title: CLE14/CLE20 peptides may interact with CLAVATA2/CORYNE receptor-like kinases to irreversibly inhibit cell division in the root meristem of Arabidopsis

abstract: Towards an understanding of the interacting nature of the CLAVATA (CLV) complex, we predicted the 3D structures of CLV3/ESR-related (CLE) peptides and the ectodomain of their potential receptor proteins/kinases, and docking models of these molecules. The results show that the ectodomain of CLV1 can form homodimers and that the 12-/13-amino-acid CLV3 peptide fits into the binding clefts of the CLV1 dimers. Our results also demonstrate that the receptor domain of CORYNE (CRN), a recently identified receptor-like kinase, binds tightly to the ectodomain of CLV2, and this likely leads to an increased possibility for docking with CLV1. Furthermore, our docking models reveal that two CRN-CLV2 ectodomain heterodimers are able to form a tetramer receptor complex. Peptides of CLV3, CLE14, CLE19, and CLE20 are also able to bind a potential CLV2-CRN heterodimer or heterotetramer complex. Using a cell-division reporter line, we found that synthetic 12-amino-acid CLE14 and CLE20 peptides inhibit, irreversibly, root growth by reducing cell division rates in the root apical meristem, resulting in a short-root phenotype. Intriguingly, we observed that exogenous application of cytokinin can partially rescue the short-root phenotype induced by over-expression of either CLE14 or CLE20 in planta. However, cytokinin treatment does not rescue the short-root phenotype caused by exogenous application of the synthetic CLE14/CLE20 peptides, suggesting a requirement for a condition provided only in living plants. These results therefore imply that the CLE14/CLE20 peptides may act through the CLV2-CRN receptor kinase, and that their availabilities and/or abundances may be affected by cytokinin activity in planta.


title: Multiscale Superpixel-Level Subspace-Based Support Vector Machines for Hyperspectral Image Classification

abstract: This letter introduces a new spectral–spatial classification method for hyperspectral images. A multiscale superpixel segmentation is first used to model the distribution of classes based on spatial information. In this context, the original hyperspectral image is integrated with segmentation maps via a feature fusion process in different scales such that the pixel-level data can be represented by multiscale superpixel-level (MSP) data sets. Then, a subspace-based support vector machine (SVMsub) is adopted to obtain the classification maps with multiscale inputs. Finally, the classification result is achieved via a decision fusion process. The resulting method, called MSP-SVMsub, makes use of the spatial and spectral coherences, and contributes to better feature characterization. Experimental results based on two real hyperspectral data sets indicate that the MSP-SVMsub exhibits good performance compared with other related methods.


title: Physiological changes of pregnancy and monitoring.

abstract: Advances in medical care have led to increasing numbers of complex, high-risk obstetric patients. Specialist training and a sound knowledge of normal maternal physiology are essential to optimize outcomes. One of the earliest observed changes is peripheral vasodilatation; this causes a fall in systemic vascular resistance and triggers physiological changes in the cardiovascular and renal systems, with 40–50% increases in cardiac output and glomerular filtration rates. Safety concerns over Swan Ganz catheters have driven the increasing interest in alternative techniques, such as echocardiography, thoracic bioimpedance and pulse contour analysis, although their exact roles in future obstetric high-dependency care have yet to be established. Analysis of arterial blood gases is fundamental to the management of sick patients, and correct interpretation can be aided by a systematic approach. Observation charts are almost ubiquitous in all aspects of medicine, but little evidence exists to support their use in the high-dependency setting.


title: A Novel maximum power point tracker controlling several converters connected to photovoltaic arrays with particle swarm optimization technique

abstract: This paper deals with maximum power point tracking control of photovoltaic generators. Photovoltaic generation systems need maximum power point tracker because the PV power output depends on the operating terminal voltage and current. Further, the PV array exhibits two or more MPP's under partial shading condition and hence finding the MPP using conventional techniques is a difficult task. To overcome the difficulty, finding the MPP, the authors have proposed a novel MPPT algorithm by introducing Particle Swarm Optimization technique. Further, the proposed algorithm uses only one pair of sensors to control multiple PV arrays. This results in lower cost, higher overall efficiency and also the algorithm is simple. Proposed MPPT algorithm is verified through experimental studies. Several partial shading conditions were tested and in all these cases the algorithm takes about one second to reach the global MPP. The reachability to MPP is good in both shading and unshading.


title: ambiPad: enriching mobile digital media with ambient feedback

abstract: With the recent increase in sales figures of tablet computers, a corresponding boost of mobile media consumption on such devices can be observed. Even though tablets provide a comparably larger screen in contrast to smartphones - which are taking the main share of mobile media consumption - the experience of consuming media content on tablets is still limited. For movie theatres the notion of 4 dimensional (4D) films exist, describing a 3D movie that is enhanced with ambient feedback such as environmental light, air streams or moist. In this paper we present ambiPad, a tablet prototype that enriches mobile digital media with ambient feedback around the tablet's display. Besides a light emitting frame, ambiPad allows for thermal stimuli as well. We report on the results of a qualitative user study, in which the feedback channels of ambiPad were rated as highly attractive and desirable by the participants.


title: For Whom the Bell Trolls: Troll Behaviour in the Twitter Brexit Debate

abstract: In a review into automated and malicious activity Twitter released a list of accounts that they believed were connected to state sponsored manipulation of the 2016 American Election. This list details 2,752 accounts Twitter believed to be controlled by Russian operatives. In the absence of a similar list of operatives active within the debate on the 2016 UK referendum on membership of the European Union (Brexit) we investigated the behaviour of the same American Election focused accounts in the production of content related to the UK-EU referendum. We found that within our dataset we had Brexit-related content from 419 of these accounts, leading to 3,485 identified tweets gathered between the 29th August 2015 and 3rd October 2017. The behaviour of the accounts altered radically on the day of the referendum, shifting from generalised disruptive tweeting to retweeting each other in order to amplify content produced by other troll accounts. We also demonstrate that, while these accounts are, in general, designed to resemble American citizens, accounts created in 2016 often contained German locations and terms in the user profiles.


title: Orthogonal nonnegative matrix t-factorizations for clustering

abstract: Currently, most research on nonnegative matrix factorization (NMF)focus on 2-factor $X=FG^T$ factorization. We provide a systematicanalysis of 3-factor $X=FSG^T$ NMF. While it unconstrained 3-factor NMF is equivalent to it unconstrained 2-factor NMF, itconstrained 3-factor NMF brings new features to it constrained 2-factor NMF. We study the orthogonality constraint because it leadsto rigorous clustering interpretation. We provide new rules for updating $F,S, G$ and prove the convergenceof these algorithms. Experiments on 5 datasets and a real world casestudy are performed to show the capability of bi-orthogonal 3-factorNMF on simultaneously clustering rows and columns of the input datamatrix. We provide a new approach of evaluating the quality ofclustering on words using class aggregate distribution andmulti-peak distribution. We also provide an overview of various NMF extensions andexamine their relationships.


title: A cloud-assisted design for autonomous driving

abstract: This paper presents Carcel, a cloud-assisted system for autonomous driving. Carcel enables the cloud to have access to sensor data from autonomous vehicles as well as the roadside infrastructure. The cloud assists autonomous vehicles that use this system to avoid obstacles such as pedestrians and other vehicles that may not be directly detected by sensors on the vehicle. Further, Carcel enables vehicles to plan efficient paths that account for unexpected events such as road-work or accidents. We evaluate a preliminary prototype of Carcel on a state-of-the-art autonomous driving system in an outdoor testbed including an autonomous golf car and six iRobot Create robots. Results show that Carcel reduces the average time vehicles need to detect obstacles such as pedestrians by 4.6x compared to today's systems that do not have access to the cloud.


title: The Coming Paradigm Shift in Forensic Identification Science

abstract: Converging legal and scientific forces are pushing the traditional forensic identification sciences toward fundamental change. The assumption of discernible uniqueness that resides at the core of these fields is weakened by evidence of errors in proficiency testing and in actual cases. Changes in the law pertaining to the admissibility of expert evidence in court, together with the emergence of DNA typing as a model for a scientifically defensible approach to questions of shared identity, are driving the older forensic sciences toward a new scientific paradigm.


title: Click Fraud Detection: Adversarial Pattern Recognition over 5 Years at Microsoft

abstract: Microsoft adCenter is the third largest Search advertising platform in the United States behind Google and Yahoo, and services about 10 % of US traffic. At this scale of traffic approximately 1 billion events per hour, amounting to 2.3 billion ad dollars annually, need to be scored to determine if it is fraudulent or bot-generated [32, 37, 41]. In order to accomplish this, adCenter has developed arguably one of the largest data mining systems in the world to score traffic quality, and has employed them successfully over 5 years. The current paper describes the unique challenges posed by data mining at massive scale, the design choices and rationale behind the technologies to address the problem, and shows some examples and some quantitative results on the effectiveness of the system in combating click fraud.


title: Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving

abstract: The virtual validation of automated driving functions requires meaningful simulation models of environment perception sensors such as radar, lidar, and cameras. There does not yet exist an unrivaled standard for perception sensor models, and radar especially lacks modeling approaches that consistently produce realistic results. In this paper, we present measurements that exemplify challenges in the development of meaningful radar sensor models. We highlight three major challenges: multi-path propagation, separability, and sensitivity of radar cross section to the aspect angle. We also review previous work addressing these challenges and suggest further research directions towards meaningful automotive radar simulation models.


