Reviewerly/exHarmony huggingface dataset:
Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentation

Zero-shot instance segmentation aims to detect and precisely segment objects of unseen categories without any training samples. Since the model is trained on seen categories, there is a strong bias that the model tends to classify all the objects into seen categories. Besides, there is a natural confusion between background and novel objects that have never shown up in training. These two challenges make novel objects hard to be raised in the final instance segmentation results. It is desired to rescue novel objects from background and dominated seen categories. To this end, we propose D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Zero with Semantic-Promoted Debiasing and Background Disambiguation to enhance the performance of Zero-shot instance segmentation. Semantic-promoted debiasing utilizes inter-class semantic relationships to involve unseen categories in visual feature training and learns an input-conditional classifier to conduct dynamical classification based on the input image. Background disambiguation produces image-adaptive background representation to avoid mistaking novel objects for background. Extensive experiments show that we significantly outperform previous state-of-the-art methods by a large margin, e.g., 16.86% improvement on COCO.
Zero-Shot Dual-Lens Super-Resolution

The asymmetric dual-lens configuration is commonly available on mobile devices nowadays, which naturally stores a pair of wide-angle and telephoto images of the same scene to support realistic super-resolution (SR). Even on the same device, however, the degradation for modeling realistic SR is image-specific due to the unknown acquisition process (e.g., tiny camera motion). In this paper, we propose a zero-shot solution for dual-lens SR (ZeDuSR), where only the dual-lens pair at test time is used to learn an image-specific SR model. As such, ZeDuSR adapts itself to the current scene without using external training data, and thus gets rid of generalization difficulty. However, there are two major challenges to achieving this goal: 1) dual-lens alignment while keeping the realistic degradation, and 2) effective usage of highly limited training data. To overcome these two challenges, we propose a degradation-invariant alignment method and a degradation-aware training strategy to fully exploit the information within a single dual-lens pair. Extensive experiments validate the superiority of Ze-DuSR over existing solutions on both synthesized and real-world dual-lens datasets. The implementation code is available at https://github.com/XrKang/ZeDuSR.
Independent Component Alignment for Multi-Task Learning

In a multi-task learning (MTL) setting, a single model is trained to tackle a diverse set of tasks Jointly. Despite rapid progress in the field, MTL remains challenging due to optimization issues such as conflicting and dominating gradients. In this work, we propose using a condition number of a linear system of gradients as a stability criterion of an MTL optimization. We theoretically demonstrate that a condition number reflects the afore-mentioned optimization issues. Accordingly, we present Aligned-MTL, a novel MTL optimization approach based on the proposed criterion, that eliminates instability in the training process by aligning the orthogonal components of the linear system of gradients. While many recent MTL approaches guaran-tee convergence to a minimum, task trade-offs cannot be specified in advance. In contrast, Aligned-MTL provably converges to an optimal point with pre-defined task-specific weights, which provides more control over the optimization result. Through experiments, we show that the proposed approach consistently improves performance on a diverse set of MTL benchmarks, including semantic and instance segmentation, depth estimation, surface normal estimation, and reinforcement learning. The source code is publicly available at https://github.com/SamsungLabs/MTL.
Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking

This work proposes an end-to-end multi-camera 3D multi-object tracking (MOT) framework. It emphasizes spatio-temporal continuity and integrates both past and future reasoning for tracked objects. Thus, we name it “Past- and-Future reasoning for Tracking” (PF-Track). Specifically, our method adopts the “tracking by attention” framework and represents tracked instances coherently over time with object queries. To explicitly use historical cues, our “Past Reasoning” module learns to refine the tracks and enhance the object features by cross-attending to queries from previous frames and other objects. The “Future Reasoning” module digests historical information and predicts robust future trajectories. In the case of long-term occlusions, our method maintains the object positions and enables re-association by integrating motion predictions. On the nuScenes dataset, our method improves AMOTA by a large margin and remarkably reduces ID-Switches by 90% compared to prior approaches, which is an order of magnitude less. The code and models are made available at https://github.com/TRI-ML/PF-Track.
Unsupervised Continual Semantic Adaptation Through Neural Rendering

An increasing amount of applications rely on data-driven models that are deployed for perception tasks across a sequence of scenes. Due to the mismatch between training and deployment data, adapting the model on the new scenes is often crucial to obtain good performance. In this work, we study continual multi-scene adaptation for the task of semantic segmentation, assuming that no ground-truth labels are available during deployment and that performance on the previous scenes should be maintained. We propose training a Semantic-NeRF network for each scene by fusing the predictions of a segmentation model and then using the view-consistent rendered semantic labels as pseudo-labels to adapt the model. Through joint training with the segmentation model, the Semantic-NeRF model effectively enables 2D-3D knowledge transfer. Furthermore, due to its compact size, it can be stored in a long-term memory and subsequently used to render data from arbitrary viewpoints to reduce forgetting. We evaluate our approach on Scan-Net, where we outperform both a voxel-based baseline and a state-of-the-art unsupervised domain adaptation method.
Cross-Training with Prototypical Distillation for improving the generalization of Federated Learning

