Reviewerly/exHarmony huggingface dataset:
Reciprocal Recommendation: Matching Users with the Right Users

Reciprocal recommender systems, which recommend users to each other, have gained significant importance in various Internet services for connecting people in a personalized manner, such as: online dating, recruitment, socializing, learning, or skill-sharing. Unlike classical item-to-user recommenders, a fundamental requirement in reciprocal recommendation is that both parties, namely the requester user and the recommended user, must be satisfied with the "user match" recommendation in order to deem it as successful. Therefore, bidirectional preferences indicating mutual compatibility between pairs of users need to be estimated predicated on information fusion. This tutorial introduces the emerging and novel topic of reciprocal recommender systems, by analyzing their information retrieval, data-driven preference modelling and integration mechanisms for predicting suitable user matches. The tutorial will also discuss the current trends, practical use, impact and challenges of reciprocal recommenders in different application domains.
Resource Allocation for Edge Computing-Based Blockchain: A Game Theoretic Approach

Blockchain has been progressively applied to various Internet of Things (IoT) platforms. As the efficiency of the blockchain depends on its computing capability, how to make sure the acquisition of the computational resources and participation of the devices would be the driving force. In this work, an edge computing-based blockchain network is considered, where the edge service provider (ESP) offers computational resources for the miners. The focus is to investigate an efficient incentive mechanism for the miners to purchase the computational resources. Accordingly, a two-stage Stackelberg game is formulated between the miners and ESP. By exploring the Stackelberg equilibrium of the optimal mining strategy under two different mining schemes. The aim is to find the optimal incentive for the ESP and miners to choose auto-fit strategies. Through theoretical analysis and numerical simulation, we demonstrate the effectiveness of the proposed scheme on encouraging devices to participate the Blockchain.
Learning Deeply Aggregated Alternating Minimization for General Inverse Problems

Regularization-based image restoration is one of the most powerful tools in image processing and computer vision thanks to its flexibility for handling various inverse problems. However, designing an optimal regularization function still remains unsolved since natural images and related scene types have a complex structure. In this paper, we present a general and principled framework, called deeply aggregated alternating minimization (DeepAM). We design a convolutional neural network (CNN) to implicitly parameterize the regularizer of the alternating minimization (AM) algorithm. Contrary to the conventional AM algorithm based on a point-wise proximal mapping, the DeepAM projects intermediate estimate into a set of natural images via deep aggregation. Since the CNN is fully integrated into the AM procedure, all parameters can be jointly optimized through end-to-end training. These properties enable the DeepAM to converge with a small number of iterations, while maintaining an algorithmic simplicity. We show that the DeepAM outperforms state-of-the-art methods, including nonlocal-based methods, Plug-and-Play regularization, and recent data-driven approaches. The effectiveness of our framework is demonstrated in a variety of image restoration tasks: Guassian denoising, deraining, deblurring, super-resolution, color-guided depth upsampling, and RGB/NIR restoration.
A Selection Method for Computing the Ensemble Size of Base Classifier in Multiple Classifier System

As a discipline, Machine Learning has been adopted and leveraged widely by researchers from several domains. There is a huge range of classifiers already available in machine learning and it has kept on growing with the advancement of this field. However, it is very hard to pick the best classifier among the several similar classifiers suitable for any problem. Recent advancement in this field for solving this issue is the Multiple Classifier System (MCS). It comes under the umbrella of ensemble learning and gives comparatively a better and definite result than a single classifier. MCS has two layers—(i) Base layer—contains a number of ML Classifiers appropriate for any specific task—and (ii) Meta Learner Layer—which aggregates the results from base layer classifiers by using techniques, such as Voting and Stacking. However, the job of selecting the appropriate classifiers from various classifiers or from a family of classifiers for a specific classification or prediction task on any dataset is still unraveling. This work emphasizes determining the characteristics of the selection method of base classifiers in the MCS. Moreover, which Meta Learner layer from Stacking and Voting aggregates the better result according to the different sizes of the base classifiers?
Dynamic Economic Optimization of a Continuously Stirred Tank Reactor Using Reinforcement Learning

