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Contact Logics provide a natural framework for representing and reasoning about regions in several areas of computer science. In this paper, we focus our attention on reasoning methods for Contact Logics and address the satisfiability problem and the unifiability problem. Firstly, we give sound and complete tableaux-based decision procedures in Contact Logics and we obtain new results about the decidability/complexity of the satisfiability problem in these logics. Secondly, we address the computability of the unifiability problem in Contact Logics and we obtain new results about the unification type of the unifiability problem in these logics.

A common task of Web users is querying structured information from Web pages. In this paper we propose a novel query processor for systematically discovering any-k relations from Web search results with conjunctive queries. The 'any-k' phrase denotes that retrieved tuples are not ranked by the system.For realizing this interesting scenario the query processor transfers a structured query into keyword queries that are submitted to a search engine, forwards search results to relation extractors, and then combines relations into result tuples.Unfortunately, relation extractors may fail to return a relation for a result tuple. We propose a solid information theory-based approach for retrieving missing attribute values of partially retrieved relations. Moreover, user-defined data sources may not return at least k complete result tuples. To solve this problem, we extend the Eddy query processing mechanism [14] for our 'querying the Web' scenario with a continuous, adaptive routing model. The model determines the most promising next incomplete row for returning any-k complete result tuples at any point during the query execution process.We report a thorough experimental evaluation over multiple relation extractors. Our experiments demonstrate that our query processor returns complete result tuples while processing only very few Web pages.

The classification of color image that contain skin pixel is a challenging process. It is challenging due to certain factor such as brightness, background similar to human skin represent the obstacles thereof. The basic approach of skin detection is color based classification. The human skin image is the composition of RGB color. The data values of skin is calculated by randomly sampling RGB color values of different face images which have different age, race, genders sets. However, its performance has not really been high because of the high overlapped degree between “skin” and “non-skin” pixels. This paper describes the linear norm fuzzy based twin support vector machine (LN-FTSVM)) approach for discriminate skin and non-skin data values of skin dataset and to enhance the skin recognition performance. The concept of fuzzy is resolved the unclassified and overlapped data region problems. If no decision function is positive for a data set, this data set is classified into a class with the large membership value. By computational experiments shows that Experiments result shows that the accuracy is improved by linear norm fuzzy based TSVM (LN-FTSVM)) over the conventional methods.

To improve the intelligent image recognition abilities of edge devices, a parallel-optimization-based framework called POWER is introduced in this paper. With FPGA (Field-Programmable Gate Array) as its hardware module, POWER provides well extensibility and flexible customization capability for developing intelligent firmware suitable for different types of edge devices in various scenarios. Through an actual case study, we design and implement a firmware prototype following the specification of POWER and explore its performance improvement using parallel optimization. Our experimental results show that the firmware prototype we implement exhibits good performance and is applicable to substation inspection robots, which also validate the effectiveness of our POWER framework in designing edge intelligent firmware modules indirectly.

In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a &ldquo, cyber-pilot&rdquo, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a &ldquo, virtual sensor&rdquo, which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory.

In this paper, a new configuration for a multi-terminal offshore wind farms based VSC-HVDC and large scale photovoltaic system using modified unified adaptive linear neuron (Adaline) algorithm is presented. In the offshore station, the permanent magnet synchronous generator (PMSG)-based wind turbines are tied to the bus via back-to-back converters and a step-up transformer. The large scale photovoltaic power is connected to the dc bus between the offshore voltage source converters (VSC) and onshore VSC-HVDC stations via a modular multilevel converter (MMC). The Power management and the synchronization between two buses are achieved by controlling the two VSC converters. The performance of the topology proposed is tested using Matlab/Simulink during permanent operation mode and in the presence of DC fault.

There are a number of methods for classification of mobile apps, but most of them rely on a fixed set of app categories and text descriptions associated with the apps. Often, one may need to classify apps into a different taxonomy and might have limited app usage data for the purpose. In this paper, we present an app classification system that uses object detection and recognition in images associated with apps, along with text based metadata of the apps, to generate a more accurate classification for a given app according to a given taxonomy. Our image based approach can, in principle, complement any existing text based approach for app classification. We train a fast RCNN to learn the coordinates of bounding boxes in an app image for effective object detection, as well as labels for the objects. We then use the detected objects in the app images in an ensemble with a text based system that uses a hierarchical supervised active learning pipeline based on uncertainty sampling for generating the training samples for a classifier. Using the ensemble, we are able to obtain better classification accuracy than if either of the text or image systems are used on their own.

