Reviewerly/exHarmony huggingface dataset:
Design of Intelligent Recommendation System of Smart Library Under Big Data Environment and Its Application Research in Applied University

With the development of society and the progress of the times, the era of big data has gradually become a new stage of development. "Big data" is a collection of data that is difficult to obtain, manage and analyze in a short time under the calculation of conventional software. The so-called "big data environment" is a large-scale data integration that can be developed by the support of the current network technology and computer technology. The recommendation system of the smart library is to be established and founded under such an environment. The recommendation system of smart library is designed to provide more beneficial help for the education system of application-oriented universities on the basis of perceiving students' preference in reading and learning. In the context of big data, the intelligent library recommendation system designed in this paper is to collect data on students' learning and reading needs with the help of big data. Integrate, process and analyze on the basis of collection, and finally send the results presented after this process to students to form a specialized smart library recommendation system suitable for applied universities. The establishment of this system can better meet the needs of students of applied universities, recommend students to adapt to their unique learning resources, and help the data-driven development of applied universities to be carried out better.
A Fireworks Algorithm Based Path Planning Method for Amphibious Robot

This paper proposes a fireworks algorithm based path planning method for amphibious robots that operate in terraqueous environments. Firstly, the amphibious path planning is formulated into a constrained optimization problem by coordinate transformation and line segmentation. Distance, energy consumption, and robot mode switching costs regarding the unconventional characteristics of the amphibious environment are considered. Secondly, the fireworks algorithm (FWA) and the bare bones fireworks algorithm (BBFWA) are briefly introduced. Finally, both methods have been implemented for solving the amphibious path planning problem. Simulation experiments have been conducted to explore the effectiveness and performance of both methods. Results indicate that our proposed path planning method for terraqueous environments is effective, and the fireworks algorithm outperforms the bare bones fireworks algorithm despite the latter surpasses several enhanced variants of the former on certain benchmarks.
Two-loop controller design and implementations for an inverted pendulum system with optimal self-adaptive fuzzy-proportional–integral–derivative control

Since inverted pendulum systems (IPSs) are under-actuated systems, controlling these systems has become an important problem. Because of their irregular movements and being structurally unstable, the balancing of these systems poses a major problem. In this study, the dynamic model of a linear IPS has been obtained by using the Newton–Euler method and also conventional proportional–integral–derivative (PID), fuzzy logic (FLC), and self-adaptive fuzzy-proportional–integral–derivative (SAF-PID) control algorithms have been designed for the control of the system. The purpose of these designed controllers is to keep the arms of the IPS on the moving cart in a vertical position and bring the cart to the specified equilibrium position. In conventional control methods, unwanted oscillations have occurred during the movement of the cart. By using two-loop control methods, it is aimed to reduce these oscillations and to design a robust system. The results have been obtained using the conventional PID, FLC, and SAF-PID controllers. To increase the performance of the SAF-PID control type, the optimum value of the coefficients of the points where the legs of the membership functions touch were calculated using the firefly optimization algorithm. To conclude, the designed controllers were performed on computer simulation. The results are given graphically and numerically. Mean absolute error (MAE), mean squared error, integral absolute error IAE, and integral squared error have been compared and examined with each other and with the studies in the literature using performance criteria. As a result, the proposed control method was at least 26.31% more successful in the angular control of the pendulum and at least 9.26% better in the position control of the cart than the studies in the literature.
Robust P2P Connectivity Estimation for Permissionless Bitcoin Network

Blockchain relies on the underlying peer-to-peer (p2p) networking to broadcast and get up-to-date on the blocks and transactions. It is therefore imperative to have high p2p connectivity for the quality of the blockchain system operations. High p2p networking connectivity ensures that a peer node is connected to multiple other peers providing a diverse set of observers of the current state of the blockchain and transactions. However, in a permissionless blockchain network, using the peer identifiers—including the current approach of counting the number of distinct IP addresses and port numbers—can be ineffective in measuring the number of peer connections and estimating the networking connectivity. Such current approach is further challenged by the networking threats manipulating the identifiers. We build a robust estimation engine for the p2p networking connectivity by sensing and processing the p2p networking traffic. We implement a working Bitcoin prototype connected to the Bitcoin Mainnet to validate and improve our engine’s performances and evaluate the estimation accuracy and cost efficiency of our estimation engine.
A particle swarm optimization approach for large-scale many-objective software architecture recovery

