OpenRaiser/Intern-Atlas huggingface dataset:
Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running
Venue: arXiv cs.DC 2022
Year: 2022

Energy is an essential, but often forgotten aspect in large-scale federated systems. As most of the research focuses on tackling computational and statistical heterogeneity from the machine learning algorithms, the impact on the mobile system still remains unclear. In this paper, we design and implement an online optimization framework by connecting asynchronous execution of federated training with application co-running to minimize energy consumption on battery-powered mobile devices. From a series of experiments, we find that co-running the training process in the background with foreground applications gives the system a deep energy discount with negligible performance slowdown. Based on these results, we first study an offline problem assuming all the future occurrences of applications are available, and propose a dynamic programming-based algorithm. Then we propose an online algorithm using the Lyapunov framework to explore the solution space via the energy-staleness trade-off. The extensive experiments demonstrate that the online optimization framework can save over 60% energy with 3 times faster convergence speed compared to the previous schemes.
Czech Dataset for Cross-lingual Subjectivity Classification
Venue: arXiv cs.CL 2022
Year: 2022

In this paper, we introduce a new Czech subjectivity dataset of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. Our prime motivation is to provide a reliable dataset that can be used with the existing English dataset as a benchmark to test the ability of pre-trained multilingual models to transfer knowledge between Czech and English and vice versa. Two annotators annotated the dataset reaching 0.83 of the Cohen's \k{appa} inter-annotator agreement. To the best of our knowledge, this is the first subjectivity dataset for the Czech language. We also created an additional dataset that consists of 200k automatically labeled sentences. Both datasets are freely available for research purposes. Furthermore, we fine-tune five pre-trained BERT-like models to set a monolingual baseline for the new dataset and we achieve 93.56% of accuracy. We fine-tune models on the existing English dataset for which we obtained results that are on par with the current state-of-the-art results. Finally, we perform zero-shot cross-lingual subjectivity classification between Czech and English to verify the usability of our dataset as the cross-lingual benchmark. We compare and discuss the cross-lingual and monolingual results and the ability of multilingual models to transfer knowledge between languages.
KERMIT -- A Transformer-Based Approach for Knowledge Graph Matching
Venue: arXiv cs.CL 2022
Year: 2022

One of the strongest signals for automated matching of knowledge graphs and ontologies are textual concept descriptions. With the rise of transformer-based language models, text comparison based on meaning (rather than lexical features) is available to researchers. However, performing pairwise comparisons of all textual descriptions of concepts in two knowledge graphs is expensive and scales quadratically (or even worse if concepts have more than one description). To overcome this problem, we follow a two-step approach: we first generate matching candidates using a pre-trained sentence transformer (so called bi-encoder). In a second step, we use fine-tuned transformer cross-encoders to generate the best candidates. We evaluate our approach on multiple datasets and show that it is feasible and produces competitive results.
Tailored Uncertainty Estimation for Deep Learning Systems
Venue: arXiv cs.LG 2022
Year: 2022

Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable. Standard techniques for uncertainty estimation, however, come along with specific combinations of strengths and weaknesses, e.g., with respect to estimation quality, generalization abilities and computational complexity. To actually harness the potential of uncertainty quantification, estimators are required whose properties closely match the requirements of a given use case. In this work, we propose a framework that, firstly, structures and shapes these requirements, secondly, guides the selection of a suitable uncertainty estimation method and, thirdly, provides strategies to validate this choice and to uncover structural weaknesses. By contributing tailored uncertainty estimation in this sense, our framework helps to foster trustworthy DL systems. Moreover, it anticipates prospective machine learning regulations that require, e.g., in the EU, evidences for the technical appropriateness of machine learning systems. Our framework provides such evidences for system components modeling uncertainty.
Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction
Venue: arXiv cs.RO 2022
Year: 2022

We approach the problem of high-DOF reaching-and-grasping via learning joint planning of grasp and motion with deep reinforcement learning. To resolve the sample efficiency issue in learning the high-dimensional and complex control of dexterous grasping, we propose an effective representation of grasping state characterizing the spatial interaction between the gripper and the target object. To represent gripper-object interaction, we adopt Interaction Bisector Surface (IBS) which is the Voronoi diagram between two close by 3D geometric objects and has been successfully applied in characterizing spatial relations between 3D objects. We found that IBS is surprisingly effective as a state representation since it well informs the fine-grained control of each finger with spatial relation against the target object. This novel grasp representation, together with several technical contributions including a fast IBS approximation, a novel vector-based reward and an effective training strategy, facilitate learning a strong control model of high-DOF grasping with good sample efficiency, dynamic adaptability, and cross-category generality. Experiments show that it generates high-quality dexterous grasp for complex shapes with smooth grasping motions.
Local Explanation of Dimensionality Reduction
Venue: arXiv cs.LG 2022
Year: 2022

Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data. Reduced data representations are less computationally intensive and easier to manage and visualize, while retaining a significant percentage of their original information. Aside from these advantages, these reduced representations can be difficult or impossible to interpret in most circumstances, especially when the DR approach does not provide further information about which features of the original space led to their construction. This problem is addressed by Interpretable Machine Learning, a subfield of Explainable Artificial Intelligence that addresses the opacity of machine learning models. However, current research on Interpretable Machine Learning has been focused on supervised tasks, leaving unsupervised tasks like Dimensionality Reduction unexplored. In this paper, we introduce LXDR, a technique capable of providing local interpretations of the output of DR techniques. Experiment results and two LXDR use case examples are presented to evaluate its usefulness.
Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data
Venue: arXiv cs.DC 2022
Year: 2022

Federated Learning (FL) is one of the hot research topics, and it utilizes Machine Learning (ML) in a distributed manner without directly accessing private data on clients. However, FL faces many challenges, including the difficulty to obtain high accuracy, high communication cost between clients and the server, and security attacks related to adversarial ML. To tackle these three challenges, we propose an FL algorithm inspired by evolutionary techniques. The proposed algorithm groups clients randomly in many clusters, each with a model selected randomly to explore the performance of different models. The clusters are then trained in a repetitive process where the worst performing cluster is removed in each iteration until one cluster remains. In each iteration, some clients are expelled from clusters either due to using poisoned data or low performance. The surviving clients are exploited in the next iteration. The remaining cluster with surviving clients is then used for training the best FL model (i.e., remaining FL model). Communication cost is reduced since fewer clients are used in the final training of the FL model. To evaluate the performance of the proposed algorithm, we conduct a number of experiments using FEMNIST dataset and compare the result against the random FL algorithm. The experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of accuracy, communication cost, and security.
The Need for Knowledge Organization. Introduction to the book Linking Knowledge: Linked Open Data for Knowledge Organization
Venue: arXiv cs.DL 2022
Year: 2022

This book is not restricted to semantic web (SW) technologies. An aspiration was to contribute to the awakening of a dialogue between information and documentation concerned with knowledge organization systems (KOSs), and branches in computer science with an emphasis on machines, algorithms and ontologies. The technological evolution of the last decades has not only fostered the emergence of ever more KOSs but also semantic web technologies. Both the actions of 'making a KOS' and 'applying existing KOSs' represent research. The design of an information layer for a knowledge domain and the design of a domain specific research process are intrinsically interwoven. We extended our intervention to KOS practices into education, by presenting a translation of existing standards and recommendations about linked open data (LOD) publishing for non-experts. The chapters describe the state of the art in providing KOSs as semantic artefacts; how the state of the art is applied in new fields; how the state of the art is pushed towards new technological solutions by being confronted with new applications; how best practices need to be tailored towards specific solutions; and what challenges occur when merging new and old ways of expressing KOSs. The linked data (LD) ecosystem represents a source of knowledge generation, acquisition, production and dissemination. The underlying discourse shows historical vision alongside the promise of linking knowledge for interaction. The already maturing ecosystems of the SW are interlocking information institutions clearly devoted to the expansion of human experience through the growth of knowledge interaction.
A Deep Learning based No-reference Quality Assessment Model for UGC Videos
Venue: arXiv cs.CV 2022
Year: 2022

Quality assessment for User Generated Content (UGC) videos plays an important role in ensuring the viewing experience of end-users. Previous UGC video quality assessment (VQA) studies either use the image recognition model or the image quality assessment (IQA) models to extract frame-level features of UGC videos for quality regression, which are regarded as the sub-optimal solutions because of the domain shifts between these tasks and the UGC VQA task. In this paper, we propose a very simple but effective UGC VQA model, which tries to address this problem by training an end-to-end spatial feature extraction network to directly learn the quality-aware spatial feature representation from raw pixels of the video frames. We also extract the motion features to measure the temporal-related distortions that the spatial features cannot model. The proposed model utilizes very sparse frames to extract spatial features and dense frames (i.e. the video chunk) with a very low spatial resolution to extract motion features, which thereby has low computational complexity. With the better quality-aware features, we only use the simple multilayer perception layer (MLP) network to regress them into the chunk-level quality scores, and then the temporal average pooling strategy is adopted to obtain the video-level quality score. We further introduce a multi-scale quality fusion strategy to solve the problem of VQA across different spatial resolutions, where the multi-scale weights are obtained from the contrast sensitivity function of the human visual system. The experimental results show that the proposed model achieves the best performance on five popular UGC VQA databases, which demonstrates the effectiveness of the proposed model. The code will be publicly available.
Multimodal Transformer-based Model for Buchwald-Hartwig and Suzuki-Miyaura Reaction Yield Prediction
Venue: arXiv cs.LG 2022
Year: 2022

