Reviewerly/exHarmony huggingface dataset:
High performance reconciliation for practical quantum key distribution systems

Quantum key distribution (QKD) is a promising technique for secure communication based on quantum mechanical principles. To improve the secure key rate (SKR) of a practical QKD system, most studies on reconciliation primarily focused on improving the reconciliation efficiency. With the increasing performance of practical QKD systems, the research priority has shifted to the improvement of both throughput and efficiency. In this paper, we propose a high-performance solution of Cascade reconciliation, including a high-throughput-oriented framework and an integrated-optimization-oriented scheme. Benefiting from the fully utilizing computation and storage resources, effectively dealing with communication delays, the integrated-optimization-oriented parameters setting, etc., an excellent overall performance was achieved. Experimental results showed that, the throughput of up to 570 Mbps with an efficiency of 1.038 was achieved, which, to our knowledge, was more than four times faster than any throughput previously demonstrated. Furthermore, throughputs on real data sets were capable of reaching up to 86 Mbps even on embedded platforms. Additionally, our solution offers good adaptability to the fluctuating communication delay and quantum bit error rate (QBER). Based on our study, low performance (i.e. low power-consumption and cost-effective) CPU platforms will be sufficient for reconciliation in the existing and near-term QKD systems.
Trust Your Neighbors: A Comprehensive Survey of Neighborhood-Based Methods for Recommender Systems

Collaborative recommendation approaches based on nearest-neighbors are still highly popular today due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter offers a comprehensive survey of neighborhood-based methods for the item recommendation problem. It presents the main characteristics and benefits of such methods, describes key design choices for implementing a neighborhood-based recommender system, and gives practical information on how to make these choices. A broad range of methods is covered in the chapter, including traditional algorithms like k-nearest neighbors as well as advanced approaches based on matrix factorization, sparse coding and random walks.
Personalized Session-Based Recommendation Using Graph Attention Networks

Personalized session-based recommendation systems aim to predict the next items that a user would be interested in based on previously recorded user interactions. The task contains two connotations. Firstly, “personalized” indicates the recommendation generating process does not involve other users. A recommendation system is expected to capture the preference patterns of the target user only. Secondly, “session-based” signifies that the preference patterns are expressed by sequences of items or interactions. Personalized session-based recommendation technologies are of high practical values in many application areas as explosive volumes of information available for customers. In this research we model user-item sessions using graphs and propose a novel graph neural network based model, namely, Personalized Session-based Recommendation using Graph Attention Networks (PSR-GAT). The PSR-GAT makes preferred item predictions by exploiting not only instantaneous preference patterns in the current session, but also more generic preference patterns in users' historical records. The proposed PSR-GAT is built on an attention mechanism basis with a consideration of item transition linkage. PSR-GAT does not have assumptions of preference expressions in sessions. Extensive experiments are conducted to evaluate the PSR-GAT model using three datasets, one is a vehicle infotainment dataset provided by the Ford Motor Company, and the other two are publicly accessible datasets. The performances of the PSR-GAT are compared with seven state-of-art methods for session-based recommendations. The results attest to the effectiveness and advantages of the PSR-GAT in the personalized session-based next interested items prediction.
SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series

Providing a lightweight self-adaptive approach that does not need offline training in advance and meanwhile is able to detect anomalies in real time could be highly beneficial. Such an approach could be immediately applied and deployed on any commodity machine to provide timely anomaly alerts. To facilitate such an approach, this paper introduces SALAD, which is a Self-Adaptive Lightweight Anomaly Detection approach based on a special type of recurrent neural networks called Long Short-Term Memory (LSTM). Instead of using offline training, SALAD converts a target time series into a series of average absolute relative error (AARE) values on the fly and predicts an AARE value for every upcoming data point based on short-term historical AARE values. If the difference between a calculated AARE value and its corresponding forecast AARE value is higher than a self-adaptive detection threshold, the corresponding data point is considered anomalous. Otherwise, the data point is considered normal. Experiments based on a real-world time series dataset demonstrates that SALAD outperforms five other state-of-the-art anomaly detection approaches in terms of detection accuracy. In addition, the results also show that SALAD is lightweight and can be deployed on a commodity machine.
On Facial Expression Recognition Benchmarks

