Reviewerly/exHarmony huggingface dataset:
A Survey on Uncertainty Toolkits for Deep Learning

The success of deep learning (DL) fostered the creation of unifying frameworks such as tensorflow or pytorch as much as it was driven by their creation in return. Having common building blocks facilitates the exchange of, e.g., models or concepts and makes developments easier replicable. Nonetheless, robust and reliable evaluation and assessment of DL models has often proven challenging. This is at odds with their increasing safety relevance, which recently culminated in the field of "trustworthy ML". We believe that, among others, further unification of evaluation and safeguarding methodologies in terms of toolkits, i.e., small and specialized framework derivatives, might positively impact problems of trustworthiness as well as reproducibility. To this end, we present the first survey on toolkits for uncertainty estimation (UE) in DL, as UE forms a cornerstone in assessing model reliability. We investigate 11 toolkits with respect to modeling and evaluation capabilities, providing an in-depth comparison for the three most promising ones, namely Pyro, Tensorflow Probability, and Uncertainty Quantification 360. While the first two provide a large degree of flexibility and seamless integration into their respective framework, the last one has the larger methodological scope.
Rethinking Gradient Operator for Exposing AI-enabled Face Forgeries

For image forensics, convolutional neural networks (CNNs) tend to learn content features rather than subtle manipulation traces, which limits forensic performance. Existing methods predominantly solve the above challenges by following a general pipeline, that is, subtracting the original pixel value from the predicted pixel value to make CNNs pay attention to the manipulation traces. However, due to the complicated learning mechanism, these methods may bring some unnecessary performance losses. In this work, we rethink the advantages of gradient operator in exposing face forgery, and design two plug-and-play modules by combining gradient operator with CNNs, namely tensor pre-processing (TP) and manipulation trace attention (MTA) module. Specifically, TP module refines the feature tensor of each channel in the network by gradient operator to highlight the manipulation traces and improve the feature representation. Moreover, MTA module considers two dimensions, namely channel and manipulation traces, to force the network to learn the distribution of manipulation traces. These two modules can be seamlessly integrated into CNNs for end-to-end training. Experiments show that the proposed network achieves better results than prior works on five public datasets. Especially, TP module greatly improves the accuracy by at least 4.60% compared with the existing pre-processing module only via simple tensor refinement. The code is available at: https://github.com/EricGzq/GocNet-pytorch.
Optimizing an Adaptive Fuzzy Logic Controller of a 3-DOF Helicopter with a Modified PSO Algorithm

This paper investigates the controller optimization for a helicopter system with three degrees of freedom (3-DOF). To control the system, we combined fuzzy logic with adaptive control theory. The system is extensively nonlinear and highly sensitive to the controller's parameters, making it a real challenge to study these parameters' effect on the controller's performance. Using metaheuristic algorithms for determining these parameters is a promising solution. This paper proposes using a modified particle swarm optimization (MPSO) algorithm to optimize the controller. The algorithm shows a high ability to perform the global search and find a reasonable search space. The algorithm modifies the search space of each particle based on its fitness function value and substitutes weak particles for new ones. These modifications have led to better accuracy and convergence rate. We prove the efficiency of the MPSO algorithm by comparing it with the standard PSO and six other well-known metaheuristic algorithms when optimizing the adaptive fuzzy logic controller of the 3-DOF helicopter. The proposed method's effectiveness is shown through computer simulations while the system is subject to uncertainties and disturbance. We demonstrate the method's superiority by comparing the results when the MPSO and the standard PSO optimize the controller.
PERBANDINGAN MOTIVASI BELAJAR DAN HASIL BELAJAR MATEMATIKA SISWA YANG DIAJAR DENGAN MODEL PEMBELAJARAN DISCOVERY LEARNING DAN ACCEKERATED LEARNING PADA MATERI INTEGRAL DI MAN 4 MARTUBUNG

This study aims to determine the differences in the use of the Discovery Learning learning model and the Accelerated Learning learning model on the learning outcomes of highly motivated students, the differences in the use of the Discovery Learning learning model and the Accelerated Learning learning model on the learning outcomes of students who have low motivation, the difference in the use of the Discovery Learning model. Learning on the learning outcomes of students who have high and low motivation, the difference in the use of Accelerated Learning learning models on learning outcomes of students who have high and low motivation. This research is a quantitative research with a quasi-experimental research type. The population is all students of class XI MAN 4 Medan in the 2019/2020 academic year, which consists of 4 classes, while the 2 class samples are class XI IPA-1 and XI IPA-2, totaling 30 people in each class. Data analysis was performed using regression analysis. The instruments used were tests and questionnaires. The results of these findings indicate: 1) The mathematics learning outcomes of students who have high motivation are taught with the Discovery Learning learning model better; 2) The mathematics learning outcomes of students who have low motivation are taught using the Discovery Learning learning model better; 3) The learning outcomes of students who have the low motivation is taught with the Discovery Learning learning model better; 4). Mathematics learning outcomes of students who have high motivation are taught with the Accelerated Learning model better
DIRA: Dynamic Domain Incremental Regularised Adaptation

