Reviewerly/exHarmony huggingface dataset:
Co-mining: Mining informative samples with noisy labels

Due to the memorization effect of Deep Neural Networks, training with noisy labels typically results in performance degradation. Existing methods have devoted numerous efforts to training a robust model. However, rare literature pays attention to deep metric learning in the presence of label noise due to the challenge of discriminating between noisy examples and informative (hard) ones. To this end, we propose a simple yet effective approach Co-mining to build a robust and discriminative feature space. Specifically, more confident yet fewer instances are selected to guarantee the label quality and make up a relatively reliable gallery for exploring under-exploited patterns. Considering that some unexpected noisy instances are inevitably registered as clean ones as training progresses, we make a trade-off between cleanliness and informativeness. A triplet loss, pulling positive pairs closer and pushing negative pairs apart, is imposed on semi-hard samples rather than the hardest counterparts. Lastly, our method employs a threshold to distinguish out-of-distribution noisy images and discards them directly. We also use a re-ranking strategy for in-distribution noisy data to correct corrupted labels based on their consistently high predicted probabilities. Comprehensive experiments on various synthetic and real-world benchmarks demonstrate the superiority of our Co-mining. Code is available at https://github.com/NUST-Machine-Intelligence-Laboratory/Co-mining.
Introducing multi-dimensional hierarchical classification: Characterization, solving strategies and performance measures

Classification problems where there exist multiple class variables that need to be jointly predicted are known as Multi-dimensional classification problems. If the labels of these class variables are organized as hierarchies, we can take advantage of specific strategies designed for the Hierarchical classification paradigm. In this paper we present the Multi-dimensional hierarchical classification (MDHC) paradigm, a result of the combination of Multi-dimensional and Hierarchical classification paradigms. We propose four MDHC learning strategies which are designed to exploit the particularities of this new paradigm, combining characteristics of Multi-dimensional and Hierarchical classification strategies. Along with these strategies, we present a framework for classifier comparison in which we use a set of performance measures specifically designed for MDHC, and a procedure to create MDHC synthetic scenarios. Using this framework and the performance measures presented, we study how characteristics of the MDHC problems influence the performance of the different MDHC strategies proposed, and compare them to other non-MDHC strategies.
Faster parameterized algorithms for Bicluster Editing and Flip Consensus Tree

In the Bicluster Editing (resp., Flip Consensus Tree) problem the input is a bipartite graph G=(V1,V2,E) and an integer k, and the goal is to decide whether there is a set F⊆V1×V2 such that the graph (V1,V2,E△F) does not contain an induced path on four vertices (resp., an induced path on five vertices whose endpoints are in V2). In this paper we give algorithms for Bicluster Editing and Flip Consensus Tree whose running times are O⁎(2.22k) and O(3.24k), respectively. This improves over the O⁎(2.636k)-time algorithm for Bicluster Editing of Tsur [IPL 2021] and the O⁎(3.68k)-time algorithm for Flip Consensus Tree of Komusiewicz and Uhlmann [Algorithmica 2014].
Self-labeling video prediction

Learning to predict future visual dynamics given input video sequences is a challenging but essential task. Although many stochastic video prediction models are proposed, they still suffer from “multi-modal entanglement”, which refers to the ambiguity of learned representations for multi-modal dynamics modeling. While most existing video prediction models are label-free, we propose a self-supervised labeling strategy to improve spatiotemporal prediction networks without extra supervision. Starting from a set of clustered pseudo-labels, our framework alternates between model optimization and label updating. The key insight of our method lies in that we exploit the reconstruction error from the optimized model itself as an indicator to progressively refine the label assignment on the training set. The two steps are interdependent, with the predictive model guiding the direction of label updates, and in turn, effective pseudo-labels further help the model learn better disentangled multi-modal representation. Experiments on two different video prediction datasets demonstrate the effectiveness of the proposed method.
An algorithm for trajectory optimization of dual-arm coordination based on arm angle constraints

<ns4:p>In this paper, the motion planning of a dual-arm robot with kinematic constraints is studied based on arm-angle constraints. When a dual-arm robot moves a common object, a closed kinematic chain is formed between the dual-arm and the object. The standard sampling-based trajectory planning algorithm solves the problem with closed-chain constraint, but this causes other problems; the running time increases, the success rate decreases, and the motion trajectory of the end effector is not smooth resulting in large output error. Therefore, this paper proposes a dual-arm coordinated trajectory optimization algorithm based on arm-angle constraints. Firstly, the kinematics of the dual-arm robot is modeled and analyzed, and the definition of the arm-angle in a seven-axis robot is proposed, the workspace of the dual-arm coordinated operation is considered to constrain it, the kinematics equation combined with the single/multi-objective optimization algorithm is used to optimize the end output error, and the joint trajectory is parameterized. This paper solves the problems that the slave arm lags behind the main arm, the motion trajectory of the dual-arm is not smooth, and the dual-arm are squeezed due to internal force during the coordinated movement of the dual-arm. The trajectory optimization improves the synchronization of the coordinated operation of the dual-arm, reduces the output error of the velocity and acceleration at the end of the dual-arm. After limiting the arm-angle, dual-arm manipulation is anthropomorphic the robot does not produce distorted arm configurations.</ns4:p>
A four-aperture super-resolution camera based on adaptive regularization parameter tuning

