OpenRaiser/Intern-Atlas huggingface dataset:
Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud
Venue: arXiv cs.DC 2023
Year: 2023

The precise estimation of resource usage is a complex and challenging issue due to the high variability and dimensionality of heterogeneous service types and dynamic workloads. Over the last few years, the prediction of resource usage and traffic has received ample attention from the research community. Many machine learning-based workload forecasting models have been developed by exploiting their computational power and learning capabilities. This paper presents the first systematic survey cum performance analysis-based comparative study of diversified machine learning-driven cloud workload prediction models. The discussion initiates with the significance of predictive resource management followed by a schematic description, operational design, motivation, and challenges concerning these workload prediction models. Classification and taxonomy of different prediction approaches into five distinct categories are presented focusing on the theoretical concepts and mathematical functioning of the existing state-of-the-art workload prediction methods. The most prominent prediction approaches belonging to a distinct class of machine learning models are thoroughly surveyed and compared. All five classified machine learning-based workload prediction models are implemented on a common platform for systematic investigation and comparison using three distinct benchmark cloud workload traces via experimental analysis. The essential key performance indicators of state-of-the-art approaches are evaluated for comparison and the paper is concluded by discussing the trade-offs and notable remarks.
Multi-Task Self-Supervised Learning for Image Segmentation Task
Venue: arXiv cs.LG 2023
Year: 2023

Thanks to breakthroughs in AI and Deep learning methodology, Computer vision techniques are rapidly improving. Most computer vision applications require sophisticated image segmentation to comprehend what is image and to make an analysis of each section easier. Training deep learning networks for semantic segmentation required a large amount of annotated data, which presents a major challenge in practice as it is expensive and labor-intensive to produce such data. The paper presents 1. Self-supervised techniques to boost semantic segmentation performance using multi-task learning with Depth prediction and Surface Normalization . 2. Performance evaluation of the different types of weighing techniques (UW, Nash-MTL) used for Multi-task learning. NY2D dataset was used for performance evaluation. According to our evaluation, the Nash-MTL method outperforms single task learning(Semantic Segmentation).
Handwriting and Drawing for Depression Detection: A Preliminary Study
Venue: arXiv cs.CV 2023
Year: 2023

The events of the past 2 years related to the pandemic have shown that it is increasingly important to find new tools to help mental health experts in diagnosing mood disorders. Leaving aside the longcovid cognitive (e.g., difficulty in concentration) and bodily (e.g., loss of smell) effects, the short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients. To this aim, patients with clinical depression (n = 14), individuals with high sub-clinical (diagnosed by a test rather than a doctor) depressive traits (n = 15) and healthy individuals (n = 20) were recruited and asked to perform four online drawing /handwriting tasks using a digitizing tablet and a special writing device. From the raw collected online data, seventeen drawing/writing features (categorized into five categories) were extracted, and compared among the three groups of the involved participants, through ANOVA repeated measures analyses. Results shows that Time features are more effective in discriminating between healthy and participants with sub-clinical depressive characteristics. On the other hand, Ductus and Pressure features are more effective in discriminating between clinical depressed and healthy participants.
Learning Trees of $\ell_0$-Minimization Problems
Venue: arXiv cs.LG 2023
Year: 2023

The problem of computing minimally sparse solutions of under-determined linear systems is $NP$ hard in general. Subsets with extra properties, may allow efficient algorithms, most notably problems with the restricted isometry property (RIP) can be solved by convex $\ell_1$-minimization. While these classes have been very successful, they leave out many practical applications. In this paper, we consider adaptable classes that are tractable after training on a curriculum of increasingly difficult samples. The setup is intended as a candidate model for a human mathematician, who may not be able to tackle an arbitrary proof right away, but may be successful in relatively flexible subclasses, or areas of expertise, after training on a suitable curriculum.
Cooperative Task-Oriented Communication for Multi-Modal Data with Transmission Control
Venue: arXiv cs.IT 2023
Year: 2023

Real-time intelligence applications in Internet of Things (IoT) environment depend on timely data communication. However, it is challenging to transmit and analyse massive data of various modalities. Recently proposed task-oriented communication methods based on deep learning have showed its superiority in communication efficiency. In this paper, we propose a cooperative task-oriented communication method for the transmission of multi-modal data from multiple end devices to a central server. In particular, we use the transmission result of data of one modality, which is with lower rate, to control the transmission of other modalities with higher rate in order to reduce the amount of transmitted date. We take the human activity recognition (HAR) task in a smart home environment and design the semantic-oriented transceivers for the transmission of monitoring videos of different rooms and acceleration data of the monitored human. The numerical results demonstrate that by using the transmission control based on the obtained results of the received acceleration data, the transmission is reduced to 2% of that without transmission control while preserving the performance on the HAR task.
Uncertainty Calibration and its Application to Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