The software systems with a well-documented architecture are easy to understand and evolve. However, in most cases, either the documented architecture is unavailable or get eroded badly. In these cases, to understand and evolve the software systems, developers often need to recover the architectural components from the system implementation code. Recently, a variety of heuristic-based multi-objective optimization algorithms (MoOAs) for software architecture recovery (SAR) have been introduced. Most of the existing SAR approaches are designed by adopting the traditional MoOAs. However, the performance of such approaches degrades drastically with large-scale many-objective SAR (LSMaO-SAR). To address the challenges the MoOAs caused by the LSMaO-SAR, we introduce a large-scale many-objective particle swarm optimization (LSM-PSO) by customizing the framework of the PSO algorithm. For this, we adopt various strategies such as Balance Fitness Evaluation (BFE), Quality Indicator (QI) based fitness evaluation, Fuzzy-Pareto dominance (FPD), and Two-archive external storage, and incorporate into the PSO model. To test the effectiveness of the LSM-PSO, it is applied over five software projects and compared with the existing SAR approaches. The results show that the proposed LSM-PSO outperforms the existing optimization-based SAR approaches.
Graph similarity rectification for person search

In person search task, it is hard to retrieve the query persons undergoing large visual changes. To tackle this problem, we propose to exploit the context information to rectify the original individual similarity for better retrieval. To this end, we propose to model a query frame and a gallery frame as a graph pair, and then design the Siamese Residual Graph Convolutional Networks (SR-GCN) to aggregate context information to generate graph similarity as a complement of the original similarity. To model the relationships between context persons, we define the joint similarity adjacency matrix which assigns the proposed joint similarity as the edge weight to measure the contributions a context person makes to the aggregation. Therefore, the context node with a higher possibility to be a co-traveler of the target node makes more contributions to the matching of the target node. To further enhance the discriminative power of individual features, we also design a Random Proxy Center loss which explicitly constrains the intra-class variations to be smaller than the inter-class variations in the feature space and could make use of unlabeled samples. Experimental results on two public datasets show that our approach performs favorably against the state-of-the-art methods.
Detector<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mo>+</mml:mo></mml:msup></mml:math>: An approach for detecting, isolating, and preventing timing attacks

In this work, we present a novel approach, called Detector+, to detect, isolate, and prevent timing-based side channel attacks (i.e., timing attacks) at runtime. The proposed approach is based on a simple observation that the time measurements required by the timing attacks differ from those required by the benign applications as these attacks need to measure the execution times of typically quite short-running operations. Detector+, therefore, monitors the time readings made by processes and mark consecutive pairs of readings that are close to each other in time as suspicious. In the presence of suspicious time measurements, Detector+ introduces noise into the measurements to prevent the attacker from extracting information by using these measurements. The sequence of suspicious time measurements are then analyzed by using a sliding window based approach to pinpoint the malicious processes at runtime. We have empirically evaluated the proposed approach by using five well known timing attacks, including Meltdown, together with their variations, representing some of the mechanisms that an attacker can employ to become stealthier. In one evaluation setup, each type of attack was carried out concurrently by multiple processes. In the other setup, multiple types of attacks were carried out concurrently. In all the experiments, Detector+ detected all the malicious time measurements with almost a perfect accuracy, prevented all the attacks, and correctly pinpointed all the malicious processes involved in the attacks without any false positives after they have made a few time measurements with an average runtime overhead of 1.56%.
A Novel Target-Aware Dual Matching and Compensatory Segmentation Tracker for Aerial Videos