Reinforcement learning (RL) algorithms are a set of goal-oriented machine learning algorithms that can perform control and optimization in a system. Most RL algorithms do not require any information about the underlying dynamics of the system, they only require input and output information. RL algorithms can therefore be applied to a wide range of systems. This paper explores the use of a custom environment to optimize a problem pertinent to process engineers. In this study the custom environment is a continuously stirred tank reactor (CSTR). The purpose of using a custom environment is to illustrate that any number of systems can readily become RL environments. Three RL algorithms are investigated: deep deterministic policy gradient (DDPG), twin-delayed DDPG (TD3), and proximal policy optimization. They are evaluated based on how they converge to a stable solution and how well they dynamically optimize the economics of the CSTR. All three algorithms perform 98% as well as a first principles model, coupled with a non-linear solver, but only TD3 demonstrates convergence to a stable solution. While itself limited in scope, this paper seeks to further open the door to a coupling between powerful RL algorithms and process systems engineering.
Towards safe and secure computer based railway interlocking systems

Railway interlocking systems (RIS) are responsible for the control of trains' movements by allowing or denying their routing according to safety rules.Originally built as mechanical systems then as electrical mechanical systems, they are modelled as relay-based electrical circuit diagrams and checked manually.Even recent computer based RIS are still modelled as relay diagrams.Manual checking of safety in such a critical context is an issue that needs to be addressed by the railway domain.In this context, one of the objectives of the LChIP project is the development of computer-based RIS based on the formal specification of the RIS behaviour, which allows the generation of formally checked computer binaries by refinement.however, the produced binaries may be accessed and tampered with by ill-intended persons as computer code is easier to analyse and understand than electrical mechanical systems.In the context of the LChIP project, binaries are run on a Microchip PIC32 MCU embedding two independent 32-bit MIPS processors.The choice of such generic architecture allows easier production and maintainability, lower production costs and easier replacement in case of hardware failure.It also makes it easier to reverse engineer compiled software as the MIPS architecture is well documented and has a small instruction set.This paper presents how binaries can be obtained using the B-method formal language and it discusses the usage of software obfuscation as a way to ensure security of these binaries.Obfuscation transforms a piece of software to make it impossible to understand for potential attackers, while keeping its observable behaviour: obfuscated software give the same outputs for the same inputs as their unobfuscated counterparts.As the obfuscation transformations do not alter the behaviour of formally checked generated binaries in a way that would void the safety checking, it is possible to secure computer based RIS and prevent ill-intended persons from tampering with them.
Traffic Behavior in Cloud Data Centers: A Survey

Data centers (DCs) nowadays house tens of thousands of servers and switches, interconnected by high-speed communication links. With the rapid growth of cloud DCs, in both size and number, tremendous efforts have been undertaken to efficiently design the network and manage the traffic within these DCs. However, little effort has been made toward measuring, understanding and chattelizing how the network-level traffic of these DCs behave. In this paper, we aim to present a systematic taxonomy and survey of these DC studies. Specifically, our survey first decomposes DC network traffic behavior into two main stages, namely (1) data collection methodologies and (2) research findings, and then classifies and discusses the recent research studies in each stage. Finally, the survey highlights few research challenges related to DC network traffic that require further research investigation.
Analysis on Electronic Warehouse Receipt of Bulk Commodity in Supply Chain Finance Practice

With the continuous enrichments of blockchain application scenarios, digital assets based on blockchain tend to mature. As the goods right certificate of supply chain financial business, electronic warehouse receipt based on blockchain technology has gradually become an important digital asset, which has been valued and applied in the field of bulk commodities. Based on comparative analysis, the paper studies the differences between paper warehouse receipts and electronic warehouse receipts, discusses the application scenarios of electronic warehouse receipts in supply chain service. Then, the paper studies the business links such as confirmation and transfer of commodity rights, warehouse receipt endorsement, warehouse receipt financing, cancellation and so on. It gives some business guidance and feasibility suggestions for banks and other fund providers to provides supply chain financial services.
Multi-agent Path Planning Problem Under a Multi-objective Optimization Framework

In this study, a mixed-integer programming formulation is developed for a team of homogeneous sensing agents under a bi-objective optimization framework to solve a discrete open-loop centralized multi-agent search and rescue path planning problem. The first objective represents the maximization of probability of target detection to ensure the success of mission planning and the second objective represents minimization of the cumulative path length of all the agents to ensure resource utilization and ensure adequate area coverage. A two-phase fuzzy programming technique is used to find the Pareto optimal solution. Numerical experiments are conducted with CPLEX to evaluate the effectiveness of the solution procedure with varying number of agents, and the impact of the size of a grid-based rectangular map with a sparsely distributed non-cooperative finite number of stationary targets.
An interactive system based on personal area networks, serious games and data mining to provide rehabilitation activities for older adults with cognitive decline

