Reviewerly/exHarmony huggingface dataset:
A Robust Autonomous Following Method for Mobile Robots in Dynamic Environments

Autonomous following is one of the key issues in mobile robotics with a wide range of applications in commercial, industrial and military areas. To achieve autonomous following, the robot needs not only to robustly detect and track the target, but also efficiently follow the target while avoiding obstacles. In this paper, we propose a method that combines a Line-of-sight sensor (a 2D laser range finder) and a Non-line-of-sight sensor (a low-cost Angle-of-Arrival (AOA) tag) to identify, track and follow the target person in dynamic environments. First, in order to get smooth and continuous moving trajectory of the target person, a Kalman Filter is used to fuse person tracking information from AOA data and laser data. Then, a real-time robot-centric rolling grid map is constructed using the laser data. On top of the rolling grid map, a target potential field is generated by using the fast marching method, and then a direction gradient field is created based on the target potential field. With the rolling grid map, the target potential field and the direction gradient field, an improved dynamic window algorithm, FMM-DWA, is proposed to control the robot to move towards the target. The proposed method considers not only the distance between the robot and the target, but also the difference between the heading of the robot and the reference direction provided by the direction gradient field, to avoid falling into the local optimum. To validate the performance of the proposed method, a series of person following experiments are carried out in complex dynamic environments. The experimental results show that the proposed following algorithm can effectively deal with the occlusion problem and robustly follow the target person while quickly avoiding the static and dynamic obstacles.
Multi-agent systems with virtual stigmergy

We introduce a simple language for multi-agent systems that lends itself to intuitive design of local specifications. Agents operate on (parts of) a decentralized data structure, the stigmergy, that contains their (partial) knowledge. Such knowledge is asynchronously propagated across local stigmergies. In this way, local changes may influence global behavior. The main novelty is that our interaction mechanism combines stigmergic interaction with attribute-based communication. Specific conditions for interaction can be expressed in the form of predicates over exposed features of the agents. Additionally, agents may access a global environment. After presenting the language, we show its expressiveness by considering some illustrative case studies. We also include preliminary results towards automated verification via a mechanizable symbolic encoding that enables us to exploit verification tools developed for mainstream languages.
Optimizing FFT-Based Convolution on ARMv8 Multi-core CPUs

Convolutional Neural Networks (CNNs) are widely applied in various machine learning applications and very time-consuming. Most of CNNs’ execution time is consumed by convolutional layers. A common approach to implementing convolutions is the FFT-based one, which can reduce the arithmetic complexity of convolutions without losing too much precision. As the performance of ARMv8 multi-core CPUs improves, they can also be utilized to perform CNNs like Intel X86 CPUs. In this paper, we present a new parallel FFT-based convolution implementation on ARMv8 multi-core CPUs. The implementation makes efficient use of ARMv8 multi-core CPUs through a series of computation and memory optimizations. The experiment results on two ARMv8 multi-core CPUs demonstrate that our new implementation gives much better performance than two existing approaches in most cases.
Binarization of Degraded Document Images Using Convolutional Neural Networks and Wavelet-Based Multichannel Images

Convolutional neural networks (CNNs) have previously been broadly utilized to binarize document images. These methods have problems when faced with degraded historical documents. This paper proposes the utilization of CNNs to identify foreground pixels using novel input-generated multichannel images. To create the images, the original source image is decomposed into wavelet subbands. Then, the original image is approximated by each subband separately, and finally, the multichannel image is constituted by arranging the original source image (grayscale image) as the first channel and the approximated image by each subband as the remaining channels. To achieve the best results, two scenarios are considered, that is, two-channel and four-channel images, and then fed into two types of CNN architectures, namely, single and multiple streams. To investigate the effect of the multichannel images proposed as network inputs, the CNNs used in the architectures are three popular networks, namely, U-net, SegNet, and DeepLabv3+. The experimental results of the scenarios demonstrate that our method is more successful than the three CNNs when trained by the original source images and proves competitive performance in comparison with state-of-the-art results using the DIBCO database.
Decentralized Collaborative Filtering Algorithms Based on Complex Network Modeling and Degree Centrality