In nature, many species became extinct as they could not adapt quickly enough to their environment. They were simply not fit enough to adapt to more and more challenging circumstances. Similar things happen when algorithms are too static to cope with particular challenges of their "environment", be it the workload, the machine, or the user requirements. In this regard, in this paper we explore the well-researched and fascinating family of adaptive indexing algorithms. Classical adaptive indexes solely adapt the indexedness of the data to the workload. However, we will learn that so far we have overlooked a second higher level of adaptivity, namely the one of the indexing algorithm itself. We will coin this second level of adaptivity meta-adaptivity. Based on a careful experimental analysis, we will develop an adaptive index, which realizes meta-adaptivity by (1) generalizing the way reorganization is performed, (2) reacting to the evolving indexedness and varying reorganization effort, and (3) defusing skewed distributions in the input data. As we will demonstrate, this allows us to emulate the characteristics of a large set of specialized adaptive indexing algorithms. In an extensive experimental study we will show that our meta-adaptive index is extremely fit in a variety of environments and outperforms a large amount of specialized adaptive indexes under various query access patterns and key distributions.

Searching for information about a specific person is a frequent online activity. In most cases, users are aided in the search process by queries containing a name in Web search engines. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Most existing solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic algorithm which addresses the synonym suggestion problem by utilizing automated speech generation, and deep learning to produce novel spoken name embeddings. These embeddings capture the way people pronounce names in a particular language and accent. Utilizing a name’s pronunciation can help detect names that sound alike, but are written differently. We demonstrated the proposed approach on a large-scale dataset with more than 250,000 forenames and surnames and evaluated it on two ground truth datasets containing 7400 forenames and 25,000 surnames (including their verified synonyms). The performance of SpokenName2Vec was found superior to the 10 other algorithms evaluated, including phonetic encoding, string similarity, and machine learning algorithms. The results obtained emphasize the potential of spoken name embeddings for improved synonym suggestion.

Due to the high importance of economic dispatch in planning and operating electric power systems, new methods have been researched to minimize the costs of power generation. To calculate these costs, the power generation of each thermal unit must be evaluated. When a thermal unit is modelled considering real world constraints, such as multiple fuels and valve point effect, traditional optimization methods are inefficient due to the nature of the cost function. This paper shows a study of a metaheuristic method, based on flower pollination to search for satisfactory results for economic dispatch. The results obtained are compared with results from other authors, with the purpose of evaluating how efficient the technique presented here is.

Machine vision based automatic apparatus is a necessary component of an automated production line. In this paper, taking can-end surface defect detection as an example, we presented a visual inspector and proposed a new surface defect detection framework. Firstly, the Region of Interest (ROI) of the input image is got by circle detection and prior radius knowledge of can-end. Then, the saliency map and texture filtering image are computed to overcome the problems of irregular texture and non-uniform illumination. Finally, all suspected defects are obtained by saliency map segmentation, connected component analysis. Following, the size, the average saliency value and the mean texture filtering value of each suspected defective region are calculated, and the 3-dimensional features are used to distinguish true defects in the central panel region of can-end. We employ the designed equipment to grab can-end images and do tests divided into four groups on the grabbed images. The accuracy is 98.16% and the execution time is about 65.7 milliseconds. Experimental results demonstrate that the proposed method can inspect faint and low contrast defect accurately. Meanwhile, the proposed method is simple and computationally efficient and meets the high speed of industrial automation.

The Advanced Encryption Standard (AES) enables secure transmission of confidential messages. Since its invention, there have been many proposed attacks against the scheme. For example, one can inject errors or faults to acquire the encryption keys. It has been shown that the AES algorithm itself does not provide a protection against these types of attacks. Therefore, additional techniques like error control codes (ECCs) have been proposed to detect active attacks. However, not all the proposed solutions show the adequate efficacy. For instance, linear ECCs have some critical limitations, especially when the injected errors are beyond their fault detection or tolerance capabilities. In this paper, we propose a new method based on a non-linear code to protect all four internal stages of the AES hardware implementation. With this method, the protected AES system is able to (a) detect all multiplicity of errors with a high probability and (b) correct them if the errors follow certain patterns or frequencies. Results shows that the proposed method provides much higher security and reliability to the AES hardware implementation with minimal overhead.

Virtual reality (VR) offers potential as a prototyping tool for human-robot interaction. We explored a way to utilize human-centered design (HCD) methodology to develop a collaborative VR game for understanding teens' perceptions of, and interactions with, social robots. Our paper features three stages of the design process for teen-robot interaction in VR; ideation, prototyping, and the game development. In the ideation stage, we identified three important design principles: collaboration, customization, and robot characterization. In the prototyping stage, we developed a card game, conducted gameplay, and confirmed our design principles. Finally, we developed a low-fidelity VR game and received teens' feedback. This exploratory study highlights the potential of VR, both for collaborative robot design and teen-robot interaction studies.

