applications including flexible link robots and ultra-precision positioning systems. The control problem in these systems is challenging, especially with gravity coupling and large deformations, because of inherent underactuation and the combination of lumped and distributed parameters of a nonlinear system. In this paper we consider an ultra-flexible inverted pendulum on a cart and propose a new nonlinear energy shaping controller to keep the pendulum at the upward position with the cart stopped at a desired location. The design is based on a model, obtained via the constrained Lagrange formulation, which previously has been validated experimentally. The controller design consists of a partial feedback linearization step followed by a standard PID controller acting on two passive outputs. Boundedness of all signals and (local) asymptotic stability of the desired equilibrium is theoretically established. Simulations and experimental evidence assess the performance of the proposed controller.


title: Formalisation of the level of detail in 3D city modelling

abstract: The level of detail in 3D city modelling, despite its usefulness and importance, is still an ambiguous and undefined term. It is used for the communication of how thoroughly real-world features have been acquired and modelled, as we demonstrate in this paper. Its definitions vary greatly between practitioners, standards and institutions. We fundamentally discuss the concept, and we provide a formal and consistent framework to define discrete and continuous levels of detail (LODs), by determining six metrics that constitute it, and by discussing their quantification and their relations. The resulting LODs are discretisations of functions of metrics that can be specified in an acquisition–modelling specification form that we introduce. The advantages of this approach over existing paradigms are formalisation, consistency, continuity, and finer specification of LODs. As an example of the realisation of the framework, we derive a series of 10 discrete LODs. We give a proposal for the integration of the framework within the OGC standard CityGML (through the Application Domain Extension).


title: Incorporation of application layer protocol syntax into anomaly detection

abstract: The syntax of application layer protocols carries valuable information for network intrusion detection. Hence, the majority of modern IDS perform some form of protocol analysis to refine their signatures with application layer context. Protocol analysis, however, has been mainly used for misuse detection, which limits its application for the detection of unknown and novel attacks. In this contribution we address the issue of incorporating application layer context into anomaly-based intrusion detection. We extend a payload-based anomaly detection method by incorporating structural information obtained from a protocol analyzer. The basis for our extension is computation of similarity between attributed tokens derived from a protocol grammar. The enhanced anomaly detection method is evaluated in experiments on detection of web attacks, yielding an improvement of detection accuracy of 49%. While byte-level anomaly detection is sufficient for detection of buffer overflow attacks, identification of recent attacks such as SQL and PHP code injection strongly depends on the availability of application layer context.


title: Slip-compensated path following for planetary exploration rovers

abstract: A system that enables continuous slip compensation for a Mars rover has been designed, implemented and field-tested. This system is composed of several components that allow the rover to accurately and continuously follow a designated path, compensate for slippage and reach intended goals in high-slip environments. These components include visual odometry, vehicle kinematics, a Kalman filter pose estimator and a slip-compensated path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs. The kinematics for a rocker–bogie suspension system estimates vehicle motion by measuring wheel rates, and rocker, bogie and steering angles. The Kalman filter processes measurements from an inertial measurement unit and visual odometry. The filter estimate is then compared to the kinematic estimate to determine whether slippage has occurred, taking into account estimate uncertainties. If slippage is detected, the slip vector...


title: Understanding Student Motivation, Behaviors and Perceptions in MOOCs

abstract: Massive Open Online Courses (MOOCs) have recently experienced rapid development and garnered significant attention from various populations. Despite the wide recognition of MOOCs as an important opportunity within educational practices, there are still many questions as to how we might satisfy students' needs, as evidenced by very high dropout rates. Researchers lack a solid understanding of what student needs are being addressed by MOOCs, and how well MOOCs now address (or fail to address) these needs. To help in building such an understanding, we conducted in-depth interviews probing student motivations, learning perceptions and experiences towards MOOCs, paying special attention to the MOOC affordances and experiences that might lead to high drop rates. Our study identified learning motivations, learning patterns, and a number of factors that appear to influence student retention. We proposed that the issue of retention should be addressed from two perspectives: retention as a problem but also retention as an opportunity.