Given that everyone online is saturated with information, the theoretical significance of recommendation algorithms is evident in the fact that users need help finding products and content they care about. Collaborative filtering predicts a user's rating on an item by finding similar users that rated the item or similar items that were rated by the user, and using the selected similar neighbors to “collaboratively filter” the recommendation. In the process, selected neighbors are considered equally important despite their differences in popularity. Here, we explore a method of modeling recommender systems as networks that can be constructed by considering items as nodes and similarity between them as links. Our research shows that item centrality has a negative impact on the accuracy of rating predictions, which needs to be considered for better algorithm performance. Experiments show that collaborative filtering algorithms can be decentralized by our method and provide a better accuracy of rating prediction. Furthermore, the relationship between the prediction target and its neighbors can be further evaluated based on both their similarity and their centrality.
Noiseless linear amplifiers for multimode states

The entanglement structure between different frequency components within broadband quantum light pulses, forged at entanglement creation, represents a promising route to the practical delivery of many multipartite quantum information applications. However, the scalability of such applications is largely limited by the entanglement decoherence caused by photon loss. One promising method to combat such losses is noiseless linear amplification. However, while there have been various procedures that implement noiseless linear amplification on single-mode states, no realization has thus far been proposed for noiseless linear amplification on quantum states carrying a multimode structure. In this work we close this gap, proposing a novel Noiseless Linear Amplifier (NLA) with Photon Catalysis (PC), namely, the PC-NLA. Constructing a multimode version of an existing NLA that uses Quantum Scissors (QS), the QS-NLA, we then show how the PC-NLA is compatible with the QS-NLA, even though the former uses half the physical resources of the latter. We then apply our newly developed multimode NLA frameworks to the problem of Continuous-Variable (CV) entanglement distillation, determining how the multimode structure of the entanglement impacts the performance of the NLAs. Different from single-mode NLA analyses, we find that a multimode NLA is only effective as a CV entanglement distillation strategy when the channel loss is beyond some threshold - a threshold largely dependent on the multimode structure. The results provided here will be valuable for real-world implementations of multipartite quantum information applications that utilize complex entanglement structure within broadband light pulses.
Operational limits for aquaculture operations from a risk and safety perspective

Current decision making regarding whether to abort a high-risk aquaculture operation in a Norwegian fish farm is mainly experience-driven. The on-site personnel decides whether to start/delay/abort operations primarily based on their subjective judgement about whether they can handle the situation. The risk is considered implicitly as "gut feelings". There are no explicit operational limits nor a structured process to derive these for high-risk operations. In this research, a predefine safety-critical attributes have been identified from major accident scenarios to guide machine learning process to define operational limits based on multi-source data. Bayesian network, Tree Augmented Naïve Bayes (TAN) search algorithms were selected to build up prediction model so that operational limits upon a given condition can be decided. The paper concludes that machine learning techniques have great potential to be used to support safe decision-making in high-risk aquaculture operation, and the risk-based operational limits facilitates better understanding of operational context, and comprehension of the meaning of several deviations which may indicate a dangerous situation.
DRLindex

Cloud database providers provision different architectures to guarantee high availability. One of these architectures is a cluster database that consists of several database engine nodes, where data is replicated among the nodes. Although the cloud database providers provide various auto-indexing tools, these tools mostly address characteristics of a database deployed on a single node, not a cluster. It is possible to install an index advisor on each node, which recommends an index set for that node. The problem with this approach is that the current index advisors for a single node aim to minimize the processing cost of the workload; however, on a cluster database, other goals such as load balancing can be considered. Hence, the better solution could be an index advisor which has a comprehensive view of the cluster node.
Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping

Real-time outlier detection from a data stream is an increasingly important problem, especially as sensor-generated data streams abound in many applications owing to the prevalence of IoT and emergence of digital twins. Several density-based approaches have been proposed to address this problem, but arguably none of them is fast enough to meet the performance demand of real applications. This paper is founded upon a novel observation that, in many regions of the data space, data distributions hardly change across window slides. We propose a new algorithm, abbr. STARE, which identifies local regions in which data distributions hardly change and then skips updating the densities in those regions-a notion called stationary region skipping. Two techniques, data distribution approximation and cumulative net-change-based skip, are employed to efficiently and effectively implement the notion. Extensive experiments using synthetic and real data streams as well as a case study show that STARE is several orders of magnitude faster than the existing algorithms while achieving comparable or higher accuracy.
Hierarchical attention model for personalized tag recommendation

Abstract With the development of Web‐based social networks, many personalized tag recommendation approaches based on multi‐information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user‐item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real‐world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.
Faster Secure Data Mining via Distributed Homomorphic Encryption

Due to the rising privacy demand in data mining, Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field. By using the HE technique, it is possible to securely outsource model learning to the not fully trustful but powerful public cloud computing environments. However, HE-based training scales badly because of the high computation complexity. It is still an open problem whether it is possible to apply HE to large-scale problems. In this paper, we propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem. The main idea of our approach is to use the slightly more communication overhead in exchange of shallower computational circuit in HE, so as to reduce the overall complexity. We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets. For example, we successfully train a logistic regression model to recognize the digit 3 and 8 within around 5 minutes, while a centralized counterpart needs almost 2 hours.
Anti-noise FCM image segmentation method based on quadratic polynomial

Image segmentation is an important and challenging task in computer vision and image understanding, in which the segmentation model is the key to improve the quality of image segmentation. In this paper, a new fuzzy c-means clustering(FCM) algorithm based on quadratic polynomials is proposed, which can better distinguish the weak edge region in the image and has certain noise resistance. Firstly, the algorithm proposes to define the segmentation center using a quadratic polynomial surface,and divide the set of data points according to the algebraic distance between data points and the segmentation surfaces. The existing model with constant as the segmentation center is a special case with quadratic polynomial surface as the segmentation center, so the new model has higher segmentation accuracy. Secondly, based on the quadratic polynomial surface as the segmentation center, a new fuzzy factor is designed, and the deviation value is used to represent the difference between the mean algebraic distance of neighborhood points and the algebraic distance of center pixel. By calculating the deviation value, the influence of neighborhood points on the center point can be better measured and the segmentation accuracy can be improved. Experimental results show that the new algorithm has better anti-noise ability as well. Thirdly, select a local window on the edge of the global segmentation result for local window segmentation, which is equivalent to using a clustering center that is more in line with local information to do segmentation in a local small window, optimize misclassified pixels, and obtain the final segmentation result. The experimental results show that in the final segmentation results of medical images with 5% noise, segmentation accuracy can reach above 96%, partition coefficient Vpc has been increased by 0.14. So the algorithm can get the membership matrix with less ambiguity, which means more reliable segmentation results, and can effectively eliminate noise effects, retain the image details.
VR Training for Warehouse Management

Virtual reality (VR) has evolved into a trending technology that has proven its worth in various application domains. VR is especially helpful for the training of complex tasks in special environments. In the area of logistics, warehouse management involves a complex workflow that consists of different order picking activities. Due to this complex workflow, stock discrepancies and misplaced wares are typical problems that often occur. To overcome this problem, we have developed a VR training application that integrates an existing warehouse management system and trains typical order picking processes. In our VR training demo, we simulate a real warehouse that is supplied with real stocks and real orders.
Numerically Stable Binary Gradient Coding