title: NN-grams: Unifying neural network and n-gram language models for Speech Recognition

abstract: We present NN-grams, a novel, hybrid language model integrating n-grams and neural networks (NN) for speech recognition. The model takes as input both word histories as well as n-gram counts. Thus, it combines the memorization capacity and scalability of an n-gram model with the generalization ability of neural networks. We report experiments where the model is trained on 26B words. NN-grams are efficient at run-time since they do not include an output soft-max layer. The model is trained using noise contrastive estimation (NCE), an approach that transforms the estimation problem of neural networks into one of binary classification between data samples and noise samples. We present results with noise samples derived from either an n-gram distribution or from speech recognition lattices. NN-grams outperforms an n-gram model on an Italian speech recognition dictation task.


title: #nowplaying the future billboard: mining music listening behaviors of twitter users for hit song prediction

abstract: Microblogs are rich sources of information because they provide platforms for users to share their thoughts, news, information, activities, and so on. Twitter is one of the most popular microblogs. Twitter users often use hashtags to mark specific topics and to link them with related tweets. In this study, we investigate the relationship between the music listening behaviors of Twitter users and a popular music ranking service by comparing information extracted from tweets with music-related hashtags and the Billboard chart. We collect users' music listening behavior from Twitter using music-related hashtags (e.g., #nowplaying). We then build a predictive model to forecast the Billboard rankings and hit music. The results show that the numbers of daily tweets about a specific song and artist can be effectively used to predict Billboard rankings and hits. This research suggests that users' music listening behavior on Twitter is highly correlated with general music trends and could play an important role in understanding consumers' music consumption patterns. In addition, we believe that Twitter users' music listening behavior can be applied in the field of Music Information Retrieval (MIR).


title: RFID Circuit Design with Optimized CMOS Inductor for Monitoring Biomedical Signals

abstract: RFID is evolving as a major technology enabler for identifying and tracking goods. RFID applications in biomedical area not only need to detect but also require monitoring and transmitting vital signals like the electrocardiogram (and heartbeat), blood pressure, body temperature, etc. The basic building blocks of an RFID tag for biomedical application are studied. The sizing and powering up of the tags are critical issues and this paper mainly focuses on the design and optimization of the inductor for an RFID circuit. UHF is chosen for this application mainly because of the practical values of inductance that can be realized in CMOS chip to operate at this operating frequency. The layout parameters of inductor are optimized for a maximum Q-factor value at the desired frequency. Using the optimized inductor, the functioning of the important blocks of an RFID tag such as power feeding circuit, heartbeat detection circuit as well as the modulation circuit is verified. This work would be very relevant for the remote monitoring of biomedical signals.


title: Signal compensation and extraction of high resolution position for sinusoidal magnetic encoders

abstract: MEs (magnetic encoders) are widely used in industrial control systems because of its significant advantages, such as high resolution, high speed, operation in harsh environments, and low cost. This paper describes a method to extract the position information with high resolution for sinusoidal MEs. A code compensator based on optimization theories is applied to correct non-ideal signals of the encoder outputs. This algorithm can effectively eliminate the DC offset, phase-shift, amplitude difference and sinusoidal deformation from these outputs. Then, a size-reduced look-up table is built-up off-line to generate the high resolution quadrature pulses suitable for standard incremental encoder signals. These LUTs (Look-up tables) rely on the approximate linear property of the amplitude of sinusoids in section [-sin(pi/4) ; sin(pi/4)] and is decreased by converting the values of LUTs into binary values (0 and 1). The firmware is implemented on a 16-bit DSP (digital signal processor) TMS320F2812 hardware platform with minimum computation. The experimental results are also presented in this paper.


title: Two-Dimensional Canonical Correlation Analysis

abstract: In this letter, we present a method of two-dimensional canonical correlation analysis (2D-CCA) where we extend the standard CCA in such a way that relations between two different sets of image data are directly sought without reshaping images into vectors. We stress that 2D-CCA dramatically reduces the computational complexity, compared to the standard CCA. We show the useful behavior of 2D-CCA through numerical examples of correspondence learning between face images in different poses and illumination conditions.


title: A survey on resource allocation in software defined networks (SDN)

abstract: With emerging use of real time communication (i.e., high band width videos, video calls etc.) and IoT, this become challenge to manage network and handle traffic. SDN is a technology which provides scalability and flexibility to network by decoupling of control plane and data plane. It is also difficult to allocate resources depending upon types of tasks and user requirements. SDN central controller provides global view of network which makes resource allocation more reliable. In this paper, survey of recently proposed techniques for research allocation in SDN will be presented.


title: MUTARCH: Architectural diversity for FPGA device and IP security

abstract: Field Programmable Gate Arrays (FPGAs) are being increasingly deployed in diverse applications including the emerging Internet of Things (IoT), biomedical, and automotive systems. However, security of the FPGA configuration file (i.e. bitstream), especially during in-field reconfiguration, as well as effective safeguards against unauthorized tampering and piracy during operation, are notably lacking. The current practice of bitstreram encryption is only available in high-end FPGAs, incurs unacceptably high overhead for area/energy-constrained devices, and is susceptible to side channel attacks. In this paper, we present a fundamentally different and novel approach to FPGA security that can protect against all major attacks on FPGA, namely, unauthorized in-field reprogramming, piracy of FPGA intellectual property (IP) blocks, and targeted malicious modification of the bitstream. Our approach employs the security through diversity principle to FPGA, which is often used in the software domain. We make each device architecturally different from the others using both physical (static) and logical (time-varying) configuration keys, ensuring that attackers cannot use a priori knowledge about one device to mount an attack on another. It therefore mitigates the economic motivation for attackers to reverse engineering the bitstream and IP. The approach is compatible with modern remote upgrade techniques, and requires only small modifications to existing FPGA tool flows, making it an attractive addition to the FPGA security suite. Our experimental results show that the proposed approach achieves provably high security against tampering and piracy with worst-case 14% latency overhead and 13% area overhead.


title: Byzantine Stochastic Gradient Descent

abstract: This paper studies the problem of distributed stochastic optimization in an adversarial setting where, out of the $m$ machines which allegedly compute stochastic gradients every iteration, an $\alpha$-fraction are Byzantine, and can behave arbitrarily and adversarially. Our main result is a variant of stochastic gradient descent (SGD) which finds $\varepsilon$-approximate minimizers of convex functions in $T = \tilde{O}\big( \frac{1}{\varepsilon^2 m} + \frac{\alpha^2}{\varepsilon^2} \big)$ iterations. In contrast, traditional mini-batch SGD needs $T = O\big( \frac{1}{\varepsilon^2 m} \big)$ iterations, but cannot tolerate Byzantine failures. Further, we provide a lower bound showing that, up to logarithmic factors, our algorithm is information-theoretically optimal both in terms of sampling complexity and time complexity.


title: Single-Shot Multi-Person 3D Body Pose Estimation From Monocular RGB Input

abstract: We propose a new efficient single-shot method for multi-person 3D pose estimation in general scenes from a monocular RGB camera. Our fully convolutional DNN-based approach jointly infers 2D and 3D joint locations on the basis of an extended 3D location map supported by body part associations. This new formulation enables the readout of full body poses at a subset of visible joints without the need for explicit bounding box tracking. It therefore succeeds even under strong partial body occlusions by other people and objects in the scene. We also contribute the first training data set showing real images of sophisticated multi-person interactions and occlusions. To this end, we leverage multi-view video-based performance capture of individual people for ground truth annotation and a new image compositing for user-controlled synthesis of large corpora of real multi-person images. We also propose a new video-recorded multi-person test set with ground truth 3D annotations. Our method achieves state-of-the-art performance on challenging multi-person scenes.


title: FuSA touch display: a furry and scalable multi-touch display

abstract: We propose a furry and scalable multi-touch display called the "FuSA2 Touch Display." The furry type of tactile sensation of this surface affords various interactions such as stroking or clawing. The system utilizes plastic fiber optic bundles to realize a furry-type texture. The system can show visual feedback by projection and detects multi-touch input using a diffused illumination technique. We employed the optical feature of plastic fiber optics to integrate the input and output systems into such a simple configuration that the display becomes scalable. We implemented a 24-inch display, evaluated the visual feedback and touch detection features, and found that our implemented display encourages users to interact with it in various actions.


title: Radar Communications for Combating Mutual Interference of FMCW Radars

abstract: Commercial automotive radars used today are based on frequency modulated continuous wave signals due to the simple and robust detection method and good accuracy. However, the increase in both the number of radars deployed per vehicle and the number of such vehicles leads to mutual interference, cutting short future plans for autonomous driving and active safety functionality. We propose and analyze a radar communications (RadCom) approach to reduce this mutual interference while simultaneously offering communication functionality. We achieve this by frequency division multiplexing radar and communications, where communications is built on a decentralized carrier sense multiple access protocol and is used to adjust the timing of radar transmissions. Our simulation results indicate that radar interference can be significantly reduced, at no cost in radar accuracy.