title: Algorithm 954: An Accurate and Efficient Cubic and Quartic Equation Solver for Physical Applications

abstract: We report on an accurate and efficient algorithm for obtaining all roots of general real cubic and quartic polynomials. Both the cubic and quartic solvers give highly accurate roots and place no restrictions on the magnitude of the polynomial coefficients. The key to the algorithm is a proper rescaling of both polynomials. This puts upper bounds on the magnitude of the roots and is very useful in stabilizing the root finding process. The cubic solver is based on dividing the cubic polynomial into six classes. By analyzing the root surface for each class, a fast convergent Newton-Raphson starting point for a real root is obtained at a cost no higher than three additions and four multiplications. The quartic solver uses the cubic solver in getting information about stationary points and, when the quartic has real roots, stable Newton-Raphson iterations give one of the extreme real roots. The remaining roots follow by composite deflation to a cubic. If the quartic has only complex roots, the present article shows that a stable Newton-Raphson iteration on a derived symmetric sixth degree polynomial can be formulated for the real parts of the complex roots. The imaginary parts follow by solving suitable quadratics.


title: Automated driving: The role of forecasts and uncertainty—A control perspective

abstract: Abstract Driving requires forecasts. Forecasted movements of objects in the driving scene are uncertain. Inevitably, decision and control algorithms for autonomous driving need to cope with such uncertain forecasts. In assisted driving, the uncertainty in the human/vehicle interaction further increases the complexity of the control design task. Our research over the past ten years has focused on control design methods which systematically handle uncertain forecasts for autonomous and semi-autonomous vehicles. This paper presents an overview of our findings and discusses relevant aspects of our recent results.


title: A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes

abstract: Many school districts have developed successful intervention programs to help students graduate high school on time. However, identifying and prioritizing students who need those interventions the most remains challenging. This paper describes a machine learning framework to identify such students, discusses features that are useful for this task, applies several classification algorithms, and evaluates them using metrics important to school administrators. To help test this framework and make it practically useful, we partnered with two U.S. school districts with a combined enrollment of approximately 200,000 students. We together designed several evaluation metrics to assess the goodness of machine learning algorithms from an educator's perspective. This paper focuses on students at risk of not finishing high school on time, but our framework lays a strong foundation for future work on other adverse academic outcomes.


Atmospheric circulation features in the ACCESS model simulations for CMIP5: historical simulation and future projections


title: Circuits for a Cubic-Millimeter Energy-Autonomous Wireless Intraocular Pressure Monitor

abstract: Glaucoma is the leading cause of blindness, affecting 67 million people worldwide. The disease damages the optic nerve due to elevated intraocular pressure (IOP) and can cause complete vision loss if untreated. IOP is commonly assessed using a single tonometric measurement, which provides a limited view since IOP fluctuates with circadian rhythms and physical activity. Continuous measurement can be achieved with an implanted monitor to improve treatment regiments, assess patient compliance to medication schedules, and prevent unnecessary vision loss. The most suitable implantation location is the anterior chamber of the eye, which is surgically accessible and out of the field of vision. The desired IOP monitor (IOPM) volume is limited to 1.5mm3 (0.5x1.5x2mm3) by the size of a self-healing incision, curvature of the cornea, and dilation of the pupil.


title: The 2.4 GHz band SOI-CMOS bridge rectifier IC

abstract: This paper describes the 2.4 GHz band SOI-CMOS bridge rectifier for sub-W class rectification. The bridge rectifier topology is suitable for IC integration because of full-wave-rectification without large sized filters. Furthermore, to realize high power operation, the series connected gate connected diodes made by NMOS FETs are employed for higher breakdown voltage. And the LPF is added to reduce efficiency degradation coming from junction capacitances. The bridge rectifier IC is fabricated on Global Foundry 0.18 μm SOI-CMOS (7RFSOI) that includes FETs with high breakdown voltage. The developed bridge rectifier achieves the rectification efficiency of 44.2 % at the input power of 27 dBm. This is the top level performance in high power CMOS rectifiers.