A major hurdle in machine learning is scalability to massive datasets. One approach to overcoming this is to distribute the computational tasks among several workers. Gradient coding has been recently proposed in distributed optimization to compute the gradient of an objective function using multiple, possibly unreliable, worker nodes. By designing distributed coded schemes, gradient coded computations can be made resilient to stragglers, nodes with longer response time compared to other nodes in a distributed network. Most such schemes rely on operations over the real or complex numbers and are inherently numerically unstable. We present a binary scheme which avoids such operations, thereby enabling numerically stable distributed computation of the gradient. Also, some restricting assumptions in prior work are dropped, and a more efficient decoding is given.
Cloud-to-end Rendering and Storage Management for Virtual Reality in Experimental Education

Real-time 3D rendering and interaction is important for virtual reality (VR) experimental education. Unfortunately, standard end-computing methods prohibitively escalate computational costs. Thus, reducing or distributing these requirements needs urgent attention, especially in light of the COVID-19 pandemic In this study, we design a cloud-to-end rendering and storage system for VR experimental education comprising two models: background and interactive. The cloud server renders items in the background and sends the results to an end terminal in a video stream. Interactive models are then lightweight-rendered and blended at the end terminal. An improved 3D warping and hole-filling algorithm is also proposed to improve image quality when the user's viewpoint changes. We build three scenes to test image quality and network latency. The results show that our system can render 3D experimental education scenes with higher image quality and lower latency than any other cloud rendering systems. Our study is the first to use cloud and lightweight rendering for VR experimental education. The results demonstrate that our system provides good rendering experience without exceeding computation costs.
A Carrier Tracking Loop Using Adaptive Strong Tracking Kalman Filter in GNSS Receivers

The Kalman filter (KF) has been widely used in the carrier track to improve the tracking performance of receivers under challenging environments. The KF-based tracking assumes that the noise statistics are exactly known in advance and kept fixed during the whole iteration process. However, the noise statistics are difficult to know accurately and the fixed noise statistics cannot reflect the practical situations under time-varying environments. To further enhance the performance of carrier tracking, the adaptive strong tracking Kalman filter (STKF) is proposed. The adaptive fading factors are employed in the state prediction covariance to adjust the Kalman gain. Moreover, to improve the accuracy of fading factors in STKF tracking, the measurement noise covariance is adjusted based on the $C/N_{0}$ estimations. In addition, the working state is checked, and fading factors are used only when the system is not steady. The proposed algorithm has been implemented in the software receiver. The test results demonstrate that the proposed method has more superior tracking performance under challenging environments than other tracking methods.
Aesthetics-Assisted Multi-task Learning with Attention for Image Memorability Prediction

The rapid development of multimedia produces a huge amount of images every day. Some can be remembered by us for a long time, while most of them are quickly forgotten. This intrinsic property of an image is called memorability, which has wide applications in advertising, photography, education and so on. Therefore, image memorability has been drawing increasing interest recently. People's unique taste of beauty influences their memory of images, however, there are few works assisting memorability prediction by utilizing visual aesthetics assessment. In this paper, we propose a new aesthetics-assisted multi-task deep learning network for image memorability estimation. Visual attention mechanism is employed in our network to focus on the most useful context information. In order to capture the common features between image memorability and aesthetics, we use image memorability data and image aesthetics data to train the multi-task deep network with attention alternately. The experimental results demonstrate that the proposed method outperforms the state-of-the-arts.
Rate-Diversity Optimal Multiblock Space-Time Codes via Sum-Rank Codes

Just as rank-metric or Gabidulin codes may be used to construct rate-diversity tradeoff optimal space-time codes, a recently introduced generalization for the sum-rank metric, linearized Reed-Solomon codes, accomplishes the same in the case of multiple fading blocks. We provide the first explicit construction of minimal-delay rate-diversity optimal multiblock space-time codes as an application of linearized Reed-Solomon codes. We then demonstrate in simulation an example of a 2-block 2-by-2 code which, with a small performance penalty-less than 1 dB at a codeword error rate of 1e-4-matches the bit rate of a full diversity alternative while using a much smaller transmitted constellation. A stack decoder for this code is then suggested.