Facial expression is an important form of nonverbal communication, as it is noted that 55% of what humans communicate is expressed in facial expressions. There are several applications of facial expressions in diverse fields including medicine, security, gaming, and even business enterprises. Thus, currently, automatic facial expression recognition is a hotbed research area that attracts lots of grants and therefore the need to understand the trends very well. This study, as a result, aims to review selected published works in the domain of study and conduct valuable analysis to determine the most common and useful algorithms employed in the study. We selected published works from 2010 to 2021 and extracted, analyzed, and summarized the findings based on the most used techniques in feature extraction, feature selection, validation, databases, and classification. The result of the study indicates strongly that local binary pattern (LBP), principal component analysis (PCA), saturated vector machine (SVM), CK+, and 10-fold cross-validation are the most widely used feature extraction, feature selection, classifier, database, and validation method used, respectively. Therefore, in line with our findings, this study provides recommendations for research specifically for new researchers with little or no background as to which methods they can employ and strive to improve.
Translation of Aerial Image Into Digital Map via Discriminative Segmentation and Creative Generation

Automatic translation of aerial images into digital maps is an important and challenging task which is widely used in practical applications. Most of the existing works view it either as a creative image-to-image translation problem or a discriminative semantic segmentation problem. However, we notice that human annotators need to extract and understand the information in aerial images first and then translate them to online maps in a creative way, which helps them draw accurate and visually appealing online maps. In this article, we propose an end-to-end online map generation method that combines a discriminative module with a creative module based on this observation to mimic human behavior. Specifically, we first utilize a semantic segmentation module to obtain a rough aerial map, in which each region is labeled with its category, and then further improve its quality with a creative module. To train a robust network that generalizes well to unfamiliar regions, we also collect a large aerial image dataset for online map generation (AIDOMG). AIDOMG consists of 40 087 pairs of aerial images and corresponding online maps collected from nine regions of six continents. We conduct extensive experiments to verify the superiority of the new design that combines discrimination and creativity and experimental results show that the performance of the proposed method significantly outperforms baseline methods.
Continuous Multi-View Human Action Recognition

Human action recognition which recognizes human actions in a video is a fundamental task in computer vision field. Although multiple existing methods with single-view or multi-view have been presented for human action recognition, these recognition approaches cannot be extended into new action recognition or action classification tasks, as well as discover underlying correlations among different views. To tackle the above problem, this paper proposes a new lifelong multi-view subspace learning framework for continuous human action recognition, which could exploit the complementary information amongst different views from a lifelong learning perspective. More specifically, a set of view-specific libraries is established to gradually store the useful information within multiple views. As a new action recognition task comes, we decompose the model parameters into a set of embedded parameters over view-specific libraries. A latent representation subspace is constructed via encouraging it to be close to different view-specific libraries, which can leverage the high-order correlations among different views and further avoid partial information for action recognition task. Meanwhile, we propose to employ an alternating direction strategy to optimize our proposed method. Empirical studies on real-world multi-view action recognition datasets have shown that our proposed framework attains the superior recognition performance and saves the computational time when continually learning new action recognition tasks.
A holistic approach on the gold metallogeny of the Singhbhum crustal province: Implications from tectono-metamorphic events during the Archean-Proterozoic regime

Studies on a wide range of gold deposit types suggest that the crustal accumulation of gold throughout the Earth's history was a consequence of changing tectonic regimes and variable paleo-redox conditions. The Singhbhum crustal province in eastern India is no exception to these events. The gold occurrences in the Singhbhum crustal province are confined to the Paleo-Mesoarchean greenstone belts within the Archean cratonic core region and the Paleo-to Mesoproterozoic North Singhbhum mobile belt (NSMB). Gold mineralization within the cratonic core region occurs either as greenstone hosted lode-gold or as modified paleoplacer gold terrains, whereas in the North Singhbhum mobile belt, gold is associated with quartz reefs and auriferous sulfides within rocks metamorphosed to greenschist-amphibolite facies. The concentration of gold within these Archean and Proterozoic terrains has been highly episodic with different sedimentary, metamorphic and hydrothermal mineralizing events operating during the respective periods. However, information regarding the genesis of gold in these occurrences remains fragmental and lacks regional-scale integration with the tectonic evolution of the Singhbhum crustal province. The present work provides a comprehensive review of the various gold occurrences/deposits in the Singhbhum crustal province and their possible genesis with respect to the tectonic evolution of the crustal province. This work recognizes that the gold endowment of the Paleo-to Mesoarchean paleoplacers was influenced by the presence of underlying high-MgO komatiites, resulting in the lean Au-assays in these paleoplacer deposits. On the other hand, the Paleo-to Mesoarchean greenstone hosted lode-gold occurrences indicate a mafic proto-source from which Au was mobilized into the Paleoarchean ocean and fixed into the contemporaneous inter-layered carbonaceous sedimentary sinks, which was ultimately reconcentrated to economic grades with the advent of plate-tectonics and coeval metamorphism. From the Proterozoic onwards, tectono-metamorphic events took over as the driving mechanism for gold accumulation in the Singhbhum crustal province. Pervasive intraplate rifting and simultaneous deposition of organic matter rich black shales set the stage for a possible Au-rich carbonaceous sedimentary sink, similar to a wide-range of Proterozoic sediment hosted gold deposits.
A Method of Enhancing Rapidly-Exploring Random Tree Robot Path Planning Using Midpoint Interpolation