Multi-aperture imaging can improve spatial resolution by fusing complementary information from multiple low-resolution (LR) frames using super-resolution (SR) algorithms. The existing SR algorithms are offline used due to the lack of fast regularization parameter update solution. The classical general cross validation (GCV) and the L-curve algorithms search for a suitable solution on each updated logarithmic parameter set for a long time. In this work, we proposed an adaptive regularization parameter tuning (ARPT) method. The regularization parameter is updated by a function of the fidelity term, the standard deviation of the noise, the weighted bilateral total variation and its standard deviation. Simulations demonstrate that the PSNR and SSIM of the ARPT algorithm, are 0.27 dB and 0.002 larger than the GCV algorithm on average, and the computation time is reduced by more than four times. A four-aperture SR camera is built by randomly shifting the sensors on a sub-pixel scale. An SR image was obtained by the ARPT method using 20 LR images. The average consuming time is reduced by 6.17 times compared with the GCV algorithm. Experiments indicate that the proposed four-aperture SR camera can improve the spatial resolution, suppress the noise, and realize a high-quality and more efficient reconstruction.
Equivalence between time series predictability and Bayes error rate

Predictability is an emerging metric that quantifies the highest possible prediction accuracy for a given time series, being widely utilized in assessing known prediction algorithms and characterizing intrinsic regularities in human behaviors. Lately, increasing criticisms aim at the inaccuracy of the estimated predictability, caused by the original entropy-based method. In this brief report, we strictly prove that the time series predictability is equivalent to a seemingly unrelated metric called Bayes error rate that explores the lowest error rate unavoidable in classification. This proof bridges two independently developed fields, and thus each can immediately benefit from the other. For example, based on three theoretical models with known and controllable upper bounds of prediction accuracy, we show that the estimation based on Bayes error rate can largely solve the inaccuracy problem of predictability.
Anthropomorphic diagnosis of runtime hidden behaviors in OpenMP multi-threaded applications

Extreme-scale computing involves hundreds of millions of threads with multi-level parallelism running on large-scale hierarchical and heterogeneous hardware. Some OpenMP multi-threaded applications increasingly suffer from runtime hidden behaviors owning to shared resource contention as well as software- and hardware-related problems. Such hidden behaviors can result in failure and inefficiencies and are among the main challenges in system resiliency. To minimize the impact of hidden behaviors, one must quickly and accurately detect and diagnose the hidden behaviors that cause the failures. However, it is difficult to identify hidden behaviors in the dynamic and noisy data collected by OpenMP multi-threaded monitoring infrastructures. This paper presents an anthropomorphic diagnosis framework for hidden behaviors of OpenMP multi-threaded applications. In the framework, we first design injected heartbeat functions for OpenMP multi-threaded applications. Then, we leverage the heartbeat sequences to extract features of hidden behaviors. Finally, we develop a feature learning-based algorithm using heartbeat analysis, namely HSA, to diagnose hidden behaviors. To evaluate our framework, the NAS Parallel NPB benchmark, EPCC OpenMP micro-benchmark suite, and Jacobi benchmark are used to test the performance of our proposed framework. The experimental results demonstrate that our framework successfully identifies 90.3% of the injected hidden behaviors of OpenMP multi-threaded applications while acquiring low overhead.
Photochemical Anchoring of Singly Er<sup>3+</sup> Ion-Doped NaYF<sub>4</sub> Nanoparticles for Scalable Fabrication of Single-Photon Emitting Devices: Implications for Quantum Light Sources in the Telecom Window

Scalable methods to access single-photon sources on demand are highly sought after. As a potential strategy, we demonstrate the optical trapping and chemical anchoring of NaYF4 nanoparticles (NPs) and NaYF4 NPs doped with on average a single Er3+ ion. The anchoring method we present involves surface coating the NPs with thiol-functionalized phospholipids, where the thiol group is protected with a chemical group photoremovable at 340 nm 2-bromo-4′-hydroxyacetophenone. Functionalized NPs are trapped optically in a gold double-nanohole aperture using a 980 nm laser. A 340 nm light beam is focused on the particle, resulting in deprotection of the thiol groups and attachment of the thiols to the gold surface, permanently anchoring the NPs. Electron microscopic imaging proves the successful anchoring after removal of the trapping laser, 340 nm light source, and solvent. The approach is promising for reliably fabricating a single-photon emitting material in a scalable and potentially automatable manner.
RUE: A robust personalized cost assignment strategy for class imbalance cost-sensitive learning