title: Simpler but More Accurate Semantic Dependency Parsing

abstract: While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves state-of-the-art performance, beating the previous, substantially more complex state-of-the-art system by 0.6% labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.


title: A multiobjective placement of switching devices in distribution networks incorporating distributed energy resources

abstract: Abstract With the growth of Distributed Energy Resources (DER) in electrical systems, an increase in the benefits with the reconfiguration of radial networks related to the use of renewable energy is expected. However, in some countries the reconfiguration of radial networks has some limitations like the impossibility of island operation. Also, many of them have contracts with Distributed Generation (DG) operators that force them to buy all the available generation of electricity. After a fault these limitations increase DG unavailability resulting in a waste of resources in the form of payments of available electricity generation not used. In this paper we propose a multiobjective optimal placement of switching devices considering DG unavailability, network reliability and equipment cost, with no island network operation. Two approaches are proposed in this multiobjective problem. In the first approach, besides the minimization of the equipment cost and the reliability indices, it is proposed an extra objective function that considers the minimization of the Distributed Generation Unavailability Index (DGUI). In the second approach two new composite indices comprising DG, interruption duration and interruption frequency are presented. Results obtained from a case study using a real utility distribution network are presented and discussed.


title: Rapid determination of acrylamide contaminant in conventional fried foods by gas chromatography with electron capture detector.

abstract: Gas chromatography coupled with electron capture detector (GC-ECD) was successfully developed and applied for the rapid determination of acrylamide in conventional fried foods, such as potato crisps, potato chips, and fried chicken wings. The method included defatting with n-hexane, extraction with aqueous solution of sodium chloride (NaCl), derivatization with potassium bromate (KBrO3) and potassium bromide (KBr), and liquid-liquid extraction with ethyl acetate. The final acrylamide extract was analyzed by GC-ECD for quantification and by GC-MS for confirmation. The chromatographic analysis was performed on the HP-INNOWax capillary column, and good retention and peak response of acrylamide were achieved under the optimal conditions (numbers of theoretical plates N = 83,815). The limit of detection (LOD) was estimated to be 0.1 microg kg(-1) on the basis of ECD technique. Recoveries of acrylamide from conventional samples spiked at levels of 150, 500 and 1000 microg kg(-1) (n = 4 for each level) ranged between 87 and 97% with relative standard deviations (RSD) of less than 4%. Furthermore, the GC-ECD method showed that no clean-up steps of acrylamide derivative would be performed prior to injection and was slightly more sensitive than the MS/MS-based methods. Validation and quantification results demonstrated that this method should be regarded as a new, low-cost, and robust alternative for conventional investigation of acrylamide.


Glycyl-Glutamine , an Endogenous β-Endorphin Derived Peptide , Inhibits Morphine Conditioned Place Preference , Tolerance , Dependence and Withdrawal


title: Overview and Outlook on the Semantic Desktop

abstract: In this paper we will give an overview of the Semantic Desktop paradigm, beginning with the history of the term, a definition, current work and its relevance to knowledge management of the future. Existing applications and research results are listed and their role as building blocks of the future Semantic Desktop described. Based on the analysis of existing systems we propose two software architecture paradigms, one for the Semantic Desktop at large and another for applications running on a Semantic Desktop. A view on the context aspect of the Semantic Desktop and the Knowledge Management aspect is given. Based on the current events and projects, we give an outlook on the next steps.


title: Power minimization in IC design: principles and applications

abstract: Low power has emerged as a principal theme in today's electronics industry. The need for low power has caused a major paradigm shift in which power dissipation is as important as performance and area. This article presents an in-depth survey of CAD methodologies and techniques for designing low power digital CMOS circuits and systems and describes the many issues facing designers at architectural, logical, and physical levels of design abstraction. It reviews some of the techniques and tools that have been proposed to overcome these difficulties and outlines the future challenges that must be met to design low power, high performance systems.


title: Different underlying impairments in decisionmaking following ventromedial and dorsolateral frontal lobe damage in humans

abstract: Ventromedial prefrontal cortex (VMF) damage can lead to impaired decision-making. This has been studied most intensively with the Iowa gambling task (IGT), a card game that asks subjects to overcome an initial attraction to high-payoff decks as losses begin to accrue. VMF subjects choose from the high risk decks more often than controls, but the fundamental impairments driving poor performance on this complex task have yet to be established. There is also conflicting evidence regarding the role of the dorsolateral prefrontal cortex (DLF) in this task. The present study examined whether poor performance on the IGT was specific for VMF damage and whether fundamental impairments in reversal learning contributed to IGT performance. We found that both VMF and DLF damage leads to impaired IGT performance. The impairment of VMF subjects, but not of DLF subjects, seems to be largely explained by an underlying reversal learning deficit.


title: Post-Quantum One-Time Linkable Ring Signature and Application to Ring Confidential Transactions in Blockchain (Lattice RingCT v1.0)

abstract: In this paper, we construct a Lattice-based one-time Linkable Ring Signature (L2RS) scheme, which enables the public to verify if two or more signatures were generated by same signatory, whilst still preserving the anonymity of the signatory. The L2RS provides unconditional anonymity and security guarantees under the Ring Short Integer Solution (Ring-SIS) lattice hardness assumption. The proposed L2RS scheme is extended to be applied in a protocol that we called Lattice Ring Confidential transaction (Lattice RingCT) v1.0, which forms the foundation of the privacy-preserving protocol in any post-quantum secure cryptocurrency such as Hcash.


Accelerating Somewhat Homomorphic Evaluation using FPGAs.


title: Moving beyond untagging: photo privacy in a tagged world

abstract: Photo tagging is a popular feature of many social network sites that allows users to annotate uploaded images with those who are in them, explicitly linking the photo to each person's profile. In this paper, we examine privacy concerns and mechanisms surrounding these tagged images. Using a focus group, we explored the needs and concerns of users, resulting in a set of design considerations for tagged photo privacy. We then designed a privacy enhancing mechanism based on our findings, and validated it using a mixed methods approach. Our results identify the social tensions that tagging generates, and the needs of privacy tools to address the social implications of photo privacy management.


title: Fuzzy AHP for determining the key features and cognitive differences of mobile game development among designer and game player

abstract: Previous studies have assessed the elements of a game design from game players’ viewpoints. Scant research has been conducted from the perspectives of both game players and game developers. This study explored the cognitive difference among game players and game developers in the importance of online game designs by employing the fuzzy analytic hierarchy process (FAHP) to analyze the importance of puzzle game designs. The online game design hierarchical framework comprising 3 systems, 9 components, and 29 factors was established. The research findings are summarized as follows: 1) System quality is the most crucial design component. 2) Game development should primarily focus on technology followed by aesthetics. 3) A gap exists in the perception of game players and game developers on the design component “service quality.” Finally, researchers can adopt the multiple criteria decision-making model used in this current study to rank the importance of design contents for cultural and creative industries.


title: Vehicle Routing with Drones

abstract: We introduce a package service model where trucks as well as drones can deliver packages. Drones can travel on trucks or fly; but while flying, drones can only carry one package at a time and have to return to a truck to charge after each delivery. We present a heuristic algorithm to solve the problem of finding a good schedule for all drones and trucks. The algorithm is based on two nested local searches, thus the definition of suitable neighbourhoods of solutions is crucial for the algorithm. Empirical tests show that our algorithm performs significantly better than a natural Greedy algorithm. Moreover, the savings compared to solutions without drones turn out to be substantial, suggesting that delivery systems might considerably benefit from using drones in addition to trucks.


title: RF energy harvesting system and circuits for charging of mobile devices

abstract: RF energy harvesting holds a promise able future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. This paper presents the overview and progress achieved in RF energy harvesting field. A modified form of existing CMOS based voltage doubler circuit is presented to achieve 160% increase in output power over traditional circuits at 0 dBm input power. A schottky diode based RF energy harvesting circuit performance is also studied with practical and simulations results.


title: NetFlow based intrusion detection system

abstract: In this paper, a NetFlow based anomaly intrusion detection system is presented. In addition, guidelines to properly configure and setup network device to minimize the possibilities that network attacks come from inside are also proposed. As the Internet becomes the platform of daily activities, the threat of network attack is also become more serious. Firewall along is not able to protect the system from being attacked through normal service channel. Furthermore, most of the current intrusion detection system focuses on the border of organization network. If the attack comes from inside, this setup does not provide any protection to hosts in the local network and the network itself. Therefore, we need to use other mechanism to protect the critical system as well as the network itself. We propose an inexpensive and easy to implement way to perform the anomaly type intrusion detection based on the NetFlow data exported from the routers or other network probes. Our system can detect several types of network attack from inside or outside and perform counter maneuver accordingly.


title: The antimicrobial skin barrier in patients with atopic dermatitis.

abstract: Keratinocytes represent the major cell population in the epithelial skin barrier and actively participate in innate immune responses by recognizing pathogenic microorganisms, followed by a fine-tuned production of cytokines, chemokines and antimicrobial peptides or proteins (AMPs). Patients with atopic dermatitis (AD) suffer from a defective permeability barrier which favors pathogen infection indicating that the permeability and antimicrobial barrier functions are interdependent. Several early studies showed that the inducible AMPs LL-37, HBD-2 and HBD-3 are expressed at lower levels in atopic skin compared to psoriatic skin. However, recent data indicate that AMP induction is not compromised in AD patients and that several AMPs are expressed at significantly higher amounts in AD compared to healthy skin. AD patients have an increased susceptibility to Staphylococcus aureus skin infection suggesting that AMP levels expressed by keratinocytes of AD patients might not be sufficient to combat pathogenic skin infection or that AMP function is disturbed. Increasing AMP expression in AD skin and repairing the skin barrier defect might have a therapeutic effect in AD patients enabling the skin to mount an enhanced response to pathogens.


