Reviewerly/exHarmony huggingface dataset:
The Semantic Conception of Logic: Essays on Consequence, Invariance, and Meaning

The semantic tradition in logic descends from Tarski's seminal work on truth and logical consequence. In the introduction to this volume, Sagi and Woods remind us that this tradition prominently uses model theory to study languages and their interpretations. Tarski's model-theoretic definition of logical consequence is the prime example of this approach, seeking as it does to reduce logical properties to a class of operations on classical, iterative (ZF) sets. Sagi and Woods explain with admirable clarity the origins, implications, and philosophical questions surrounding this project. The contributions to the volume go on to address whether this approach successfully elucidates logicality and how it intersects with the foundations of mathematics and natural language semantics. One of the most enduring topics in the semantic tradition is the divide between logical and non-logical vocabulary. This plays an important role in Tarski's definition of logical consequence, but does the value and success of this definition require an absolute criterion of logicality? Gila Sher previously developed the view that vocabulary are properly called ‘logical’ when they express operations that are stable under certain transformations of the domain of quantification. This is known as invariantism. Mario Gómez-Torrente previously argued not only against invariantism, but more generally against the need for any such criteria. He contends that the semantic approach functions fine even if the demarcation of logical term is merely pragmatic and interest relative. In the first two chapters of the present volume, Sher and Gómez-Torrente rehash and expand on this debate.
A novel chaotic Runge Kutta optimization algorithm for solving constrained engineering problems

Abstract This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being incorporated with the base Runge Kutta optimization (RUN) algorithm to improve their performance. An imperative analysis was conducted to check CRUN’s convergence proficiency, sustainability of critical constraints, and effectiveness. The proposed algorithm was tested on six well-known design engineering tasks, namely: gear train design, coupling with a bolted rim, pressure vessel design, Belleville spring, and vehicle brake-pedal optimization. The results demonstrate that CRUN is superior compared to state-of-the-art algorithms in the literature. So, in each case study, CRUN was superior to the rest of the algorithms and furnished the best-optimized parameters with the least deviation. In this study, 10 chaotic maps were enhanced with the base RUN algorithm. However, these chaotic maps improve the solution quality, prevent premature convergence, and yield the global optimized output. Accordingly, the proposed CRUN algorithm can also find superior aspects in various spectrums of managerial implications such as supply chain management, business models, fuzzy circuits, and management models.
An archived dataset from the ECMWF Ensemble Prediction System for probabilistic solar power forecasting

Ensemble numerical weather prediction (NWP) is the backbone of the state-of-the-art solar forecasting for horizons ranging between a few hours and a few days. Dynamical ensemble forecasts are generated by perturbing the initial condition, and thereby obtaining a set of equally likely trajectories of the future weather. Generating dynamical ensemble forecasts demands extensive knowledge of atmospheric science and significant computational resources. Hence, the task is often performed by international and national weather centers and space agencies. Solar forecasters, on the other hand, are primarily interested in post-processing those ensemble forecasts disseminated by weather service providers, as to arrive at forecasts of solar power output. To facilitate the uptake of ensemble NWP forecasts in solar power forecasting research, this paper offers an archived dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System, over a four-year period (2017–2020) and over an extensive geographical region (e.g., most of Europe and North America), under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Two case studies are presented to demonstrate the usage of the dataset. One case study elaborates how ensemble forecasts can be summarized and calibrated, which constitute two common forms of probabilistic forecast post-processing. The other demonstrates how the dataset can be used in solar power forecasting applications, which compares machine learning with the physical model chain in terms of their irradiance-to-power conversion capability. The Python code used to produce the results shown in this paper is made available on GitHub.
Patch attention convolutional vision transformer for facial expression recognition with occlusion

Despite substantial progress in Facial Expression Recognition (FER) in recent decades, most previous methods have been developed to recognize constrained facial expressions. Real-world occlusions lead to invisible facial regions and contaminated facial features, which undoubtedly increase the difficulty of FER in the wild. Therefore, a Patch Attention Convolutional Vision Transformer (PACVT) is proposed to tackle the occlusion FER problem. The backbone convolutional neural network is used to extract facial feature maps, which are cropped into multiple regional patches to extract local and global features. The Patch Attention Unit (PAU) is designed to perceive occluded regions by adaptively calculating the patch-level attention weights of local features for expression recognition. The facial patches are mapped into sequences of visual tokens, and the Vision Transformer (ViT) is employed to capture the interactions and correlations between these visual tokens from a global perspective. The self-attention in ViT enables the PACVT to focus on the salient patches with discriminative features and ignore the occlusion. Experiments are conducted on three widely used expression datasets and their occlusion subsets, and the results demonstrate that the proposed PACVT outperforms state-of-the-art methods on occlusion FER. Cross-dataset experiment results evidence the generalization ability of the PACVT.
Spatial and temporal variation of vegetation cover in the main mining area of Qibaoshan Town, China: Potential impacts from mining damage, solid waste discharge and land reclamation

