Reviewerly/exHarmony huggingface dataset:
Dynamic activation and enhanced image contour features for object detection

Object detection technology is a popular research direction which is widely used in areas such as autonomous driving and medical diagnosis. At this stage mobile devices often have limited storage resources to deploy large object detection networks and need to meet real-time requirements. This paper proposes a lightweight and efficient object detection model based on YOLOv4, first using the lightweight network GhostNet to extract image features and reduce the number of parameters and computation of the backbone structure; then combining AFmodule and Meta-ACON activation function to enhance the feature extraction capability of the backbone network, which strengthen the mode’s ability to capture image spatial feature information; this paper also designs the RL-PAFPN feature fusion structure is with the Reslayer module to further improve the model’s ability to extract and fuse image features. By comparing other mainstream object detection models, the YOLOv4-Ghost-AMR network in this paper has less computation and fewer parameters, and the accuracy of the model reaches 86.83%, which is suitable for deployment in mobile devices with limited storage. The model proposed in this paper can be applied to medical, traffic and fault detection fields, which changes the traditional manual detection method and saves manpower and time costs, achieving high precision real-time object detection.
Comparison of Google Lens recognition performance with other plant recognition systems

In the context of the STEM approach to education, motivating pupils through tailored research and leveraging IT in the classroom is relevant. The justification of these approaches hasn't received much examination, though. The purpose of the study is to support the decision to use an AR-plant recognition application to give tailored instruction throughout both extracurricular activities and the school day. Every phase of an app's interaction with a user was examined and used to categorize every app. Also described were the social settings of the applications and how they were used for extracurricular activities. There has been discussion on the didactics of using AR recognition apps in biology classes. A survey of experts in digital education regarding the ease of installation, the friendliness of the interface, and the accuracy of image processing was conducted to give usability analysis. Applications were examined for their ability to accurately identify plants on the "Dneprovskiy district of Kiev" list in order to assess the rationale of usage. It has been established that Google Lens is the best option. As an alternative to Seek or Flora Incognita, according to the analysis's findings, these apps were less accurate.
From classical to soft computing based watermarking techniques: A comprehensive review

With evolution of digital technology, it is challenging to protect personal data from access without consent. Digital watermarking conceals some information as watermark to protect from unauthorized users while granting access to authorized users. This paper presents a detailed review of existing watermarking techniques belonging to spatial domain and frequency domain. Moreover, a detailed discussion on the meta-heuristic-based watermarking techniques is included which focuses on performing optimal watermarking. For a digital watermarking technique, the basic characteristics, application area, watermarking attacks, quality assessment metrics and challenges are presented. Furthermore, experimental evaluation of existing watermarking techniques is conducted for qualitative and quantitative analysis on gray-scale image dataset as well as video dataset. Finally, this paper highlights research challenges for the future research. Therefore, this study may be an alternative tool for researchers in designing an efficient watermarking techniques while solving a real-world applications.
Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement

We investigate the problem of coordinating two robot arms to solve non-monotone tabletop multi-object re- arrangement tasks. In a non-monotone rearrangement task, complex object-object dependencies exist that require moving some objects multiple times to solve an instance. In working with two arms in a large workspace, some objects must be handed off between the robots, which further complicates the planning process. For the challenging dual-arm tabletop rearrangement problem, we develop effective task planning algorithms for scheduling the pick-n-place sequence that can be properly distributed between the two arms. We show that, even without using a sophisticated motion planner, our method achieves significant time savings in comparison to greedy approaches and naive parallelization of single-robot plans.
An empirical study on the survival rate of GitHub projects

The number of Open Source projects hosted in social coding platforms such as GitHub is constantly growing. However, many of these projects are not regularly maintained and some are even abandoned shortly after they were created. In this paper we analyze early project development dynamics in software projects hosted on GitHub, including their survival rate. To this aim, we collected all 1,127 GitHub repositories from four different ecosystems (i.e., NPM packages, R packages, WordPress plugins and Laravel packages) created in 2016. We stored their activity in a time series database and analyzed their activity evolution along their lifespan, from 2016 to now. Our results reveal that the prototypical development process consists of intensive coding-driven active periods followed by long periods of inactivity. More importantly, we have found that a significant number of projects die in the first year of existence with the survival rate decreasing year after year. In fact, the probability of surviving longer than five years is less than 50% though some types of projects have better chances of survival.
Manipulating Twitter through Deletions

Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. By design, these approaches ignore deleted tweets. However, bad actors can delete content strategically to manipulate the system. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. Estimates based on publicly available Twitter data underestimate the true deletion volume. A small fraction of accounts delete a large number of tweets daily. We uncover two abusive behaviors that exploit deletions. First, limits on tweet volume are circumvented, allowing certain accounts to flood the network with over 26 thousand daily tweets. Second, coordinated networks of accounts engage in repetitive likes and unlikes of content that is eventually deleted, which can manipulate ranking algorithms. These kinds of abuse can be exploited to amplify content and inflate popularity, while evading detection. Our study provides platforms and researchers with new methods for identifying social media abuse.
Neural Radiance Transfer Fields for Relightable Novel-View Synthesis with Global Illumination

Given a set of images of a scene, the re-rendering of this scene from novel views and lighting conditions is an important and challenging problem in Computer Vision and Graphics. On the one hand, most existing works in Computer Vision usually impose many assumptions regarding the image formation process, e.g. direct illumination and predefined materials, to make scene parameter estimation tractable. On the other hand, mature Computer Graphics tools allow modeling of complex photo-realistic light transport given all the scene parameters. Combining these approaches, we propose a method for scene relighting under novel views by learning a neural precomputed radiance transfer function, which implicitly handles global illumination effects using novel environment maps. Our method can be solely supervised on a set of real images of the scene under a single unknown lighting condition. To disambiguate the task during training, we tightly integrate a differentiable path tracer in the training process and propose a combination of a synthesized OLAT and a real image loss. Results show that the recovered disentanglement of scene parameters improves significantly over the current state of the art and, thus, also our re-rendering results are more realistic and accurate.
MTTrans: Cross-domain Object Detection with Mean Teacher Transformer

Recently, DEtection TRansformer (DETR), an end-to-end object detection pipeline, has achieved promising performance. However, it requires large-scale labeled data and suffers from domain shift, especially when no labeled data is available in the target domain. To solve this problem, we propose an end-to-end cross-domain detection Transformer based on the mean teacher framework, MTTrans, which can fully exploit unlabeled target domain data in object detection training and transfer knowledge between domains via pseudo labels. We further propose the comprehensive multi-level feature alignment to improve the pseudo labels generated by the mean teacher framework taking advantage of the cross-scale self-attention mechanism in Deformable DETR. Image and object features are aligned at the local, global, and instance levels with domain query-based feature alignment (DQFA), bi-level graph-based prototype alignment (BGPA), and token-wise image feature alignment (TIFA). On the other hand, the unlabeled target domain data pseudo-labeled and available for the object detection training by the mean teacher framework can lead to better feature extraction and alignment. Thus, the mean teacher framework and the comprehensive multi-level feature alignment can be optimized iteratively and mutually based on the architecture of Transformers. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance in three domain adaptation scenarios, especially the result of Sim10k to Cityscapes scenario is remarkably improved from 52.6 mAP to 57.9 mAP. Code will be released https://github.com/Lafite-Yu/MTTrans-OpenSource .
A Novel Smartphone-Based Human Activity Recognition Using Deep Learning in Health care

Nowadays, most smartphones feature a number of strong sensors, such as direction, network, location, and motion sensors. Motion and inertial sensors (i.e., accelerometer, gyroscope, etc.) are particularly popular in human activity recognition (HAR) for detecting human physical activity which helps in many research areas of the Internet of healthcare things (IoHT) including several patient’s diseases such as Parkinson, obesity, cardiovascular, and diabetes. Deep learning (DL) techniques have been increasingly popular over the years as a result of their significant outcomes. The use of deep learning techniques to recognize human physical activity in wearable and mobile sensor situations has also gotten a lot of attention from around the world. In this paper, a deep neural network (DNN) combining bidirectional long short-term memory (Bi-LSTM) and convolutional neural network (CNN) is proposed. The performance of the model has been evaluated on two publicly available datasets: WISDM and UCI-HAR. The model has achieved 97.96 and 97.15% accuracy for the WISDM and UCI-HAR, respectively. Moreover, the simulation results show the effectiveness of the proposed work compared to other state-of-the-art methods.
Blockchain in Supply Chain Management: A Synthesis of Barriers and Enablers for Managers

