Reviewerly/exHarmony huggingface dataset:
A Penalty-Based Differential Evolution for Multimodal Optimization

It is very difficult to locate multiple global optimal solutions (GOSs) of multimodal optimization problems (MMOPs). To deal with this issue, a penalty-based multimodal optimization differential evolution (DE), called PMODE, is developed in this article. In PMODE, a penalty strategy with a dynamic penalty radius is constructed to solve MMOPs. An elite selection mechanism is designed to identify and select elite solutions. The neighboring areas of these elite solutions are penalized. PMODE uses a popular DE variant-JADE as its search engine. The proposed PMODE is compared with several other state-of-the-art multimodal optimization algorithms on 20 MMOPs used in the IEEE CEC2013 special session. The experimental results show that PMODE performs better than other state-of-the-art methods.
A survey of OCR evaluation tools and metrics

Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development.IoT routing is the basis for the efficient operation of the perception-layer network.As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication.In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority.Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k.To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer.This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer.RLATT has triple-optimized the trickle timer algorithm.To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm.Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.
Data-free Universal Adversarial Perturbation and Black-box Attack

Universal adversarial perturbation (UAP), i.e. a single perturbation to fool the network for most images, is widely recognized as a more practical attack because the UAP can be generated beforehand and applied directly during the at-tack stage. One intriguing phenomenon regarding untargeted UAP is that most images are misclassified to a dominant label. This phenomenon has been reported in previous works while lacking a justified explanation, for which our work attempts to provide an alternative explanation. For a more practical universal attack, our investigation of untargeted UAP focuses on alleviating the dependence on the original training samples, from removing the need for sample labels to limiting the sample size. Towards strictly data-free untargeted UAP, our work proposes to exploit artificial Jigsaw images as the training samples, demonstrating competitive performance. We further investigate the possibility of exploiting the UAP for a data-free black-box attack which is arguably the most practical yet challenging threat model. We demonstrate that there exists optimization-free repetitive patterns which can successfully attack deep models. Code is available at https://bit.ly/3y0ZTIC.
OpenRDW: A Redirected Walking Library and Benchmark with Multi-User, Learning-based Functionalities and State-of-the-art Algorithms

Redirected walking (RDW) is a locomotion technique that guides users on virtual paths, which might vary from the paths they physically walk in the real world. Thereby, RDW enables users to explore a virtual space that is larger than the physical counterpart with near-natural walking experiences. Several approaches have been proposed and developed; each using individual platforms and evaluated on a custom dataset, making it challenging to compare between methods. However, there are seldom public toolkits and recognized benchmarks in this field. In this paper, we introduce OpenRDW, an open-source library and benchmark for developing, deploying and evaluating a variety of methods for walking path redirection. The OpenRDW library provides application program interfaces to access the attributes of scenes, to customize the RDW controllers, to simulate and visualize the navigation process, to export multiple formats of the results, and to evaluate RDW techniques. It also supports the deployment of multi-user real walking, as well as reinforcement learning-based models exported from TensorFlow or PyTorch. The OpenRDW benchmark includes multiple testing conditions, such as walking in size varied tracking spaces or shape varied tracking spaces with obstacles, multiple user walking, etc. On the other hand, procedurally generated paths and walking paths collected from user experiments are provided for a comprehensive evaluation. It also contains several classic and state-of-the-art RDW techniques, which include the above mentioned functionalities.
How Good is Good Enough? Quantifying the Impact of Benefits, Accuracy, and Privacy on Willingness to Adopt COVID-19 Decision Aids

An increasing number of data-driven decision aids are being developed to provide humans with advice to improve decision-making around important issues such as personal health and criminal justice. For algorithmic systems to support human decision-making effectively, people must be willing to use them. Yet, prior work suggests that accuracy and privacy concerns may both deter potential users and limit the efficacy of these systems. We expand upon prior research by empirically modeling how accuracy and privacy influence intent to adopt algorithmic systems. We focus on an algorithmic system designed to aid people in a globally-relevant decision context with tangible consequences: the COVID-19 pandemic. We use statistical techniques to analyze surveys of 4,615 Americans to (1) evaluate the effect of both accuracy and privacy concerns on reported willingness to install COVID-19 apps; (2) examine how different groups of users weigh accuracy relative to privacy; and (3) we empirically develop the first statistical models, to our knowledge, of how the amount of benefit (e.g., error rate) and degree of privacy risk in a data-driven decision aid may influence willingness to adopt.
Why or Why Not? The Effect of Justification Styles on Chatbot Recommendations