It is difficult to guarantee optimality using the sampling-based rapidly-exploring random tree (RRT) method. To solve the problem, this paper proposes the post triangular processing of the midpoint interpolation method to minimize the planning time and shorten the path length of the sampling-based algorithm. The proposed method makes a path that is closer to the optimal path and somewhat solves the sharp path problem through the interpolation process. Experiments were conducted to verify the performance of the proposed method. Applying the method proposed in this paper to the RRT algorithm increases the efficiency of optimization by minimizing the planning time.
Faster BiSeNet: A Faster Bilateral Segmentation Network for Real-time Semantic Segmentation

Since the rising demand for dense estimation tasks in mobile applications, real-time semantic segmentation is increasingly desirable. Our work proposes a faster bilateral segmentation network (Faster BiSeNet) based on BiSeNetV2, which promotes feature fusion of the spatial and semantic branch with the more compact structure to improve real-time performance. Our light-weight design enhances the mutual connection between two branches in the shallow and deep layers by 1) Shallow feature sharing: using the simple linear operation to transform shallow features of the spatial branch as those needed in the semantic branch; 2) Deep feature aggregation: introducing a Gated Guided Aggregation Layer to guide appropriate spatial information to supplement the missing details of the semantic branch through gating mechanism, which avoids using unnecessary convolutions to select the aggregated features; 3) Auxiliary edge loss: making the gated output of the spatial branch focus on important boundary-related information, which facilitates the fusion of valuable edge information into the predicted results. Extensive experiments demonstrate that our approach obtains better realtime performance against a few state-of-the-art real-time semantic segmentation methods. Compared with BiSeNetV2, Faster BiSeNet respectively achieves 72.8% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\uparrow 0.2\%)$</tex> I, 74.5% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\uparrow 2.1\%)$</tex> ) Mean IoU at the inference of 187 FPS <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\uparrow 31$</tex> FPS), 154 FPS <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\uparrow 29.5$</tex> FPS) on the challenging Cityscapes test set and CamVid validation set on one NVIDIA GeForce GTX 1080Ti card. This demonstrates that our method achieves the competitive segmentation performance at real-time speed.
End-to-End Deep Reinforcement Learning for Image-Based UAV Autonomous Control

To achieve the perception-based autonomous control of UAVs, schemes with onboard sensing and computing are popular in state-of-the-art work, which often consist of several separated modules with respective complicated algorithms. Most methods depend on handcrafted designs and prior models with little capacity for adaptation and generalization. Inspired by the research on deep reinforcement learning, this paper proposes a new end-to-end autonomous control method to simplify the separate modules in the traditional control pipeline into a single neural network. An image-based reinforcement learning framework is established, depending on the design of the network architecture and the reward function. Training is performed with model-free algorithms developed according to the specific mission, and the control policy network can map the input image directly to the continuous actuator control command. A simulation environment for the scenario of UAV landing was built. In addition, the results under different typical cases, including both the small and large initial lateral or heading angle offsets, show that the proposed end-to-end method is feasible for perception-based autonomous control.
Variations of the elephant random walk