Ambient Assisted Living (AAL) services are emerging as context-awareness solutions to support elderly people’s autonomy. The context-aware paradigm makes applications more user-adaptive. In this way, context and user models expressed in ontologies are employed by applications to describe user and environment characteristics. The rapid advance of technology allows creating context server to relieve applications of context reasoning techniques. Specifically, the Next Generation Networks (NGN) provides by means of the presence service a framework to manage the current user’s state as well as the user’s profile information extracted from Internet and mobile context. This paper propose a user modeling ontology for AAL services which can be deployed in a NGN environment with the aim at adapting their functionalities to the elderly’s context information and state.

Location-based services are quickly becoming immensely popular. In addition to services based on users' current location, many potential services rely on users' location history, or their spatial-temporal provenance. Malicious users may lie about their spatial-temporal provenance without a carefully designed security system for users to prove their past locations. In this paper, we present the Spatial-Temporal provenance Assurance with Mutual Proofs (STAMP) scheme. STAMP is designed for ad-hoc mobile users generating location proofs for each other in a distributed setting. However, it can easily accommodate trusted mobile users and wireless access points. STAMP ensures the integrity and non-transferability of the location proofs and protects users' privacy. A semi-trusted Certification Authority is used to distribute cryptographic keys as well as guard users against collusion by a light-weight entropy-based trust evaluation approach. Our prototype implementation on the Android platform shows that STAMP is low-cost in terms of computational and storage resources. Extensive simulation experiments show that our entropy-based trust model is able to achieve high $(>0.9)$ collusion detection accuracy.

Elasticity makes cloud computing an attractive platform for executing complex large-scale expensive dataflows, as it enables different trade-offs between execution time and monetary cost, by varying the number of resources to be provisioned. With cloud providers offering heterogeneous types of resources with different performance and price characteristics, the problem of identifying the various trade-offs available is a great challenge, as the number of possible alternative configurations increases significantly compared to a homogeneous environment, which is itself already computationally difficult. This paper proposes a novel algorithm for dataflow scheduling on heterogeneous clouds that identifies solutions (schedules) close to the optimal pareto front, by exploring the search space in an efficient way. The results of an experimental comparison with the state of the art show that, in several cases, the proposed algorithm provides a richer, more diverse set of solutions, several of which are characterized by significantly better time-money trade-offs.

An original and an effective method for the analysis of the dc-link voltage ripple for single-phase multilevel inverters based on H-bridge and level doubling network (LDN) is given in this paper. The LDN is basically a half-bridge fed by a floating capacitor, without the need of an additional dc supply and with voltage self-balancing capability, able to increase (double) the number of output voltage levels. This configuration is particularly suitable for single-phase single-stage grid-connected photovoltaic (PV) systems, and the LDN cell can be seen also as a retrofit for existing H-bridge configurations. In order to address the design of the conversion system, with particular reference to the dc-link capacitor, PV current and voltage ripples are analytically determined and verified by experimental tests.

The paper presents a novel short-range wireless sensor node architecture, based on Integral Pulse Frequency Modulator (IPFM) and Ultra-Wideband (UWB) pulse generator. Due to the lack of internal clock signal source and multi user implementation without application of a microprocessor, the architecture is simple and energy efficient. Multi-user coding is performed using delay elements, where each user has unique delay time value. The output of the IPFM is fed to the delay element. Delayed and original signal from the IPFM are feeding UWB pulse generator. The paper presents the transmitter architecture and measurements of the transmitter signals in time and frequency domain.

International audience; This paper addresses the localization and super-resolutionof license plate in a unifying approach. Higher qualitylicense plate can be obtained using super-resolution onsuccessive lower resolution plate images. All existingmethods assume that plate zones are correctly extractedfrom every frame. However, the accurate localization needsa sufficient quality of the image, which is not always truein real video. Super-resolution on all pixels is a possiblebut much time consuming alternative. We propose a frameworkwhich interlaces successfully these two modules. First,coarse candidates are found by an weak but fast licenseplate detection based on edge map sub-sampling. Then, animproved fast MAP-based super-resolution, using local phaseaccurate registration and edge preserving prior, is appliedon these regions of interest. Finally, our robustICHT-based localizer rejects false-alarms and localizes thehigh resolution license plate more accurately. Experimentswhich were conducted on synthetic and real data, proved therobustness of our approach with real-time possibility.