title: Tracking the Spatiotemporal Neural Dynamics of Real-world Object Size and Animacy in the Human Brain

abstract: Animacy and real-world size are properties that describe any object and thus bring basic order into our perception of the visual world. Here, we investigated how the human brain processes real-worl...


title: A portable graphics library for introductory CS

abstract: For several decades, instructors who focus on introductory computer science courses have recognized the value of graphical examples. Supporting a graphics library that is appropriate for beginning students has become more difficult over time. This paper describes a new approach to building a graphics library that allows for multiple source languages and a wide range of target architectures and platforms. The key to this approach is using an interprocess pipe to communicate between a platform independent client library and a Java based process to perform the graphical operations specific to each platform.


title: Predicting and Understanding Urban Perception with Convolutional Neural Networks

abstract: Cities' visual appearance plays a central role in shaping human perception and response to the surrounding urban environment. For example, the visual qualities of urban spaces affect the psychological states of their inhabitants and can induce negative social outcomes. Hence, it becomes critically important to understand people's perceptions and evaluations of urban spaces. Previous works have demonstrated that algorithms can be used to predict high level attributes of urban scenes (e.g. safety, attractiveness, uniqueness), accurately emulating human perception. In this paper we propose a novel approach for predicting the perceived safety of a scene from Google Street View Images. Opposite to previous works, we formulate the problem of learning to predict high level judgments as a ranking task and we employ a Convolutional Neural Network (CNN), significantly improving the accuracy of predictions over previous methods. Interestingly, the proposed CNN architecture relies on a novel pooling layer, which permits to automatically discover the most important areas of the images for predicting the concept of perceived safety. An extensive experimental evaluation, conducted on the publicly available Place Pulse dataset, demonstrates the advantages of the proposed approach over state-of-the-art methods.


title: Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech

abstract: In this paper, we propose a novel deep neural network architecture, Speech2Vec, for learning fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the underlying spoken words, and are close to other vectors in the embedding space if their corresponding underlying spoken words are semantically similar. The proposed model can be viewed as a speech version of Word2Vec. Its design is based on a RNN Encoder-Decoder framework, and borrows the methodology of skipgrams or continuous bag-of-words for training. Learning word embeddings directly from speech enables Speech2Vec to make use of the semantic information carried by speech that does not exist in plain text. The learned word embeddings are evaluated and analyzed on 13 widely used word similarity benchmarks, and outperform word embeddings learned by Word2Vec from the transcriptions.


title: New World Tryptamine Hallucinogens and the Neuroscience of Ayahuasca

abstract: New World indigenous peoples are noted for their sophisticated use of psychedelic plants in shamanic and ethnomedical practices. The use of psychedelic plant preparations among New World tribes is far more prevalent than in the Old World. Yet, although these preparations are botanically diverse, almost all are chemically similar in that their active principles are tryptamine derivatives, either DMT or related constituents. Part 1 of this paper provides an ethnopharmacological overview of the major tryptamine-containing New World hallucinogens. Part 2 focuses on ayahuasca and its effects on the human brain. Using complementary neurophysiological and neuroimaging techniques, we have identified brain areas involved in the cognitive effects induced by this complex botanical preparation. Initial SPECT data showed that ayahuasca modulated activity in higher order association areas of the brain. Increased blood perfusion was observed mainly in anterior brain regions encompassing the frontomedial and anterior cingulate cortices of the frontal lobes, and in the medial regions of the temporal lobes. On the other hand, applying spectral analysis and source location techniques to cortical electrical signals, we found changes in neuronal activity that predominated in more posterior sensory-selective areas of the brain. Now, using functional connectivity analysis of brain oscillations we have been able to reconcile these seemingly contradictory findings. By measuring transfer entropy, a metric based on information theory, we have shown that ayahuasca temporarily modifies the ordinary flow of information within the brain. We propose a model in which ayahuasca reduces top-down constraints and facilitates bottom-up information transfer. By simultaneously enhancing endogenous cortical excitability and reducing higher-order cognitive control, ayahuasca temporarily disrupts neural hierarchies allowing inner exploration and a new outlook on reality.


title: Bidirectional Soft-Switching Series-Resonant Converter With Simple PWM Control and Load-Independent Voltage-Gain Characteristics for Energy Storage System in DC Microgrids

abstract: A fixed frequency operated bidirectional series-resonant (BSR) converter is proposed for energy storage system in dc microgrid. Simple pulsewidth modulation (PWM) control is applied to the proposed converter to regulate the power flows and achieve the following attractive features: 1) the voltage gain of the converter is only determined by the effective duty cycles of switches and has nothing to do with the amplitude and direction of the transferred power, which is a significant innovation and improvement in comparison with most of the existing resonant converters; 2) theoretically, the normalized voltage gain can be regulated from zero to infinite, which means the proposed BSR converter is a Buck–Boost converter and suitable for wide voltage range applications; 3) the direction of power flow and the operation mode, i.e., the forward or backward mode, can be changed automatically and smoothly with the simple PWM control; and 4) zero-voltage switching turn-on is achieved for all the active switches within the entire operation range. Operation principles, control strategies, characteristics, and design considerations of the proposed BSR are analyzed. A 1.6 kW prototype is built and tested to evaluate the feasibility of the proposed converter.


abstract: Swinton, PA, Lloyd, R, Keogh, JWL, Agouris, I, and Stewart, AD. A biomechanical comparison of the traditional squat, powerlifting squat, and box squat. JS trength Cond Res26(7): 1805–1816, 2012. The purpose of this study was to compare the biomechanics of the traditional squat with 2 popular exercise variations commonly referred to as the powerlifting squat and box squat. Twelve male powerlifters performed the exercises with 30, 50, and 70% of their measured 1 repetition maximum (1RM), with instruction to lift the loads as fast as possible. Inverse dynamics and spatial tracking of the external resistance were used to quantify biomechanical variables. A range of significant kinematic and kinetic differences (p , 0.05) emerged between the exercises. The traditional squat was performed with a narrow stance, whereas the powerlifting squat and box squat were performed with similar wide stances (48.3 6 3.8, 89.6 6 4.9, 92.1 6 5.1 cm, respectively). During the eccentric phase of the traditional squat, the knee traveled past the toes resulting in anterior displacement of the system center of mass (COM). In contrast, during the powerlifting squat and box squat, a more vertical shin position was maintained, resulting in posterior displacements of the system COM. These differences in linear displacements had a significant effect (p , 0.05) on a number of peak joint moments, with the greatest effects measured at the spine and ankle. For both joints, the largest peak moment was produced during the traditional squat, followed by the powerlifting squat, then box squat. Significant differences (p , 0.05) were also noted at the hip joint where the largest moment in all 3 planes were produced during the powerlifting squat. Coaches and athletes should be aware of the biomechanical differences between the squatting variations and select according to the kinematic and kinetic profile that best match the training goals.


title: Reverse Engineering iOS Mobile Applications

abstract: As a result of the ubiquity and popularity of smart phones, the number of third party mobile applications is explosively growing. With the increasing demands of users for new dependable applications, novel software engineering techniques and tools geared towards the mobile platform are required to support developers in their program comprehension and analysis tasks. In this paper, we propose a reverse engineering technique that automatically (1) hooks into, dynamically runs, and analyzes a given iOS mobile application, (2) exercises its user interface to cover the interaction state space and extracts information about the runtime behaviour, and (3) generates a state model of the given application, capturing the user interface states and transitions between them. Our technique is implemented in a tool called iCrawler. To evaluate our technique, we have conducted a case study using six open-source iPhone applications. The results indicate that iCrawler is capable of automatically detecting the unique states and generating a correct model of a given mobile application.


title: Can two-sided messages increase the helpfulness of online reviews?

abstract: Purpose – Online reviews are increasingly available for a wide range of products and services. Several studies have demonstrated the benefits of the presence of customer reviews to an online retailer, but the issue of what makes online reviews helpful to a consumer in the process of making a purchase decision remains uninvestigated. The paper aims to discuss this issue. Design/methodology/approach – Given the strategic potential of online reviews, this study drew on past research to develop a conceptual understanding of the components of helpfulness and to further empirically test the model using actual online review data from iPeen.com in Taiwan. A content analysis of 989 reviews across four products identified the interplay effects of review sidedness, reviewer’s expertise, and product type on the helpfulness of an online review. Findings – For search goods, consumers consider two-sided reviews to be more helpful than one-sided reviews when the reviewers are experts in writing such articles, whereas the...