The latest estimates of the World Health Organization (WHO) point out that between 2015 and 2050, the proportion of the world's population over 60 years will increase from 12% to 22%. Likewise, by 2050, around 80% of senior citizens will be living in low- and middle-income countries. They tend to suffer from several disorders such as hearing loss, cataracts, refractive errors, back and neck pain and osteoarthritis, chronic obstructive pulmonary disease, diabetes, depression, and dementia. Cognitive decline is one of the most complex conditions that a senior citizen can face. For this reason, this paper proposes an interactive system that delivers collaborative playful activities to senior citizens through Personal Area Networks and serious games. In the same way, the proposal incorporates electronics kits to stimulate visuomotor coordination, spatial reasoning, and visual perception. The proposal was tested on 30 senior citizens with promising results.
A Blockchain based Framework for Secure Data Offloading in Tactile Internet Environment

The rapid increase in the number of mobile devices across the globe has brought a new challenge to the forefront, one of mobile traffic management. The ever-increasing number of mobile devices leads to the generation of a large amount of data and computationally intensive applications, which contributes heavily to cellular network congestion. To solve this issue, we propose a mobile data offloading scheme based on a distributed ledger technology (DLT). Existing mobile data offloading schemes based on DLT employ conventional blockchain to set up a peer-to-peer (P2P) network of mobile users. Although these schemes have gained ground in improving the Quality of Experience (QoE) for end-users, they lack efficiency and scalability. Furthermore, generic blockchain does not provide timestamp ordering of events, which is necessary to ensure the computation of delay-sensitive tasks. To overcome these challenges, we propose the use of a directed acyclic graph (DAG) data structure for mobile data offloading. Finally, to ensure time and cost optimality, a game-theoretic approach has been proposed in this paper.
ROS Based Real-Time Motion Control for Robotic Visual Arts Exhibit Using Decawave Local Positioning System

In this work, we address the problem of real-time control and localization of an autonomous art exhibit designed using Mecanum wheeled omnidirectional mobile robots. Currently, the exhibit is animated using open-loop control as there are no practical means of implementing a localization system in art galleries. This paper proposes a nonlinear model predictive controller (NMPC) supported by a Decawave localization system for trajectory tracking of the robots. A Robot Operating System (ROS) based NMPC is implemented to control the motion of the robot for a smooth and drift-free trajectory tracking. The Automatic Control and Dynamic Optimization (ACADO) toolkit is used to find the optimal control action while considering all constraints on system states and inputs. A Local Positioning System (LPS) is implemented using Decawave 1001 modules to provide position feedback with an Optitrack motion capture system providing ground truth information for validation. The proposed control approach is first evaluated using the Virtual Robot Experimentation Platform (V-REP), which additionally provides means of trajectory design and simulation of the robot in the desired exhibition space. Thereafter, laboratory experiments are conducted to evaluate the performance of the proposed control and localization systems. The results show a smooth and drift-free performance of the system with less than 10% error of the robot size, which can be deployed at gallery spaces with minimal setup requirements.
Modified Capsule Neural Network (Mod-CapsNet) for Indoor Home Scene Recognition

In this paper, a Modified Capsule Neural Network (Mod-CapsNet) with a pooling layer but without the squash function is used for recognition of indoor home scenes which are represented in grayscale. This Mod-CapsNet produced an accuracy of 70% compared to the 17.2% accuracy produced by a standard CapsNet. Since there is a lack of larger datasets related to indoor home scenes, to obtain better accuracy with smaller datasets is also one of the important aims in the paper. The number of images used for training and testing is 20,000 and 5000 respectively, all of dimension 128X128. The analysis proves that in the indoor home scene recognition task the combination of the capsule without a squash function and with max-pooling layers works better than by using capsules with convolutional layers. Indoor home scenes are specifically focused towards analysing capsules performance on datasets whose images have similarities but are, nonetheless, quite different. For example, tables may be present in living rooms and dining rooms even though these are quite different rooms.
Exploiting Computation Reuse in Cloud-Based Deep Learning via Input Reordering