Blockchain is an emerging and disruptive technology and has the potential to change how supply chains manage their information. However, Blockchain is accompanied by challenges, such as increased information technology complexity, issues of scalability, incompatibility with existing laws and regulations, and a lack of awareness among organisations and customers. This research conducts a bibliometric analysis based on a sample of 68 papers which address the barriers and enablers of blockchain adoption in supply chain management. A recurring theme in the papers was managers’ lack of understanding of Blockchain, which acted as a barrier to adoption. This study proposes a possible explanation by arguing that the academic models used in literature are too obscure from a manager’s perspective and that there is a need to synthesise literature into a framework which is easily understood and familiar. Therefore, the barriers and enablers identified in this study were grouped into the robust Political, Economic, Social, Technological, Legal, and Environment (PESTLE) framework. A key finding from this framework was the absence of political barriers or enablers, which is surprising since blockchain adoption challenges the current status quo in multiple ways. Furthermore, the environmental enablers and barriers were scarcely discussed, with little empirical evidence.
Robust transformer with locality inductive bias and feature normalization

Vision transformers have been demonstrated to yield state-of-the-art results on a variety of computer vision tasks using attention-based networks. However, research works in transformers mostly do not investigate robustness/accuracy trade-off, and they still struggle to handle adversarial perturbations. In this paper, we explore the robustness of vision transformers against adversarial perturbations and try to enhance their robustness/accuracy trade-off in white box attack settings. To this end, we propose Locality iN Locality (LNL) transformer model. We prove that the locality introduction to LNL contributes to the robustness performance since it aggregates local information such as lines, edges, shapes, and even objects. In addition, to further improve the robustness performance, we encourage LNL to extract training signal from the moments (a.k.a., mean and standard deviation) and the normalized features. We validate the effectiveness and generality of LNL by achieving state-of-the-art results in terms of accuracy and robustness metrics on German Traffic Sign Recognition Benchmark (GTSRB) and Canadian Institute for Advanced Research (CIFAR-10). More specifically, for traffic sign classification, the proposed LNL yields gains of 1.1% and 35% in terms of clean and robustness accuracy compared to the state-of-the-art studies.
Design of an interval type-2 fuzzy neural network sliding mode robust controller for higher stability of magnetic spacecraft attitude control

This paper designs an interval type-2 fuzzy neural network sliding mode robust controller (IT2FNNSMRC) to improve the stability of the vibrational angle of the orbital plane in magnetic rigid spacecraft attitude control. The control system consists of an interval type-2 fuzzy neural network (IT2FNN) controller, a PD controller, and a robust controller in parallel connection. The IT2FNN controller, as a nonlinear regulator, compensates the nonlinearity of the controlled object; the PD controller, as a feedback controller, ensures the global asymptotic stability of the control system; the robust controller inhibits input load disturbance. The IT2FNN controller hereof has a self-organizing function which enables it to automatically determine the network structure and parameters online. At the stage of IT2FNN structure learning, the standard on rule growth is set according to the incentive intensities of IT2FNN rule premises. A new rule is generated when the incentive intensities of rules are all smaller than a certain threshold; next, a significance index is set for each rule. When the significance index of some rule decays to a certain threshold, the corresponding rule shall be deleted to achieve the goals of optimizing IT2FNN structure and reducing system complexity. At the stage of parameter learning, adaptive adjustment of IT2FNN parameters is made via the sliding mode control theory learning algorithm, and the stabilities of the algorithm and control system are proven using Lyapunov function. Finally, the proposed control scheme is used in the control of a magnetic rigid spacecraft, as compared to three other designed control methods. Simulation results show that IT2FNNSMRC has superior control precision and stability. And the IT2FNN which adopts the proposed learning algorithm can address uncertainty satisfactorily, with higher computational implementability.
T<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e698" altimg="si280.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>L: Trans-transfer Learning for few-shot fine-grained visual categorization with extended adaptation

Fine-grained visual categorization requires the ability to distinguish categories with subtle differences, which is also a problem constantly burdened by collecting and labeling massive volume of samples. While transfer learning is extensively used in fine-grained visual categorization, these approaches generally does not work under few-shot regime. As apposed to meta-learning which suffers limitations such as shallow adaptation, and traditional transfer learning which is not task-oriented, we propose a novel transfer learning approach which we refer to as Trans-transfer Learning (T2L). Firstly, a two-phase learning framework is proposed to facilitate task orientation and deep adaptation. Secondly, a novel explainable-learning based procedure is integrated into the second phase to reconfigure the network for training on few samples, after which a deep adaptation procedure is conducted by simultaneously optimizing the feature extractor and the classification boundaries. It is motivated by the fact that a large factor of the over-fitting under few-shot regime, comes from noises that appear in every layers of the activation. The reconfigured model can train solely based on inter-class differences, which not only alleviates over-fitting, but also rediscovers more discriminating features. The proposed approach is comparatively evaluated with state-of-the-art approaches in this field and demonstrates better performances consistently.
A Generalized Deep Learning Approach to Seismic Activity Prediction