Predicting the yield percentage of a chemical reaction is useful in many aspects such as reducing wet-lab experimentation by giving the priority to the reactions with a high predicted yield. In this work we investigated the use of multiple type inputs to predict chemical reaction yield. We used simplified molecular-input line-entry system (SMILES) as well as calculated chemical descriptors as model inputs. The model consists of a pre-trained bidirectional transformer-based encoder (BERT) and a multi-layer perceptron (MLP) with a regression head to predict the yield. We experimented on two high throughput experimentation (HTE) datasets for Buchwald-Hartwig and Suzuki-Miyaura reactions. The experiments show improvements in the prediction on both datasets compared to systems using only SMILES or chemical descriptors as input. We also tested the model's performance on out-of-sample dataset splits of Buchwald-Hartwig and achieved comparable results with the state-of-the-art. In addition to predicting the yield, we demonstrated the model's ability to suggest the optimum (highest yield) reaction conditions. The model was able to suggest conditions that achieves 94% of the optimum reported yields. This proves the model to be useful in achieving the best results in the wet lab without expensive experimentation.
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering
Venue: arXiv cs.LG 2022
Year: 2022

The computational demands of neural architecture search (NAS) algorithms are usually directly proportional to the size of their target search spaces. Thus, limiting the search to high-quality subsets can greatly reduce the computational load of NAS algorithms. In this paper, we present Clustering-Based REDuction (C-BRED), a new technique to reduce the size of NAS search spaces. C-BRED reduces a NAS space by clustering the computational graphs associated with its architectures and selecting the most promising cluster using proxy statistics correlated with network accuracy. When considering the NAS-Bench-201 (NB201) data set and the CIFAR-100 task, C-BRED selects a subset with 70% average accuracy instead of the whole space's 64% average accuracy.
Combining Lipschitz and RBF Surrogate Models for High-dimensional Computationally Expensive Problems
Venue: arXiv cs.NE 2022
Year: 2022

Standard evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward and computationally cheap. However, in many real-world optimization problems, these evaluations involve computationally expensive numerical simulations or physical experiments. Surrogate-assisted evolutionary algorithms (SAEAs) have recently gained increased attention for their performance in solving these types of problems. The main idea of SAEAs is the integration of an evolutionary algorithm with a selected surrogate model that approximates the computationally expensive function. In this paper, we propose a surrogate model based on a Lipschitz underestimation and use it to develop a differential evolution-based algorithm. The algorithm, called Lipschitz Surrogate-assisted Differential Evolution (LSADE), utilizes the Lipschitz-based surrogate model, along with a standard radial basis function surrogate model and a local search procedure. The experimental results on seven benchmark functions of dimensions 30, 50, 100, and 200 show that the proposed LSADE algorithm is competitive compared with the state-of-the-art algorithms under a limited computational budget, being especially effective for the very complicated benchmark functions in high dimensions.
Finite Entailment of UCRPQs over ALC Ontologies
Venue: arXiv cs.LO 2022
Year: 2022

We investigate the problem of finite entailment of ontology-mediated queries. We consider the expressive query language, unions of conjunctive regular path queries (UCRPQs), extending the well-known class of union of conjunctive queries, with regular expressions over roles. We look at ontologies formulated using the description logic ALC, and show a tight 2EXPTIME upper bound for entailment of UCRPQs. At the core of our decision procedure, there is a novel automata-based technique introducing a stratification of interpretations induced by the deterministic finite automaton underlying the input UCRPQ
Polyglot Prompt: Multilingual Multitask PrompTraining
Venue: arXiv cs.CL 2022
Year: 2022

This paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i.e. without any task/language-specific module? The benefit of achieving this could open new doors for future multilingual research, including allowing systems trained on low resources to be further assisted by other languages as well as other tasks. We approach this goal by developing a learning framework named Polyglot Prompting to exploit prompting methods for learning a unified semantic space for different languages and tasks with multilingual prompt engineering. We performed a comprehensive evaluation of 6 tasks, namely topic classification, sentiment classification, named entity recognition, question answering, natural language inference, and summarization, covering 24 datasets and 49 languages. The experimental results demonstrated the efficacy of multilingual multitask prompt-based learning and led to inspiring observations. We also present an interpretable multilingual evaluation methodology and show how the proposed framework, multilingual multitask prompt training, works. We release all datasets prompted in the best setting and code.
Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI
Venue: arXiv cs.CL 2022
Year: 2022

Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean. However, such learning, particularly in Markov-based query rewriting systems have far from addressed the impact of these models on future training where successive feedback is inevitably contingent on the rewrite itself, especially in a continually updating environment. In this paper, we explore the consequences of this inherent lack of self-awareness towards impairing the model performance, ultimately resulting in both Type I and II errors over time. To that end, we propose augmenting the Markov Graph construction with a superposition-based adjacency matrix. Here, our method leverages an induced stochasticity to reactively learn a locally-adaptive decision boundary based on the performance of the individual rewrites in a bi-variate beta setting. We also surface a data augmentation strategy that leverages template-based generation in abridging complex conversation hierarchies of dialogs so as to simplify the learning process. All in all, we demonstrate that our self-aware model improves the overall PR-AUC by 27.45%, achieves a relative defect reduction of up to 31.22%, and is able to adapt quicker to changes in global preferences across a large number of customers.
Birds' Eye View: Measuring Behavior and Posture of Chickens as a Metric for Their Well-Being
Venue: arXiv cs.CV 2022
Year: 2022

Chicken well-being is important for ensuring food security and better nutrition for a growing global human population. In this research, we represent behavior and posture as a metric to measure chicken well-being. With the objective of detecting chicken posture and behavior in a pen, we employ two algorithms: Mask R-CNN for instance segmentation and YOLOv4 in combination with ResNet50 for classification. Our results indicate a weighted F1 score of 88.46% for posture and behavior detection using Mask R-CNN and an average of 91% accuracy in behavior detection and 86.5% average accuracy in posture detection using YOLOv4. These experiments are conducted under uncontrolled scenarios for both posture and behavior measurements. These metrics establish a strong foundation to obtain a decent indication of individual and group behaviors and postures. Such outcomes would help improve the overall well-being of the chickens. The dataset used in this research is collected in-house and will be made public after the publication as it would serve as a very useful resource for future research. To the best of our knowledge no other research work has been conducted in this specific setup used for this work involving multiple behaviors and postures simultaneously.
Line of Sight Curvature for Missile Guidance using Reinforcement Meta-Learning
Venue: arXiv cs.RO 2022
Year: 2022

We use reinforcement meta learning to optimize a line of sight curvature policy that increases the effectiveness of a guidance system against maneuvering targets. The policy is implemented as a recurrent neural network that maps navigation system outputs to a Euler 321 attitude representation. The attitude representation is then used to construct a direction cosine matrix that biases the observed line of sight vector. The line of sight rotation rate derived from the biased line of sight is then mapped to a commanded acceleration by the guidance system. By varying the bias as a function of navigation system outputs, the policy enhances accuracy against highly maneuvering targets. Importantly, our method does not require an estimate of target acceleration. In our experiments, we demonstrate that when our method is combined with proportional navigation, the system significantly outperforms augmented proportional navigation with perfect knowledge of target acceleration, achieving improved accuracy with less control effort against a wide range of target maneuvers.
MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud
Venue: arXiv cs.DC 2022
Year: 2022

Existing general purpose frameworks for gigantic model training, i.e., dense models with billions of parameters, cannot scale efficiently on cloud environment with various networking conditions due to large communication overheads. In this paper, we propose MiCS, which Minimizes the Communication Scale to bring down communication overhead. Specifically, by decreasing the number of participants in a communication collective, MiCS can utilize heterogeneous network bandwidth, reduce network traffic over slower links, reduce the latency of communications for maintaining high network bandwidth utilization, and amortize expensive global gradient synchronization overhead. Our evaluation on AWS shows that the system throughput of MiCS is up to 2.89$\times$ that of the state-of-the-art large model training systems. MiCS achieves near-linear scaling efficiency, which is up to 1.27$\times$ that of DeepSpeed. MiCS allows us to train a proprietary model with 100 billion parameters on 512 GPUs with 99.4% weak-scaling efficiency, and it is able to saturate over 54.5% theoretical computation power of each GPU on a public cloud with less GPU memory and more restricted networks than DGX-A100 clusters.