Cross-training has become a promising strategy to handle data heterogeneity problem in federated learning, which re-train a local model across different clients to improve its generalization capability in a privacy-preserving manner. Its main idea is to make the local models to fit the data of all clients. However, the heterogeneity between data sources may lead the local models to quickly forget the knowledge learned in several rounds of cross-training. To address the problem, this paper presents a novel prototype guided cross training mechanism, termed PGCT, to regularize the change of class-level data representations across clients. It includes two main modules, where the prototype guided representation learning module employs client-aware prototypes of data patterns learned by clustering to guide the learning of consistency representation across feature spaces. This maintains the similar decision boundary across different clients. The prototype-based feature augmentation module uses prototypes as soft attention regularizers to further aggregate rich information to enhance the discrimination of historical features. Experiments were conducted on four datasets in terms of performance comparison, ablation study and case study, and the results verified that PGCT can learn discriminative features with different classes under the guidance of prototypes, which leads to better performance than the state-of-the-art methods.
Central Bank Digital Currency Is Good As Long As All Stakeholders Are Involved

Recent changes in the money supply landscape like the rise of Cryptocurrencies like Bitcoin and also China pushing for broader use of its Digital Yuan soon [1] are forcing governments around the world to investigate about central bank [issued] digital currency (CBDC).In this work We want to analyse the level of understanding of CBDC, opportunities, risks, limitations and obstacles.We explore the pros cons and possibilities of implementing a central bank digital currency (CBDC) by highlighting the different levels of acceptance of a CBDC from four groups of stakeholders: Central Bankers, Bankers, Fintech and General Public.The result from our survey carried out during the month of February 2022 shows different concerns depending on the stakeholder, we can highlight: that Fintech, Digital Bankers and Bankers agree that digital currencies could lessen the risk of money laundering, Central Banks do not believe this.All groups agreed that credit card companies like Visa, Mastercard will lose up market share.Also, while Central Bankers believe that cutting off traditional banks from the supply of digital currency means that there is no multiplier effect in the economy, Bankers and people in Fintech and the general public do not believe so.From what we conclude that the creation of the CBDC is in good taste so long as all stakeholders are involved, and big issues such as privacy, and risk of it affecting the current financial system are kept in mind.
Systematic Literature Review on Click Through Rate Prediction

The ability to anticipate whether a user will click on an item is one of the most crucial aspects of operating an e-commerce business, and clickthrough rate prediction is an attempt to provide an answer to this question. Beginning with the simplest multilayer perceptrons and progressing to the most sophisticated attention networks, researchers employ a variety of methods to solve this issue. In this paper, we present the findings of a comprehensive literature review that will assist researchers in getting a head start on developing new solutions. The most prevalent models were variants of the state-of-the-art DeepFM model.
PENGEMBANGAN MEDIA POP-UP BOOK PADA PEMBELAJARAN IPA DI SD

Development of Pop-Up Book media on science learning in elementary schools. This study aims to determine the validity level of pop-up book media in grade 1 SDS Syalom. The type of research used is development research (R&amp;D). Researchers used the ADDIE development model. The ADDIE model development method consists of 5 development stages including: (1) Analysis (analysis stage), (2) Design (initial product design stage), (3) Development (product development stage), (4) Implementation (product implementation stage) , (5) Evaluation (product evaluation stage). Due to the limitations of researchers and the occurrence of the Covid19 pandemic during the study, the steps used in media validation were carried out online. Based on the validation results from material experts, media experts, design experts, it can be concluded that the quality of the pop-up book media developed is in the appropriate category or can be used for grade 1 elementary school students. Student responses to pop-up book media in the field implementation test got a good response.&#x0D; &#x0D; Keywords: Pop-up book media, science learning, learning media
NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks

Siamese networks are one of the most trending methods to achieve self-supervised visual representation learning (SSL). Since hand labeling is costly, SSL can play a crucial part by allowing deep learning to train on large unlabeled datasets. Meanwhile, Neural Architecture Search (NAS) is becoming increasingly important as a technique to discover novel deep learning architectures. However, early NAS methods based on reinforcement learning or evolutionary algorithms suffered from ludicrous computational and memory costs. In contrast, differentiable NAS, a gradient-based approach, has the advantage of being much more efficient and has thus retained most of the attention in the past few years. In this article, we present NASiam, a novel approach that uses for the first time differentiable NAS to improve the multilayer perceptron projector and predictor (encoder/predictor pair) architectures inside siamese-networks-based contrastive learning frameworks (e.g., SimCLR, SimSiam, and MoCo) while preserving the simplicity of previous baselines. We crafted a search space designed explicitly for multilayer perceptrons, inside which we explored several alternatives to the standard ReLU activation function. We show that these new architectures allow ResNet backbone convolutional models to learn strong representations efficiently. NASiam reaches competitive performance in both small-scale (i.e., CIFAR-10/CIFAR-100) and large-scale (i.e., ImageNet) image classification datasets while costing only a few GPU hours. We discuss the composition of the NAS-discovered architectures and emit hypotheses on why they manage to prevent collapsing behavior.
Palaeoenvironment Conditions During Deposition of Sargelu, Naokelekan, and Najmah Formations in Zey Gawara Area, Kurdistan Region, Iraq: Implications from Major and Trace Elements Proportions

Palaeoenvironment conditions during deposition of Jurassic Sargelu, Naokelekan, and Najmah formations were studied based on samples from well Z1 in Zey Gawara area, Kurdistan region, Iraq. For the purpose of determining the palaeoredox, palaeoclimate conditions as well as palaeoweathering, tectonic setting, and provenance indices, the inorganic geochemical analysis to indicate the number and amount of different elements were performed. Based on the occurrence and proportionalities of trace elements e.g., V, Ni, Cu, Sr, the Sargelu Formation was probably accumulated in suboxic to anoxic conditions, while the Naokelekan and Najmah formations were deposited under the suboxic to dysoxic conditions. Relatively semiarid condition have suggested for Sargelu and Naokelekan formations, and relatively semi humid condition for Najmah Formation. The chemical index of alteration and the high plagioclase index of alteration suggest for modest to intense weathering source area for the Sargelu and Naokelekan formations. The index of chemical variability (&lt;1) implies that the Sargelu, Naokelekan, and Najmah formations are probably immature sediments and deposited inside a tectonically active setting. Based on the tectonic indicators, the Sargelu and Naokelekan formations were deposited in an active continental margin, and the Najmah Formation was deposited in an oceanic island arc. The felsic for the Sargelu and Naokelekan and the felsic- intermediate igneous rocks were proposed as plausible source rocks for the Najmah Formation.
Multi-Model Fusion Framework Using Deep Learning for Visual-Textual Sentiment Classification

Multimodal Sentiment Analysis (SA) is gaining popularity due to its broad application potential. The existing studies have focused on the SA of single modalities, such as texts or photos, posing challenges in effectively handling social media data with multiple modalities. Moreover, most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations, leading to unsatisfactory sentiment classification results. Motivated by this, we propose a new visual-textual sentiment classification model named Multi-Model Fusion (MMF), which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual and textual content. The proposed model comprises three deep neural networks. Two different neural networks are proposed to extract the most emotionally relevant aspects of image and text data. Thus, more discriminative features are gathered for accurate sentiment classification. Then, a multichannel joint fusion model with a self-attention technique is proposed to exploit the intrinsic correlation between visual and textual characteristics and obtain emotionally rich information for joint sentiment classification. Finally, the results of the three classifiers are integrated using a decision fusion scheme to improve the robustness and generalizability of the proposed model. An interpretable visual-textual sentiment classification model is further developed using the Local Interpretable Model-agnostic Explanation model (LIME) to ensure the model’s explainability and resilience. The proposed MMF model has been tested on four real-world sentiment datasets, achieving (99.78%) accuracy on Binary_Getty (BG), (99.12%) on Binary_iStock (BIS), (95.70%) on Twitter, and (79.06%) on the Multi-View Sentiment Analysis (MVSA) dataset. These results demonstrate the superior performance of our MMF model compared to single-model approaches and current state-of-the-art techniques based on model evaluation criteria.
FORMING LOWER SECONDARY EDUCATION STUDENTS' READINESS FOR INNOVATIVE ACTIVITIES IN NATURAL-MATHEMATICAL COURSES