Object tracking in aerial scenes is widely used in intelligent transportation and national defense security. Template-based matching trackers currently dominate tracking pattern due to their remarkable performance, but are confined to rectangular bounding box tracking and the lack of effective transformation models, which lead to their low position accuracy in aerial scenes of special difficulties, such as low target resolution, large field of view, and multiple angle changes. To address this issue, we develop a novel target-aware dual matching and compensatory segmentation tracker for aerial videos-TMCS, which transforms visual object tracking into a video object segmentation problem. Due to deep features of offline trained network are less discriminative in representing these targets of arbitrary form, we develop a regression loss for selecting the most representative convolutional filters to learn salient features of aerial target. In contrast to general trackers, the proposed approach designs the dual match mechanism, which matches the target activation features with the foreground and background features of the target template to generate geometric invariant models, rather than offline remembering the appearance of the target. In addition, most tracking benchmarks require marking the object position as a rectangular bounding box. In this paper, an online fast fitting method is proposed to interpret the output form of the target as a rotating rectangular bounding box that is more suitable for the target, which improves object location accuracy. Extensive results on the challenging UAV123 dataset show that our method achieves comparable performance, which basically meets the aerial target tracking accuracy and real-time requirements.
TIMo—A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera

We present TIMo (Time-of-flight Indoor Monitoring), a dataset for video-based monitoring of indoor spaces captured using a time-of-flight (ToF) camera. The resulting depth videos feature people performing a set of different predefined actions, for which we provide detailed annotations. Person detection for people counting and anomaly detection are the two targeted applications. Most existing surveillance video datasets provide either grayscale or RGB videos. Depth information, on the other hand, is still a rarity in this class of datasets in spite of being popular and much more common in other research fields within computer vision. Our dataset addresses this gap in the landscape of surveillance video datasets. The recordings took place at two different locations with the ToF camera set up either in a top-down or a tilted perspective on the scene. Moreover, we provide experimental evaluation results from baseline algorithms.
Differentially-Private Federated Learning with Long-Term Constraints Using Online Mirror Descent

This paper discusses a fully decentralized online federated learning setting with long-term constraints. The fully decentralized setting removes communication and computational bottlenecks associated with a central server communicating with a large number of clients. Also, online learning is introduced to the federated learning setting to capture situations with time-varying data distribution. Practical federated learning settings are imposed with long-term constraints such as energy constraints, money cost constraints, time constraints etc. The clients are not obligated to satisfy any per round constraint, but they must satisfy these long-term constraints. To provide privacy to the shared local model updates of the clients, local differential privacy is introduced. An online mirror descent-based algorithm is proposed and its regret bound is obtained. The regret bound is compared with the regret bound of a differentially-private version of online gradient descent algorithm proposed for federated learning.
CapsNet meets SIFT: A robust framework for distorted target categorization

• We realize distorted target recognition by presenting a CapsNetSIFT architecture (cf. Fig. 1). It can simultaneously enable accurate positioning of interest regions and comprehensive learning of discriminative features whilst boasting invariance to visual distortions. • We propose a parallel MD-CapsNet in CapsNetSIFT for boosting representability. The customized structure, hyperparameter setting, and dynamic routing agreement have been demonstrated. • We propose a VM-SIFT in CapsNetSIFT to establish correspondence among capsule encoding vectors of standard images and distorted ones. This may represent the first attempt to realize feature matching of capsule vectors. • We conduct quantitative experiments of distorted target categorization on four benchmarks (CUB-200-2011, Stanford Dogs, Stanford Cars, and our hand-crafted rice growth dataset). Evaluation results reveal our advantage over state-of-the-arts. Due to overexposure, jitter, motion, and other spatiotemporal-varying perturbations, the collected images always undergo various visual distortions (e.g., deformation, partially occluded signs, fisheye respective, affine or 3D projections, in-plane and out-of-plane rotation) during acquisition or transmission procedure. Deep neural networks (DNNs) perform poorly on such pristine images in terms of high-level abstract operations, e.g., object categorization and semantic segmentation. To conquer this legacy, a distortion-tolerant model denoted as CapsNetSIFT is proposed to enhance representability and detectability of target in distorted imagery. We modify and integrate capsule network (CapsNet) with scale invariant feature transform (SIFT) together, both of which boast innate invariance to spacial-scale transformations. Two key insights, the customized multi-dimensional CapsNet (MD-CapsNet) and vector matching SIFT (VM-SIFT), can cooperate together and reinforce each other: the former encodes and provides representative feature vectors for the later, whilst the later localizes space-scale invariant interval dimensions (instead of pixels) and establish correspondence between source standard images (high-quality training images) and distorted ones (testing images). Thus, the category of one source standard image owning the most associations is the ground-truth category. Evaluation results reveal that employing CapsNetSIFT for distorted target recognition (CUB-200–2011, Stanford Dogs, Stanford Cars, and our hand-crafted dataset), significantly improves the resistance against various simulated distortions, and outperforms state-of-the-arts with relatively higher training and testing accuracy (93.97% and 91.03%).
Uniform Interpolation from Cyclic Proofs: The Case of Modal Mu-Calculus

