Reviewerly/exHarmony huggingface dataset:
Clickbait Headline Detection in Indonesian News Sites using Robustly Optimized BERT Pre-training Approach (RoBERTa)

The abuse of clickbait headlines by online news media has kept increasing, causing bad experiences for the reader and reducing online news reading engagement. Since the advances in self-attention model, model such as BERT and other variant has been considered to be the state-of-the-art method in many NLP (natural language processing) related tasks. In this experiment, we'll be focusing on one of BERT variants called RoBERTa which is an improved model over BERT. The goal of this experiment is to compare how well the currently available RoBERTa model perform in detecting clickbait In-donesia news headline. Using a total of 6632 annotated news headlines, sampled from the CLICK-ID dataset, we experimented with some of huggingface community's top voted RoBERTa and BERT Indonesia language models, and compare each of their performances in classifying Indonesia clickbait headlines. We evaluate the accuracy, precission, recall, and F1 score of each models and found that cahya/XLM-RoBERTa-large and indobenchmarkIndoBERT-p1 as our top-performing model in this experiment with a 92% accuracy. Resource and performance wise we recommend indobenchmark/IndoBERT-p1 as a more suitable model, however, XLM-RoBERTa-large also comes with its merit in terms of having a more consistent output across validation and unseen set. In this paper, we propose each of the model configurations along with the exploratory data analysis, preprocessing method on the data, and the training performance of the configuration of each model.
Real time sign language detection system using deep learning techniques

Humans require communication in order to survive.It is a fundamental and effective method for communicating thoughts, feelings, and points of view.A greater number of babies are being born with hearing abnormalities, which puts them at a communication disadvantage with the rest of the world, according to data on physically challenged children during the last decade.People who are deaf or hard of hearing typically use sign languages to communicate.Hand gestures are used by Deaf or Mute persons to communicate; as a result, non-Deaf people have a hard time understanding their messages.Systems that can detect different signs and provide information to common people are thus necessary.To address this issue, we have developed an automated sign language detection system using deep learning, which helps deaf/mute people can communicate with normal people.
Learning High Speed Precision Table Tennis on a Physical Robot

Learning goal conditioned control in the real world is a challenging open problem in robotics. Reinforcement learning systems have the potential to learn autonomously via trial-and-error, but in practice the costs of manual reward design, ensuring safe exploration, and hyperparameter tuning are often enough to preclude real world deployment. Imitation learning approaches, on the other hand, offer a simple way to learn control in the real world, but typically require costly cu-rated demonstration data and lack a mechanism for continuous improvement. Recently, iterative imitation methods have been shown to be effective at relaxing both these constraints, learning goal directed control from undirected demonstration data, and improving continuously via self-supervised goal reaching. These approaches, however, have not yet been shown to scale beyond simple simulated environments. In this work, we present the first evidence that simple iterative imitation learning can scale to goal-directed behavior on a real robot in a dynamic setting: high speed, precision table tennis (e.g. “land the ball on this particular target”). We find that this approach offers a straightforward way to do continuous on-robot learning, without complexities such as reward design, value function learning, or sim-to-real transfer. We also find that this approach is scalable-sample efficient enough to train on a physical robot in just a few hours. In real world evaluations, we find that that the resulting policy can perform on par or better than amateur humans (with players sampled randomly from a robotics lab) at the task of returning the ball to specific targets on the table. Finally, we analyze the effect of an initial undirected bootstrap dataset size on performance, finding that a modest amount of unstructured demonstration data provided up-front drastically speeds up the convergence of a general purpose goal-reaching policy. See supplementary video for examples of the policy on a physical robot.
Anomaly Detection on Bitcoin, Ethereum Networks Using GPU-accelerated Machine Learning Methods

Blockchain technology is continually gaining momentum, with applications expanding in sectors beyond digital assets and financial services. With the existence of a public distributed ledger, the validity of transactions and accounts on the blockchain can be easily reviewed. Nevertheless, there are malicious persons that attempt to fraud cryptocurrency holders, undermining the reliability of the blockchain. This study focuses on identifying fraudulent transactions and accounts by detecting anomalies in the Bitcoin and the Ethereum transaction networks, the two largest cryptocurrencies. By leveraging GPU-accelerated machine learning models, including Support Vector Machines, Random Forest, and Logistic Regression, we draw the metadata of over 30 million transactions on the Bitcoin network and confirmed transactions from over 500 thousand accounts on the Ethereum network. We offer insight into feature importance through sensitivity analysis, as well as train accurate models that allow for method adoption in automated fraud detection systems. The trained models achieve an accuracy and recall of 96.9% and 0.987 on the Bitcoin dataset, and 80.2% and 0.835 on the Ethereum dataset. The study of anomaly detection in the cryptocurrency blockchain done in this paper can be generalized to other blockchain networks, including health service blockchains, public sector blockchains, and financial intelligence blockchains.
Edge–IoT Computing and Networking Resource Allocation for Decomposable Deep Learning Inference