Cost-sensitive learning is a popular paradigm to address class-imbalance learning (CIL) problem. Traditional cost-sensitive learning approaches always solve CIL problem by assigning a constant higher training error penalty for all minority instances than that of majority instances, but ignore the significance of location information. Therefore, several recent studies began to focus on the personalized cost assignment, i.e., designating different costs for different instances based on their location information. The emerging personalized cost-sensitive approaches always perform better than those traditional ones; however, the estimation for location information may be inaccurate as it is apt to be impacted by data density variation. To address this problem, we propose a novel location information estimation and cost assignment strategy called RUE. Unlike previous approaches, our proposed strategy explores location information by an indirect way: the error rate feed backed from a random undersampling ensemble. The strategy is robust towards data distribution, and is helpful for accurately estimating the significance of each instance regardless the complexity of data distribution. In context of Fuzzy Support Vector Machine (FSVM) and Weighted Extreme Learning Machine (WELM), the proposed cost assignment strategy is compared with several popular and state-of-the-art approaches, and the results show its effectiveness and superiority.
Metaheuristics with deep learning driven phishing detection for sustainable and secure environment

Information technologies have intervened in every aspect of human life. This growth of connectivity, however, has radically changed the phishing attack landscape. In a phishing attack, users are tricked into providing data they would not willingly share otherwise. This attack is a persistent threat to the sustainability and security of ubiquitous systems. Hence, this paper introduces a novel metaheuristics deep learning-oriented phishing detection (MDLPD-SSE) technique for a sustainable and secure environment. The presented MDLPD-SSE model majorly focuses on identifying phishing websites. For this, the MDLPD-SSE method pre-processes the input URL to transform it into a compatible format. In addition, an improved simulated annealing-based feature selection (ISA-FS) approach was used to derive feature subsets. Furthermore, the long short-term memory (LSTM) model is utilized in this study to identify phishing. Finally, the bald eagle search (BES) optimization methodology was exploited to fine-tune the hyperparameters relevant to the LSTM model. Our outcomes demonstrated the superiority of the proposed model with an improved accuracy of 95.78%.
QE-DAL: A quantum image feature extraction with dense distribution-aware learning framework for object counting and localization

Object counting and localization (OCL) was an essential problem in intelligent transportation fields. The convolutional neural network (CNN)-based models transformed the OCL problems into a regression task. However, the abundant semantic information of the crowd scenes may lead the CNN framework hard to extract adequate features in order to ensure good precision In this work, a Quantum Image Feature Extraction with Dense Distribution-Aware Learning (QE-DAL) framework was proposed to handle this problem. The crowd features were extracted by Quantum layers, which were extracted by encoding, quantum circuits and decode procedures based on the multi-scale architecture. For handling objects, the refined distance compensating operator was adopted to fuse the multi-scale architecture. To relieve the computation burden, a Gaussian distribution estimation mechanism was proposed to initiate and update the bounding sizes of the objects via a point-supervised manner. Finally, the joint loss function, which describes pixel classes, density maps and offset bounding boxes, was built for QE-DAL. The ablation experiment results demonstrated that the effectiveness of the quantum feature extraction architecture and the Gaussian distribution estimation mechanism of QE-DAL was validated to show superior performance than the other state-of-the-art framework. Moreover, the generalization of the QE-DAL was evidenced by the Cross-scene learning evaluation.
Exploration of Player Behaviours from Broadcast Badminton Videos

Abstract Understanding an opposing player's behaviours and weaknesses is often the key to winning a badminton game. This study presents a system to extract game data from broadcast badminton videos, and visualize the extracted data to help coaches and players develop effective tactics. Specifically, we apply state‐of‐the‐art machine learning methods to partition a broadcast video into segments, in which each video segment shows a badminton rally. Next, we detect players' feet in each video frame and transform the player positions into the court coordinate system. Finally, we detect hit frames in each rally, in which the shuttle moves towards the opposite directions. By visualizing the extracted data, our system conveys when and where players hit the shuttle in historical games. Since players tend to smash or drop shuttles under a specific location, we provide users with interactive tools to filter data and focus on the distributions conditioned by player positions. This strategy also reduces visual clutter. Besides, our system plots the shuttle hitting distributions side‐by‐side, enabling visual comparison and analysis of player behaviours under different conditions. The results and the use cases demonstrate the feasibility of our system.
A hybrid discrete state transition algorithm for combinatorial optimization problems

