OpenRaiser/Intern-Atlas huggingface dataset:
A Multi-faceted Analysis of the Performance Variability of Virtual Machines
Venue: arXiv cs.SE 2023
Year: 2023

Cloud computing and virtualization solutions allow one to rent the virtual machines (VMs) needed to run applications on a pay-per-use basis, but rented VMs do not offer any guarantee on their performance. Cloud platforms are known to be affected by performance variability, but a better understanding is still required. This paper moves in that direction and presents an in-depth, multi-faceted study on the performance variability of VMs. Unlike previous studies, our assessment covers a wide range of factors: 16 VM types from 4 well-known cloud providers, 10 benchmarks, and 28 different metrics. We present four new contributions. First, we introduce a new benchmark suite (VMBS) that let researchers and practitioners systematically collect a diverse set of performance data. Second, we present a new indicator, called Variability Indicator, that allows for measuring variability in the performance of VMs. Third, we illustrate an analysis of the collected data across four different dimensions: resources, isolation, time, and cost. Fourth, we present multiple predictive models based on Machine Learning that aim to forecast future performance and detect time patterns. Our experiments provide important insights on the resource variability of VMs, highlighting differences and similarities between various cloud providers. To the best of our knowledge, this is the widest analysis ever conducted on the topic.
Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views
Venue: arXiv cs.CV 2023
Year: 2023

We present Ego3DPose, a highly accurate binocular egocentric 3D pose reconstruction system. The binocular egocentric setup offers practicality and usefulness in various applications, however, it remains largely under-explored. It has been suffering from low pose estimation accuracy due to viewing distortion, severe self-occlusion, and limited field-of-view of the joints in egocentric 2D images. Here, we notice that two important 3D cues, stereo correspondences, and perspective, contained in the egocentric binocular input are neglected. Current methods heavily rely on 2D image features, implicitly learning 3D information, which introduces biases towards commonly observed motions and leads to low overall accuracy. We observe that they not only fail in challenging occlusion cases but also in estimating visible joint positions. To address these challenges, we propose two novel approaches. First, we design a two-path network architecture with a path that estimates pose per limb independently with its binocular heatmaps. Without full-body information provided, it alleviates bias toward trained full-body distribution. Second, we leverage the egocentric view of body limbs, which exhibits strong perspective variance (e.g., a significantly large-size hand when it is close to the camera). We propose a new perspective-aware representation using trigonometry, enabling the network to estimate the 3D orientation of limbs. Finally, we develop an end-to-end pose reconstruction network that synergizes both techniques. Our comprehensive evaluations demonstrate that Ego3DPose outperforms state-of-the-art models by a pose estimation error (i.e., MPJPE) reduction of 23.1% in the UnrealEgo dataset. Our qualitative results highlight the superiority of our approach across a range of scenarios and challenges.
Variational Connectionist Temporal Classification for Order-Preserving Sequence Modeling
Venue: arXiv cs.LG 2023
Year: 2023

Connectionist temporal classification (CTC) is commonly adopted for sequence modeling tasks like speech recognition, where it is necessary to preserve order between the input and target sequences. However, CTC is only applied to deterministic sequence models, where the latent space is discontinuous and sparse, which in turn makes them less capable of handling data variability when compared to variational models. In this paper, we integrate CTC with a variational model and derive loss functions that can be used to train more generalizable sequence models that preserve order. Specifically, we derive two versions of the novel variational CTC based on two reasonable assumptions, the first being that the variational latent variables at each time step are conditionally independent; and the second being that these latent variables are Markovian. We show that both loss functions allow direct optimization of the variational lower bound for the model log-likelihood, and present computationally tractable forms for implementing them.
ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers
Venue: arXiv cs.CV 2023
Year: 2023