Seismic activity prediction has been a challenging research domain: in this regard, accurate prediction using historical data is an intricate task. Numerous machine learning and traditional approaches have been presented lately for seismic activity prediction; however, no generalizable model exists. In this work, we consider seismic activity predication as a binary classification problem, and propose a deep neural network architecture for the classification problem, using historical data from Chile, Hindukush, and Southern California. After obtaining the data for the three regions, a data cleaning process was used, which was followed by a feature engineering step, to create multiple new features based on various seismic laws. Afterwards, the proposed model was trained on the data, for improved prediction of the seismic activity. The performance of the proposed model was evaluated and compared with extant techniques, such as random forest, support vector machine, and logistic regression. The proposed model achieved accuracy scores of 98.28%, 95.13%, and 99.29% on the Chile, Hindukush, and Southern California datasets, respectively, which were higher than the current benchmark model and classifiers. In addition, we also conducted out-sample testing, where the evaluation metrics confirmed the generality of our proposed approach.
Comparing encoding mechanisms in realistic virtual reality and conventional 2D laboratory settings: Event-related potentials in a repetition suppression paradigm

Although the human brain is adapted to function within three-dimensional environments, conventional laboratory research commonly investigates cognitive mechanisms in a reductionist approach using two-dimensional stimuli. However, findings regarding mnemonic processes indicate that realistic experiences in Virtual Reality (VR) are stored in richer and more intertwined engrams than those obtained from the conventional laboratory. Our study aimed to further investigate the generalizability of laboratory findings and to differentiate whether the processes underlying memory formation differ between VR and the conventional laboratory already in early encoding stages. Therefore, we investigated the Repetition Suppression (RS) effect as a correlate of the earliest instance of mnemonic processes under conventional laboratory conditions and in a realistic virtual environment. Analyses of event-related potentials (ERPs) indicate that the ERP deflections at several electrode clusters were lower in VR compared to the PC condition. These results indicate an optimized distribution of cognitive resources in realistic contexts. The typical RS effect was replicated under both conditions at most electrode clusters for a late time window. Additionally, a specific RS effect was found in VR at anterior electrodes for a later time window, indicating more extensive encoding processes in VR compared to the laboratory. Specifically, electrotomographic results (VARETA) indicate multimodal integration involving a broad cortical network and higher cognitive processes during the encoding of realistic objects. Our data suggest that object perception under realistic conditions, in contrast to the conventional laboratory, requires multisensory integration involving an interconnected functional system, facilitating the formation of intertwined memory traces in realistic environments.
Deep Learning-Based Real-Time Traffic Sign Recognition System for Urban Environments

A traffic sign recognition system is crucial for safely operating an autonomous driving car and efficiently managing road facilities. Recent studies on traffic sign recognition tasks show significant advances in terms of accuracy on several benchmarks. However, they lack performance evaluation in driving cars in diverse road environments. In this study, we develop a traffic sign recognition framework for a vehicle to evaluate and compare deep learning-based object detection and tracking models for practical validation. We collect a large-scale highway image set using a camera-installed vehicle for training models, and evaluate the model inference during a test drive in terms of accuracy and processing time. In addition, we propose a novel categorization method for urban road scenes with possible scenarios. The experimental results show that the YOLOv5 detector and strongSORT tracking model result in better performance than other models in terms of accuracy and processing time. Furthermore, we provide an extensive discussion on possible obstacles in traffic sign recognition tasks to facilitate future research through numerous experiments for each road condition.
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances

With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area. However, despite promising results, the field still faces challenges that require further research, e.g., allowing flexible upsampling, more effective loss functions, and better evaluation metrics. We review the domain of SR in light of recent advances and examine state-of-the-art models such as diffusion (DDPM) and transformer-based SR models. We critically discuss contemporary strategies used in SR and identify promising yet unexplored research directions. We complement previous surveys by incorporating the latest developments in the field, such as uncertainty-driven losses, wavelet networks, neural architecture search, novel normalization methods, and the latest evaluation techniques. We also include several visualizations for the models and methods throughout each chapter to facilitate a global understanding of the trends in the field. This review ultimately aims at helping researchers to push the boundaries of DL applied to SR.