It is inevitable for data collection that IoT sensors are distributed to interested areas. However, not only the proper placement of sensors, but also the replacement of sensors that have run out of energy is very difficult. As a remedy, wireless charging systems for IoT sensors have been researched recently, but it is apparent that the availability of charging system is limited especially for IoT sensors scattered in rugged terrain. Thus, it is important that the sensor relocation models to recover sensing holes employ energy-efficient scheme. While there are various methods in the mobile model of wireless sensors, well-known wheel-based movements in rough areas are hard to achieve. Thus, research is ongoing in various areas of the hopping mobile model in which wireless sensors jump. Many past studies about hopping sensor relocation assume that all sensor nodes are aware of entire network information throughout the network. These assumptions do not fit well to the actual environment, and they are nothing but classical theoretical research. In addition, the physical environment (sand, mud, etc.) of the area in which the sensor is deployed can change from time to time. In this paper, we overcome the theoretical-based problems of the past researches and propose a new realistic hopping sensor relocation protocol considering terrain conditions. Since the status of obstacles around the sensing hole is unknown, the success rate of the hopping sensor relocation is used to predict the condition of the surrounding environment. Also, we are confident that our team is uniquely implementing OMNeT++ (Objective Modular Network Testbed in C++) simulation in the hopping sensor relocation protocol to reflect the actual communication environment. Simulations have been performed on various obstacles for performance evaluation and analysis, and we are confident that better energy efficiency with later appearance of sensing holes can be achieved compared to well-known relocation protocols.

In this study a multi-objective problem considering uncertainty and flexibility of job sequence in an automated flexible job shop (AFJS) is considered using manufacturing simulation. The AFJS production system is considered as a complex problem due to automatic elements requiring planning and optimization. Several solution approaches are proposed lately in different categories of meta-heuristics, combinatorial optimization and mathematically originated methods. This paper provides the metamodel using simulation optimization approach based on multi-objective efficiency. The proposed metamodel includes different general techniques and swarm intelligent technique to reach the optimum solution of uncertain resource assignment and job sequences in an AFJS. In order to show the efficiency and productivity of the proposed approach, various experimental scenarios are considered. Results show the optimal resources assignment and optimal job sequence which cause efficiency and productivity maximization. The makespan, number of late jobs, total flow time and total weighted flow time minimization have been resulted in an automated flexible job shop too.

The increasing transistor scale integration poses, among others, the thermal-aware floorplanning problem consisting of how to place the hardware components in order to reduce overheating by dissipation. Because of the huge amount of feasible floorplans, most of the solutions found in the literature include an evolutionary algorithm for, either partially or completely, carrying out the task of floorplanning. Evolutionary algorithms usually have a bottleneck in the fitness evaluation. In the problem of thermal-aware floorplanning, the layout evaluation by the thermal model takes 99.5% of the computational time for the best floorplanning algorithm proposed so far. The contribution of this paper is to present a parallelization of this evaluation phase in a master-worker model to achieve a dramatic speed-up of the thermal-aware floorplanning process. Exhaustive experimentation was carried out over 3D integrated circuits, with 48 and 128 cores, outperforming previous published works. Copyright (c) 2012 John Wiley & Sons, Ltd.

A good smart contract can provide safe and reliable services to efficiently solve real-world problems. However, smart contracts written by developers create potential security vulnerabilities and can result in huge economic losses. Therein, detecting potential vulnerabilities in smart contracts plays an important role in maintaining the security of the blockchain. There are a series of schemes work on finding bugs in smart contracts, such as Oyente and ContractFuzzer, etc. However, existing solutions only address several types of security vulnerabilities on a single platform. In this paper, we propose a general framework to detect potential vulnerabilities of smart contracts. In order to abstract the execution logic of smart contracts, we need to convert them into control flow graphs. We use the Ullmann algorithm to discover vulnerabilities from these graphs. Also, we give a running example to illustrate how our framework can be used to detect the reentrancy problems written by solidity.

In this paper we propose a Sliding Mode Controller for a class of scalar bilinear systems with delay in both the input and the state. Such a class is considered since it has shown to be suitable for modelling and control of a class of turbulent flow systems. The stability and robustness analysis for the reaching phase in the controlled system are Lyapunov-based. However, since the sliding dynamics is infinite dimensional and described by an integral equation, we show that the stability and robustness analysis is simplified by using Volterra operator theory.

The high energy consumption has become one bottleneck in the development of the data centers (DCs), where the main energy consumers are the cooling system and the servers. Therefore, the joint optimization for the energy efficiency of the cooling system and the servers is a crucial problem, while most of previous works on energy saving only studies one of these two components in an isolated manner. In this paper, we propose a real-time strategy, rTCS (real-time Task Classification and Scheduling strategy), to jointly optimize the energy efficiency of these two components in the scenario where the tasks arrive dynamically. Strategy rTCS first labels the tasks to classify them according to their run time and end time with a time complexity of O(1) and a bounded space complexity. Then, rTCS schedules the tasks in real time based on their labels and the energy consumption model of the DC. Simulation results show that rTCS can effectively improve the energy efficiency of DCs.