title: Design and validation of modular MPPT electric power system for multi-U CubeSat

abstract: To meet the power demand for multi-U CubeSat from 1U up to 20U, a kind of innovative modular electric power system(EPS) is designed and verified. The system can convert three independent photovoltaic power inputs with each channel up to 40Watts, and the maximum power point (MPP) for each channel can be set independently by hardware. The solar power is converted and stored in lithium-ion batteries. Using a high efficiency charge controller, the battery voltage is maintained between 6.5V to 8.4V. Two buck-converters are applied to change the battery power to 5V and 3.3V to supply other subsystems with 5A maximum current. A TI MPS430 ultralow power microcontroller is used to monitor the system status, switch the power outputs and communicate with onboard computer via I2C. The proposed EPS has already been applied in Aoxiang ZhiXing(Aoxiang-Sat), the world's first 12U scientific CubeSat, which is launched on June 25, 2016. The telemetry data shows that the power produced by EPS is abundant for the satellite, and it worked with excellent performance during the satellite lifetime.


title: Smart integrated temperature sensor - mixed-signal circuits and systems in 32-nm and beyond

abstract: Integrating smart temperature sensors into digital platforms facilitates information to be processed and transmitted, and open up new applications. Furthermore, temperature sensors are crucial components in computing platforms to manage power-efficiency trade-offs reliably under a thermal budget. This paper presents a holistic perspective about smart temperature sensor design from system- to device-level including manufacturing concerns. Through smart sensor design evolutions, we identify some scaling paths and circuit techniques to surmount analog/mixed-signal design challenges in 32-nm and beyond. We close with opportunities to design smarter temperature sensors.


title: Practical 3d reconstruction based on Photometric Stereo

abstract: Photometric Stereo is a powerful image based 3D reconstruction technique that has recently been used to obtain very high quality reconstructions. However, in its classic form, Photometric Stereo suffers from two main limitations: Firstly, one needs to obtain images of the 3D scene under multiple different illuminations. As a result the 3D scene needs to remain static during illumination changes, which prohibits the reconstruction of deforming objects. Secondly, the images obtained must be from a single viewpoint. This leads to depth-map based 2.5 reconstructions, instead of full 3D surfaces. The aim of this Chapter is to show how these limitations can be alleviated, leading to the derivation of two practical 3D acquisition systems: The first one, based on the powerful Coloured Light Photometric Stereo method can be used to reconstruct moving objects such as cloth or human faces. The second, permits the complete 3D reconstruction of challenging objects such as porcelain vases. In addition to algorithmic details, the Chapter pays attention to practical issues such as setup calibration, detection and correction of self and cast shadows. We provide several evaluation experiments as well as reconstruction results.


title: SVM training time reduction using vector quantization

abstract: In this paper, we describe a new method for training SVM on large data sets. Vector quantization is applied to reduce a large data set by replacing examples by prototypes. Training time for choosing optimal parameters is greatly reduced. Some experimental results yields to demonstrate that this method can reduce training time by a factor of 100, while preserving classification rate. Moreover this method allows to find a decision function with a low complexity when the training data set includes noisy or error examples.


title: ‘It's just drama’: teen perspectives on conflict and aggression in a networked era

abstract: Contemporary youth conflict often plays out through social media like Facebook and Twitter. ‘Drama’ is an emergent concept describing performative, interpersonal conflict that takes place in front of an active, engaged audience, often on social media. Using ethnographic data, this paper examines how American teenagers conceptualize the term drama; the relationship between drama and social media; and the implications drama has for understanding contemporary teenage conflict. The emic use of drama distances teens from practices conceptualized by adults as bullying or relational aggression, while acknowledging the role of the audience in social media interactions. Drama also serves to reinforce the conventional gendered norms of high school, perpetrating the systemic undervaluing of feminine subjects and re-inscribing heteronormativity. Understanding how drama operates helps illuminate how widespread use of social media among teenagers has altered dynamics of aggression and conflict.


