Reviewerly/exHarmony huggingface dataset:
MCDM approach to investigate the effectiveness of SCRUM events in minimizing risk factors in project management

Traditional project management methods have been used for many years in the implementation and management of projects. With the publication of the agile manifesto in 2001, the interest in agile methods increased and successful results were obtained in the execution and solution of complex projects with agile approaches. Agile approach consists of different structures that can be named as framework or method. Among these agile methods, which are selected and applied according to the suitability and purpose of the project, the most frequently preferred one is SCRUM. In this study, SCRUM, an agile project management technique that has been used frequently in recent years in order to develop a flexible project management process, has been examined, and the effectiveness of the events of the SCRUM technique in minimizing the risks that arise in project management has been evaluated. Determining the effectiveness of these SCRUM events, which each agile project team implements in turn, is very important for the correct and effective allocation of resources. For this purpose, a multi-criteria decision-making model has been proposed to evaluate the effectiveness of 4 SCRUM events within the scope of this study. While the importance weights of the 24 project management risk factors proposed in the solution phase of the created model were determined by the SWARA method, the SCRUM events were evaluated with the WASPAS, COPRAS and EDAS methods and the solution values found by three different methods were compared.
Testing Platform-Independent Quantum Error Mitigation on Noisy Quantum Computers

We apply quantum error mitigation techniques to a variety of benchmark problems and quantum computers to evaluate the performance of quantum error mitigation in practice. To do so, we define an empirically motivated, resource-normalized metric of the improvement of error mitigation which we call the improvement factor, and calculate this metric for each experiment we perform. The experiments we perform consist of zero-noise extrapolation and probabilistic error cancellation applied to two benchmark problems run on IBM, IonQ, and Rigetti quantum computers, as well as noisy quantum computer simulators. Our results show that error mitigation is on average more beneficial than no error mitigation — even when normalized by the additional resources used — but also emphasize that the performance of quantum error mitigation depends on the underlying computer.
Generalized Autonomous Path Proxy Re-Encryption Scheme to Support Branch Functionality

Proxy Re-Encryption (PRE), a special cryptographic primitive, can efficiently perform ciphertext conversion on the cloud. To enable the data owner (i.e. delegator) to authorize a file access path according to the different priorities of the users (i.e. delegatees), autonomous path proxy re-encryption (AP-PRE) was proposed, where the delegator can generate a proxy re-encryption autonomous path in order of the delegatees’ priority. If one delegatee does not hold the decryption right, the ciphertext can be converted to a new ciphertext that can be decrypted by the next delegatee with lower priority in the path. Although AP-PRE enables the delegator to pre-define the whole decryption path, the access policy only supports a linked path and the data owner disallows the nodes in the proxy path to generate delegating branches to access its data. Such a linked path may be too long in practice, especially when the system scales up (i.e. the average complexity of encrypted data access is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(n)</i> where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> denotes the number of delegatees). Hence, we propose a generalized autonomous path proxy re-encryption (APB-PRE) scheme for supporting branch functionality. Firstly, by setting the token and a carefully designed ciphertext structure, the branch functionality of the path delegation is realized. Specifically, we utilize the bilinearity of bilinear pairing to construct the token for the transition of the label embedded in the ciphertext in different paths, resulting in a far more flexible access structure with a tree-like topology. In APB-PRE, the delegatees with lower priority who need to share data can complete the decryption task earlier, without affecting the decryption of the high-priority delegatees. Finally, we prove that it achieves IND-HRA security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. Benefiting from the creation of branching paths, users on the branching path can get the re-encrypted ciphertext much earlier. Therefore, the average complexity of encrypted data access reduces to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(log n)</i> compared to AP-PRE. The experimental results show that our proposal can extend the branching functionalities of AP-PRE with only moderate computational cost.
Enhancing the Self-Universality for Transferable Targeted Attacks