Due to the existing cryptographic schemes based on the satisfiability problem (SAT) lack the specific security model and the security analysis of the provable security theory, the security is questioned. For this problem, combining with the advantages of SAT and Elgamal scheme, we construct a secure, verifiable encryption scheme in this paper. Firstly, combining and improving the SRR(N, k, s) model based on the difficulty problem of the random regular formula near the phase transition point, we construct an encryption algorithm. Secondly, on the basis of the existing Damgârd Elgamal scheme, a more secure scheme based on SAT and variants of discrete logarithm problem is proposed. Lastly, the analysis results show that compared with the existing SAT-based hybrid cryptographic schemes, the private key of the hybrid encryption scheme proposed in this paper is shorter and the ciphertext satisfies the verifiability, and the scheme can secure against adaptive chosen ciphertext attacks (CCA2).

International audience; Mimicry and deferred imitation have often been considered as separate kinds of imitation. In this paper, we present a simple architecture for robotic arm control which can be used for both. The model is based on some dynamical equations, which provide a motor control with exploring and converging capacities. A visuo-motor map is used to associate positions of the end effector in the visual space with proprioceptive position of the robotic arm. It enables a fast learning of the visuo-motor associations without needing to embed a priori information. The controller can be used both for accurate control and interaction. It has been implemented on a minimal robotic setup showing some interesting emergent properties. The robot can reproduce simple gestures in mimicry situation and finalized actions in deferred imitation situation. Moreover, it can even show some “intention” recognition abilities. Finally, the experiment of deferred imitation which inherits from learning by demonstration, also provides a good basis for cooperative and interactive experiments.

Chemical reaction system, dynamic operation can significantly increase the average rate of reaction, improve the time-average selectivity of complex reactions and enhance the molecular weight distribution of certain free-radical polymerization reactions, overcome the thermodynamic limitations of reversible reactions. It even can be used as integrated means of exothermic/ endothermic reaction and catalytic reaction/catalyst regeneration, opens up new ways to strengthen and control the reaction process, reduce waste emissions, and increase economic and social benefits. Therefore, it has great significance to model, simulate and calculate the process of chemical reaction. In this paper, we propose a firefly algorithm with memory and adaptive step length to solve the optimal dynamic model of chemical reaction. The characteristics of the proposed algorithm are analyzed in detailed and the simulation results of the algorithm are given. It provides a feasible solution to solve such problems and the simulation results also show the effectiveness of the proposed algorithm.

Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research., Accepted for publication at 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA 2020

The paper presents the concept of using a Protege environment to extract knowledge about the properties and features of discrete problems described by ALMM technology. The concept of an Intelligent ALMM system was proposed firstly by Dudek-Dyduch E. The main purpose of the ALMM system is not only to solve problems with discreet optimization, but also to help the user in choosing the right method and algorithm for solving them. The authors, based on earlier work connected with component structure based solvers using ALMM technology, proposed the exploitation of description of the properties of individual problems in order to create the corresponding knowledge base. This paper presents an example of a knowledge base structure for the problem of scheduling tasks on a single machine that can be used to solve various optimization tasks. Using the OWL language convention, the architecture of classes and related properties of solved problems: axioms and assertions were proposed. An on-line knowledge base representing specific classes of problems together with its own query language can be a scenario for pre-modeling the architecture of the final application. For the ontological architecture, the examples of knowledge extraction scenarios to solve the optimization problem of the tasks scheduling on a single machine are presented too.

As mobile technologies become more ubiquitous, design work at the intersection of mixed reality and embodied learning is growing. While much of this work focuses on designing technologies and environments for children, we contribute a unique perspective of children as designers of these technologies. In this paper, we explore how children embody and debug computational algorithms through designing their own mixed reality games. We conducted two afterschool workshops with 19 middle school aged children (3 girls, 16 boys, ages 10-13) during which participants designed mobile, location-based games with mixed reality technologies about local plants and animals. Findings reveal how participants across workshops embody a key game mechanic (digitally spawning characters in the real world) by engaging in an iterative digital-to-physical-to-digital debugging process that led to their understanding of the underlying computational algorithm. We further present design considerations for authoring platforms that allow children to design with mixed reality, place-based technologies.