The article examines the issue of pedagogical conditions for the formation of the readiness of lower secondary school students for innovative activities during the lessons of the courses of the natural-mathematical cycle. A set of pedagogical conditions that positively influence the formation of students' readiness has been proposed. It has been proven that providing a comfortable educational environment, constant support of the motivation and readiness of pedagogical workers for innovative activities, systematic use of digital educational resources and a complex of creative, research and applied tasks has a positive effect on the level of readiness of lower secondary school students for innovative activities during the lessons of the courses of the natural-mathematical cycle. It has also been stated that the formation of basic school students' readiness for innovative activities is a complex and long-term process that is impossible without the creation of appropriate pedagogical conditions. A comprehensive and multifaceted approach to the formation of readiness would give an innovative product (result) only with a clearly developed algorithm, which would be followed by all participants in the educational process at secondary education institutions. It has been noted that innovative activity in the lessons of mathematics, physics, economics, chemistry, biology, geography, astronomy, etc., is a priority task of the respective teachers, because these subjects cause anxiety among the students of education and due to their complexity, students lose interest in studying them.
BCAF-3D: Bilateral Content Awareness Fusion for cross-modal 3D object detection

As two major data modalities in autonomous driving, LiDAR point clouds and RGB images include rich geometric clues and semantic features. Compared with using a single data modality, fusing two data modalities can provide complementary information for the 3D object detection task. However, some prevalent cross-modal methods (Vora et al., 2020; Huang et al., 2020; Sindagi et al., 2019) cannot effectively obtain favorable information, and only adopt a unilateral fusion mechanism. In this paper, we propose a novel fusion strategy named Bilateral Content Awareness Fusion (BCAF) to address these issues. Specifically, BCAF adopts a two-stream structure consisting of a LiDAR Content Awareness (LCA) branch and an Image Content Awareness (ICA) branch along with a Soft Fusion (SF) module. First, the LCA and ICA are used to enhance instance-relevant clues. Then, with two awareness features given by the LCA and ICA branches, the aggregation features can be generated to choose favorable image features and LiDAR features. Finally, the SF module fuses the bilateral favorable features and outputs the cross-modal feature. Experiments of our method are conducted on the KITTI dataset, including 3D object detection evaluation and bird’s eye view evaluation. Compared with the previous art method, our approach achieves significant improvements. Especially for the metric of mean Average Precision (mAP) on the Car category, our approach obtains 0.5 and 0.62 gains for the tasks of 3D object detection and bird’s eye view, respectively.
Safe Reinforcement Learning and Adaptive Optimal Control With Applications to Obstacle Avoidance Problem

This paper presents a novel composite obstacle avoidance control method to generate safe motion trajectories for autonomous systems in an adaptive manner. First, system safety is described using forward invariance, and the barrier function is encoded into the cost function such that the obstacle avoidance problem can be characterized by an infinite-horizon optimal control problem. Next, a safe reinforcement learning framework is proposed by combining model-based policy iteration and state-following-based approximation. Upon real-time data and extrapolated experience data, this learning design is implemented through the actor-critic structure, in which critic networks are tuned by gradient-descent adaption and actor networks produce adaptive control policies via gradient projection. Then, system stability and weight convergence are theoretically analyzed using Lyapunov method. Finally, the proposed learning-based controller is demonstrated on a two-dimensional single integrator system and a nonlinear unicycle kinematic system. Simulation results reveal that the system or agent can smoothly reach the target point while keeping a safe distance from each obstacle; at the same time, other three avoidance control methods are used to provide side-by-side comparisons and to verify some claimed advantages of the present method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the obstacle avoidance problem of real-time navigation of an agent to the target point, which applies to practical autonomous systems such as vehicles and robots. Pre-generative methods and reactive methods have been widely employed to generate safe motion trajectories in the obstacle environment. However, these methods cannot strike a good balance between safety and optimality. In this paper, the obstacle avoidance problem is formulated in the sense of optimal control, and a safe reinforcement learning method is designed to generate safe motion trajectories. This method combines the advantages of model-based policy iteration and state-following-based approximation, in which the former ensures regional optimality while the latter ensures local safety. Based on the proposed adaptive tuning laws, engineers are able to design learning-based avoidance controllers in the environment with static obstacles. In future research, we will address the dynamic avoidance problem against moving obstacles.
Identifying Informative Predictor Variables With Random Forests

Random forests are a nonparametric machine learning method, which is currently gaining popularity in the behavioral sciences. Despite random forests’ potential advantages over more conventional statistical methods, a remaining question is how reliably informative predictor variables can be identified by means of random forests. The present study aims at giving a comprehensible introduction to the topic of variable selection with random forests and providing an overview of the currently proposed selection methods. Using simulation studies, the variable selection methods are examined regarding their statistical properties, and comparisons between their performances and the performance of a conventional linear model are drawn. Advantages and disadvantages of the examined methods are discussed, and practical recommendations for the use of random forests for variable selection are given.
Dataset Distillation Using Parameter Pruning

In this study, we propose a novel dataset distillation method based on parameter pruning. The proposed method can synthesize more robust distilled datasets and improve distillation performance by pruning difficult-to-match parameters during the distillation process. Experimental results on two benchmark datasets show the superiority of the proposed method.