Chatbots or conversational recommenders have gained increasing popularity as a new paradigm for Recommender Systems (RS). Prior work on RS showed that providing explanations can improve transparency and trust, which are critical for the adoption of RS. Their interactive and engaging nature makes conversational recommenders a natural platform to not only provide recommendations but also justify the recommendations through explanations. The recent surge of interest inexplainable AI enables diverse styles of justification, and also invites questions on how styles of justification impact user perception. In this article, we explore the effect of “why” justifications and “why not” justifications on users’ perceptions of explainability and trust. We developed and tested a movie-recommendation chatbot that provides users with different types of justifications for the recommended items. Our online experiment ( n = 310) demonstrates that the “why” justifications (but not the “why not” justifications) have a significant impact on users’ perception of the conversational recommender. Particularly, “why” justifications increase users’ perception of system transparency, which impacts perceived control, trusting beliefs and in turn influences users’ willingness to depend on the system’s advice. Finally, we discuss the design implications for decision-assisting chatbots.
Autonomous Exploration Development Environment and the Planning Algorithms

Autonomous Exploration Development Environment is an open-source repository released to facilitate development of high-level planning algorithms and integration of com-plete autonomous navigation systems. The repository contains representative simulation environment models, fundamental navigation modules, e.g., local planner, terrain traversability analysis, waypoint following, and visualization tools. Together with two of our high-level planner releases - TARE planner for exploration and FAR planner for route planning, we detail usage of the three open-source repositories and share experiences in integration of autonomous navigation systems. We use DARPA Subterranean Challenge as a use case where the repositories together form the main navigation system of the CMU-OSU Team. In the end, we discuss a few potential use cases in extended applications.
Multiparty quantum key agreement

In this paper, we discuss in some detail how multiparty quantum key agreement protocols must be carefully designed and present a collusion attack to illustrate this point. Then, a secure circle-type multiparty quantum key agreement with Bell states is proposed. Bell states are used as the information carriers and transmitted among the participants, who embed their secrets into the traveling particles via certain encoding operations. In this way, all participants simultaneously obtain the same agreement key, i.e., the sum of their secret inputs, at the end of the protocol. Here, quantum state discrimination is utilized to design the encoding operations, which ensures that the proposed protocol is correct and secure against the presented collusion attack. Furthermore, it is shown that the proposed protocol satisfies three conditions of a secure quantum key agreement protocol in theory. In addition, these encoding operations consist of some common single-qubit gates, the Hadamard operator and four Pauli operators, which makes the proposed protocol feasible using current technology.
MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning using an Anchor Free Approach

Multitask learning is a common approach in machine learning, which allows to train multiple objectives with a shared architecture. It has been shown that by training multiple tasks together inference time and compute resources can be saved, while the objectives performance remains on a similar or even higher level. However, in perception related multitask networks only closely related tasks can be found, such as object detection, instance and semantic segmentation or depth estimation. Multitask networks with diverse tasks and their effects with respect to efficiency on one another are not well studied.In this paper we augment the CenterNet anchor-free approach for training multiple diverse perception related tasks together, including the task of object detection and semantic segmentation as well as human pose estimation. We refer to this DNN as Multitask-CenterNet (MCN). Additionally, we study different MCN settings for efficiency. The MCN can perform several tasks at once while maintaining, and in some cases even exceeding, the performance values of its corresponding single task networks. More importantly, the MCN architecture decreases inference time and reduces network size when compared to a composition of single task networks.
Organic matter enrichment of the Chang 7 member in the Ordos Basin: Insights from chemometrics and element geochemistry