Abstract In the classical simple random walk the steps are independent, that is, the walker has no memory. In contrast, in the elephant random walk, which was introduced by Schütz and Trimper [19] in 2004, the next step always depends on the whole path so far. Our main aim is to prove analogous results when the elephant has only a restricted memory, for example remembering only the most remote step(s), the most recent step(s), or both. We also extend the models to cover more general step sizes.
Reasoning structural relation for occlusion-robust facial landmark localization

In facial landmark localization tasks, various occlusions heavily degrade the localization accuracy due to the partial observability of facial features. This paper proposes a structural relation network (SRN) for occlusion-robust landmark localization. Unlike most existing methods that simply exploit the shape constraint, the proposed SRN aims to capture the structural relations among different facial components. These relations can be considered a more powerful shape constraint against occlusion. To achieve this, a hierarchical structural relation module (HSRM) is designed to hierarchically reason the structural relations that represent both long- and short-distance spatial dependencies. Compared with existing network architectures,the HSRM can efficiently model the spatial relations by leveraging its geometry-aware network architecture, which reduces the semantic ambiguity caused by occlusion. Moreover, the SRN augments the training data by synthesizing occluded faces. To further extend our SRN for occluded video data, we formulate the occluded face synthesis as a Markov decision process (MDP). Specifically, it plans the movement of the dynamic occlusion based on an accumulated reward associated with the performance degradation of the pre-trained SRN. This procedure augments hard samples for robust facial landmark tracking. Extensive experimental results indicate that the proposed method achieves outstanding performance on occluded and masked faces. Code is available at https://github.com/zhuccly/SRN
A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation

Clustering objects from the LiDAR point cloud is an important research problem with many applications such as autonomous driving. To meet the real-time requirement, existing research proposed to apply the connected-component-labeling (CCL) technique on LiDAR spherical range image with a heuristic condition to check if two neighbor points are connected. However, LiDAR range image is different from a binary image which has a deterministic condition to tell if two pixels belong to the same component. The heuristic condition used on the LiDAR range image only works empirically, which suggests the LiDAR clustering algorithm should be robust to potential failures of the empirical heuristic condition. To overcome this challenge, this paper proposes a divide-and-merge LiDAR clustering algorithm. This algorithm firstly conducts clustering in each evenly divided local region, then merges the local clustered small components by voting on edge point pairs. Assuming there are <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> LiDAR points of objects in total with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> divided local regions, the time complexity of the proposed algorithm is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(N)+O(m^{2})$</tex> . A smaller <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> means the voting will involve more neighbor points, but the time complexity will become larger. So the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> controls the trade-off between the time complexity and the clustering accuracy. A proper <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> helps the proposed algorithm work in real-time as well as maintain good performance. We evaluate the divide-and-merge clustering algorithm on the SemanticKITTI panoptic segmentation benchmark by cascading it with a state-of-the-art semantic segmentation model. The final performance evaluated through the leaderboard achieves the best among all published methods. The proposed algorithm is implemented with C++ and wrapped as a python function. It can be easily used with the modern deep learning framework in python. We released the code under the following link <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/placeforyiming/Divide-and-Merge-LiDAR-Panoptic-Cluster.
Knowledge Management System Analysis of Smart Regency Mobile-Apps Service with Software Usability Measurement Inventory (SUMI) Approach

In the Indonesian context, the number of districts is four times the number of cities, so regency development needs serious attention. However, there are still very few studies that explore districts' existence from the perspective of ICT utility governance. This study aims to measure the mobile-based smart regency information system through usability evaluation. The method applies the adoption of the life cycle of the Knowledge Management System (KMSLC). Evaluation of Existing Infrastructure Analysis, Capture Knowledge, Implementing the Knowledge Management (KM) Model, and Evaluation are the stages used. The measures taken include system analysis, application mapping knowledge, implementation results, and usability assessment. The questionnaire was conducted on ten respondents using the mobile-based smart regency application. The questionnaire was conducted in 5 categories: Effectiveness, Efficiency, Control, Support, and Simplicity. Each of 10 questions, so there are 60 questions and three linkers, namely 4 if all agree, 2 if they don't know, and 0 don't agree. The Median SUMI Scale results for the mobile-based Smart Regency application are 60, 62.5, 60, 60, and 57.5. The usability evaluation results above the average mean that the mobile-based smart regency information system's usability is in a good category. This research helps in determining knowledge management in mobile smart regency services. The study also provides insight into the factors affecting the success of knowledge management of smart regency services for application developers and policymakers.