This paper presents a new method to optimize, from a working cycle defined by torque and speed profiles, both the design and the control strategy of permanent magnet synchronous generators (PMSGs). The case of a 10 MW direct-drive permanent magnet generator for an Offshore wind turbine was chosen to illustrate this method, which is based on the d–q axis equivalent circuit model. It allows to optimize, with a reduced computation time, the design, considering either a flux weakening control strategy (FW) or a maximum torque per Ampere control (MTPA) strategy, while respecting all the constraints—particularly the thermal constraint, which is characterized by a transient regime. The considered objective is to minimize the mass and the average electric losses over all working points. Thermal and magnetic analytical models are validated by a 2D finite element analysis (FEA).

In this paper, we investigate the adaptive video delivery for multiple users over time-varying and mutually interfering multi-cell wireless networks. The key research challenge is to jointly design the physical-layer resource allocation scheme and application-layer rate adaptation logic, such that the users’ long-term fair quality of experience (QoE) can be maximized. Due to the timescale mismatch between these two layers and the asynchrony of user requests, however, it is difficult to directly model the cross-layer stochastic control problem by using a reinforcement learning framework. To address this difficulty, we propose a novel two-level decision framework where an optimization-based beamforming scheme (performed at the base stations) and a deep reinforcement learning (DRL)-based rate adaptation scheme (performed at the user terminals) are, respectively, developed, such that a highly complex long-term multi-user QoE fairness problem is decomposed into some relatively simple problems and solved effectively. Our strategy represents a significant departure from the existing schemes with consideration of either a short-term multi-user QoE maximization or a long-term single-user point-to-point QoE maximization. Extensive simulations demonstrate that the proposed cross-layer design is effective and promising.

Most existing high-power solar array simulators are suitable for simulating solar array energy sources in terrestrial applications. However, they are not sufficiently fast for spacecraft power-system testing using terrestrial testing applications. This paper proposes a UI-RI hybrid lookup table method with high linearity and high-speed convergence performance, which outperforms the traditional single UI lookup table method by employing a field-programmable gate array digital I–V outer loop. The proposed method overcomes the difficulty in guaranteeing stable output characteristics of the current output-type space solar array simulator (SSAS) because of the nonlinear I–V curves and slow convergence speed to the target operating point of the latter. The new lookup method is applied to a 2.4-kW hardware power platform of SSAS, and the SSAS can quickly respond to a load step between the short-circuit and open-circuit (OC) states, which is the harshest working condition, using the UI-RI hybrid lookup table method with a shunt switching frequency of 2 kHz. It can also quickly respond to a load step between the OC and rated-load states using a series-switching frequency of 10 kHz. The experimental data-point distribution trajectory of the switching process shows a greater tendency to converge to the designed I–V curve compared with the single UI lookup table method.

Identifying butterfly species by image patterns is a challenging task in computer vision and pattern recognition community due to many butterfly species having similar shape patterns with complex interior structures and considerable pose variation. In additional, geometrical transformation and illumination variation also make this task more difficult. In this paper, a novel image descriptor, named Gaussian convolution angle (GCA) is proposed for butterfly species classification. The proposed GCA projects the butterfly vein image function and intensity image function along a group of vectors that start from a common contour points and ends at the remaining contour points which results a group of vectors that capture the complex vein patterns and texture patterns of butterfly images. The Gaussian convolutions of different scales are conducted to the resulting vector functions to generate a multiscale GCA descriptors. The proposed GCA is not only invariant to geometrical transformation including rotation, scaling and translation, but also invariant to lighting change. The proposed method has been tested on a publicly available butterfly image dataset that has 832 samples of 10 species. It achieves a classification accuracy of 92.03% which is higher than the benchmark methods.

The objectives set for smart grids are as diverse as they are exciting and ambitious. Instead of overloads, bottlenecks and blackouts, smart grids will ensure the reliability, sustainability and efficiency of the Electric Vehicle Supply Equipment (EVSEs). Therefore, EVSEs and Electric Vehicles (EVs) must integrate wireless communication between them to discover the availability and make pre-reservations of charging time slots. In this paper, we propose a novel multi-objective EV Charging Slots Assignment (CSA) optimization model that, besides balancing energy usage between EVSEs, minimizes the latency time of EVs. Moreover, we integrate our optimization model to a communication protocol between EVs and EVSEs that allows a reliable reservation process, called Reliable Broadcast for EV Charging Assignment (REBECA) [1]. Then, we propose a centralized heuristic to efficiently solve our model as an offline CSA process.

