Reviewerly/exHarmony huggingface dataset:
An island-based differential evolution algorithm with the multi-size populations

Computational intelligence methods can provide high-quality solutions to a variety of complex optimization problems where exact analytical solutions are impossible to obtain within a reasonable time and other resources used. The article proposes a novel concept of island model with islands of different sizes as well as differential evolution algorithms implementing this concept. Such a multi-size approach facilitates the design of island-based algorithms and brings a variety of benefits. Among them: improved fitness dynamics throughout the entire time of operation even without migration of solutions among the islands. The absence of migration eliminates the need to establish the topology and the policy of migration. It also makes the efficiency of multi-size island-based algorithms independent of the particular islands' size and practically eliminates the need of tuning the size of islands which is usually done in the case of the canonical island model. All these features indicate the superiority of the proposed multi-size island model over the canonical one. The efficiency of the proposed multi-size approach has been tested by solving one of the most difficult scheduling problems which is the discrete-continuous scheduling with continuous resource discretization.
Privacy-Preserving Wildcards Pattern Matching Protocol for IoT Applications

With the continuous development of the Internet of Things (IoT), various IoT devices create an incomprehensible amount of data all the time. However, the IoT devices have limited computing and storage resources and are difficult to process massive data locally, so they often introduce servers to help them for calculating or analyzing data. At present, the “IoT + Cloud” mode has been widely accepted. How to protect users' privacy in the public cloud environment has become critical. Among the common methods of processing data in the server, pattern matching is an important one which aims to identify the appearance and location of a string (called pattern) within a larger string or text. There are a lot of studies on privacy-preserving pattern matching protocols, but most protocols are constructed using heavy public-key cryptographic operations, which are not applicable to IoT devices. In this paper, we propose a new protocol using secret sharing and oblivious transfer (OT) and latter improve its efficiency with OT extension, so it is very efficient for lightweight IoT devices. In addition, our protocol also supports query with wildcards which can be used for the batch search. This protocol is provable-secure against honest-but-curious adversaries. Both the theoretical and experimental results show that our protocol can be used in real-world IoT applications.
Automatic system supporting multicopter swarms with manual guidance

Currently, there are some scenarios, such as search and rescue operations,where the deployment of manually guided swarms of UAVs can be necessary. In such cases, the pilot’s commands are unknown a priori (unpredictable), meaning that the UAVs must respond in near real time to the movements of the leader UAV in order to maintain swarm consistency. In this paper we develop a protocol for the coordination of UAVs in a swarm where the swarm leader is controlled by a real pilot, and the other UAVs must follow it in real time to maintain swarm cohesion. We validate our solution using a realistic simulation software that we developed (ArduSim), testing flights with multiple numbers of UAVs and different swarm configurations. Simulation results show the validity of the proposed swarm coordination protocol, detailing the responsiveness limits of our solution, and finding the minimum distances between UAVs to avoid collisions.
Reduce False Positive Alerts for Elderly Person Fall Video-Detection Algorithm by convolutional neural network model

Currently, image acquisition and understanding has become a necessity. In fact, it’s what allows machines to become one of the most powerful tools. Nowadays, machines that replace humans and experts in making decisions in several areas have seen their success from what so-called deep learning, a powerful learning in Machine learning tool for processing, classifying and object recognition tasks. The idea behind deep learning is training machines, adapting their skills and applying them to many tasks. In the same way that human brain learns, the information is entered through our senses (eyes...) and goes through billions of neurons before it was processed to get an output, deep learning also takes information as in input then start proceeding through several hidden layers before an output layer. For that, we choose to profit from this powerful learning to improve the Video fall detection algorithm which suffers from generating a huge amount of false alarms. So we propose in our work to minimize these false alerts using a CNN model that can classify a person sitting in a wheelchair from others to eliminate them. We present in this paper, on the one hand, a survey of the most recent and powerful architectures of CNN, on the other, we propose to add a CNN model into the elderly person fall Video-Detection Algorithm to improve its accuracy.
SATE: Providing Stable and Agile Adaptation in HTTP-Based Video Streaming