Autonomous systems (AS) often use Deep Neural Network (DNN) classifiers to allow them to operate in complex, high-dimensional, non-linear, and dynamically changing environments. Due to the complexity of these environments, DNN classifiers may output misclassifications during operation when they face domains not identified during development. Removing a system from operation for retraining becomes impractical as the number of such AS increases. To increase AS reliability and overcome this limitation, DNN classifiers need to have the ability to adapt during operation when faced with different operational domains using a few samples (e.g. 2 to 100 samples). However, retraining DNNs on a few samples is known to cause catastrophic forgetting and poor generalisation. In this paper, we introduce Dynamic Incremental Regularised Adaptation (DIRA), an approach for dynamic operational domain adaption of DNNs using regularisation techniques. We show that DIRA improves on the problem of forgetting and achieves strong gains in performance when retraining using a few samples from the target domain. Our approach shows improvements on different image classification benchmarks aimed at evaluating robustness to distribution shifts (e.g.CIFAR-10C/100C, ImageNet-C), and produces state-of-the-art performance in comparison with other methods from the literature.
Implementation of Encryption and decryption Methodologies

Abstract: With the internet having reached a level that merges with our lives, growing explosively during the last several decades, data security has become a main concern for anyone connected to the web. Data security ensures that our data is only accessible by the intended receiver and prevents any modification or alteration of data. In order to achieve this level of security, various algorithms and methods have been developed. Cryptography can be defined as techniques that cipher data, depending on specific algorithms that make the data unreadable to the human eye unless decrypted by algorithms that are predefined by the sender.
openEO for GEO.INFORMED

The GEO.INFORMED project aims at transforming freely available satellite images into information for environmental policy. We make use of the openEO API for accessing and preprocessing our satellite data, as this API ensures transparency, free access, FAIR data, and sharing.
Self-Programming Artificial Intelligence Using Code-Generating Language Models

Recent progress in large-scale language models has enabled breakthroughs in previously intractable computer programming tasks. Prior work in meta-learning and neural architecture search has led to substantial successes across various task domains, spawning myriad approaches for algorithmically optimizing the design and learning dynamics of deep learning models. At the intersection of these research areas, we implement a code-generating language model with the ability to modify its own source code. Self-programming AI algorithms have been of interest since the dawn of AI itself. Although various theoretical formulations of generalized self-programming AI have been posed, no such system has been successfully implemented to date under real-world computational constraints. Applying AI-based code generation to AI itself, we develop and experimentally validate the first practical implementation of a self-programming AI system. We empirically show that a self-programming AI implemented using a code generation model can successfully modify its own source code to improve performance and program sub-models to perform auxiliary tasks. Our model can self-modify various properties including model architecture, computational capacity, and learning dynamics.
Dynamic data structures for parameterized string problems

We revisit classic string problems considered in the area of parameterized complexity, and study them through the lens of dynamic data structures. That is, instead of asking for a static algorithm that solves the given instance efficiently, our goal is to design a data structure that efficiently maintains a solution, or reports a lack thereof, upon updates in the instance. We first consider the Closest String problem, for which we design randomized dynamic data structures with amortized update times $d^{\mathcal{O}(d)}$ and $|\Sigma|^{\mathcal{O}(d)}$, respectively, where $\Sigma$ is the alphabet and $d$ is the assumed bound on the maximum distance. These are obtained by combining known static approaches to Closest String with color-coding. Next, we note that from a result of Frandsen et al.~[J. ACM'97] one can easily infer a meta-theorem that provides dynamic data structures for parameterized string problems with worst-case update time of the form $\mathcal{O}(\log \log n)$, where $k$ is the parameter in question and $n$ is the length of the string. We showcase the utility of this meta-theorem by giving such data structures for problems Disjoint Factors and Edit Distance. We also give explicit data structures for these problems, with worst-case update times $\mathcal{O}(k2^{k}\log \log n)$ and $\mathcal{O}(k^2\log \log n)$, respectively. Finally, we discuss how a lower bound methodology introduced by Amarilli et al.~[ICALP'21] can be used to show that obtaining update time $\mathcal{O}(f(k))$ for Disjoint Factors and Edit Distance is unlikely already for a constant value of the parameter $k$.
Fusion Label Enhancement for Multi-Label Learning