Image-based environment perception is an important component especially for driver assistance systems or autonomous driving. In this scope, modern neuronal networks are used to identify multiple objects as well as the according position and size information within a single frame. The performance of such an object detection model is important for the overall performance of the whole system. However, a detection model might also predict these objects under a certain degree of uncertainty. [...] In this work, we examine the semantic uncertainty (which object type?) as well as the spatial uncertainty (where is the object and how large is it?). We evaluate if the predicted uncertainties of an object detection model match with the observed error that is achieved on real-world data. In the first part of this work, we introduce the definition for confidence calibration of the semantic uncertainty in the context of object detection, instance segmentation, and semantic segmentation. We integrate additional position information in our examinations to evaluate the effect of the object's position on the semantic calibration properties. Besides measuring calibration, it is also possible to perform a post-hoc recalibration of semantic uncertainty that might have turned out to be miscalibrated. [...] The second part of this work deals with the spatial uncertainty obtained by a probabilistic detection model. [...] We review and extend common calibration methods so that it is possible to obtain parametric uncertainty distributions for the position information in a more flexible way. In the last part, we demonstrate a possible use-case for our derived calibration methods in the context of object tracking. [...] We integrate our previously proposed calibration techniques and demonstrate the usefulness of semantic and spatial uncertainty calibration in a subsequent process. [...]
An Estimation of Distribution Algorithm based on interactions between requirements to solve the bi-objective Next Release Problem
Venue: arXiv cs.SE 2023
Year: 2023

Selecting the appropriate requirements to develop in the next release of an open market software product under evolution, is a compulsory step of each software development project. This selection should be done by maximizing stakeholders' satisfaction and minimizing development costs, while keeping constraints. In this work we investigate what is the requirements interactions impact when searching for solutions of the bi-objective Next Release Problem. In one hand, these interactions are explicitly included in two algorithms: a branch and bound algorithm and an estimation of distribution algorithm (EDA). And on the other, we study the performance of these not previously used solving approaches by applying them in several instances of small, medium and large size data sets. We find that interactions inclusion do enhance the search and when time restrictions exists, as in the case of the bi-objective Next Release Problem, EDAs have proven to be stable and reliable locating a large number of solutions on the reference Pareto front.
Toward a normative theory of (self-)management by goal-setting
Venue: arXiv cs.AI 2023
Year: 2023

People are often confronted with problems whose complexity exceeds their cognitive capacities. To deal with this complexity, individuals and managers can break complex problems down into a series of subgoals. Which subgoals are most effective depends on people's cognitive constraints and the cognitive mechanisms of goal pursuit. This creates an untapped opportunity to derive practical recommendations for which subgoals managers and individuals should set from cognitive models of bounded rationality. To seize this opportunity, we apply the principle of resource-rationality to formulate a mathematically precise normative theory of (self-)management by goal-setting. We leverage this theory to computationally derive optimal subgoals from a resource-rational model of human goal pursuit. Finally, we show that the resulting subgoals improve the problem-solving performance of bounded agents and human participants. This constitutes a first step towards grounding prescriptive theories of management and practical recommendations for goal-setting in computational models of the relevant psychological processes and cognitive limitations.
1st Place Solution for PSG competition with ECCV'22 SenseHuman Workshop
Venue: arXiv cs.CV 2023
Year: 2023

Panoptic Scene Graph (PSG) generation aims to generate scene graph representations based on panoptic segmentation instead of rigid bounding boxes. Existing PSG methods utilize one-stage paradigm which simultaneously generates scene graphs and predicts semantic segmentation masks or two-stage paradigm that first adopt an off-the-shelf panoptic segmentor, then pairwise relationship prediction between these predicted objects. One-stage approach despite having a simplified training paradigm, its segmentation results are usually under-satisfactory, while two-stage approach lacks global context and leads to low performance on relation prediction. To bridge this gap, in this paper, we propose GRNet, a Global Relation Network in two-stage paradigm, where the pre-extracted local object features and their corresponding masks are fed into a transformer with class embeddings. To handle relation ambiguity and predicate classification bias caused by long-tailed distribution, we formulate relation prediction in the second stage as a multi-class classification task with soft label. We conduct comprehensive experiments on OpenPSG dataset and achieve the state-of-art performance on the leadboard. We also show the effectiveness of our soft label strategy for long-tailed classes in ablation studies. Our code has been released in https://github.com/wangqixun/mfpsg.
When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations
Venue: arXiv cs.LG 2023
Year: 2023