Online video streaming service, such as Youtube and Netflix, is emerging as a killer application that constitutes most IP traffic. Users want high-quality video streaming service; however, network bandwidth cannot keep up user's demand. In the HTTP-based video streaming technology, the video provider divides the video into multiple chunks, encodes it at various bitrates, and stores it in the media server. The client requests a video chunk using an adaptive bitrate (ABR) algorithm, considering the network conditions. While there are many works to maximize user satisfaction, the state-of-the-art ABR algorithms are still vulnerable to rebuffering due to inaccurate estimates and decisions. To address this problem, we first analyze the real-world traces and find several design inferences for an efficient ABR algorithm. Then, we introduce SATE, an ABR algorithm that provides a stable and agile adaptation even in bandwidth constraint and dramatically changing network conditions. We evaluate the performance of SATE in various network settings and demonstrate its efficacy in practice. SATE provides a stable adaptation and dramatically reduced rebuffering while delivering a similar average bitrate compared to the state-of-the-art ABR algorithms.
A Damped Oscillation Model for Tracking Near Space Hypersonic Gliding Targets

Maneuver models are dedicated to accurate representation of unknown motion pattern. However, for near space hypersonic jump gliding targets of high speed, diverse movement pattern, and mobility, the conventional maneuver models are difficult to describe the complex movement characteristics, and then leading to high and unstable tracking error. In order to solve this problem, a new model is proposed based on the attenuation of oscillation function. The core of the model is to consider the target acceleration as a zero mean random process with attenuation oscillation. With the model, the equations of the maneuvering target are constructed and the system dynamic error of this model was deduced taking Kalman filter as the filtering algorithm. Moreover, the corresponding relations among parameters are discussed, and an adaptive method is proposed for setting parameters appropriately in the situations when a priori information is unknown. In this way, the parameters can be adjusted online through jumping point identification. Theoretical analysis and simulation experiments are conducted to demonstrate the effectiveness of the proposed model. Comparing to commonly used models, it shows lower filtering errors tracking near space hypersonic jump gliding targets. Finally, the rationality and validity of the parameters adaptive method are explained.
Parallel mining of contextual outlier using sparse subspace

In this paper, we present a parallel computing solution for an existing outlier mining method (LOMA)–a local outlier detection technique. As datasets increase radically in size, there is a pressing demand to develop highly scalable outlier detection algorithms that leverages modern distributed and parallel computing infrastructures. It is challenging to devise parallel outlier detection techniques, because sharing and accessing global data across multiple computing nodes inevitably impose I/O overheads. To address this concern, we design a parallel LOMA computing framework called PLOMA, which is composed of three modules, namely, parallel data reduction, local sparse-subspace construction, and sparse-subspace validation. The parallel data reduction module exhibits superb efficiency by the virtue of the sampling technology to compute k nearest neighbor. At the heart of PLOMA is a local sparse-subspace construction module, which extends the discrete particle swarm optimization method to search local sparse subspaces on multiple datanodes in parallel. The validation module allows PLOMA to maintain high mining accuracy by verifying the correctness of the local sparse subspaces obtained by the construction module. Furthermore, PLOMA produces contextual information serving as insightful explanations on detected outliers, thereby achieving high interpretability. We apply PLOMA algorithm to the astronomical spectral data for discovering peculiar celestial objects. Our comprehensive experimental study using a 24-nodes Hadoop cluster confirms that PLOMA achieves high performance in terms of extensibility, scalability, and interpretability.
XFER

Real-time inference with low latency requirement has become increasingly important for numerous applications in both cloud computing and edge computing. The FPGA-based Deep Neural Network (DNN) accelerators have demonstrated the superior performance and energy efficiency over CPUs and GPUs; in addition, for real-time AI with low batch size, FPGA is expected to achieve further performance improvement over the general purpose computing platform. However, the performance gain of the single-FPGA design is hindered by the limited on-chip resource. In this paper, we leverage a cluster of FPGAs to fully exploit the parallelism in DNNs with the objective of obtaining super-linear performance. To achieve this goal, a novel design, "XFER", is proposed to deploy DNNs to FPGA cluster by splitting the DNN layer to multiple FPGAs and moving traffics from memory bus to inter-FPGA links. The resultant system can achieve both workload balance and traffic balance. As a case study, we implement Convolutional Neural Networks (CNNs) on ZCU102 FPGA boards. Evaluation results demonstrate that XFER on two FPGAs can achieve 3.48x speedup compared with state-of-the-art FPGA designs, achieving super-linear speedup.
A Hybrid Regularization Semi-Supervised Extreme Learning Machine Method and Its Application

