OpenRaiser/Intern-Atlas huggingface dataset:
Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments
Venue: arXiv cs.LG 2021
Year: 2021

This work presents a fully data-driven, black-box pipeline to obtain an optimal control policy for a multi-loop building control problem based on historical building and weather data, thus without the need for complex physics-based modelling. We demonstrate the method for joint control of room temperature and bidirectional EV charging to maximize the occupant thermal comfort and energy savings while leaving enough energy in the EV battery for the next trip. We modelled the room temperature with a recurrent neural network and EV charging with a piece-wise linear function. Using these models as a simulation environment, we applied a deep reinforcement learning (DRL) algorithm to obtain an optimal control policy. The learnt policy achieves on average 17% energy savings over the heating season and 19% better comfort satisfaction than a standard RB room temperature controller. When a bidirectional EV is additionally connected and a two-tariff electricity pricing is applied, the MIMO DRL policy successfully leverages the battery and decreases the overall cost of electricity compared to two standard RB controllers, one controlling the room temperature and another controlling the bidirectional EV (dis-)charging. Finally, we demonstrate a successful transfer of the learnt DRL policy from simulation onto a real building, the DFAB HOUSE at Empa Duebendorf in Switzerland, achieving up to 30% energy savings while maintaining similar comfort levels compared to a conventional RB room temperature controller over three weeks during the heating season.
Investigations on Audiovisual Emotion Recognition in Noisy Conditions
Venue: arXiv cs.SD 2021
Year: 2021

In this paper we explore audiovisual emotion recognition under noisy acoustic conditions with a focus on speech features. We attempt to answer the following research questions: (i) How does speech emotion recognition perform on noisy data? and (ii) To what extend does a multimodal approach improve the accuracy and compensate for potential performance degradation at different noise levels? We present an analytical investigation on two emotion datasets with superimposed noise at different signal-to-noise ratios, comparing three types of acoustic features. Visual features are incorporated with a hybrid fusion approach: The first neural network layers are separate modality-specific ones, followed by at least one shared layer before the final prediction. The results show a significant performance decrease when a model trained on clean audio is applied to noisy data and that the addition of visual features alleviates this effect.
Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices
Venue: arXiv cs.CV 2021
Year: 2021

Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place using visual information under environmental, viewpoint and appearance changes. An emerging trend in VPR is the use of sequence-based filtering methods on top of single-frame-based place matching techniques for route-based navigation. The combination leads to varying levels of potential place matching performance boosts at increased computational costs. This raises a number of interesting research questions: How does performance boost (due to sequential filtering) vary along the entire spectrum of single-frame-based matching methods? How does sequence matching length affect the performance curve? Which specific combinations provide a good trade-off between performance and computation? However, there is lack of previous work looking at these important questions and most of the sequence-based filtering work to date has been used without a systematic approach. To bridge this research gap, this paper conducts an in-depth investigation of the relationship between the performance of single-frame-based place matching techniques and the use of sequence-based filtering on top of those methods. It analyzes individual trade-offs, properties and limitations for different combinations of single-frame-based and sequential techniques. A number of state-of-the-art VPR methods and widely used public datasets are utilized to present the findings that contain a number of meaningful insights for the VPR community.
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making
Venue: arXiv cs.HC 2021
Year: 2021

Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts - who often have no expertise in ML or data science - are asked to use ML predictions to make high-stakes decisions. Multiple ML usability challenges can appear as result, such as lack of user trust in the model, inability to reconcile human-ML disagreement, and ethical concerns about oversimplification of complex problems to a single algorithm output. In this paper, we investigate the ML usability challenges that present in the domain of child welfare screening through a series of collaborations with child welfare screeners. Following the iterative design process between the ML scientists, visualization researchers, and domain experts (child screeners), we first identified four key ML challenges and honed in on one promising explainable ML technique to address them (local factor contributions). Then we implemented and evaluated our visual analytics tool, Sibyl, to increase the interpretability and interactivity of local factor contributions. The effectiveness of our tool is demonstrated by two formal user studies with 12 non-expert participants and 13 expert participants respectively. Valuable feedback was collected, from which we composed a list of design implications as a useful guideline for researchers who aim to develop an interpretable and interactive visualization tool for ML prediction models deployed for child welfare screeners and other similar domain experts.
Information-Centric Networking in Wireless Environments: Security Risks and Challenges
Venue: arXiv cs.NI 2021
Year: 2021

Information-Centric Networking (ICN) has emerged as a paradigm to cope with the lack of built-in security primitives and efficient mechanisms for content distribution of today's Internet. However, deploying ICN in a wireless environment poses a different set of challenges compared to a wired environment, especially when it comes to security. In this paper, we present the security issues that may arise and the attacks that may occur from different points of view when ICN is deployed in wireless environments. The discussed attacks may target both applications and the ICN network itself by exploiting elements of the ICN architecture, such as content names and in-network content caches. Furthermore, we discuss potential solutions to the presented issues and countermeasures to the presented attacks. Finally, we identify future research opportunities and directions.
Differentiable Neural Architecture Learning for Efficient Neural Network Design
Venue: arXiv cs.LG 2021
Year: 2021