In supervised learning, low quality annotations lead to poorly performing classification and detection models, while also rendering evaluation unreliable. This is particularly apparent on temporal data, where annotation quality is affected by multiple factors. For example, in the post-hoc self-reporting of daily activities, cognitive biases are one of the most common ingredients. In particular, reporting the start and duration of an activity after its finalisation may incorporate biases introduced by personal time perceptions, as well as the imprecision and lack of granularity due to time rounding. Here we propose a method to model human biases on temporal annotations and argue for the use of soft labels. Experimental results in synthetic data show that soft labels provide a better approximation of the ground truth for several metrics. We showcase the method on a real dataset of daily activities.
Multipath agents for modular multitask ML systems
Venue: arXiv cs.LG 2023
Year: 2023

A standard ML model is commonly generated by a single method that specifies aspects such as architecture, initialization, training data and hyperparameters configuration. The presented work introduces a novel methodology allowing to define multiple methods as distinct agents. Agents can collaborate and compete to generate and improve ML models for a given tasks. The proposed methodology is demonstrated with the generation and extension of a dynamic modular multitask ML system solving more than one hundred image classification tasks. Diverse agents can compete to produce the best performing model for a task by reusing the modules introduced to the system by competing agents. The presented work focuses on the study of agents capable of: 1) reusing the modules generated by concurrent agents, 2) activating in parallel multiple modules in a frozen state by connecting them with trainable modules, 3) condition the activation mixture on each data sample by using a trainable router module. We demonstrate that this simple per-sample parallel routing method can boost the quality of the combined solutions by training a fraction of the activated parameters.
A Strong Baseline for Batch Imitation Learning
Venue: arXiv cs.LG 2023
Year: 2023

Imitation of expert behaviour is a highly desirable and safe approach to the problem of sequential decision making. We provide an easy-to-implement, novel algorithm for imitation learning under a strict data paradigm, in which the agent must learn solely from data collected a priori. This paradigm allows our algorithm to be used for environments in which safety or cost are of critical concern. Our algorithm requires no additional hyper-parameter tuning beyond any standard batch reinforcement learning (RL) algorithm, making it an ideal baseline for such data-strict regimes. Furthermore, we provide formal sample complexity guarantees for the algorithm in finite Markov Decision Problems. In doing so, we formally demonstrate an unproven claim from Kearns & Singh (1998). On the empirical side, our contribution is twofold. First, we develop a practical, robust and principled evaluation protocol for offline RL methods, making use of only the dataset provided for model selection. This stands in contrast to the vast majority of previous works in offline RL, which tune hyperparameters on the evaluation environment, limiting the practical applicability when deployed in new, cost-critical environments. As such, we establish precedent for the development and fair evaluation of offline RL algorithms. Second, we evaluate our own algorithm on challenging continuous control benchmarks, demonstrating its practical applicability and competitiveness with state-of-the-art performance, despite being a simpler algorithm.
A Valid Self-Report is Never Late, Nor is it Early: On Considering the "Right" Temporal Distance for Assessing Emotional Experience
Venue: arXiv cs.HC 2023
Year: 2023

Developing computational models for automatic affect prediction requires valid self-reports about individuals' emotional interpretations of stimuli. In this article, we highlight the important influence of the temporal distance between a stimulus event and the moment when its experience is reported on the provided information's validity. This influence stems from the time-dependent and time-demanding nature of the involved cognitive processes. As such, reports can be collected too late: forgetting is a widely acknowledged challenge for accurate descriptions of past experience. For this reason, methods striving for assessment as early as possible have become increasingly popular. However, here we argue that collection may also occur too early: descriptions about very recent stimuli might be collected before emotional processing has fully converged. Based on these notions, we champion the existence of a temporal distance for each type of stimulus that maximizes the validity of self-reports -- a "right" time. Consequently, we recommend future research to (1) consciously consider the potential influence of temporal distance on affective self-reports when planning data collection, (2) document the temporal distance of affective self-reports wherever possible as part of corpora for computational modelling, and finally (3) and explore the effect of temporal distance on self-reports across different types of stimuli.
Computation vs. Communication Scaling for Future Transformers on Future Hardware
Venue: arXiv cs.AR 2023
Year: 2023