Training of Convolutional Neural Networks (CNNs) on long video sequences is computationally expensive due to the substantial memory requirements and the massive number of parameters that deep architectures demand. Early fusion of video frames is thus a standard technique, in which several consecutive frames are first agglomerated into a compact representation, and then fed into the CNN as an input sample. For this purpose, a summarization approach that represents a set of consecutive RGB frames by a single dynamic image to capture pixel dynamics is proposed recently. In this paper, we introduce a novel ordered representation of consecutive optical flow frames as an alternative and argue that this representation captures the action dynamics more effectively than RGB frames. We provide intuitions on why such a representation is better for action recognition. We validate our claims on standard benchmark datasets and demonstrate that using summaries of flow images lead to significant improvements over RGB frames while achieving accuracy comparable to the state-of-the-art on UCF101 and HMDB datasets., Comment: Accepted in WACV 2017

This paper reports a concise complexity analysis for the multilevel fast multipole algorithm. The algorithm can solve a plethora of problems governed by different kinds of physics, and therefore the analysis is done for surface and volume problems in two and three dimensions for the sake of generality.

Existing Multi-Instance learning (MIL) methods for medical image classification typically segment an image (bag) into small patches (instances) and learn a classifier to predict the label of an unknown bag. Most of such methods assume that instances within a bag are independently and identically distributed. However, instances in the same bag often interact with each other. In this paper, we propose an Induced SelfAttention based deep MIL method that uses the self-attention mechanism for learning the global structure information within a bag. To alleviate the computational complexity of the naive implementation of self-attention, we introduce an inducing point based scheme into the self-attention block. We show empirically that the proposed method is superior to other deep MIL methods in terms of performance and interpretability on three medical image data sets. We also employ a synthetic MIL data set to provide an intensive analysis of the effectiveness of our method. The experimental results reveal that the induced self-attention mechanism can learn very discriminative and different features for target and non-target instances within a bag, and thus fits more generalized MIL problems.

User stories are descriptions of functionality that a software user needs. They play an important role in determining which software requirements and bug fixes should be handled and in what order. Developers elicit user stories through meetings with customers. But user story elicitation is complex, and involves many passes to accommodate shifting and unclear customer needs. The result is that developers must take detailed notes during meetings or risk missing important information. Ideally, developers would be freed of the need to take notes themselves, and instead speak naturally with their customers. This paper is a step towards that ideal. We present a technique for automatically extracting information relevant to user stories from recorded conversations between customers and developers. We perform a qualitative study to demonstrate that user story information exists in these conversations in a sufficient quantity to extract automatically. From this, we found that roughly 10.2% of these conversations contained user story information. Then, we test our technique in a quantitative study to determine the degree to which our technique can extract user story information. In our experiment, our process obtained about 70.8% precision and 18.3% recall on the information.

A key operational decision faced by a multi-facility health care diagnostic centre serving different patient categories (for example: Health Check-up Patient (HCP), Out-Patient (OP), Emergency Patient (EP), or In-Patient) is whom to serve next at a particular facility. In this paper, we model random arrival of these patients belonging to different categories and priorities at multiple diagnostic facilities over a finite planning horizon. We formulate a mathematical model for sequential decision-making under uncertainty using Markov Decision Process (MDP) with the objective of maximising net revenue and use dynamic programming (DP) to solve it. To address dimensionality and scalability issue of MDP, we provide a decentralised MDP (D_MDP) formulation. We develop simulation-based neighbourhood search algorithm to improve DP solution for D_MDP. We compare these solutions with three other rule-based heuristics using simulation.

As the success of deep models has led to their deployment in all areas of computer vision, it is increasingly important to understand how these representations work and what they are capturing. In this paper, we shed light on deep spatiotemporal representations by visualizing the internal representation of models that have been trained to recognize actions in video. We visualize multiple two-stream architectures to show that local detectors for appearance and motion objects arise to form distributed representations for recognizing human actions. Key observations include the following. First, cross-stream fusion enables the learning of true spatiotemporal features rather than simply separate appearance and motion features. Second, the networks can learn local representations that are highly class specific, but also generic representations that can serve a range of classes. Third, throughout the hierarchy of the network, features become more abstract and show increasing invariance to aspects of the data that are unimportant to desired distinctions (e.g. motion patterns across various speeds). Fourth, visualizations can be used not only to shed light on learned representations, but also to reveal idiosyncrasies of training data and to explain failure cases of the system.

Recent renewed interest in computational writer identification has resulted in an increased number of publications. In relation to historical musicology its application has so far been limited. One of the obstacles seems to be that the clarity of the images from the scans available for computational analysis is often not sufficient. In this paper, the use of the Hinge feature is proposed to avoid segmentation and staff-line removal for effective feature extraction from low quality scans. The use of an auto encoder in Hinge feature space is suggested as an alternative to staff-line removal by image processing, and their performance is compared. The result of the experiment shows an accuracy of 87 % for the dataset containing 84 writers’ samples, and superiority of our segmentation and staff-line removal free approach. Practical analysis on Bach’s autograph manuscript of the Well-Tempered Clavier II (Additional MS. 35021 in the British Library, London) is also presented and the extensive applicability of our approach is demonstrated.