As robotic systems increasingly encounter complex and unconstrained real-world scenarios, there is a demand to recognize diverse objects. The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects. Recent novel object pose estimation methods are solving this issue using task-specific fine-tuned CNNs for deep template matching. This adaptation for pose estimation still requires expensive data rendering and training procedures. MegaPose for example is trained on a dataset consisting of two million images showing 20,000 different objects to reach such generalization capabilities. To overcome this shortcoming we introduce ZS6D, for zero-shot novel object 6D pose estimation. Visual descriptors, extracted using pre-trained Vision Transformers (ViT), are used for matching rendered templates against query images of objects and for establishing local correspondences. These local correspondences enable deriving geometric correspondences and are used for estimating the object's 6D pose with RANSAC-based PnP. This approach showcases that the image descriptors extracted by pre-trained ViTs are well-suited to achieve a notable improvement over two state-of-the-art novel object 6D pose estimation methods, without the need for task-specific fine-tuning. Experiments are performed on LMO, YCBV, and TLESS. In comparison to one of the two methods we improve the Average Recall on all three datasets and compared to the second method we improve on two datasets.
Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis
Venue: arXiv cs.LG 2023
Year: 2023

Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. However, it is still largely unclear how well users comprehend the provided explanations and whether these increase the users ability to predict the model behavior. We approach this question by conducting a user study to evaluate comprehensibility and predictability in two widely used tools: LIME and SHAP. Moreover, we investigate the effect of counterfactual explanations and misclassifications on users ability to understand and predict the model behavior. We find that the comprehensibility of SHAP is significantly reduced when explanations are provided for samples near a model's decision boundary. Furthermore, we find that counterfactual explanations and misclassifications can significantly increase the users understanding of how a machine learning model is making decisions. Based on our findings, we also derive design recommendations for future post-hoc explainability methods with increased comprehensibility and predictability.
Optimizing V2V Unicast Communication Transmission with Reinforcement Learning and Vehicle Clustering
Venue: arXiv cs.NI 2023
Year: 2023

Efficient routing algorithms based on vehicular ad hoc networks (VANETs) play an important role in emerging intelligent transportation systems. This highly dynamic topology faces a number of wireless communication service challenges. In this paper, we propose a protocol based on reinforcement learning and vehicle node clustering, the protocol is called Qucts, solve vehicle-to-fixed-destination or V2V messaging problems. Improve message delivery rates with minimal hops and latency, link stability is also taken into account. The agreement is divided into three levels, first cluster the vehicles, each cluster head broadcasts its own coordinates and speed, to get more cluster members. Also when a cluster member receives another cluster head broadcast message, the cluster head generates a list of surrounding clusters, find the best cluster to the destination as the next cluster during message passing. Second, the protocol constructs a Q-value table based on the state after clustering, used to participate in the selection of messaging clusters. Finally, we introduce parameters that express the stability of the vehicle within the cluster, for communication node selection. This protocol hierarchy makes Qucts an offline and online solution. In order to distinguish unstable nodes within a cluster, Coding of each road, will have vehicles with planned routes, For example, car hailing and public bus. Compare the overlap with other planned paths vehicles in the cluster, low overlap is labeled as unstable nodes. Vehicle path overlap rate without a planned path is set to the mean value. Comparing Qucts with existing routing protocols through simulation, Our proposed Qucts scheme provides large improvements in both data delivery rate and end-to-end delay reduction.
An Efficient Consolidation of Word Embedding and Deep Learning Techniques for Classifying Anticancer Peptides: FastText+BiLSTM
Venue: arXiv cs.LG 2023
Year: 2023

Anticancer peptides (ACPs) are a group of peptides that exhibite antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec and FastText are evaluated as word embedding techniques for the purpose of extracting peptide sequences. Then, the output of word embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and Independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for Independent dataset, thence determining new state-of-the-art.
Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives
Venue: arXiv cs.CV 2023
Year: 2023

Action scene understanding in soccer is a challenging task due to the complex and dynamic nature of the game, as well as the interactions between players. This article provides a comprehensive overview of this task divided into action recognition, spotting, and spatio-temporal action localization, with a particular emphasis on the modalities used and multimodal methods. We explore the publicly available data sources and metrics used to evaluate models' performance. The article reviews recent state-of-the-art methods that leverage deep learning techniques and traditional methods. We focus on multimodal methods, which integrate information from multiple sources, such as video and audio data, and also those that represent one source in various ways. The advantages and limitations of methods are discussed, along with their potential for improving the accuracy and robustness of models. Finally, the article highlights some of the open research questions and future directions in the field of soccer action recognition, including the potential for multimodal methods to advance this field. Overall, this survey provides a valuable resource for researchers interested in the field of action scene understanding in soccer.
SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts
Venue: arXiv cs.CL 2023
Year: 2023