title: Wafer-Level Integration of an Advanced Logic-Memory System Through the Second-Generation CoWoS Technology

abstract: State-of-the-art silicon interposer technology of chip-on-wafer-on-substrate (CoWoS) containing the second-generation high bandwidth memory (HBM) has been applied for the first time in fabricating high-performance wafer-level system-in-package. An ultralarge Si interposer up to 1200mm2 made by a two-mask stitching process is used to form the basis of the second-generation CoWoS (CoWoS-2) to accommodate chips of logic and memory and achieve the highest possible performance. Yield challenges associated with the high warpage of such a large heterogeneous system are resolved to achieve high package yield. Compared to alternative interposer integration approaches such as chip-on-substrate, CoWoS offers more competitive design rule which results in better power consumption, transmission loss, and eye diagram. CoWoS-2 has positioned itself as a flexible 3-D IC platform for logic-memory heterogeneous integration between logic system-on-chip and HBM for various high-performance computing applications.


title: DTMC Model Checking by SCC Reduction

abstract: Discrete-Time Markov Chains (DTMCs) are a widely-used formalism to model probabilistic systems. On the one hand, available tools like PRISM or MRMC offer efficient model checking algorithms and thus support the verification of DTMCs. However, these algorithms do not provide any diagnostic information in the form of counterexamples, which are highly important for the correction of erroneous systems. On the other hand, there exist several approaches to generate counterexamples for DTMCs, but all these approaches require the model checking result for completeness. In this paper we introduce a model checking algorithm for DTMCs that also supports the generation of counterexamples. Our algorithm, based on the detection and abstraction of strongly connected components, offers abstract counterexamples, which can be interactively refined by the user.


title: Synchronised oscillations of the human sensorimotor cortex.

abstract: Oscillations are a prominent feature of macroscopic human sensorimotor cortical activity as recorded non-invasively with electroencephalography (EEG) and magnetoencephalography (MEG). The advent of whole-scalp MEG systems allowing rapid non-invasive recording from the entire cortex and accurate localisation of neural sources, and the development of refined signal analysis methods are important factors that led to an increasing interest in studies of sensorimotor oscillations during the last 10 years. Investigations on healthy subjects revealed frequency-specific localisation and modality-specific reactivity of 10 Hz and 20 Hz sensorimotor oscillations. Task-specific coherence between motor cortical and electromyographic oscillations, reflecting cortico-motoneuronal coupling, point towards a functional role of precentral oscillations in the cortical control of voluntary movements. Furthermore, abnormal cortico-motoneuronal coupling may underlie clinical symptoms of motor disorders, such as tremor. Thus, investigation of oscillatory sensorimotor activity proceeds from phenomenology to function and provides an interesting approach to address questions in human motor physiology and pathophysiology.


title: Differentially private graphical degree sequences and synthetic graphs

abstract: We present an algorithm for releasing graphical degree sequences of simple undirected graphs under the framework of differential privacy. The algorithm is designed to provide utility for statistical inference in random graph models whose sufficient statistics are functions of degree sequences. Specifically, we focus on the tasks of existence of maximum likelihood estimates, parameter estimation and goodness-of-fit testing for the beta model of random graphs. We show the usefulness of our algorithm by evaluating it empirically on simulated and real-life datasets. As the released degree sequence is graphical, our algorithm can also be used to release synthetic graphs under the beta model.


title: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation

abstract: In this paper we propose a slanted plane model for jointly recovering an image segmentation, a dense depth estimate as well as boundary labels (such as occlusion boundaries) from a static scene given two frames of a stereo pair captured from a moving vehicle. Towards this goal we propose a new optimization algorithm for our SLIC-like objective which preserves connecteness of image segments and exploits shape regularization in the form of boundary length. We demonstrate the performance of our approach in the challenging stereo and flow KITTI benchmarks and show superior results to the state-of-the-art. Importantly, these results can be achieved an order of magnitude faster than competing approaches.


Improving Grounded Language Acquisition Efficiency Using Interactive Labeling


title: Distance Is Not Dead: Social Interaction and Geographical Distance in the Internet Era

abstract: The Internet revolution has made long-distance communication dramatically faster, easier, and cheaper than ever before. This, it has been argued, has decreased the importance of geographic proximity in social interactions, transforming our world into a global village with a borderless society. We argue for the opposite: while technology has undoubtedly increased the overall level of communication, this increase has been most pronounced for local social ties. We show that the volume of electronic communications is inversely proportional to geographic distance, following a Power Law. We directly study the importance of physical proximity in social interactions by analyzing the spatial dissemination of new baby names. Counter-intuitively, and in line with the above argument, the importance of geographic proximity has dramatically increased with the internet revolution.


title: Armature Reaction Field and Inductance of Coreless Moving-Coil Tubular Linear Machine

abstract: Analysis of armature reaction field and inductance is extremely important for design and control implementation of electromagnetic machines. So far, most studies have focused on magnetic field generated by permanent-magnet (PM) poles, whereas less work has been done on armature reaction field. This paper proposes a novel analytical modeling method to predict the armature reaction field of a coreless PM tubular linear machine with dual Halbach array. Unlike conventional modeling approach, the proposed method formulates the armature reaction field for electromagnetic machines with finite length, so that the analytical modeling precision can be improved. In addition, winding inductance is also analytically formulated to facilitate dynamic motion control based on the reaction field solutions. Numerical result is subsequently obtained with finite-element method and employed to validate the derived analytical models. A research prototype with dual Halbach array and single phase input is developed. Experiments are conducted on the reaction field and inductance to further verify the obtained mathematical models.


title: Dynamic analysis of toroidal winding switched reluctance motor driven by 6-switch converter

abstract: This paper presents a characteristic analysis of a wye-connected toroidal winding switched reluctance motor (TSRM) driven by 6-switch converter. The voltage equations considering both TSRM and the drive topology are introduced for steady state dynamic simulation. Inductances, which mainly affect motor characteristics, are calculated by finite element analysis and stored in a look-up table. During the dynamic analysis with the proposed voltage equations, the inductances are fitted using a spline interpolation. The dynamic analysis results are compared with those of conventional winding SRM which are already examined for verification of analysis method


title: DSDR: Dynamic Semantic Discard Reader for Open-Domain Question Answering

abstract: Open-domain question answering (QA) is an important problem in natural language processing. A QA system usually consists of an information retrieve (IR) model for finding the relevant passages that may have answers and a read comprehension (RC) model for generating answers from the selected passages. The state-of-the-art RC model uses all the passages for feature extraction when matching semantic information of text pairs. In this paper, however, we argue that the texts of passages often have different importance for specific questions and some even provide negative information that might affect the correctness of final prediction. To address this problem, we design dynamic semantic discard reader (DSDR), a deep model that tries to drop negative information irrelevant to the questions. The result of our experiment shows that DSDR outperforms existing methods in the exact-match and F1 scores on open-domain QA datasets.


title: PruNet: Class-Blind Pruning Method For Deep Neural Networks

abstract: DNNs are highly memory and computationally intensive, due to which they are unfeasible to deploy in real time or mobile applications, where power and memory resources are scarce. Introducing sparsity in the network is a way to reduce those requirements. However, systematically employing pruning under given accuracy requirements is a challenging problem. We propose a novel methodology that iteratively applies a magnitude-based Class-Blind pruning to compress a DNN for obtaining a sparse model. It is a generic methodology and can be applied to different types of DNNs. We demonstrate that retraining after pruning is essential to restore the accuracy of the network. Experimental results show that our methodology is able to reduce the model size by around two orders of magnitude, without noticeably affecting the accuracy. It requires several iterations of pruning and retraining, but can achieve up to 190x Memory Saving Ratio (for the LeNet on the MNIST dataset) when compared to the baseline model. Similar results are also obtained for more complex networks like 91x for VGG-16 on the CIFAR100 dataset. If we combine this work with an efficient coding for sparse networks, like Compressed Sparse Column (CSC) or Compressed Sparse Row (CSR), we can obtain a reduced memory footprint. Our methodology can be complemented by other compression techniques, like weight sharing, quantization or fixed-point conversion, that allows to further reduce memory and computations.


title: A Qualitative Person-Oriented Inquiry into Women’s Perspectives on Knowledge and Knowing

abstract: The current study extends theory and research on women’s beliefs about knowledge and knowing. Whereas previous research focused primarily on describing differences between domains of knowing, we focus on differences within domains. We examined individual experiences in the narratives of 8 women (ages 36–42 years) that exemplify 4 different positions in the theoretical model known as Women’s Ways of Knowing (WAYS). Analyses were conducted using an interpretative phenomenological analysis. Although we found that women in our study described views on knowledge consistent with the WAYS domain of knowing in which they were classified, some aspects of the women’s interviews did not fit with their given domain. Two women could be classified under the same WAYS domain and have very different ways of understanding knowledge. We conclude that a person-oriented approach to personal epistemology research adds to and enriches aggregate-level understanding of subject matter. Implications for theoretical models of epistemological development are discussed.