Scaling neural network models has delivered dramatic quality gains across ML problems. However, this scaling has increased the reliance on efficient distributed training techniques. Accordingly, as with other distributed computing scenarios, it is important to understand how will compute and communication scale relative to one another as models scale and hardware evolves? A careful study which answers this question can better guide the design of future systems which can efficiently train future large models. Accordingly, this work provides a comprehensive multi-axial (algorithmic, empirical, hardware evolution) analysis of compute vs. communication (Comp-vs.-Comm) scaling for future Transformer models on future hardware. First, our algorithmic analysis shows that compute generally enjoys an edge over communication as models scale. However, since memory capacity scales slower than compute, these trends are being stressed. Next, we quantify this edge by empirically studying how Comp-vs.-Comm scales for future models on future hardware. To avoid profiling numerous Transformer models across many setups, we extract execution regions and project costs using operator models. This allows a spectrum (hundreds) of future model/hardware scenarios to be accurately studied ($<$15% error), and reduces profiling costs by 2100$\times$. Our experiments show that communication will be a significant portion (40-75%) of runtime as models and hardware evolve. Moreover, communication which is hidden by overlapped computation in today's models often cannot be hidden in future, larger models. Overall, this work highlights the increasingly large role communication will play as models scale and discusses techniques and upcoming technologies that can help address it.
Information Rates for Channels with Fading, Side Information and Adaptive Codewords
Venue: arXiv cs.IT 2023
Year: 2023

Generalized mutual information (GMI) is used to compute achievable rates for fading channels with various types of channel state information at the transmitter (CSIT) and receiver (CSIR). The GMI is based on variations of auxiliary channel models with additive white Gaussian noise (AWGN) and circularly-symmetric complex Gaussian inputs. One variation uses reverse channel models with minimum mean square error (MMSE) estimates that give the largest rates but are challenging to optimize. A second variation uses forward channel models with linear MMSE estimates that are easier to optimize. Both model classes are applied to channels where the receiver is unaware of the CSIT and for which adaptive codewords achieve capacity. The forward model inputs are chosen as linear functions of the adaptive codeword's entries to simplify the analysis. For scalar channels, the maximum GMI is then achieved by a conventional codebook, where the amplitude and phase of each channel symbol are modified based on the CSIT. The GMI increases by partitioning the channel output alphabet and using a different auxiliary model for each partition subset. The partitioning also helps to determine the capacity scaling at high and low signal-to-noise ratios. A class of power control policies is described for partial CSIR, including a MMSE policy for full CSIT. Several examples of fading channels with AWGN illustrate the theory, focusing on on-off fading and Rayleigh fading. The capacity results generalize to block fading channels with in-block feedback, including capacity expressions in terms of mutual and directed information.
Erasure of Unaligned Attributes from Neural Representations
Venue: arXiv cs.CL 2023
Year: 2023

We present the Assignment-Maximization Spectral Attribute removaL (AMSAL) algorithm, which erases information from neural representations when the information to be erased is implicit rather than directly being aligned to each input example. Our algorithm works by alternating between two steps. In one, it finds an assignment of the input representations to the information to be erased, and in the other, it creates projections of both the input representations and the information to be erased into a joint latent space. We test our algorithm on an extensive array of datasets, including a Twitter dataset with multiple guarded attributes, the BiasBios dataset and the BiasBench benchmark. The last benchmark includes four datasets with various types of protected attributes. Our results demonstrate that bias can often be removed in our setup. We also discuss the limitations of our approach when there is a strong entanglement between the main task and the information to be erased.
Reliable and Unreliable Sources in Age-Based Gossiping
Venue: arXiv cs.IT 2023
Year: 2023

We consider a network consisting of $n$ nodes that aim to track a continually updating process or event. To disseminate updates about the event to the network, two sources are available, such that information obtained from one source is considered more reliable than the other source. The nodes wish to have access to information about the event that is not only latest but also more reliable, and prefer a reliable packet over an unreliable packet even when the former is a bit outdated with respect to the latter. We study how such preference affects the fraction of users with reliable information in the network and their version age of information. We derive the analytical equations to characterize the two quantities, long-term expected fraction of nodes with reliable packets and their long-term expected version age using stochastic hybrid systems (SHS) modelling and study their properties. We also compare these results with the case where nodes give more preference to freshness of information than its reliability. Finally we show simulation results to verify the theoretical results and shed further light on behavior of above quantities with respect to dependent variables.