Multi-label learning (MLL) refers to the problem of tagging a given instance with a set of relevant labels. In MLL, the implicit relative importance of different labels representing a single instance is generally different, which recently gained considerable attention and should be fully leveraged. Therefore, label enhancement (LE) has been widely applied in various MLL tasks as the ability to effectively mine the implicit relative importance information of different labels. However, due to the fact that the label enhancement process in previous LE-based MLL methods is decoupled from the training process on the predictive models, the objective of LE does not match the training process and finally affects the whole learning system. In this paper, we propose a novel approach named Fusion Label Enhancement for Multi-label learning (FLEM) to effectively integrate the LE process and the training process. Specifically, we design a matching and interaction mechanism which leverages a novel interaction label enhancement loss to avoid that the recovered label distribution does not match the need of the predictive model. In the meantime, we present a unified label distribution loss for establishing the corresponding relationship between the recovered label distribution and the training of the predictive model. With the proposed loss, the label distributions recovered from the LE process can be efficiently utilized for training the predictive model. Experimental results on multiple benchmark datasets validate the effectiveness of the proposed approach.
Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers

Existing Math Word Problem (MWP) solvers have achieved high accuracy on benchmark datasets. However, prior works have shown that such solvers do not generalize well and rely on superficial cues to achieve high performance. In this paper, we first conduct experiments to showcase that this behaviour is mainly associated with the limited size and diversity present in existing MWP datasets. Next, we propose several data augmentation techniques broadly categorized into Substitution and Paraphrasing based methods. By deploying these methods we increase the size of existing datasets by five folds. Extensive experiments on two benchmark datasets across three state-of-the-art MWP solvers show that proposed methods increase the generalization and robustness of existing solvers. On average, proposed methods significantly increase the state-of-the-art results by over five percentage points on benchmark datasets. Further, the solvers trained on the augmented dataset perform comparatively better on the challenge test set. We also show the effectiveness of proposed techniques through ablation studies and verify the quality of augmented samples through human evaluation.
Reducing the Cost of Training Security Classifier (via Optimized Semi-Supervised Learning)

Background: Most of the existing machine learning models for security tasks, such as spam detection, malware detection, or network intrusion detection, are built on supervised machine learning algorithms. In such a paradigm, models need a large amount of labeled data to learn the useful relationships between selected features and the target class. However, such labeled data can be scarce and expensive to acquire. Goal: To help security practitioners train useful security classification models when few labeled training data and many unlabeled training data are available. Method: We propose an adaptive framework called Dapper, which optimizes 1) semi-supervised learning algorithms to assign pseudo-labels to unlabeled data in a propagation paradigm and 2) the machine learning classifier (i.e., random forest). When the dataset class is highly imbalanced, Dapper then adaptively integrates and optimizes a data oversampling method called SMOTE. We use the novel Bayesian Optimization to search a large hyperparameter space of these tuning targets. Result: We evaluate Dapper with three security datasets, i.e., the Twitter spam dataset, the malware URLs dataset, and the CIC-IDS-2017 dataset. Experimental results indicate that we can use as low as 10% of original labeled data but achieve close or even better classification performance than using 100% labeled data in a supervised way. Conclusion: Based on those results, we would recommend using hyperparameter optimization with semi-supervised learning when dealing with shortages of labeled security data.
A Conceptual Trust Management Framework under Uncertainty for Smart Vehicular Networks

Trust is a fundamental concept in large-scale distributed systems like the Internet of Things (IoT). Trust helps to resolve choices into a decision. However, the trust calculation depends on the amount of uncertainty present in data sources. Trust in an IoT network is proportional to the amount of uncertainty generated by such sources as hardware malfunctions, network stability, adversarial issues, and the nature of data exchanged between the entities. The relationship between trust and uncertainty warrants approaches designed to maximize the former quality whilst minimizing the latter. Unfortunately, there is no consensus on an approach to ensure the trustworthiness of IoT networks, in particular, addressing the uncertainty issues in a fine-grained way. This paper aims to explore a generalized framework designed to manage trust in IoT networks of varying scales. In the proposed framework, several sources of uncertainty are expressed as quantities, trust ratings are calculated for individual entities in an IoT network, and a network model capable of effectively distributing workloads to trustworthy nodes is proposed. We consider a practical use case of smart vehicular networks. By realizing this paper, a standardized approach to building trustworthy IoT networks can be established, which can further guide subsequent works in the field of trust management under uncertainty.
Exposing Deepfake Face Forgeries with Guided Residuals