Augmentation for Visual Question Answering


title: Numerical optimization of transverse flux induction heating systems

abstract: Transverse flux induction heating (TFH) of flat metal products is one of the most effective induction technologies. This method provides very high efficiency in combination with unique technological flexibility and extremely low floor space required. It makes TFH beyond competition to be applied in continuous strip production and processing lines. However, to realise all potential advantages of TFH concept is possible only by optimal design and control of heating installations. Experience of last years shows that successful creation of TFH systems can be only based on numerical modelling. At the same time even advanced 3D simulation codes do not provide exactable engineering solution by themselves. Additional application of automatic optimisation techniques is only the way to overcome this situation. This paper is devoted to application of most effective optimisation algorithms for atomised design of TFH systems. Genetic algorithms of optimisation are more and more often used in engineering science because of their unique possibilities. Use of genetic algorithms in combination with 3D electromagnetic and thermal analysis of TFH systems allows to make optimal 3D shape design of inductors. This problem can not be solved in effective way by conventional design methods.


title: Optimization of heterogeneous Bin packing using adaptive genetic algorithm

abstract: This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach. The optimal and feasible packing of goods for transportation and distribution to various locations by satisfying the practical constraints are the key points in this project work. As the number of boxes for packing can not be predicted in advance and the boxes may not be of same category always. It also involves many practical constraints that are why the optimal packing makes much importance to the industries. This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering most of the practical constraints facing in logistic industries. This goal was achieved in this research by optimizing the empty volume inside the container using genetic approach. Feasible packing pattern was achieved by satisfying various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint. 3D bin packing problem consists of 'n' number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and in-turn profit. Furthermore, Boxes to be packed may be of arbitrary sizes. The user input data are the number of bins, its size, shape, weight, and constraints if any along with standard container dimension. This user input were stored in the database and encoded to string (chromosomes) format which were normally acceptable by GA. GA operators were allowed to act over these encoded strings for finding the best solution.


title: Comparing Raw Data and Feature Extraction for Seizure Detection with Deep Learning Methods

abstract: Personalized health monitoring is slowly becoming a reality due to advances in small, high-fidelity sensors, low-power processors, as well as energy harvesting techniques. The ability to efficiently and effectively process this data and extract useful information is of the utmost importance. In this paper, we aim at dealing with this challenge for the application of automated seizure detection. We explore the use of a variety of representations and machine learning algorithms to the particular task of seizure detection in high-resolution, multi-channel EEG data. In doing so, we explore the classification accuracy, computational complexity and memory requirements with a view toward understanding which approaches are most suitable. In particular, we show that layered learning approaches such as Deep Belief Networks excel along these dimensions.


title: A Dynamic Histogram Equalization for Image Contrast Enhancement

abstract: A smart contrast enhancement technique, dynamic histogram equalization (DHE), is proposed. It takes control over traditional histogram equalization for appropriate contrast enhancement of images without introducing any severe side affects such as washed out appearance, over-enhancement of some features and noises, checkerboard effects etc.


title: Navigating nets: simple algorithms for proximity search

abstract: We present a simple deterministic data structure for maintaining a set S of points in a general metric space, while supporting proximity search (nearest neighbor and range queries) and updates to S (insertions and deletions). Our data structure consists of a sequence of progressively finer e-nets of S, with pointers that allow us to navigate easily from one scale to the next.We analyze the worst-case complexity of this data structure in terms of the "abstract dimensionality" of the metric S. Our data structure is extremely efficient for metrics of bounded dimension and is essentially optimal in a certain model of distance computation. Finally, as a special case, our approach improves over one recently devised by Karger and Ruhl [KR02].


title: Extending enterprise architecture modelling with business goals and requirements

abstract: The methods for enterprise architecture (EA), such as The Open Group Architecture Framework, acknowledge the importance of requirements modelling in the development of EAs. Modelling support is needed to specify, document, communicate and reason about goals and requirements. The current modelling techniques for EA focus on the products, services, processes and applications of an enterprise. In addition, techniques may be provided to describe structured requirements lists and use cases. Little support is available however for modelling the underlying motivation of EAs in terms of stakeholder concerns and the high-level goals that address these concerns. This article describes a language that supports the modelling of this motivation. The definition of the language is based on existing work on high-level goal and requirements modelling and is aligned with an existing standard for enterprise modelling: the ArchiMate language. Furthermore, the article illustrates how EA can benefit from analysis techniques from the requirements engineering domain.


title: Big Data in Product Development: Need for a Data Strategy

abstract: Products nowadays do not only consist of mechanical and electrical components but also of connectivity components. Connected products set up a feedback-loop and provide manufacturers with use phase data. Such data enables manufacturers to better understand how products are used, which could help to design products that better fit the actual use. The number of connected devices will increase, but there is little knowledge about problems that engineering companies face when using data analytics for product development. The literature review and the experience gained through case studies highlight that inexperienced companies especially run into problems at the planning phase even before data analytics can start. The findings emphasize that companies need to invest time to develop a data strategy beforehand. However, an analysis of existing process models for data initiatives reveals that only little support is provided for the systematic development of a data strategy. Therefore, this paper derives a framework for a process model that supports companies in developing a data strategy for product development. The paper ends with suggestions for future research.


title: Development of a soft robotic glove with high gripping force using force distributing compliant structures

abstract: This paper presents a high-force grip assist glove composed of soft and flexible materials. By using a new force distributing compliant structure, the proposed glove can assist high gripping force while adapting to human hand motion, which is difficult for conventional soft robotic gloves. The force distributing compliant structure is comprised of flexible frames, soft fabrics and belts, and can transmit a high force or moment to the distal end, even though the whole structure is flexible. Therefore, the proposed glove can assist gripping without a harmful load or misalignment on the human fingers. The palmar side of the glove is made of a thin and soft fabric and belts to facilitate the transfer of tactile sensation. The proposed assist glove enables the user to manipulate various objects owing to both the softness and high gripping force. The basic concept as well as thorough theoretical analysis about the force distributing compliant structures is described, and the experiments for quantitative validation of the developed assist gripper are presented.


title: A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy

abstract: Early detection of microaneurysms (MAs), the first sign of Diabetic Retinopathy (DR), is an essential first step in automated detection of DR to prevent vision loss and blindness. This study presents a novel and different algorithm for automatic detection of MAs in fluorescein angiography (FA) fundus images, based on Radon transform (RT) and multi-overlapping windows. This project addresses a novel method, in detection of retinal land marks and lesions to diagnose the DR. At the first step, optic nerve head (ONH) was detected and masked. In preprocessing stage, top-hat transformation and averaging filter were applied to remove the background. In main processing section, firstly, we divided the whole preprocessed image into sub-images and then segmented and masked the vascular tree by applying RT in each sub-image. After detecting and masking retinal vessels and ONH, MAs were detected and numbered by using RT and appropriated thresholding. The results of the proposed method were evaluated reported on three different retinal images databases, the Mashhad Database with 120 FA fundus images, Second Local Database from Tehran with 50 FA retinal images and a part of Retinopathy Online Challenge (ROC) database with 22 images. Automated DR detection demonstrated a sensitivity and specificity of 94% and 75% for Mashhad database and 100% and 70% for the Second Local Database respectively.


title: Texture-based fruit detection

abstract: In this paper, a technique based on texture analysis is proposed for detecting green fruits on plants. The method involves interest point feature extraction and descriptor computation, interest point classification using support vector machines, candidate fruit point mapping, morphological closing and fruit region extraction. In an empirical study using low-cost web camera sensors suitable for use in mechanized systems, 24 combinations of interest point features and interest point descriptors were evaluated on two fruit types (pineapple and bitter melon). The method is highly accurate, with single-image detection rates of 85 % for pineapples and 100 % for bitter melons. The method is thus sufficiently accurate for precise location and monitoring of textured fruit in the field. Future work will explore combination of detection and tracking for further improved results.


title: Novel Frequency-Agile Bandpass Filter With Wide Tuning Range and Spurious Suppression

abstract: A new frequency-agile scheme for the loaded stepped-impedance resonator (SIR) is introduced, which can be employed for designing a wideband tunable bandpass filter (BPF). Two identical capacitors are added to the two open ends, and a capacitor is added to the center of the SIR. By properly controlling their respective values, the even-mode resonant frequency $(f_\mathrm{even})$ can be bidirectionally tuned, resulting in a wideband tuning range (approximately double those of the traditional tunable resonators). However, in such frequency range, the odd-mode resonant frequency $(f_\mathrm{odd})$ functions as a spurious resonance would appear. To overcome this, the feed line is attached to the center of the SIR where it is always short-circuited for $f_\mathrm{odd}$ and its odd-order harmonics, which cannot be excited accordingly. Thus, the wideband tuning range of the passband $(f_\mathrm{even})$ in the proposed BPF is validated to be meaningful, and the selectivity of the passband is improved due to the suppression of odd-mode spurious near the passband. For demonstration, a wideband tunable BPF is designed, and the simulated and measured results are presented, showing good agreement. The results showcase that the tuning range of passband reaches 84.4%, i.e., from 0.77 to 1.42 GHz with insertion loss from 3.1 to 1 dB.


title: Decentralized active gate control for current balancing of parallel connected IGBT modules

abstract: In modern power converters operating at currents of few kA and voltages of several kV, Insulated Gate Bipolar Transistor (IGBT) modules are typically used. The increased power demand of modern inverters and rectifiers results in higher currents and voltages. Currents up to tens of kA and voltages up to tens of kV are required, which exceed the ratings of IGBT semiconductors currently available. Multiple IGBT modules can be connected in parallel or in series to provide the requested current or voltage rating. Depending on the interconnection of the switches, parameter variations in the semiconductors and in the system, as well as tolerances and delay times in the gate driving circuits, an unbalanced current or voltage distribution may occur. Therefore, the IGBT modules are generally derated, which results in an increased number of devices and volume being required. In this paper, a modular concept of a decentralized active gate control for current balancing of parallel connected IGBT modules is presented. It operates distributed to the gate drive units (GDU), the hardware and software configurations are independent of the system design and no restrictions on the number of parallel connected IGBT modules exist.