Recently, deep learning (DL) becomes increasingly important since its transformative effect on a wide range of applications. During inference process, the DL model is deployed on the cloud to answer online queries. One crucial issue in the progress of DL inference is energy consumption, which significantly retards computation performance. Therefore, many previous investigations decrease the energy consumption via computation reuse technique based on similarity. However, if input data consists individually from mobile devices, applying these schemes will significantly decline computation performance. Because in disordered individual inputs, similarity for reuse is difficult to exploit directly. Results of initial experimental observations show that (1) individual input data also has high similarity for reuse, and (2) the total similarity during computation process has a relation with the characteristics of input data. This motivates us to design a reordering scheme to enhance similarity for computation reuse. Our main approaches are using statistical theory to predict the similarities among input data, and determining the execution sequence. Based on these approaches, we propose an effective input reordering scheme for computation reuse to save energy consumption. The evaluation under various benchmarks demonstrates that the reordering scheme significantly outperforms the previous schemes, for instance, the computation reuse is enhanced to $1.1 \times$ and the energy consumption is minimized to 40% according to the configuration of traditional computation reuse technique.
Boolean Searchable Symmetric Encryption with Filters on Trusted Hardware

The prevalence and availability of cloud infrastructures has made them the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de facto</i> solution for storing and archiving data, both for organizations and individual users. Nonetheless, the cloud’s wide spread adoption is still hindered by dependability and security concerns, particularly in applications with large data collections where efficient search and retrieval services are also major requirements. This leads to an increased tension between security, efficiency, and search expressiveness. In this article we tackle this tension by proposing BISEN, a new provably-secure boolean searchable symmetric encryption scheme that improves these three complementary dimensions by exploring the design space of isolation guarantees offered by novel commodity hardware such as Intel SGX, abstracted as Isolated Execution Environments (IEEs). BISEN is the first scheme to support multiple users and enable highly expressive and arbitrarily complex boolean queries, with minimal information leakage regarding performed queries and accessed data, and verifiability regarding fully malicious adversaries. Furthermore, BISEN extends the traditional SSE model to support filter functions on search results based on generic metadata created by the users. Experimental validation and comparison with the state of art shows that BISEN provides better performance with enriched search semantics and security properties.
Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization

Abstract We introduce and analyze a parallel sequential Monte Carlo methodology for the numerical solution of optimization problems that involve the minimization of a cost function that consists of the sum of many individual components. The proposed scheme is a stochastic zeroth-order optimization algorithm which demands only the capability to evaluate small subsets of components of the cost function. It can be depicted as a bank of samplers that generate particle approximations of several sequences of probability measures. These measures are constructed in such a way that they have associated probability density functions whose global maxima coincide with the global minima of the original cost function. The algorithm selects the best performing sampler and uses it to approximate a global minimum of the cost function. We prove analytically that the resulting estimator converges to a global minimum of the cost function almost surely and provide explicit convergence rates in terms of the number of generated Monte Carlo samples and the dimension of the search space. We show, by way of numerical examples, that the algorithm can tackle cost functions with multiple minima or with broad “flat” regions which are hard to minimize using gradient-based techniques.
Online, Model-Free Motion Planning in Dynamic Environments: An Intermittent, Finite Horizon Approach with Continuous-Time Q-Learning

This paper presents an online kinodynamic motion planning scheme for dynamically evolving environments, by employing Q-learning. The methodology addresses the finite horizon continuous-time optimal control problem with completely unknown system dynamics. An actor-critic structure is employed along with a buffer of previous experiences, to approximate the optimal policy and alleviate the learning signal requirements. The methodology is equipped with a terminal state evaluation to achieve fast navigation. The path planning is assigned to the RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sup> . An obstacle augmentation and a local re-planning strategy are responsible for collision-free navigation. Rigorous Lyapunov-based proofs are provided to guarantee closed-loop stability of the equilibrium point. We evaluate the efficacy of the methodology with simulations.
Accelerating Chan–Vese model with cross-modality guided contrast enhancement for liver segmentation

Accurate and fast liver segmentation remains a challenging and important task for clinicians. Segmentation algorithms are slow and inaccurate due to noise and low quality images in computed tomography (CT) abdominal scans. Chan–Vese is an active contour based powerful and flexible method for image segmentation due to superior noise robustness. However, it is quite slow due to time-consuming partial differential equations, especially for large medical datasets. This can pose a problem for a real-time implementation of liver segmentation and hence, an efficient parallel implementation is highly desirable. Another important aspect is the contrast of CT liver images. Liver slices are sometimes very low in contrast which reduces the overall quality of liver segmentation. Hence, we implement cross-modality guided liver contrast enhancement as a pre-processing step to liver segmentation. GPU implementation of Chan–Vese improves average speedup by 99.811 (± 7.65) times and 14.647 (± 1.155) times with and without enhancement respectively in comparison with the CPU. Average dice, sensitivity and accuracy of liver segmentation are 0.656, 0.816 and 0.822 respectively on the original liver images and 0.877, 0.964 and 0.956 respectively on the enhanced liver images improving the overall quality of liver segmentation.