Deep learning (DL) applications have attracted significant attention with the rapidly growing demand for Internet of Things (IoT) systems. However, performing the inference tasks for DL applications on IoT devices is challenging due to the large computational demands of DL models. Recently, edge computing has offered us a solution by deploying resources near the end users. However, resources at the edge are still limited; thus, management issues, such as allocating the networking resources as well as the computing capabilities and configuring the devices appropriately for different applications, become essential. For knobs in such edge management, we consider multiple application tasks with different options of DL models and different hyperparameter settings, along with possible decomposition points that utilize the split DL concept to design the configuration tables. Layer-level decomposition in split DL provides greater flexibility by splitting a single DL inference model into parts on different computing devices, and each part consists of several consecutive layers. We then propose the SplitDL-Image and the SplitDL-Video algorithms based on the Vickrey–Clarke–Groves (VCG) mechanism by considering model performance and frames per second (FPS) requirements with the preferences of the heterogeneous IoT devices. The proposed method allocates networking and edge server computing resources according to the designed configuration tables by assigning the appropriate configuration to each IoT device. Simulation results based on real-world applications show that the proposed method indeed allocates more resources to IoT devices with more urgent/important tasks, preference for better accuracy, or higher local computational cost. In addition, other desired properties, such as truthful bidding, individual rationality, and weakly budget balance, are also guaranteed.
A Comparative Analysis of Tertius, Apriori, and FP-Growth Algorithm in Groceries Dataset

A technique that can help search for patterns or associative relationships is the association rules technique. Association rules that are widely used are based on those that meet the minimum support and confidence requirements, these association rules are in the form of if antecedent then consequent. The association rule algorithm used is the Tertius algorithm, the Apriori algorithm and the FP-Growth algorithm, preceded by the preparation of a wholesale transaction database and the determination of the minimum support and confidence limits with a minimum support of 2%. This study aims to perform a comparative analysis of the Apriori algorithm and the FP-Growth algorithm to find purchasing patterns, using a wholesale dataset sourced from kaggle.com with a total of 4119 data, which is data from cafeteria wholesale sales. The results of the rules from the Apriori algorithm, the Tertius algorithm and the FP-Growth algorithm using the same support and confidence, obtained the same results. From transactions at Grocier with a minimum support of 30% and a minimum confidence of 60% are Rolls/Buns products with Pop plants. The conclusion is that if the buyer buys Rolls / Buns, there is a 75% chance of buying Pip fruit and Whole milk.
Dual-Resolution and Deformable Multihead Network for Oriented Object Detection in Remote Sensing Images

Compared with general object detection, the scale variations, arbitrary orientations, and complex backgrounds of objects in remote sensing images make it more challenging to detect oriented objects. Especially for oriented objects that have large aspect ratios, it is more difficult to accurately detect their boundary. Many methods show excellent performance on oriented object detection, most of which are anchor-based algorithms. To mitigate the performance gap between anchor-free algorithms and anchor-based algorithms, this article proposes an anchor-free algorithm called dual-resolution and deformable multihead network (DDMNet) for oriented object detection. Specifically, the dual-resolution network with bilateral fusion is adopted to extract high-resolution feature maps which contain both spatial details and multiscale contextual information. Then, the deformable convolution is incorporated into the network to alleviate the misalignment problem of oriented object detection. And a dilated feature fusion module is performed on the deformable feature maps to expand their receptive fields. Finally, box boundary-aware vectors instead of the angle are leveraged to represent the oriented bounding box and the multihead network is designed to get robust predictions. DDMNet is a single-stage oriented object detection method without using anchors and exhibits promising performance on the public challenging benchmarks. DDMNet obtains 90.49%, 93.25%, and 78.66% mean average precision on the HRSC2016, FGSD2021, and DOTA datasets. In particular, DDMNet achieves 79.86% at mAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">75</sub> and 53.85% at mAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">85</sub> on the HRSC2016 dataset, respectively, outperforming the current state-of-the-art methods.
Optimal Nonprehensile Interception Strategy for Objects in Flight