The increasing frequency of mining activities in the world has led to many environmental pollution problems, such as mine wastewater discharge, mine solid waste dumps, and mine dust dispersion. These problems have negative implications for the environment and the public health of people living nearby the mining areas. Despite this, there are few methods to determine the state of mine pollution on a regional scale. Therefore, we applied remote sensing technologies to assess the mine pollution situation, especially the mine solid waste pollution, of a mining area, taking Qibaoshan Town, Liuyang City, Hunan Province, China, as an example. In our research, we have calculated the vegetation cover change of the Qibaoshan Town over the years (2000-2020), charted the vegetation coverage grade maps, and analysed the tendency of vegetation cover changes, to infer the mine pollution situation, the progress of pollution treatment and the efforts made by the local government and the mines on mine pollution disposal and the land reclamation. Additionally, mining damage can bring about geological hazards such as surface subsidence leading to vegetation destruction, while mining solid waste pollution and discharge can occupy a large amount of land and thus lead to vegetation reduction. As a result, this method of calculating FVC changes in a mining area is particularly suitable for assessing the extent of mining damage, the status of solid waste pollution and discharge, and the progress of land reclamation. In the abstract, we claim that this short communication article serves as a guide to start a conversation, and encourages experts and scholars to engage in this area of research.
It’s a Match! The effects of chatbot anthropomorphization and chatbot gender on consumer behavior

Chatbots are increasingly used as substitutes for human service agents in online shops. This has led researchers to analyze how chatbot characteristics influence consumer responses. However, while the relevance of chatbot characteristics has been examined, to date, consumers’ personalities have remained unattended in the research on this innovative mode of online support. Therefore, this study aims to understand how the interaction of consumer characteristics and chatbot characteristics influences consumer behavior. In doing so, we focus on how chatbots’ visual cues (i.e., anthropomorphization, gender) influence consumer behavior while also considering consumers’ self-concept. To answer the research question, we first conceptually discuss why consumer behavior depends on perceived self-congruence between consumers and a chatbot, which can be reached by anthropomorphizing chatbots and giving them the “right” gender. Subsequently, based on multiple studies, we empirically test the hypotheses considering male, female, and non-binary consumers. Our results demonstrate the relevance of both chatbot anthropomorphization and chatbot gender.
A collaborative filtering recommendation algorithm based on embedding representation

This aims to propose a collaborative filtering algorithm based on embedding representation and word embedding techniques, namely UI2vec. According to the joint feature extraction network designed in this paper, UI2vec embeds users and items on the potential space at the same time, and uses the item similarity between them to predict the user’s content of interest. Then a generative model VUI2vec with more stable performance is proposed based on UI2vec, which maps users and items as independent Gaussian distributions and obtains the approximate posterior distribution of both by variational inference. The recommendation performance of UI2vec and VUI2vec is evaluated on TaFeng, Movielens, and Netflix datasets. The impact of important superparameters within the model on performance is investigated. The experimental results show that compared with the baseline model, the proposed methods performs consistently well.
A systematic literature review on blockchain governance

Blockchain has been increasingly used as a component to enable decentralisation in software architecture for a variety of applications. Blockchain governance has received considerable attention to ensure the safe and appropriate use and evolution of blockchain, especially after the Ethereum DAO attack in 2016. However, there are no systematic efforts to analyse existing governance solutions. To understand the state-of-the-art of blockchain governance, we conducted a systematic literature review with 37 primary studies. The extracted data from primary studies are synthesised to answer identified research questions. The study results reveal several major findings: (1) governance can improve the adaptability and upgradability of blockchain, whilst the current studies neglect broader ethical responsibilities as the objectives of blockchain governance; (2) governance is along with the development process of a blockchain platform, while ecosystem-level governance process is missing, and; (3) the responsibilities and capabilities of blockchain stakeholders are briefly discussed, whilst the decision rights, accountability, and incentives of blockchain stakeholders are still under studied. We provide actionable guidelines for academia and practitioners to use throughout the lifecycle of blockchain, and discuss future trends to support researchers in this area.
Traditional machine learning algorithms for breast cancer image classification with optimized deep features