The lacustrine black shales of the Chang 7 member are the main source rocks and the most important unconventional (shale oil) petroleum resources in the Ordos Basin, North China. A series of continuous core samples was analysed for main, trace, and rare earth elements. The development of thin or laminar tuffs and tuffaceous mudstones and the enrichment of Fe, Mn, and other elements demonstrate that the Chang 7 member was influenced by event deposition, such as volcanic and hydrothermal activity. The ancient lake had brackish water and a relatively high sedimentation rate during the deposition of the Chang 7 member. The palaeoclimate in the early stage was warmer and wetter than that in the middle-late stage of the Chang 7 period. The geochemical proxies for palaeoproductivity and palaeoredox indicate that the ancient lake had a relatively high primary productivity with an anoxic to euxinic sulphide bottom water in the Chang 73 stage, which decreased to dyoxic-oxic water during the Chang 71+2 periods. A parameter dataset comprising 11 elemental proxies related to the reconstruction or evaluation of the depositional environment and palaeoproductivity was analysed using principal component analysis (PCA). The first principal component (PC1) accounted for 59.88% of the total variance in the original dataset and mainly represented the characteristics of palaeoproductivity and redox conditions; thus, it can be interpreted as an indicator of these factors. Based on the excellent positive linear correlation between the total organic matter content of the Chang 7 samples and the PC1 scores, the enrichment of organic matter in the Chang 7 member was mainly controlled by palaeoproductivity and the preservation environment. PCA is an effective method for dimensionality reduction and visualisation of multi-element geochemical data, which allows for a more comprehensive and in-depth understanding of geological samples. This work provides a practical application of chemometrics in elemental geochemistry. This can serve as a model for other studies.
A Marine Organism Detection Framework Based on the Joint Optimization of Image Enhancement and Object Detection

Underwater vision-based detection plays an increasingly important role in underwater security, ocean exploration and other fields. Due to the absorption and scattering effects of water on light, as well as the movement of the carrier, underwater images generally have problems such as noise pollution, color cast and motion blur, which seriously affect the performance of underwater vision-based detection. To address these problems, this study proposes an end-to-end marine organism detection framework that can jointly optimize the image enhancement and object detection. The framework uses a two-stage detection network with dynamic intersection over union (IoU) threshold as the backbone and adds an underwater image enhancement module (UIEM) composed of denoising, color correction and deblurring sub-modules to greatly improve the framework's ability to deal with severely degraded underwater images. Meanwhile, a self-built dataset is introduced to pre-train the UIEM, so that the training of the entire framework can be performed end-to-end. The experimental results show that compared with the existing end-to-end models applied to marine organism detection, the detection precision of the proposed framework can improve by at least 6%, and the detection speed has not been significantly reduced, so that it can complete the high-precision real-time detection of marine organisms.
From text to signatures: Knowledge transfer for efficient deep feature learning in offline signature verification

Handwritten signature is a common biometric trait, widely used for confirming the presence or the consent of a person. Offline Signature Verification (OSV) is the task of verifying the signer using static signature images captured after the finish of signing process, with many applications especially in the domain of forensics. Deep Convolutional Neural Networks (CNNs) can generate efficient feature representations, but their training is data-intensive. Since limited training data is an intrinsic problem of an OSV system’s development, this work focuses on addressing the problem of learning informative features by employing prior knowledge from a similar task in a domain with an abundance of training data. In particular, we demonstrate that an appropriate pre-training of a CNN model in the task of handwritten text-based writer identification task, can dramatically improve the efficiency of the CNN in the OSV task, enabling to obtain state-of-the-art performance with an order of magnitude less training signature samples. In the proposed scheme, after the pre-training of the CNN in writer identification task through specially processed handwritten text data, the learned features are tailored to the signature problem though a metric learning stage that utilizes contrastive loss to learn a mapping of the signatures’ features to a latent space that suits the OSV task. At the final stage, the proposed scheme utilizes Writer-Dependent (WD) classifiers learned on a few reference samples from each writer. Our system is tested on the three challenging signature datasets, CEDAR, MCYT-75 and GPDS300GRAY. The obtained accuracy in terms of Equal Error Rates (EER) is statistically equivalent to the popular SigNet CNN, despite a significantly smaller training set of signature images and no use of skilled forgeries signatures during training.
Loki

Maximizing the quality of experience (QoE) for real-time video is a long-standing challenge. Traditional video transport protocols, represented by a few deterministic rules, can hardly adapt to the heterogeneous and highly dynamic modern Internet. Emerging learning-based algorithms have demonstrated potential to meet the challenge. However, our measurement study reveals an alarming long tail performance issue: these algorithms tend to be bottle-necked by occasional catastrophic events due to the built-in exploration mechanisms. In this work, we propose Loki, which improves the robustness of learning-based model by coherently integrating it with a rule-based algorithm. To enable integration at feature level, we first reverse-engineer the rule-based algorithm into an equivalent "black-box" neural network. Then, we design a dual-attention feature fusion mechanism to fuse it with a reinforcement learning model. We train Loki in a commercial real-time video system through online learning, and evaluate it over 101 million video sessions, in comparison to state-of-the-art rule-based and learning-based solutions. The results show that Loki improves not only the average but also the tail performance substantially (26.30% to 44.24% reduction of stall rate and 1.76% to 2.17% increase in video throughput at 95-percentile).
The Improved A* Obstacle Avoidance Algorithm for the Plant Protection UAV with Millimeter Wave Radar and Monocular Camera Data Fusion