title: Effects of joint torque constraints on humanoid robot balance recovery in the presence of external disturbance

abstract: A humanoid robot should be able to keep balance under unexpected disturbance, and can take three strategies, i.e. ankle strategy, hip strategy and step strategy, to recover balance from biomechanics research. In this paper, the relationship between limited joint torque and balance recovery strategy is analyzed using Zero moment Point Manipulability Ellipsoid. Furthermore, during balance control, the constraints between the feet and the ground must be maintained. The satisfaction of these constraints, namely the gravity constraints, the friction constraints and the CoP constraints, imposed bounds on the control torque are investigated. Such control bounds have significant effects on designing balance recovery strategies and can be used to predict the type of falls.


title: An Interface Algebra for Real-Time Components

abstract: We present an assume-guarantee interface algebra for real-time components. In our formalism a component implements a set of task sequences that share a resource. A component interface consists of an arrival rate function and a latency for each task sequence, and a capacity function for the shared resource. The interface specifies that the component guarantees certain task latencies depending on assumptions about task arrival rates and allocated resource capacities. Our algebra defines compatibility and refinement relations on interfaces. Interface compatibility can be checked on partial designs, even when some component interfaces are yet unknown. In this case interface composition computes as new assumptions the weakest constraints on the unknown components that are necessary to satisfy the specified guarantees. Interface refinement is defined in a way that ensures that compatible interfaces can be refined and implemented independently. Our algebra thus formalizes an interface-based design methodology that supports both the incremental addition of new components and the independent stepwise refinement of existing components. We demonstrate the flexibility and efficiency of the framework through simulation experiments.


title: Application of Business Process Reengineering as a Process Improvement Tool: A Case Study

abstract: The environment in which organisations function at present is characterised by precipitous variation and development. In consequence, if they are to remain competitive, organisations must constantly improve their processes and systems. The improvements referred to must relate mainly to the effective and efficient utilisation of scarce resources within the organisation, and cause a shift in organisational priorities. The shift from product centredness to customer centredness makes change inevitable. Globally organisations are eagerly seeking innovative philosophies and theories that will help them achieve growth, competitiveness and productivity. Business process reengineering (BPR) is recognised as a methodology which could be beneficial in attaining the goals. Developed during the 1990s, BPR is a derivative of other Japanese methodologies such as just in time and process focus. Its hallmark is the sweeping restructuring of processes, transformation of alignment and streamlining of processes within an organisation. The aim of the paper is to identify a specific application which could facilitate improvements to the processes of a selected organisation. The correct application of the BPR methodology would result in effective and efficient processes.


title: Robust Bilayer Segmentation and Motion/Depth Estimation with a Handheld Camera

abstract: Extracting high-quality dynamic foreground layers from a video sequence is a challenging problem due to the coupling of color, motion, and occlusion. Many approaches assume that the background scene is static or undergoes the planar perspective transformation. In this paper, we relax these restrictions and present a comprehensive system for accurately computing object motion, layer, and depth information. A novel algorithm that combines different clues to extract the foreground layer is proposed, where a voting-like scheme robust to outliers is employed in optimization. The system is capable of handling difficult examples in which the background is nonplanar and the camera freely moves during video capturing. Our work finds several applications, such as high-quality view interpolation and video editing.


Exploring the impact of artificial intelligence on teaching and learning in higher education


title: Braking chopper solutions for Modular Multilevel Converters

abstract: In many converter applications, the usage of a braking chopper system is required for energy dissipation. Thus, braking chopper solutions need to be found for Modular Multilevel Converters (MMLC). This paper describes braking chopper solutions for the indirect and the direct MMLC. For each converter topology a solution utilizing cells is analyzed. Additionally, taking the direct MMLC as an example, a solution which utilizes thyristors is shown. All chopper solutions have been simulated; this paper shows some of these simulation results.