We show how to construct uniform interpolants in the context of the modal mu-calculus. D'Agostino and Hollenberg (2000) were the first to prove that this logic has the uniform interpolation property, employing a combination of semantic and syntactic methods. This article outlines a purely proof-theoretic approach to the problem based on insights from the cyclic proof theory of mu-calculus. We argue the approach has the potential to lend itself to other temporal and fixed point logics.
GraphMineSuite

We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by &gt;2X), maximal clique listing (by &gt;9×), k -clique listing (by up to 1.1×), and subgraph isomorphism (by 2.5×), also obtaining better theoretical performance bounds.
Effect of Fixing Earthquake Depth in ShakeAlert Algorithms on Performance for Intraslab Earthquakes

Abstract We investigate whether assuming a fixed shallow depth in the ShakeAlert network-based earthquake early warning system is sufficient to produce accurate ground-motion based alerts for intraslab earthquakes. ShakeAlert currently uses a fixed focal depth of 8 km to estimate earthquake location and magnitude. This is an appropriate way to reduce computational costs without compromising alert accuracy in California, where earthquakes typically occur on shallow crustal faults. In the Pacific Northwest (PNW), however, the most common moderate-magnitude events occur within the subducting Juan de Fuca slab at depths between ∼35 and 65 km. Using a dataset of seismic recordings from 37 Mw 4.5+ intraslab earthquakes from the PNW and Chile, we replay events through the Earthquake Point-Source Integrated Code and eqInfo2GM algorithms to estimate source parameters and compute modified Mercalli intensity (MMI) alert threshold contours. Each event is replayed twice—once using a fixed 8 km depth and a second time using the actual catalog earthquake depth. For each depth scenario, we analyze MMI III and IV contours using various performance metrics to determine the number of correctly alerted sites and measure warning times. We determine that shallow depth replays are more likely to produce errors in location estimates of greater than 50 km if the event is located outside of a seismic network. When located within a seismic network, shallow and catalog depth replays have similar epicenter estimates. Results show that applying catalog earthquake depth does not improve the accuracy of magnitude estimates or MMI alert threshold contours, or increase warning times. We conclude that using a fixed shallow earthquake depth for intraslab earthquakes will not significantly impact alert accuracy in the PNW.
DRRNets: Dynamic Recurrent Routing via Low-Rank Regularization in Recurrent Neural Networks

Recurrent neural networks (RNNs) continue to show outstanding performance in sequence learning tasks such as language modeling, but it remains difficult to train RNNs for long sequences. The main challenges lie in the complex dependencies, gradient vanishing or exploding, and low resource requirement in model deployment. In order to address these challenges, we propose dynamic recurrent routing neural networks (DRRNets), which can: 1) shorten the recurrent lengths by allocating recurrent routes dynamically for different dependencies and 2) reduce the number of parameters significantly by imposing low-rank constraints on the fully connected layers. A novel optimization algorithm via low-rank constraint and sparsity projection is developed to train the network. We verify the effectiveness of the proposed method by comparing it with multiple competitive approaches in several popular sequential learning tasks, such as language modeling and speaker recognition. The results in terms of different criteria demonstrate the superiority of our proposed method.
Digital abilities, between instrumentalization and empowerment: a discourse analysis of Chilean Secondary Technical and Vocational public policy documents

(2021). Digital abilities, between instrumentalization and empowerment: a discourse analysis of Chilean Secondary Technical and Vocational public policy documents. Journal of Vocational Education & Training. Ahead of Print.