Residual-domain feature is very useful for Deepfake detection because it suppresses irrelevant content features and preserves key manipulation traces. However, inappropriate residual prediction will bring side effects on detection accuracy. In addition, residual-domain features are easily affected by image operations such as compression. Most existing works exploit either spatial-domain features or residual-domain features, while neglecting that two types of features are mutually correlated. In this paper, we propose a guided residuals network, namely GRnet, which fuses spatial-domain and residual-domain features in a mutually reinforcing way, to expose face images generated by Deepfake. Different from existing prediction based residual extraction methods, we propose a manipulation trace extractor (MTE) to directly remove the content features and preserve manipulation traces. MTE is a fine-grained method that can avoid the potential bias caused by inappropriate prediction. Moreover, an attention fusion mechanism (AFM) is designed to selectively emphasize feature channel maps and adaptively allocate the weights for two streams. The experimental results show that the proposed GRnet achieves better performances than the state-of-the-art works on four public fake face datasets including HFF, FaceForensics++, DFDC and Celeb-DF. Especially, GRnet achieves an average accuracy of 97.72% on the HFF dataset, which is at least 5.25% higher than the existing works.
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models

Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging challenge for automated detection where code-mixing or more than one language have been used for conversation in social media. Typically, hate speech detection models are evaluated by measuring their performance on the held-out test data using metrics such as accuracy and F1-score. While these metrics are useful, it becomes difficult to identify using them where the model is failing, and how to resolve it. To enable more targeted diagnostic insights of such multilingual hate speech models, we introduce a set of functionalities for the purpose of evaluation. We have been inspired to design this kind of functionalities based on real-world conversation on social media. Considering Hindi as a base language, we craft test cases for each functionality. We name our evaluation dataset HateCheckHIn. To illustrate the utility of these functionalities , we test state-of-the-art transformer based m-BERT model and the Perspective API.
MineObserver: A Deep Learning Framework for Assessing Natural Language Descriptions of Minecraft Imagery

This paper introduces a novel approach for learning natural language descriptions of scenery in Minecraft. We apply techniques from Computer Vision and Natural Language Processing to create an AI framework called MineObserver for assessing the accuracy of learner-generated descriptions of science-related images. The ultimate purpose of the system is to automatically assess the accuracy of learner observations, written in natural language, made during science learning activities that take place in Minecraft. Eventually, MineObserver will be used as part of a pedagogical agent framework for providing in-game support for learning. Preliminary results are mixed, but promising with approximately 62% of images in our test set being properly classified by our image captioning approach. Broadly, our work suggests that computer vision techniques work as expected in Minecraft and can serve as a basis for assessing learner observations.
Baselines and Protocols for Household Speaker Recognition

Speaker recognition on household devices, such as smart speakers, features several challenges: (i) robustness across a vast number of heterogeneous domains (households), (ii) short utterances, (iii) possibly absent speaker labels of the enrollment data (passive enrollment), and (iv) presence of unknown persons (guests). While many commercial products exist, there is less published research and no publicly-available evaluation protocols or open-source baselines. Our work serves to bridge this gap by providing an accessible evaluation benchmark derived from public resources (VoxCeleb and ASVspoof 2019 data) along with a preliminary pool of open-source baselines. This includes four algorithms for active enrollment (speaker labels available) and one algorithm for passive enrollment.
Vision Transformer with Deformable Attention

Transformers have recently shown superior performances on various vision tasks. The large, sometimes even global, receptive field endows Transformer models with higher representation power over their CNN counterparts. Nevertheless, simply enlarging receptive field also gives rise to several concerns. On the one hand, using dense attention e.g., in ViT, leads to excessive memory and computational cost, and features can be influenced by irrelevant parts which are beyond the region of interests. On the other hand, the sparse attention adopted in PVT or Swin Transformer is data agnostic and may limit the ability to model long range relations. To mitigate these issues, we propose a novel deformable self-attention module, where the positions of key and value pairs in self-attention are selected in a data-dependent way. This flexible scheme enables the self-attention module to focus on relevant regions and capture more informative features. On this basis, we present Deformable Attention Transformer, a general backbone model with deformable attention for both image classification and dense prediction tasks. Extensive experiments show that our models achieve consistently improved results on comprehensive benchmarks. Code is available at https://github.com/LeapLabTHU/DAT.
Fantastic Data and How to Query Them

It is commonly acknowledged that the availability of the huge amount of (training) data is one of the most important factors for many recent advances in Artificial Intelligence (AI). However, datasets are often designed for specific tasks in narrow AI sub areas and there is no unified way to manage and access them. This not only creates unnecessary overheads when training or deploying Machine Learning models but also limits the understanding of the data, which is very important for data-centric AI. In this paper, we present our vision about a unified framework for different datasets so that they can be integrated and queried easily, e.g., using standard query languages. We demonstrate this in our ongoing work to create a framework for datasets in Computer Vision and show its advantages in different scenarios. Our demonstration is available at https://vision.semkg.org.