In this paper, we propose a novel transfer-based targeted attack method that optimizes the adversarial perturbations without any extra training efforts for auxiliary networks on training data. Our new attack method is proposed based on the observation that highly universal adversarial perturbations tend to be more transferable for targeted attacks. Therefore, we propose to make the perturbation to be agnostic to different local regions within one image, which we called as self-universality. Instead of optimizing the perturbations on different images, optimizing on different regions to achieve self-universality can get rid of using extra data. Specifically, we introduce a feature similarity loss that encourages the learned perturbations to be universal by maximizing the feature similarity between adversarial perturbed global images and randomly cropped local regions. With the feature similarity loss, our method makes the features from adversarial perturbations to be more dominant than that of benign images, hence improving targeted transferability. We name the proposed attack method as Self-Universality (SU) attack. Extensive experiments demonstrate that SU can achieve high success rates for transfer-based targeted attacks. On ImageNet-compatible dataset, SU yields an improvement of 12% compared with existing state-of-the-art methods. Code is available at https://github.com/zhipengwei/Self-Universality.
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning

This work focuses on sign language retrieval—a recently proposed task for sign language understanding. Sign language retrieval consists of two sub-tasks: text-to-sign-video (T2V) retrieval and sign-video-to-text (V2T) retrieval. Different from traditional video-text retrieval, sign language videos, not only contain visual signals but also carry abundant semantic meanings by themselves due to the fact that sign languages are also natural languages. Considering this character, we formulate sign language retrieval as a cross-lingual retrieval problem as well as a video-text retrieval task. Concretely, we take into account the linguistic properties of both sign languages and natural languages, and simultaneously identify the fine-grained cross-lingual (i.e., sign-to-word) mappings while contrasting the texts and the sign videos in a joint embedding space. This process is termed as cross-lingual contrastive learning. Another challenge is raised by the data scarcity issue—sign language datasets are orders of magnitude smaller in scale than that of speech recognition. We alleviate this issue by adopting a domain-agnostic sign encoder pre-trained on large-scale sign videos into the target domain via pseudo-labeling. Our framework, termed as domain-aware sign language retrieval via Cross-lingual Contrastive learning or CiCo for short, outperforms the pioneering method by large margins on various datasets, e.g., +22.4 T2V and +28.0 V2T R@1 improvements on How2Sign dataset, and +13.7 T2V and +17.1 V2T R@1 improvements on PHOENIX-2014T dataset. Code and models are available at: https://github.com/FangyunWei/SLRT.
Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding

Combining multiple datasets enables performance boost on many computer vision tasks. But similar trend has not been witnessed in object detection when combining multiple datasets due to two inconsistencies among detection datasets: taxonomy difference and domain gap. In this paper, we address these challenges by a new design (named Detection Hub) that is dataset-aware and category-aligned. It not only mitigates the dataset inconsistency but also provides coherent guidance for the detector to learn across multiple datasets. In particular, the dataset-aware design is achieved by learning a dataset embedding that is used to adapt object queries as well as convolutional kernels in detection heads. The categories across datasets are semantically aligned into a unified space by replacing one-hot category representations with word embedding and leveraging the semantic coherence of language embedding. Detection Hub fulfills the benefits of large data on object detection. Experiments demonstrate that joint training on multiple datasets achieves significant performance gains over training on each dataset alone. Detection Hub further achieves SoTA performance on UODB benchmark with wide variety of datasets.
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning

Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles few-shot learning across different domains. It aims at transferring prior knowledge learned on the source dataset to novel target datasets. The CD-FSL task is especially challenged by the huge domain gap between different datasets. Critically, such a domain gap actually comes from the changes of visual styles, and wave-SAN [10] empirically shows that spanning the style distribution of the source data helps alleviate this issue. However, wave-SAN simply swaps styles of two images. Such a vanilla operation makes the generated styles “real” and “easy”, which still fall into the original set of the source styles. Thus, inspired by vanilla adversarial learning, a novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL. Particularly, our style attack method synthesizes both “virtual” and “hard” adversarial styles for model training. This is achieved by perturbing the original style with the signed style gradients. By continually attacking styles and forcing the model to recognize these challenging adversarial styles, our model is gradually robust to the visual styles, thus boosting the generalization ability for novel target datasets. Besides the typical CNN-based backbone, we also employ our StyleAdv method on large-scale pre-trained vision transformer. Extensive experiments conducted on eight various target datasets show the effectiveness of our method. Whether built upon ResNet or ViT, we achieve the new state of the art for CD-FSL. Code is available at https://github.com/lovelyqian/StyleAdv-CDFSL.
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm

Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a popular paradigm in multiple computer vision tasks. Previous research has covered both the unsupervised pretraining and supervised finetuning in this paradigm, while little attention is paid to exploiting the annotation budget for finetuning. To fill in this gap, we formally define this new active finetuning task focusing on the selection of samples for annotation in the pretraining-finetuning paradigm. We propose a novel method called ActiveFT for active finetuning task to select a subset of data distributing similarly with the entire unlabeled pool and maintaining enough diversity by optimizing a parametric model in the continuous space. We prove that the Earth Mover's distance between the distributions of the selected subset and the entire data pool is also reduced in this process. Extensive experiments show the leading performance and high efficiency of ActiveFT superior to baselines on both image classification and semantic segmentation. Our code is released at https://github.com/yichen928/ActiveFT.
Towards Effective Visual Representations for Partial-Label Learning

Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning has recently boosted the performance of PLL on vision tasks, attributed to representations learned by contrasting the same/different classes of entities. Without access to true labels, positive points are predicted using pseudolabels that are inherently noisy, and negative points often require large batches or momentum encoders, resulting in unreliable similarity information and a high computational overhead. In this paper, we rethink a state-of-the-art contrastive PLL method PiCO [24], inspiring the design of a simple framework termed PaPi (Partial-label learning with a guided Prototypical classifier), which demonstrates significant scope for improvement in representation learning, thus contributing to label disambiguation. PaPi guides the optimization of a prototypical classifier by a linear classifier with which they share the same feature encoder, thus explicitly encouraging the representation to reflect visual similarity between categories. It is also technically appealing, as PaPi requires only a few components in PiCO with the opposite direction of guidance, and directly eliminates the contrastive learning module that would introduce noise and consume computational resources. We empirically demonstrate that PaPi significantly outperforms other PLL methods on various image classification tasks.
Decoupling-and-Aggregating for Image Exposure Correction

The images captured under improper exposure conditions often suffer from contrast degradation and detail distortion. Contrast degradation will destroy the statistical properties of low-frequency components, while detail distortion will disturb the structural properties of high-frequency components, leading to the low-frequency and high-frequency components being mixed and inseparable. This will limit the statistical and structural modeling capacity for exposure correction. To address this issue, this paper proposes to decouple the contrast enhancement and detail restoration within each convolution process. It is based on the observation that, in the local regions covered by convolution kernels, the feature response of low-/high-frequency can be decoupled by addition/difference operation. To this end, we inject the addition/difference operation into the convolution process and devise a Contrast Aware (CA) unit and a Detail Aware (DA) unit to facilitate the statistical and structural regularities modeling. The proposed CA and DA can be plugged into existing CNN-based exposure correction networks to substitute the Traditional Convolution (TConv) to improve the performance. Furthermore, to maintain the computational costs of the network without changing, we aggregate two units into a single TConv kernel using structural re-parameterization. Evaluations of nine methods and five benchmark datasets demonstrate that our proposed method can comprehensively improve the performance of existing methods without introducing extra computational costs compared with the original networks. The codes will be publicly available.
ALOFT: A Lightweight MLP-Like Architecture with Dynamic Low-Frequency Transform for Domain Generalization

Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are based on convolutional neural networks (CNNs). However, the local operation of the convolution kernel makes the model focus too much on local representations (e.g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability. Recently, several MLP-based methods have achieved promising results in supervised learning tasks by learning global interactions among different patches of the image. Inspired by this, in this paper, we first analyze the difference between CNN and MLP methods in DG and find that MLP methods exhibit a better generalization ability because they can better capture the global representations (e.g., structure) than CNN methods. Then, based on a recent lightweight MLP method, we obtain a strong baseline that outperforms most state-of-the-art CNN-based methods. The baseline can learn global structure representations with a filter to suppress structureirrelevant information in the frequency space. Moreover, we propose a dynAmic LOw-Frequency spectrum Transform (ALOFT) that can perturb local texture features while preserving global structure features, thus enabling the filter to remove structure-irrelevant information sufficiently. Extensive experiments on four benchmarks have demonstrated that our method can achieve great performance improvement with a small number of parameters compared to SOTA CNN-based DG methods. Our code is available at https://github.com/lingeringlight/ALOFT/.
Viewpoint Equivariance for Multi-View 3D Object Detection