title: A rail-to-rail I/O operational amplifier with 0.5% g/sub m/ fluctuation using double p-channel differential input pairs

abstract: A new operational amplifier working with supply voltages 1.8V has been designed in SMIC 0.18-/spl mu/m CMOS technology. The p-channel differential input pair and a level-shift p-channel differential input pair operate in weak inversion. A constant transconductance (within 0.5% of maximum fluctuation) based on current mode is ensured for the entire input common mode range by a simple and novel small signal control circuit that offsets second-order effects under constant tail current, such as variation of the slope factors of weak inversion transistors and finite output resistance. The class AB output stage biased by a new cross-coupling voltage level shifter has a full voltage swing and better high frequency performance. This circuit provides a 46MHz gain bandwidth for a power consumption of 0.6mW with 5pF load.


title: DeepV2D: Video to Depth with Differentiable Structure from Motion

abstract: We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video. DeepV2D combines the representation ability of neural networks with the geometric principles governing image formation. We compose a collection of classical geometric algorithms, which are converted into trainable modules and combined into an end-to-end differentiable architecture. DeepV2D interleaves two stages: motion estimation and depth estimation. During inference, motion and depth estimation are alternated and converge to accurate depth.


title: A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization

abstract: Recent theoretical results describing the sum capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the power of the transmitted signal. This work is comprised of two parts. In this first part, we show that while the sum capacity grows linearly with the minimum of the number of antennas and users, the sum rate of channel inversion does not. This poor performance is due to the large spread in the singular values of the channel matrix. We introduce regularization to improve the condition of the inverse and maximize the signal-to-interference-plus-noise ratio at the receivers. Regularization enables linear growth and works especially well at low signal-to-noise ratios (SNRs), but as we show in the second part, an additional step is needed to achieve near-capacity performance at all SNRs.


title: Gender and sexual orientation differences in sexual response to sexual activities versus gender of actors in sexual films.

abstract: In this study, the authors investigated the hypothesis that women's sexual orientation and sexual responses in the laboratory correlate less highly than do men's because women respond primarily to the sexual activities performed by actors, whereas men respond primarily to the gender of the actors. The participants were 20 homosexual women, 27 heterosexual women, 17 homosexual men, and 27 heterosexual men. The videotaped stimuli included men and women engaging in same-sex intercourse, solitary masturbation, or nude exercise (no sexual activity); human male-female copulation; and animal (bonobo chimpanzee or Pan paniscus) copulation. Genital and subjective sexual arousal were continuously recorded. The genital responses of both sexes were weakest to nude exercise and strongest to intercourse. As predicted, however, actor gender was more important for men than for women, and the level of sexual activity was more important for women than for men. Consistent with this result, women responded genitally to bonobo copulation, whereas men did not. An unexpected result was that homosexual women responded more to nude female targets exercising and masturbating than to nude male targets, whereas heterosexual women responded about the same to both sexes at each activity level.


Knowledge Integration and Rule Extraction in Neural Networks


A Pencil-and-Paper Algorithm for Solving Sudoku Puzzles


title: Multi-caste An t Colony Optimization Algorithms

abstract: In this paper we present a multi-caste ant colony optimiza- tion approach, where each caste has its own set of parameters. Two vari- ants are proposed: in the rst, the composition of the castes remains xed throughout the optimization, whilst the other allows ants to move from one caste to another. Results obtained in several traveling salesperson problem instances reveal that the adoption of a multi-caste framework increases the robustness of ACO algorithms. In concrete, we show that the existence of dierent castes removes the need to carefully dene q0, an essential parameter for the success of Ant Colony System.


title: A memory-based realization of a binarized deep convolutional neural network

abstract: A pre-trained deep convolutional neural network (CNN) is a feed-forward computation perspective, which is widely used for the embedded systems, requires high power-and-area efficiency. This paper realizes a binarized CNN which treats only binary 2-values (+1/−1) for the inputs and the weights. In this case, the multiplier is replaced with an EX-NOR circuit. To reduce both power and area, we realize the 2-valued CNN by off- and on-chip memories. Since our 2D convolution operations are realized by the on-chip memory, our implementation consumes lower power than the DSP block. We decompose the memory part, and realize them by a cascade of memories (LUT cascade). By introducing a batch normalization technique, the classification error for the binarized CNN can be improved. We implemented the CIFAR-10 benchmark on the NetFPGA-SUME board, which has the Xilinx Inc. Virtex 7 FPGA and three on-chip QDR II+ Synchronous SRAMs. Compared with the conventional FPGA realizations, the performance is 2.82 times faster, the power efficiency is 1.76 times, and the area efficiency is 29.13 times better.


title: Design of a 28/38 GHz dual-band printed slot antenna for the future 5G mobile communication Networks

abstract: In this article, a dual-band printed slot antenna for the future fifth generation (5G) mobile networks are proposed. The antenna is compact with size of 0.8 λ 0 × 0.75 λ 0 at 28 GHz. Matching between a sector-disk shaped radiating patch and the 50-Ω microstrip line is manipulated through aproximity-feed technique. An elliptically shaped aperture is etched in the ground plane to enhance the antenna bandwidth. A shunt stub is used to get more enhancement of the impedance bandwidth of the antenna. To reduce the interference between the 5G system and other systems, π-shaped slot is etched off in the feed line to create a notched band of 30–34 GHz. The simulated results show that the designed antenna has a dual band function at 28/38 GHz that covers future 5G applications. The proposed antenna provides almost omni-directional patterns, relatively flat gain, and high radiation efficiency through the frequency band excluding the rejected band.


title: Position Sensorless Control for Four-Switch Three-Phase Brushless DC Motor Drives

abstract: This paper develops a new method of sensorless control for four-switch three-phase (FSTP) brushless dc (BLDC) motor drives to achieve the lowest manufacturing cost. The novel sensorless control includes the advantages of four-switch three-phase inverter (FSTPI) and sensorless control scheme in the same time. Formerly, it was impossible to achieve sensorless control for FSTPI using the traditional four-space-vectors PWM scheme, because there was no floating phase to detect back EMF. So, this paper proposes a novel asymmetric PWM scheme for FSTP BLDC motor drives to make six commutations and produce four floating phases to detect back-EMF. The operational principle of the sensorless control for FSTP BLDC motor drives is demonstrated by experimental results


title: A methodology for estimating the value of privacy in information disclosure systems

abstract: In many types of information systems, users face an implicit tradeoff between disclosing personal information and receiving benefits, such as discounts by an electronic commerce service that requires users to divulge some personal information. While these benefits are relatively measurable, the value of privacy involved in disclosing the information is much less tangible, making it hard to design and evaluate information systems that manage personal information. Meanwhile, existing methods to assess and measure the value of privacy, such as self-reported questionnaires, are notoriously unrelated of real-world behavior. To overcome this obstacle, we propose a methodology called VOPE (Value of Privacy Estimator), which relies on behavioral economics' Prospect Theory (Kahneman & Tversky, 1979) and valuates people's privacy preferences in information disclosure scenarios. VOPE is based on an iterative and responsive methodology in which users take or leave a transaction that includes a component of information disclosure. To evaluate the method, we conduct an empirical experiment (nź=ź195), estimating people's privacy valuations in electronic commerce transactions. We report on the convergence of estimations and validate our results by comparing the values to theoretical projections of existing results (Tsai, Egelman, Cranor, & Acquisti, 2011), and to another independent experiment that required participants to rank the sensitivity of information disclosure transactions. Finally, we discuss how information systems designers and regulators can use VOPE to create and to oversee systems that balance privacy and utility. Display Omitted We propose a methodology (called VOPE) to valuate people's perceptions of privacy.System designers and regulators can use VOPE to balance users' privacy and utility.VOPE applies behavioral economics Prospect Theory in an iterative feedback process.A user study (nź=ź195) shows that valuations converge with the number of iterations.We validated VOPE by comparing valuations to ranking of privacy choices.


title: An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance

abstract: The nature of problem and claim management environment are well-defined.Very comprehensive literature revive is presented.An IML based novel DSS detecting fraud and abuse in healthcare insurance is developed.Unlike earlier studies, the system embraces all relevant actors and commodities.Real data are used for the evaluation and perform better results wrt previous studies. Detecting fraudulent and abusive cases in healthcare is one of the most challenging problems for data mining studies. However, most of the existing studies have a shortage of real data for analysis and focus on a very limited version of the problem by covering only a specific actor, healthcare service, or disease. The purpose of this study is to implement and evaluate a novel framework to detect fraudulent and abusive cases independently from the actors and commodities involved in the claims and an extensible structure to introduce new fraud and abuse types. Interactive machine learning that allows incorporating expert knowledge in an unsupervised setting is utilized to detect fraud and abusive cases in healthcare. In order to increase the accuracy of the framework, several well-known methods are utilized, such as the pairwise comparison method of analytic hierarchical processing (AHP) for weighting the actors and attributes, expectation maximization (EM) for clustering similar actors, two-stage data warehousing for proactive risk calculations, visualization tools for effective analyzing, and z-score and standardization in order to calculate the risks. The experts are involved in all phases of the study and produce six different abnormal behavior types using storyboards. The proposed framework is evaluated with real-life data for six different abnormal behavior types for prescriptions by covering all relevant actors and commodities. The Area Under the Curve (AUC) values are presented for each experiment. Moreover, a cost-saving model is also presented. The developed framework, i.e., the eFAD suite, is actor- and commodity-independent, configurable (i.e., easily adaptable in the dynamic environment of fraud and abusive behaviors), and effectively handles the fragmented nature of abnormal behaviors. The proposed framework combines both proactive and retrospective analysis with an enhanced visualization tool that significantly reduces the time requirements for the fact-finding process after the eFAD detects risky claims. This system is utilized by a company to produce monthly reports that include abnormal behaviors to be evaluated by the insurance company.