We describe SemEval-2022 Task 7, a shared task on rating the plausibility of clarifications in instructional texts. The dataset for this task consists of manually clarified how-to guides for which we generated alternative clarifications and collected human plausibility judgements. The task of participating systems was to automatically determine the plausibility of a clarification in the respective context. In total, 21 participants took part in this task, with the best system achieving an accuracy of 68.9%. This report summarizes the results and findings from 8 teams and their system descriptions. Finally, we show in an additional evaluation that predictions by the top participating team make it possible to identify contexts with multiple plausible clarifications with an accuracy of 75.2%.
FourierLoss: Shape-Aware Loss Function with Fourier Descriptors
Venue: arXiv cs.CV 2023
Year: 2023

Encoder-decoder networks become a popular choice for various medical image segmentation tasks. When they are trained with a standard loss function, these networks are not explicitly enforced to preserve the shape integrity of an object in an image. However, this ability of the network is important to obtain more accurate results, especially when there is a low-contrast difference between the object and its surroundings. In response to this issue, this work introduces a new shape-aware loss function, which we name FourierLoss. This loss function relies on quantifying the shape dissimilarity between the ground truth and the predicted segmentation maps through the Fourier descriptors calculated on their objects, and penalizing this dissimilarity in network training. Different than the previous studies, FourierLoss offers an adaptive loss function with trainable hyperparameters that control the importance of the level of the shape details that the network is enforced to learn in the training process. This control is achieved by the proposed adaptive loss update mechanism, which end-to-end learns the hyperparameters simultaneously with the network weights by backpropagation. As a result of using this mechanism, the network can dynamically change its attention from learning the general outline of an object to learning the details of its contour points, or vice versa, in different training epochs. Working on 2879 computed tomography images of 93 subjects, our experiments revealed that the proposed adaptive shape-aware loss function led to statistically significantly better results for liver segmentation, compared to its counterparts.
Passage Summarization with Recurrent Models for Audio-Sheet Music Retrieval
Venue: arXiv cs.SD 2023
Year: 2023

Many applications of cross-modal music retrieval are related to connecting sheet music images to audio recordings. A typical and recent approach to this is to learn, via deep neural networks, a joint embedding space that correlates short fixed-size snippets of audio and sheet music by means of an appropriate similarity structure. However, two challenges that arise out of this strategy are the requirement of strongly aligned data to train the networks, and the inherent discrepancies of musical content between audio and sheet music snippets caused by local and global tempo differences. In this paper, we address these two shortcomings by designing a cross-modal recurrent network that learns joint embeddings that can summarize longer passages of corresponding audio and sheet music. The benefits of our method are that it only requires weakly aligned audio-sheet music pairs, as well as that the recurrent network handles the non-linearities caused by tempo variations between audio and sheet music. We conduct a number of experiments on synthetic and real piano data and scores, showing that our proposed recurrent method leads to more accurate retrieval in all possible configurations.
This is the Table I Want! Interactive Data Transformation on Desktop and in Virtual Reality
Venue: arXiv cs.HC 2023
Year: 2023

Data transformation is an essential step in data science. While experts primarily use programming to transform their data, there is an increasing need to support non-programmers with user interface-based tools. With the rapid development in interaction techniques and computing environments, we report our empirical findings about the effects of interaction techniques and environments on performing data transformation tasks. Specifically, we studied the potential benefits of direct interaction and virtual reality (VR) for data transformation. We compared gesture interaction versus a standard WIMP user interface, each on the desktop and in VR. With the tested data and tasks, we found time performance was similar between desktop and VR. Meanwhile, VR demonstrates preliminary evidence to better support provenance and sense-making throughout the data transformation process. Our exploration of performing data transformation in VR also provides initial affirmation for enabling an iterative and fully immersive data science workflow.
Estimation of the angular position of a two-wheeled balancing robot using a real IMU with selected filters
Venue: arXiv cs.RO 2023
Year: 2023