The discrete state transition algorithm (DSTA) has been wildly applied to deal with combinatorial optimization problems. However, its low convergence accuracy limits its application in large-scale optimization problems. Aiming at the convergence performance and search intensity of the algorithm, a hybrid discrete state transition algorithm (HDSTA) is proposed in this work by introducing tabu search and elite solution set. Firstly, a searching mechanism with the integration of DSTA and tabu search (TS) is established, which allows moving to adjacent solutions at an increased cost to escape from the local optimum. Specifically, a tabu list as adaptive memory is adopted to avoid the loop when deviating from local optima. Secondly, an elite solution set is introduced to integrate the information of the previous optimal solution and the global optimal solution, and the search strategy is modified to expand the range and diversity of candidate solutions. Finally, the proposed HDSTA is verified according to the real data on two well-known optimization problems (staff assignment problem and traveling salesman problem) and the real data of an industrial case. The experimental results show the effectiveness of the proposed algorithm in large-scale optimization problems.
SenGlove—A Modular Wearable Device to Measure Kinematic Parameters of The Human Hand

For technical or medical applications, the knowledge of the exact kinematics of the human hand is key to utilizing its capability of handling and manipulating objects and communicating with other humans or machines. The optimal relationship between the number of measurement parameters, measurement accuracy, as well as complexity, usability and cost of the measuring systems is hard to find. Biomechanic assumptions, the concepts of a biomechatronic system and the mechatronic design process, as well as commercially available components, are used to develop a sensorized glove. The proposed wearable introduced in this paper can measure 14 of 15 angular values of a simplified hand model. Additionally, five contact pressure values at the fingertips and inertial data of the whole hand with six degrees of freedom are gathered. Due to the modular design and a hand size examination based on anthropometric parameters, the concept of the wearable is applicable to a large variety of hand sizes and adaptable to different use cases. Validations show a combined root-mean-square error of 0.99° to 2.38° for the measurement of all joint angles on one finger, surpassing the human perception threshold and the current state-of-the-art in science and technology for comparable systems.
Multistakeholder Education Model in Strengthening Literacy and Disaster Awareness in Lampung Province

An individual level of disaster awareness ultimately influences the approach and nature of actions taken to mitigate and prepare for disasters, and can contribute to reducing community vulnerability to disasters, especially among those from low socioeconomic backgrounds. Furthermore, a better understanding of the critical differences in information-seeking behavior can assist in designing effective intervention models and approaches. Therefore, this paper discusses the model for strengthening local communities’ disaster awareness and information literacy behavior. Mixed method research is based on research questions using case studies from two local governments in Lampung Province; Bandar Lampung City and South Lampung Regency. Quantitative analysis was performed with simple statistics. Meanwhile, qualitative analysis was carried out using an interactive model assisted by triangulation tables. The solution that can be used so that disaster education and literacy become strong and equitable is to combine it with a specific approach. The approach we suggest in this research is the concept of multistakeholder education.&#x0D; Keywords: disaster literacy, disaster education, disaster awareness
A Flexible Session-Based Recommender System for e-Commerce

Research into session-based recommendation systems (SBSR) has attracted a lot of attention, but each study focuses on a specific class of methods. This work examines and evaluates a large range of methods, from simpler statistical co-occurrence methods to embeddings and SotA deep learning methods. This paper analyzes theoretical and practical issues in developing and evaluating methods for SBSR in e-commerce applications, where user profiles and purchase data do not exist. The major tasks of SBRS are reviewed and studied, namely: prediction of next-item, next-basket and purchase intent. For physical retail shopping where no information about the current session exists, we treat the previous baskets purchased by the user as previous sessions drawn from a loyalty system. Mobile application scenarios such as push notifications and calling tune recommendations are also presented. Recommender models using graphs, embeddings and deep learning methods are studied and evaluated in all SBRS tasks using different datasets. Our work contributes a number of very interesting findings. Among all tested models, LSTMs consistently outperform other methods of SBRS in all tasks. They can be applied directly because they do not need significant fine-tuning. Additionally, they naturally model the dynamic browsing that happens in e-commerce web applications. On the other hand, another important finding of our work is that graph-based methods can be a good compromise between effectiveness and efficiency. Another important conclusion is that a “temporal locality principle” holds, implying that more recent behavior is better suited for prediction. In order to evaluate these systems further in realistic environments, several session-based recommender methods were integrated into an e-shop and an A/B testing method was applied. The results of this A/B testing are in line with the experimental results, which represents another important contribution of this paper. Finally, important parameters such as efficiency, application of business rules, re-ranking issues, and the utilization of hybrid methods are also considered and tested, providing comprehensive useful insights into SBRS and facilitating the transferability of this research work to other domains and recommendation scenarios.