title: A Review of Computer Vision Techniques for the Analysis of Urban Traffic

abstract: Automatic video analysis from urban surveillance cameras is a fast-emerging field based on computer vision techniques. We present here a comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). The decreasing hardware cost and, therefore, the increasing deployment of cameras have opened a wide application field for video analytics. Several monitoring objectives such as congestion, traffic rule violation, and vehicle interaction can be targeted using cameras that were typically originally installed for human operators. Systems for the detection and classification of vehicles on highways have successfully been using classical visual surveillance techniques such as background estimation and motion tracking for some time. The urban domain is more challenging with respect to traffic density, lower camera angles that lead to a high degree of occlusion, and the variety of road users. Methods from object categorization and 3-D modeling have inspired more advanced techniques to tackle these challenges. There is no commonly used data set or benchmark challenge, which makes the direct comparison of the proposed algorithms difficult. In addition, evaluation under challenging weather conditions (e.g., rain, fog, and darkness) would be desirable but is rarely performed. Future work should be directed toward robust combined detectors and classifiers for all road users, with a focus on realistic conditions during evaluation.


title: Code block segmentation hardware architecture for LTE-Advanced

abstract: A very efficient algorithm and hardware architecture for code block segmentation used on LTE-Advanced channel coding physical layer (PHY) is presented in this paper. Code block segmentation is a generic procedure which is commonly applied before turbo encoding. Its main function is to fragment a large transport block into smaller code blocks. This approach reduces memory requirements of the turbo code interleaver, without compromising its coding gain, since turbo encoder improves its performance as the size of the code block increases. The current work presents not only an optimized procedure with reduced computational complexity, but also an architecture with very low resource count, regarding ASIC or FPGA implementations, performing at a maximum frequency of 351 MHz on a FPGA architecture.


title: Towards Linked Research Data: an Institutional Approach

abstract: For Open Science to be widely adopted, a strong institutional support for scientists will be essential. Bielefeld University and the associated Center of Excellence Cognitive Interaction Technology (CITEC) have developed a platform that enables researchers to manage their publications and the underlying research data in an easy and efficient way. Following a Linked Data approach we integrate this data into a unified linked data store and interlink it with additional data sources from inside the university and outside sources like DBpedia. Based on the existing platform, a concrete case study from the domain of biology is implemented that releases optical motion tracking data of stick insect locomotion. We investigate the cost and usefulness of such a detailed, domain-specific semantic enrichment in order to evaluate whether this approach might be considered for large-scale deployment.


title: Medicinal plants, conservation and livelihoods

abstract: Many types of action can be taken in favour of the conservation and sustainable use of medicinal plants. Some of these are undertaken directly at the places where the plants are found, while others are less direct, such as some of those relating to commercial systems, ex situ conservation and bioprospecting. In the latter cases, actions taken will not lead to in situ conservation unless they feedback to improvements in the field. Probably the single most important role for medicinal plants in biological conservation is their ‘use’ to achieve conservation of natural habitats more generally. This stems from the special meanings that medicinal plants have to people, related to the major contributions that they make to many people's lives in terms of health support, financial income, cultural identity and livelihood security. Problems associated with biopiracy or (the other side of the coin) excessive restrictions on research have come to assume policy prominence in the general thematic area of ‘medicinal plant conservation and use’. The fair and equitable sharing of benefits from bioprospecting is required under the Convention on Biological Diversity, but it is not always easy to achieve these ideals in practice. While experience is accumulated in how this may practically be achieved, it is important, at the present time, that controls imposed on scientific research to prevent biopiracy or theft of local and indigenous intellectual property do not unduly restrict research that has little or nothing to do with these matters.


title: Large-Scale Analysis of Email Search and Organizational Strategies

abstract: Email continues to be an important form of communication as well as a way to manage tasks and archive personal information. As the volume of email grows, organizing and finding relevant email remains challenging. In this paper, we present a large-scale log analysis of the activities that people perform on email mes-sages (accessing external information via links or attachments, responding to messages, and organizing messages), their search behavior, and their organizational practices in a popular web email client. First, we characterize general email activities as well as activities associated with search. We find that within search sessions, peo-ple are more likely to access information and respond to messag-es but less likely to organize. Second, we examine the relation-ship between characteristics of a person's mailbox and their search and organizational practices. People with larger mailboxes tend to organize more, respond a little more, and access infor-mation less. People with larger mailboxes and folder structures search more, but the number of folders has less influence on search. Third, we extend previous work on email organization (e.g., filers vs. pilers; cleaners vs. keepers) by examining the extent to which these strategies are evident in our large-scale analysis and influence email activities and search. People who rely heavily on one organizational strategy tend to use others less. People who organize less tend to search more. Finally, we de-scribe how these insights can influence the design of email search.


title: A systematic literature review on agile requirements engineering practices and challenges

abstract: We mapped out 17 requirements engineering practices adopted by agile practitioners so far.Identified 5 challenges of traditional requirements engineering overcome by adopting agile requirements engineering.Found 8 challenges posed by following agile requirements engineering.Findings suggest that research context needs attention in terms of more empirical.The empirical results can help to analyse impact of adopting agile requirements engineering. Unlike traditional software development methods, agile methods are marked by extensive collaboration, i.e. face-to-face communication. Although claimed to be beneficial, the software development community as a whole is still unfamiliar with the role of the requirements engineering practices in agile methods. The term "agile requirements engineering" is used to define the "agile way" of planning, executing and reasoning about requirements engineering activities. Moreover, not much is known about the challenges posed by collaboration-oriented agile way of dealing with requirements engineering activities. Our goal is to map the evidence available about requirements engineering practices adopted and challenges faced by agile teams in order to understand how traditional requirements engineering issues are resolved using agile requirements engineering. We conducted a systematic review of literature published between 2002 and June 2013 and identified 21 papers, that discuss agile requirements engineering. We formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for our research goal. The review identified 17 practices of agile requirements engineering, five challenges traceable to traditional requirements engineering that were overcome by agile requirements engineering, and eight challenges posed by the practice of agile requirements engineering. However, our findings suggest that agile requirements engineering as a research context needs additional attention and more empirical results are required to better understand the impact of agile requirements engineering practices e.g. dealing with non-functional requirements and self-organising teams.


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title: MATLAB/GUI based simulation for photovoltaic systems

abstract: Renewable energy sources are being progressively more used for providing energy needs due to the growing concern of environmental pollution. Photovoltaic (PV) systems have a significant place within the renewable energy sources. Solar cells are the most important elements of photovoltaic systems. They determine the parameters to meet the power requested. For this reason, a simulation program is necessary to see the performance and behavior of the PV systems before the establishment in the different load and environment conditions. Therefore a program has been developed to simulate photovoltaic modules and components for PV systems. The program has been written in MATLAB®/Simulink® environment. In addition, the characteristic curves of photovoltaic module according to the values of the temperature and radiation are observed in MATLAB®/GUI interface.


title: An Isolated, bridgeless, quasi-resonant ZVS-switching, buck-boost single-stage AC-DC converter with power factor correction (PFC)

abstract: The number of power supplies connected to the public household power grid is constantly increasing. For a good mains voltage quality, the harmonic input currents are limited in standards. Many applications also require a lower output voltage with isolation. Today, this is mostly obtained via a multi-stage converter, consisting of a diode bridge rectifier, a boost PFC and a DC-DC converter with galvanic isolation. For higher efficiency, a bridgeless PFC-stage can be used, but it nevertheless requires a DC-DC converter for isolation. In order to further increase efficiency and decrease component count, a single-stage concept can be used. The Cuk “True Bridgeless PFC” rectifier system, for example, can perform all the requirements in a single stage. A simple application and realization of a converter topology is also quite important, especially in consumer electronics, where costs are crucial. In this paper, a new single stage topology with a different operation principle is presented, focusing on ease of use in hardware realization.


title: Coding for Distributed Fog Computing

abstract: Redundancy is abundant in fog networks (i.e., many computing and storage points) and grows linearly with network size. We demonstrate the transformational role of coding in fog computing for leveraging such redundancy to substantially reduce the bandwidth consumption and latency of computing. In particular, we discuss two recently proposed coding concepts, minimum bandwidth codes and minimum latency codes, and illustrate their impacts on fog computing. We also review a unified coding framework that includes the above two coding techniques as special cases, and enables a trade-off between computation latency and communication load to optimize system performance. At the end, we will discuss several open problems and future research directions.