Semi-supervised extreme learning machine (SS-ELM) has been applied to many classification and regression assignments with high performance, in which both the labeled and unlabeled data are exploited to enhance accuracy and computation efficiency. The Laplacian manifold regularization method has been incorporated to explore the geometry of the underlying manifold structure. However, the Laplacian manifold regularization lacks the extrapolating ability and biases the solution to a real constant function. In this paper, we propose a novel algorithm, the Laplacian-Hessian regularization SS-ELM (LHRSS-ELM), to enhance the performance of conventional SS-ELM. The main advantages of LHRSS-ELM are as follows: 1) LHRSS-ELM exhibits the learning capability and computational efficiency of traditional SS-ELMs; 2) LHRSS-ELM algorithm combines both Laplacian and Hessian term to enhance the extrapolating power, accuracy, and robustness and also show significant performance in multiclass classification tasks; and 3) for the purpose of pursuing the best pair of hyperparameters to establish a comparable model, we dynamically update them from sequences. The proposed algorithm is evaluated on publicly available data sets and further applied for the state classification of superheating degree in the aluminum electrolysis process. The experimental results demonstrate that the proposed mechanism is superior to the existing state-of-the-art semi-supervised learning algorithms in the matter of accuracy and robustness.
Privacy-preserving multi-criteria collaborative filtering

Privacy-preserving collaborative filtering schemes focus on eliminating the privacy threats inherent in single preference values, and the privacy risks in the multi-criteria preference domain are disregarded. In this work, we introduce randomized perturbation-based privacy-preserving approaches for multi-criteria collaborative filtering systems. Initially, the privacy protection methods efficiently used in traditional single-criterion systems are adapted onto multi-criteria ratings. However, these systems require intelligent protection mechanisms that are flexible and adapting to the structure of each sub-criterion. To achieve such a goal, we introduce a novel privacy-preserving protocol by adapting an entropy-based randomness determination procedure that can recover accuracy losses. The proposed protocol adjusts privacy-controlling parameters concerning the information inherent in each criterion. We experimentally evaluate the proposed schemes on three subsets of Yahoo!Movies multi-criteria preference dataset to demonstrate the effects of the proposed privacy-preserving schemes on both user privacy levels and prediction accuracy for differing sparsity rates. According to the obtained experimental outcomes, the proposed entropy-based privacy-preserving scheme can produce significantly more accurate predictions while maintaining an identical level of privacy provided by the traditional privacy protection scenario. The experimental results also confirm that the novel entropy-based privacy-preserving scheme maintains the confidentiality of personal preferences without severely compromising prediction accuracy.
Reconfigurable Photonic Circuit for Controlled Power Delivery to Laser-Driven Accelerators on a Chip

Laser-driven particle accelerators are a promising avenue for creating tabletop accelerators and light sources, but their functionality is currently limited by a lack of controlled power delivery over a long acceleration length. This work demonstrates that reconfigurable photonic integrated circuits may be used as a control and delivery mechanism for such accelerators. Through the use of integrated Mach-Zehnder interferometers, the authors present a protocol for automatic, dynamic optimization of power delivery, and show that such an approach may enable significant performance enhancements, compared to conventional linear accelerators.
Longest Common Substring with Approximately k Mismatches

Abstract In the longest common substring problem, we are given two strings of length n and must find a substring of maximal length that occurs in both strings. It is well known that the problem can be solved in linear time, but the solution is not robust and can vary greatly when the input strings are changed even by one character. To circumvent this, Leimeister and Morgenstern introduced the problem of the longest common substring with k mismatches. Lately, this problem has received a lot of attention in the literature. In this paper, we first show a conditional lower bound based on the SETH hypothesis implying that there is little hope to improve existing solutions. We then introduce a new but closely related problem of the longest common substring with approximately k mismatches and use locality-sensitive hashing to show that it admits a solution with strongly subquadratic running time. We also apply these results to obtain a strongly subquadratic-time 2-approximation algorithm for the longest common substring with k mismatches problem and show conditional hardness of improving its approximation ratio.
FE for Inner Products and Its Application to Decentralized ABE

In this work, we revisit the primitive functional encryption (FE) for inner products and show its application to decentralized attribute-based encryption (ABE). Particularly, we derive an FE for inner products that satisfies a stronger notion, and show how to use such an FE to construct decentralized ABE for the class $$\{0,1\}$$ - $$\mathsf {LSSS} $$ against bounded collusions in the plain model. We formalize the FE notion and show how to achieve such an FE under the LWE or DDH assumption. Therefore, our resulting decentralized ABE can be constructed under the same standard assumptions, improving the prior construction by Lewko and Waters (Eurocrypt 2011). Finally, we also point out challenges to construct decentralized ABE for general functions by establishing a relation between such an ABE and witness encryption for general NP statements.
A new MNF–BM4D denoising algorithm based on guided filtering for hyperspectral images