In 2019, the National Archives of Korea (NAK) developed a blockchain recordkeeping platform to conduct R&amp, D on recordkeeping approaches. This paper introduces two types of R&amp, D studies that have been conducted thus far. The first is the use of blockchain transaction audit trail technology to ensure the authenticity of audiovisual archives, i.e., the application of blockchain to a new system. The second uses blockchain technology to verify whether the datasets of numerous information systems built by government agencies are managed without forgery or tampering, i.e., the application of blockchain to an existing system. Government work environments globally are rapidly shifting from paper records to digital. However, the traditional recordkeeping methodology has not adequately kept up with these digital changes. Despite the importance of responding to digital changes by incorporating innovative technologies such as blockchain in recordkeeping practices, it is not easy for most archives to invest funds in experiments on future technologies. Owing to the Korean government’s policy of investing in digital transformation, NAK’s blockchain recordkeeping platform has been developed, and several R&amp, D tasks are underway. Hopefully, the findings of this study will be shared with archivists around the world who are focusing on the future of recordkeeping.

Web automation applications are widely used for different purposes such as B2B integration and automated testing of web applications. Most current systems build the automatic web navigation component by using the APIs of conventional browsers. While this approach has its advantages, it suffers performance problems for intensive web automation tasks which require real time responses and/or a high degree of parallelism. In this paper, we outline a set of techniques to build a web navigation component able to efficiently execute web navigation sequences. These techniques detect what elements and scripts of the pages accessed during the navigation sequence are needed for the correct execution of the sequence (and, therefore, must be loaded and executed), and what parts of the pages can be discarded. The tests executed with real web sources show that the optimized navigation sequences run significantly faster and consume significantly less resources.

This paper reports on the different engineering, social and financial challenges behind the building and deploying electric energy monitoring and eco-feedback technology in real-world scenarios, which despite being relevant to the research community are seldom reported in the literature. The objectives of this paper are two-fold: First, discuss the technical and social constraints of real-world deployments. This includes, for example, hardware and software requirements, and issues related to security and intrusiveness of the monitoring solutions. Second, identify and understand the costs associated with developing and deploying such systems. These include hardware costs and consumed energy. To this end, we rely on over five years of experience developing and improving a non-intrusive energy monitoring research platform to enable the deployment of long and short-term studies of eco-feedback technology. During this time, two versions of that platform were deployed in 50 homes for periods that lasted between 6 and 18 consecutive months. By iteratively developing and deploying our sensing and eco-feedback infrastructures, we managed to build upon previous findings and lessons learned to understand how to create, deploy, and maintain such systems. Concurrently, we gained insights regarding what are some of the most relevant costs associated with running such experiments.

This paper presents a control design for the irrigation station by sprinkling. The proposed method is applied in order to solve the problem of managing water sources and distributions systems. The main contribution is focused primarily on conceiving T-S fuzzy model for the irrigation station. In formalism of the fuzzy-coalescence algorithm, we derive sufficient conditions to determine the model parameters. Meanwhile, additional effort is developed to achieve PI control for the irrigation station model.

Password security of user accounts is a major concern in today's fast moving and advanced computing world with password authentication the most commonly used identification mechanism. Its security can be enhanced using typing biometrics as a transparent layer of user authentication. This paper describes a password hardening mechanism that is able to recognize different users based on their time latencies. Our research focuses on only using the time period between keystrokes as the measure of the individual's typing pattern. Seven individuals used the same 7-digit password which allowed the system to record the time interval between each digit. Identification improvements up to 3% were achieved with Subtractive Clustering Fuzzy Inference when rule conflicts were removed.

Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user’s out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using “Asymmetric-SVD” and “TC-SeqRec” respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor’s crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly.

This paper describes an energy harvesting solution for a wireless sensor node based on the watermill principle. This solution has been conceived to be employed for the realization of monitoring infrastructures to be deployed in underground water lines like aqueducts or drainage systems. In particular, this solution has been developed to be employed in a Wireless Sensor Network for the monitoring of the “Bottini” (the ancient medieval aqueduct) running under the streets of the Historic Centre of Siena, Italy. Through the proposed harvesting system the sensor nodes are able to operate autonomously for long spans of time, allowing the real time remote monitoring of critical environmental parameters that are crucial for the preservation of this cultural site. The power generation system includes a waterwheel exploiting the water flow of the aqueduct to drive an electric motor, and a power control system, in order to manage the power accumulation and the energy consumption of the sensor node. The operation of the whole architecture has been tested in laboratory in order to prove the effectiveness of the solution and its possible use in the real scenario.