To enhance obstacle avoidance abilities of the plant protection UAV in unstructured farmland, this article improved the traditional A* algorithms through dynamic heuristic functions, search point optimization, and inflection point optimization based on millimeter wave radar and monocular camera data fusion. Obstacle information extraction experiments were carried out. The performance between the improved algorithm and traditional algorithm was compared. Additionally, obstacle avoidance experiments were also carried out. The results show that the maximum error in distance measurement of data fusion method was 8.2%. Additionally, the maximum error in obstacle width and height measurement were 27.3% and 18.5%, respectively. The improved algorithm is more useful in path planning, significantly reduces data processing time, search grid, and turning points. The algorithm at most increases path length by 2.0%, at least reduces data processing time by 68.4%, search grid by 74.9%, and turning points by 20.7%. The maximum trajectory offset error was proportional to the flight speed, with a maximum trajectory offset of 1.4 m. The distance between the UAV and obstacle was inversely proportional to flight speed, with a minimum distance of 1.6 m. This method can provide a new idea for obstacle avoidance of the plant protection UAV.
Direct Quantum Communications in the Presence of Realistic Noisy Entanglement

To realize the Quantum Internet, quantum communications require pre-shared entanglement among quantum nodes. However, both the generation and the distribution of the maximally-entangled quantum states are inherently contaminated by quantum decoherence. Conventionally, the quantum decoherence is mitigated by performing the consecutive steps of quantum entanglement distillation followed by quantum teleportation. However, this conventional approach imposes a long delay. To circumvent this impediment, we propose a novel quantum communication scheme relying on realistic noisy pre-shared entanglement, which eliminates the sequential steps imposing delay in the standard approach. More precisely, our proposed scheme can be viewed as a direct quantum communication scheme capable of improving the quantum bit error ratio (QBER) of the logical qubits despite relying on realistic noisy pre-shared entanglement. Our performance analysis shows that the proposed scheme offers competitive QBER, yield, and goodput compared to the existing state-of-the-art quantum communication schemes, despite requiring fewer quantum gates.
DSFNet: Dynamic and Static Fusion Network for Moving Object Detection in Satellite Videos

Moving object detection (MOD) in satellite videos remains challenging due to the extremely small size of the interested targets and the highly complex background. Both the intra-frame (static) and inter-frame (dynamic) information are of great importance to MOD. In this letter, we propose a two-stream detection network named dynamic and static fusion network (DSFNet) to tackle the MOD problem in satellite videos. Specifically, the DSFNet is composed of a 2-D backbone to extract static context information from a single frame and a lightweight 3-D backbone to extract dynamic motion cues from consecutive frames. Then the extracted static and dynamic features are fused and fed into the detection head to detect the moving targets in satellite videos. We conduct extensive experiments on videos collected from Jilin-1 satellite and the results have demonstrated the effectiveness and robustness of the proposed DSFNet. Experimental results show that our DSFNet achieves the-state-of-the-art performance.
Reward Mechanism for Blockchains Using Evolutionary Game Theory

Blockchains have witnessed widespread adoption in the past decade in various fields. The growing demand makes their scalability and sustainability challenges more evident than ever. As a result, more and more blockchains have begun to adopt proof-of-stake (PoS) consensus protocols to address those challenges. One of the fundamental characteristics of any blockchain technology is its crypto-economics and incentives. Lately, each PoS blockchain has designed a unique reward mechanism, yet, many of them are prone to free-rider and nothing-at-stake problems. To better understand the ad-hoc design of reward mechanisms, in this paper, we develop a reward mechanism framework that could apply to many PoS blockchains. We formulate the block validation game wherein the rewards are distributed for validating the blocks correctly. Using evolutionary game theory, we analyze how the participants' behaviour could potentially evolve with the reward mechanism. Also, penalties are found to play a central role in maintaining the integrity of blockchains.