3D object detection from visual sensors is a corner-stone capability of robotic systems. State-of-the-art methods focus on reasoning and decoding object bounding boxes from multi-view camera input. In this work we gain intuition from the integral role of multi-view consistency in 3D scene understanding and geometric learning. To this end, we introduce VEDet, a novel 3D object detection framework that exploits 3D multi-view geometry to improve localization through viewpoint awareness and equivariance. VEDet leverages a query-based transformer architecture and encodes the 3D scene by augmenting image features with positional encodings from their 3D perspective geometry. We design view-conditioned queries at the output level, which enables the generation of multiple virtual frames during training to learn viewpoint equivariance by enforcing multi-view consistency. The multi-view geometry injected at the input level as positional encodings and regularized at the loss level provides rich geometric cues for 3D object detection, leading to state-of-the-art performance on the nuScenes benchmark. The code and model are made available at https://github.com/TRI-ML/VEDet.
ASTER VNIR‐SWIR Based Lithological Mapping of Granitoids in the Western Junggar Orogen (NW Xinjiang): Improved Inputs to Random Forest Method

Abstract Although advanced spaceborne thermal emission and reflection radiometer multispectral analysis for lithological mapping has been widely applied, traditional methods such as band ratios (BR) and principal component analysis (PCA) are still hampered by cumbersome data processing and poor classification performance. In this study, we utilize improved data inputs for random forest (RF) to extract lithological information of granitoids, which are the predominant rock type for intrusion‐related polymetallic ore deposits in the western Junggar Orogen (NW Xinjiang). Based on spectral absorption features of minerals (e.g., orthoclase, K‐feldspar, hornblende, biotite, plagioclase, and oligoclase), image statistical information and textural features, we tested different combinations of bands, BR, PCA, and texture using RF method, and found that the combination of B13678 + T1 (Mean texture) achieved the highest weighted‐F1 score for granitoids, with an accuracy of 87.32%. Compared to the support vector machine, RF effectively distinguishes lithological differences between different types of granitoid and wallrocks, especially the granite, granodiorite, and alkali granite in the Akebasito intrusion, as well as the alkali granite, plagiogranite and biotite granite in the Karamay intrusions. Moreover, the large number of rare metal deposits (including Cu, Au, Mo, etc.) distributed near the granitoid intrusions in the western Junggar, our result facilitates the analysis of regional tectonic evolution and mineralization controlling.
Making Applications for Midwives' Independent Practice Using the Waterfall Method

The patient's medical records at the Independent Midwife Practice (PMB) must be stored and kept confidential in order to provide good, quality, and responsive health services to patients. Nuning Midwife Independent Practice Nuning, S.ST,. Tanah Bumbu was founded in 2012. During its 11 years of existence, patients still have to queue in the waiting room for a long time. This is because the service process and medical records used are still manual. This study aims to make a special application on PMB. Researchers use the research and development method to facilitate the research process and the waterfall method as a reference for the PMB application development process. The resulting development product is a PMB application that can provide convenience to midwives in providing services to patients and reduce the long queue of patients in the waiting room. Data collection used interviews with 2 respondents from PMB owners and midwives. The results of the research are in the form of functional and non-functional system requirements for PMB applications. These needs are translated in the form of system design in the form of system flowcharts, use case diagrams, class diagrams, activity diagrams, entity relationship diagrams, and data flow diagrams. The design is then translated into a programming language using PHP programming and MySQL database. From the results of this study, it has several features, namely login features, patient visit report features, patient referral letter features, detailed identity data features for patients and midwives, notification features, patient registration/registration features independently, and patient examination results record features. Features that are not yet available in the PMB application are the automatic payment feature and the distance between the PMB and the referred hospital.