Intercepting an object in flight through nonpre-hensile manipulation is a challenging problem, which is aimed at catching and stopping a flying object using little contacts without completely restraining its relative motion to the robot. This paper presents a two-stage optimal trajectory generation method to tackle this problem. At the pre-catching stage, optimal position and attitude trajectories of the robot's end-effector to approach the object are generated by a variational method. At the post-catching stage, the end-effector's trajectories are generated to optimally eliminate the translational and rotational motion of the object and a convex-MPC algorithm combined with admittance control is used to realize the trajectory tracking. A series of simulations and experiments have been conducted to verify the effectiveness of the proposed method.
Collaboration-Enabled Intelligent Internet Architecture: Opportunities and Challenges

Since intelligent algorithms such as deep learning (DL) have strong expressiveness, flexibility, and scalability for complex problems, some DL-based methods have been applied to Internet architecture-related scenarios, such as congestion control and malicious traffic detection. However, DL-based models have relatively high requirements for resources such as data and computing, and the existing Internet architecture requires further evolution to break through these limitations. In this article, we propose the collaboration-enabled intelligent Internet architecture, which can leverage intelligence to facilitate the evolution of Internet architecture in more complex scenarios. Specifically, we first discuss the inherent opportunities and challenges of enabling the Internet architecture to be intelligent through collaboration, which are brought by the imbalance of Internet supply and demand, distributed organizational structure, and the lack of built-in security. Immediately after, we present the newly proposed collaboration-enabled intelligent Internet architecture, which consists of heterogeneous hardware infrastructure and a collaboration-oriented software service platform. Through the complementarity of these two components (i.e., providing hierarchical computing and full exploitation of hierarchical capabilities), it promotes the collaboration of intelligent algorithms built into the Internet architecture. Moreover, some flexible algorithmic modules for the proprietary requirements of the Internet architecture are built into the software service platform. Finally, we take multi-classification malicious traffic detection as a case study, and demonstrate the advantages of enabling Internet architecture to be intelligent through collaboration.
HybridAxes: An Immersive Analytics Tool With Interoperability Between 2D and Immersive Reality Modes

Throughout the visual analytics process, users create visualizations with different dimensionalities. Research shows that in this process users benefit from being able to simultaneously see 2D and 3D modes of their data visualizations. Towards supporting this potential need, we introduce HybridAxes, an immersive visual analytics tool that allows the users to conduct their analysis at either end of the Reality-Virtuality continuum - either in 2D on desktop monitors or 3D in an immersive AR/VR environment - while enabling them to seamlessly switch between the two modes. We believe that by using our system, users will find it easier and faster to understand and analyze multi-dimensional data. An initial pilot test indicates positive trends in terms of users' performance time and usability metrics compared to the standalone desktop or AR/VR counterparts. Our preliminary results also suggest that users experience a lower cognitive load while task-switching between these virtuality modes. This reduction in mental effort causes them to perceive the system to be unobtrusive and pleasant to work with. Going forward, we plan to conduct more rigorous studies to verify our claims and to explore other research questions on this topic.
Few-Shot Image Generation with Mixup-Based Distance Learning

Producing diverse and realistic images with generative models such as GANs typically requires large scale training with vast amount of images. GANs trained with limited data can easily memorize few training samples and display undesirable properties like “stairlike” latent space where interpolation in the latent space yields discontinuous transitions in the output space. In this work, we consider a challenging task of pretraining-free few-shot image synthesis, and seek to train existing generative models with minimal overfitting and mode collapse. We propose mixup-based distance regularization on the feature space of both a generator and the counterpart discriminator that encourages the two players to reason not only about the scarce observed data points but the relative distances in the feature space they reside. Qualitative and quantitative evaluation on diverse datasets demonstrates that our method is generally applicable to existing models to enhance both fidelity and diversity under few-shot setting. Codes are available ( https://github.com/reyllama/mixdl ).
Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding

This paper proposes a 4D backbone for long-term point cloud video understanding. A typical way to capture spatial-temporal context is using 4Dconv or transformer without hierarchy. However, those methods are neither effective nor efficient enough due to camera motion, scene changes, sampling patterns, and complexity of 4D data. To address those issues, we leverage the primitive plane as mid-level representation to capture the long-term spatial-temporal context in 4D point cloud videos, and propose a novel hierarchical backbone named Point Primitive Transformer (PPTr), which is mainly composed of intra-primitive point transformers and primitive transformers. Extensive experiments show that PPTr outperforms the previous state of the arts on different tasks.
RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups

Disease risk models can identify high-risk patients and help clinicians provide more personalized care. However, risk models de-veloped on one dataset may not generalize across diverse subpop-ulations of patients in different datasets and may have unexpected performance. It is challenging for clinical researchers to inspect risk models across different subgroups without any tools. Therefore, we developed an interactive visualization system called RMExplorer (Risk Model Explorer) to enable interactive risk model assessment. Specifically, the system allows users to define subgroups of patients by selecting clinical, demographic, or other characteristics, to ex-plore the performance and fairness of risk models on the subgroups, and to understand the feature contributions to risk scores. To demonstrate the usefulness of the tool, we conduct a case study, where we use RMExplorer to explore three atrial fibrillation risk models by applying them to the UK Biobank dataset of 445,329 individuals. RMExplorer can help researchers to evaluate the performance and biases of risk models on subpopulations of interest in their data.
Efficient Decoder-Free Object Detection with Transformers

Vision transformers (ViTs) are changing the landscape of object detection approaches. A natural usage of ViTs in detection is to replace the CNN-based backbone with a transformer-based backbone, which is straightforward and effective, with the price of bringing considerable computation burden for inference. More subtle usage is the DETR family, which eliminates the need for many hand-designed components in object detection but introduces a decoder demanding an extra-long time to converge. As a result, transformer-based object detection can not prevail in large-scale applications. To overcome these issues, we propose a novel decoder-free fully transformer-based (DFFT) object detector, achieving high efficiency in both training and inference stages, for the first time. We simplify objection detection into an encoder-only single-level anchor-based dense prediction problem by centering around two entry points: 1) Eliminate the training-inefficient decoder and leverage two strong encoders to preserve the accuracy of single-level feature map prediction; 2) Explore low-level semantic features for the detection task with limited computational resources. In particular, we design a novel lightweight detection-oriented transformer backbone that efficiently captures low-level features with rich semantics based on a well-conceived ablation study. Extensive experiments on the MS COCO benchmark demonstrate that DFFTSMALL outperforms DETR by $$2.5\%$$ AP with $$28\%$$ computation cost reduction and more than $$10\times $$ fewer training epochs. Compared with the cutting-edge anchor-based detector RetinaNet, DFFTSMALL obtains over $$5.5\%$$ AP gain while cutting down $$70\%$$ computation cost. The code is available at https://github.com/peixianchen/DFFT.
Rethinking Confidence Calibration for Failure Prediction

Reliable confidence estimation for the predictions is important in many safety-critical applications. However, modern deep neural networks are often overconfident for their incorrect predictions. Recently, many calibration methods have been proposed to alleviate the overconfidence problem. With calibrated confidence, a primary and practical purpose is to detect misclassification errors by filtering out low-confidence predictions (known as failure prediction). In this paper, we find a general, widely-existed but actually-neglected phenomenon that most confidence calibration methods are useless or harmful for failure prediction. We investigate this problem and reveal that popular confidence calibration methods often lead to worse confidence separation between correct and incorrect samples, making it more difficult to decide whether to trust a prediction or not. Finally, inspired by the natural connection between flat minima and confidence separation, we propose a simple hypothesis: flat minima is beneficial for failure prediction. We verify this hypothesis via extensive experiments and further boost the performance by combining two different flat minima techniques. Our code is available at https://github.com/Impression2805/FMFP .
Givenness Hierarchy Informed Optimal Document Planning for Situated Human-Robot Interaction

Robots that use natural language in collaborative tasks must refer to objects in their environment. Recent work has shown the utility of the linguistic theory of the Givenness Hierarchy (GH) in generating appropriate referring forms. But before referring expression generation, collaborative robots must determine the content and structure of a sequence of utterances, a task known as document planning in the natural language generation community. This problem presents additional challenges for robots in situated contexts, where described objects change both physically and in the minds of their interlocutors. In this work, we consider how robots can “think ahead” about the objects they must refer to and how to refer to them, sequencing object references to form a coherent, easy to follow chain. Specifically, we leverage GH to enable robots to plan their utterances in a way that keeps objects at a high cognitive status, which enables use of concise, anaphoric referring forms. We encode these linguistic insights as a mixed integer program within a planning context, formulating constraints to concisely and efficiently capture GH-theoretic cognitive properties. We demonstrate that this GH-informed planner generates sequences of utterances with high intersentential coherence, which we argue should enable substantially more efficient and natural human-robot dialogue.