A low-cost measurement system using filtering of measurements for two-wheeled balancing robot stabilisation purposes has been addressed in this paper. In particular, a measurement system based on gyroscope, accelerometer, and encoder has been considered. The measurements have been corrected for deterministic disturbances and then filtered with Kalman, $α$-$β$ type, and complementary filters. A quantitative assessment of selected filters has been given. As a result, the complete structure of a measurement system has been obtained. The performance of the proposed measurement system has been validated experimentally by using a dedicated research rig.
PrNet: A Neural Network for Correcting Pseudoranges to Improve Positioning with Android Raw GNSS Measurements
Venue: arXiv cs.LG 2023
Year: 2023

We present a neural network for mitigating biased errors in pseudoranges to improve localization performance with data collected from mobile phones. A satellite-wise Multilayer Perceptron (MLP) is designed to regress the pseudorange bias correction from six satellite, receiver, context-related features derived from Android raw Global Navigation Satellite System (GNSS) measurements. To train the MLP, we carefully calculate the target values of pseudorange bias using location ground truth and smoothing techniques and optimize a loss function involving the estimation residuals of smartphone clock bias. The corrected pseudoranges are then used by a model-based localization engine to compute locations. The Google Smartphone Decimeter Challenge (GSDC) dataset, which contains Android smartphone data collected from both rural and urban areas, is utilized for evaluation. Both fingerprinting and cross-trace localization results demonstrate that our proposed method outperforms model-based and state-of-the-art data-driven approaches.
Algorithmic complexity and soficness of shifts in dimension two
Venue: arXiv cs.IT 2023
Year: 2023

In this manuscript we study properties of multidimensional shifts. More precisely, we study the necessary and sufficient conditions for a shift to be sofic, i.e. the boundary between sofic shifts and effective ones. To this end, we use different versions of algorithmic complexity (a.k.a. Kolmogorov complexity). In the first part of the work we suggest new necessary conditions of soficness for multidimensional shift. These conditions are expressed in terms of Kolmogorov complexity with bounded ressources. We discuss several applications of this technique. In particular, we construct an example of a two-dimensional effective and non sofic shift that has a very low combinatorial complexity : the number of global admissible N x N patterns grows only polynomially in N. We also show that the technique developed by S.Kass and K.Madden is equivalent to a special case of our method. In the second part, we discuss properties of subshifts defined in terms of density of letters. More specifically, we study two-dimensional subshifts $S(ρ)$ in the binary alphabet (white and black cells) where a configuration is admissible if every pattern of size N x N contains at most $N^ρ$ black cells. We show that $S(^ρ)$ is sofic for every $ρ<1$. Moreover, all effectif subshifts of these shifts are also sofic. The proof of this result is principally based on the construction of a self-simulating point-fixed tile set, with several new ingredients: an ad hoc model of computation based on a non deterministic cellular automaton (which allows to implement efficiently massively parallel calculations) and some properties of flows in a specific type of planar graphs.
Soft Merging: A Flexible and Robust Soft Model Merging Approach for Enhanced Neural Network Performance
Venue: arXiv cs.LG 2023
Year: 2023

Stochastic Gradient Descent (SGD), a widely used optimization algorithm in deep learning, is often limited to converging to local optima due to the non-convex nature of the problem. Leveraging these local optima to improve model performance remains a challenging task. Given the inherent complexity of neural networks, the simple arithmetic averaging of the obtained local optima models in undesirable results. This paper proposes a {\em soft merging} method that facilitates rapid merging of multiple models, simplifies the merging of specific parts of neural networks, and enhances robustness against malicious models with extreme values. This is achieved by learning gate parameters through a surrogate of the $l_0$ norm using hard concrete distribution without modifying the model weights of the given local optima models. This merging process not only enhances the model performance by converging to a better local optimum, but also minimizes computational costs, offering an efficient and explicit learning process integrated with stochastic gradient descent. Thorough experiments underscore the effectiveness and superior performance of the merged neural networks.