For breast cancer diagnosis, computer-aided classification of histopathological images is of critical importance for correct and early diagnosis. Transfer learning approaches for feature extraction have made significant progress in recent years and are now widely used. To select the best representative features to classify breast cancer pathological images and avoid the curse of dimensionality, this work uses optimized deep features. The proposed approach firstly employs ResNet18 architecture for feature extraction to achieve deep features. Then, meta-heuristic algorithms namely Particle Swarm Optimization (PSO), Atom Search Optimization (ASO) and Equilibrium Optimizer (EO) algorithms, are employed to provide more representative features of breast cancer pathological images. To understand the effect of optimized deep features on classification, traditional machine learning (ML) algorithms are used. The experimental analysis of the proposed approach has been done on the public benchmark dataset BreakHis. Experimental results illustrate that, for features obtained from ResNet18-EO, the proposed approach achieves a 97.75% F-score by using the Support Vector Machine (SVM) with gaussian and radial-based functions (RBF).
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis

In recent years, the security of AI systems has drawn increasing research attention, especially in the medical imaging realm. To develop a secure medical image analysis (MIA) system, it is a must to study possible backdoor attacks (BAs), which can embed hidden malicious behaviors into the system. However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e.g., X-Ray, CT, and MRI) and analysis tasks (e.g., classification, detection, and segmentation). Most existing BA methods are designed to attack natural image classification models, which apply spatial triggers to training images and inevitably corrupt the semantics of poisoned pixels, leading to the failures of attacking dense prediction models. To address this issue, we propose a novel Frequency-Injection based Backdoor Attack method (FIBA) that is capable of delivering attacks in various MIA tasks. Specifically, FIBA leverages a trigger function in the frequency domain that can inject the low-frequency information of a trigger image into the poisoned image by linearly combining the spectral amplitude of both images. Since it preserves the semantics of the poisoned image pixels, FIBA can perform attacks on both classification and dense prediction models. Experiments on three benchmarks in MIA (i.e., ISIC-2019 [4] for skin lesion classification, KiTS-19 [17] for kidney tumor segmentation, and EAD-2019 [1] for endoscopic artifact detection), validate the effectiveness of FIBA and its superiority over stateof-the-art methods in attacking MIA models and bypassing backdoor defense. Source code will be available at code.
SeqFormer: Sequential Transformer for Video Instance Segmentation

In this work, we present SeqFormer for video instance segmentation. SeqFormer follows the principle of vision transformer that models instance relationships among video frames. Nevertheless, we observe that a stand-alone instance query suffices for capturing a time sequence of instances in a video, but attention mechanisms shall be done with each frame independently. To achieve this, SeqFormer locates an instance in each frame and aggregates temporal information to learn a powerful representation of a video-level instance, which is used to predict the mask sequences on each frame dynamically. Instance tracking is achieved naturally without tracking branches or post-processing. On YouTube-VIS, SeqFormer achieves 47.4 AP with a ResNet-50 backbone and 49.0 AP with a ResNet-101 backbone without bells and whistles. Such achievement significantly exceeds the previous state-of-the-art performance by 4.6 and 4.4, respectively. In addition, integrated with the recently-proposed Swin transformer, SeqFormer achieves a much higher AP of 59.3. We hope SeqFormer could be a strong baseline that fosters future research in video instance segmentation, and in the meantime, advances this field with a more robust, accurate, neat model. The code is available at https://github.com/wjf5203/SeqFormer .
RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs

Blind face restoration is to recover a high-quality face image from unknown degradations. As face image contains abundant contextual information, we propose a method, RestoreFormer, which explores fully-spatial attentions to model contextual information and surpasses existing works that use local operators. RestoreFormer has several benefits compared to prior arts. First, unlike the conventional multi-head self-attention in previous Vision Transformers (ViTs), RestoreFormer incorporates a multi-head cross-attention layer to learn fully-spatial interactions between corrupted queries and high-quality key-value pairs. Second, the key-value pairs in ResotreFormer are sampled from a reconstruction-oriented high-quality dictionary, whose elements are rich in high-quality facial features specifically aimed for face reconstruction, leading to superior restoration results. Third, RestoreFormer outperforms advanced state-of-the-art methods on one synthetic dataset and three real-world datasets, as well as produces images with better visual quality. Code is available at https://github.com/wzhouxiff/RestoreFormer.git.
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation

This paper proposes a simple transfer learning baseline for sign language translation. Existing sign language datasets (e.g. PHOENIX-2014T, CSL-Daily) contain only about 10 K-20K pairs of sign videos, gloss annotations and texts, which are an order of magnitude smaller than typical parallel data for training spoken language translation models. Data is thus a bottleneck for training effective sign language translation models. To mitigate this problem, we propose to progressively pretrain the model from general- domain datasets that include a large amount of external supervision to within-domain datasets. Concretely, we pretrain the sign-to-gloss visual network on the general domain of human actions and the within-domain of a sign-to-gloss dataset, and pretrain the gloss-to-text translation network on the general domain of a multilingual corpus and the within-domain of a gloss-to-text corpus. The joint model is fine-tuned with an additional module named the visual-language mapper that connects the two networks. This simple baseline surpasses the previous state-of-the-art results on two sign language translation benchmarks, demonstrating the effectiveness of transfer learning. With its simplicity and strong performance, this approach can serve as a solid baseline for future research.
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramatically, the client selection strategy can significantly influence the convergence rate of the FL process. Active client selection strategies are popularly proposed in recent studies. However, they neglect the loss correlations between the clients and achieve only marginal improvement compared to the uniform selection strategy. In this work, we propose FedCoran FLframework built on a correlation-based client selection strategy, to boost the convergence rate of FL. Specifically, we first model the loss correlations between the clients with a Gaussian Process (GP). Based on the GP model, we derive a client selection strategy with a significant reduction of expected global loss in each round. Besides, we develop an efficient GP training method with a low communication overhead in the FL scenario by utilizing the covariance stationarity. Our experimental results show that compared to the state-of-the-art method, FedCorr can improve the convergence rates by 34% ~ 99% and 26% ~ 51% on FMNIST and CIFAR-10, respectively.
Towards Language-Free Training for Text-to-Image Generation

One of the major challenges in training text-to-image generation models is the need of a large number of highquality image-text pairs. While image samples are often easily accessible, the associated text descriptions typically require careful human captioning, which is particularly time- and cost-consuming. In this paper, we propose the first work to train text-to-image generation models without any text data. Our method leverages the well-aligned multi-modal semantic space of the powerful pre-trained CLIP model: the requirement of text-conditioning is seamlessly alleviated via generating text features from image features. Extensive experiments are conducted to illustrate the effectiveness of the proposed method. We obtain state-of-the-art results in the standard text-to-image generation tasks. Importantly, the proposed language-free model outperforms most existing models trained with full image-text pairs. Furthermore, our method can be applied in fine-tuning pretrained models, which saves both training time and cost in training text-to-image generation models. Our pre-trained model obtains competitive results in zero-shot text-to-image generation on the MS-COCO dataset, yet with around only 1% of the model size and training data size relative to the recently proposed large DALL-E model.
Ensembling Off-the-shelf Models for GAN Training

The advent of large-scale training has produced a cor-nucopia of powerful visual recognition models. However, generative models, such as GANs, have traditionally been trained from scratch in an unsupervised manner. Can the collective “knowledge” from a large bank ofpretrained vision models be leveraged to improve GAN training? If so, with so many models to choose from, which one(s) should be selected, and in what manner are they most effective? We find that pretrained computer vision models can signif-icantly improve performance when used in an ensemble of discriminators. Notably, the particular subset of selected models greatly affects performance. We propose an effective selection mechanism, by probing the linear separability between real and fake samples in pretrained model embed-dings, choosing the most accurate model, and progressively adding it to the discriminator ensemble. Interestingly, our method can improve GAN training in both limited data and large-scale settings. Given only 10k training samples, our FID on LSUN Catmatches the StyleGAN2 trained on 1.6M images. On the full dataset, our method improves FID by 1.5 to 2x on cat, church, and horse categories of LSUN.
CenterFormer: Center-Based Transformer for 3D Object Detection

Query-based transformer has shown great potential in constructing long-range attention in many image-domain tasks, but has rarely been considered in LiDAR-based 3D object detection due to the overwhelming size of the point cloud data. In this paper, we propose CenterFormer, a center-based transformer network for 3D object detection. CenterFormer first uses a center heatmap to select center candidates on top of a standard voxel-based point cloud encoder. It then uses the feature of the center candidate as the query embedding in the transformer. To further aggregate features from multiple frames, we design an approach to fuse features through cross-attention. Lastly, regression heads are added to predict the bounding box on the output center feature representation. Our design reduces the convergence difficulty and computational complexity of the transformer structure. The results show significant improvements over the strong baseline of anchor-free object detection networks. CenterFormer achieves state-of-the-art performance for a single model on the Waymo Open Dataset, with 73.7% mAPH on the validation set and 75.6% mAPH on the test set, significantly outperforming all previously published CNN and transformer-based methods. Our code is publicly available at https://github.com/TuSimple/centerformer