This paper proposed a new MNF-BM4D denoising algorithm based on guided filtering to improve the denoising performance of the state-of-the-art Block-Matching and 4D filtering(BM4D) algorithm for hyperspectral images in the spatial and spectral domain. BM4D is firstly used to denoise hyperspectral images. Then Minimum Noise Fraction(MNF) algorithm is introduced to distinguish between the main component and the noisy component. Finally, the guided image filtering technology is utilized to further improve the denoising performance. A number of experiments on both simulated and real data are conducted to validate the effective denoising performance of the proposed method. Therefore, the proposed algorithm can be considered as a promising technique for hyperspectral imagery denoising.
A heuristic method for modeling the initial pressure drop in melt filtration using woven screens in polymer recycling

This paper addresses the use of heuristic optimization algorithms to generate generally valid analytic equations for estimating the initial pressure drop of square and Dutch woven screens in polymer recycling. We present a mathematical description of the isothermal initial pressure drop of non‐Newtonian polymer melt flows through woven screens without the need for numerical methods. We first performed numerical CFD simulations to create a set of 9,000 physically independent modeling set‐ups as a basis for heuristic modeling. Then, we applied symbolic regression based on genetic programming to develop pecScreen models, achieving coefficients of determination R 2 &gt; 0.9995. For verification of our models, we performed experiments using both virgin and slightly contaminated in‐house and post‐industrial recycling materials. The experimentally determined data are in good agreement with the approximation results, which yielded a coefficient of determination R 2 of 0.926. Our modeling approach, the accuracy of which we have proven, allows fast and stable computational modeling of the initial pressure drop of polymer melt flows through woven screens. POLYM. ENG. SCI., 59:1105–1113 2019. © 2019 The Authors. Polymer Engineering &amp; Science published by Wiley Periodicals, Inc. on behalf of Society of Plastics Engineers.
Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine

Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. This can occur in different real world applications such as Wi-Fi localization and intrusion detection. In this study, we generated a cyclic dynamic generator (CDG), which we used to convert an existing dataset into a time series dataset with cyclic and changing features. Furthermore, we developed the infinite-term memory online sequential extreme learning machine (ITM-OSELM) on the basis of the feature-adaptive online sequential extreme learning machine (FA-OSELM) transfer learning, which incorporates an external memory to preserve old knowledge. This model was compared to the FA-OSELM and online sequential extreme learning machine (OSELM) on the basis of data generated from the CDG using three datasets: UJIndoorLoc, TampereU, and KDD 99. Results corroborate that the ITM-OSELM is superior to the FA-OSELM and OSELM using a statistical t-test. In addition, the accuracy of ITM-OSELM was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively.
Tightly Secure Hierarchical Identity-Based Encryption

We construct the first tightly secure hierarchical identity-based encryption (HIBE) scheme based on standard assumptions, which solves an open problem from Blazy, Kiltz, and Pan (CRYPTO 2014). At the core of our constructions is a novel randomization technique that enables us to randomize user secret keys for identities with flexible length. The security reductions of previous HIBEs lose at least a factor of $$ Q $$ , which is the number of user secret key queries. Different to that, the security loss of our schemes is only dependent on the security parameter. Our schemes are adaptively secure based on the Matrix Diffie-Hellman assumption, which is a generalization of standard Diffie-Hellman assumptions such as $$k$$ -Linear. We have two tightly secure constructions, one with constant ciphertext size, and the other with tighter security at the cost of linear ciphertext size. Among other things, our schemes imply the first tightly secure identity-based signature scheme by a variant of the Naor transformation.
Forward Secure Conjunctive-Keyword Searchable Encryption

Searchable symmetric encryption (SSE) has been applied in constructing secure cloud storage for its simplicity, high-efficiency, and practicality. Recent works show that forward privacy has become a fundamental property required for SSE, and several forward secure dynamic SSE schemes supporting single-keyword search have been proposed. However, to the best of our knowledge, there is no forward secure searchable encryption scheme supporting conjunctive-keyword search. In this paper, we propose two forward secure SSE schemes with conjunctive-keyword search functionality. Technically, in our basic scheme, we introduce a bloom filter on the client side to check the existence of keywords in a file. This also makes it easy to delete a file in locality rather than on the server side. In order to avoid the false positive probability of the bloom filter and reduce the redundant search results, we further propose a construction by applying secret-key inner-product encryption. Both of our schemes achieve sub-linear efficiency and one-round communication in the search protocol. Moreover, our methods are generic, as they can be applied to any forward secure single-keyword searchable encryption scheme to obtain a conjunctive-keyword one.