Automated neural network design has received ever-increasing attention with the evolution of deep convolutional neural networks (CNNs), especially involving their deployment on embedded and mobile platforms. One of the biggest problems that neural architecture search (NAS) confronts is that a large number of candidate neural architectures are required to train, using, for instance, reinforcement learning and evolutionary optimisation algorithms, at a vast computation cost. Even recent differentiable neural architecture search (DNAS) samples a small number of candidate neural architectures based on the probability distribution of learned architecture parameters to select the final neural architecture. To address this computational complexity issue, we introduce a novel \emph{architecture parameterisation} based on scaled sigmoid function, and propose a general \emph{Differentiable Neural Architecture Learning} (DNAL) method to optimize the neural architecture without the need to evaluate candidate neural networks. Specifically, for stochastic supernets as well as conventional CNNs, we build a new channel-wise module layer with the architecture components controlled by a scaled sigmoid function. We train these neural network models from scratch. The network optimization is decoupled into the weight optimization and the architecture optimization. We address the non-convex optimization problem of neural architecture by the continuous scaled sigmoid method with convergence guarantees. Extensive experiments demonstrate our DNAL method delivers superior performance in terms of neural architecture search cost. The optimal networks learned by DNAL surpass those produced by the state-of-the-art methods on the benchmark CIFAR-10 and ImageNet-1K dataset in accuracy, model size and computational complexity.
VELOC: VEry Low Overhead Checkpointing in the Age of Exascale
Venue: arXiv cs.DC 2021
Year: 2021

Checkpointing large amounts of related data concurrently to stable storage is a common I/O pattern of many HPC applications. However, such a pattern frequently leads to I/O bottlenecks that lead to poor scalability and performance. As modern HPC infrastructures continue to evolve, there is a growing gap between compute capacity vs. I/O capabilities. Furthermore, the storage hierarchy is becoming increasingly heterogeneous: in addition to parallel file systems, it comprises burst buffers, key-value stores, deep memory hierarchies at node level, etc. In this context, state of art is insufficient to deal with the diversity of vendor APIs, performance and persistency characteristics. This extended abstract presents an overview of VeloC (Very Low Overhead Checkpointing System), a checkpointing runtime specifically design to address these challenges for the next generation Exascale HPC applications and systems. VeloC offers a simple API at user level, while employing an advanced multi-level resilience strategy that transparently optimizes the performance and scalability of checkpointing by leveraging heterogeneous storage.
Leading or Following? Dyadic Robot Imitative Interaction Using the Active Inference Framework
Venue: arXiv cs.RO 2021
Year: 2021

This study investigated how social interaction among robotic agents changes dynamically depending on the individual belief of action intention. In a set of simulation studies, we examine dyadic imitative interactions of robots using a variational recurrent neural network model. The model is based on the free energy principle such that a pair of interacting robots find themselves in a loop, attempting to predict and infer each other's actions using active inference. We examined how regulating the complexity term to minimize free energy determines the dynamic characteristics of networks and interactions. When one robot trained with tighter regulation and another trained with looser regulation interact, the latter tends to lead the interaction by exerting stronger action intention, while the former tends to follow by adapting to its observations. The study confirms that the dyadic imitative interaction becomes successful by achieving a high synchronization rate when a leader and a follower are determined by developing action intentions with strong belief and weak belief, respectively.
A Deep and Wide Neural Network-based Model for Rajasthan Summer Monsoon Rainfall (RSMR) Prediction
Venue: arXiv cs.LG 2021
Year: 2021

Importance of monsoon rainfall cannot be ignored as it affects round the year activities ranging from agriculture to industrial. Accurate rainfall estimation and prediction is very helpful in decision making in the sectors of water resource management and agriculture. Due to dynamic nature of monsoon rainfall, it's accurate prediction becomes very challenging task. In this paper, we analyze and evaluate various deep learning approaches such as one dimensional Convolutional Neutral Network, Multi-layer Perceptron and Wide Deep Neural Networks for the prediction of summer monsoon rainfall in Indian state of Rajasthan.For our analysis purpose we have used two different types of datasets for our experiments. From IMD grided dataset, rainfall data of 484 coordinates are selected which lies within the geographical boundaries of Rajasthan. We have also collected rainfall data of 158 rain gauge station from water resources department. The comparison of various algorithms on both these data sets is presented in this paper and it is found that Deep Wide Neural Network based model outperforms the other two approaches.
Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement
Venue: arXiv cs.CV 2021
Year: 2021

With the growing demand for hand hygiene and convenience of use, palmprint recognition with touchless manner made a great development recently, providing an effective solution for person identification. Despite many efforts that have been devoted to this area, it is still uncertain about the discriminative ability of the contactless palmprint, especially for large-scale datasets. To tackle the problem, in this paper, we build a large-scale touchless palmprint dataset containing 2334 palms from 1167 individuals. To our best knowledge, it is the largest contactless palmprint image benchmark ever collected with regard to the number of individuals and palms. Besides, we propose a novel deep learning framework for touchless palmprint recognition named 3DCPN (3D Convolution Palmprint recognition Network) which leverages 3D convolution to dynamically integrate multiple Gabor features. In 3DCPN, a novel variant of Gabor filter is embedded into the first layer for enhancement of curve feature extraction. With a well-designed ensemble scheme,low-level 3D features are then convolved to extract high-level features. Finally on the top, we set a region-based loss function to strengthen the discriminative ability of both global and local descriptors. To demonstrate the superiority of our method, extensive experiments are conducted on our dataset and other popular databases TongJi and IITD, where the results show the proposed 3DCPN achieves state-of-the-art or comparable performances.
Distributed statistical inference with pyhf enabled through funcX
Venue: arXiv cs.DC 2021
Year: 2021

In High Energy Physics facilities that provide High Performance Computing environments provide an opportunity to efficiently perform the statistical inference required for analysis of data from the Large Hadron Collider, but can pose problems with orchestration and efficient scheduling. The compute architectures at these facilities do not easily support the Python compute model, and the configuration scheduling of batch jobs for physics often requires expertise in multiple job scheduling services. The combination of the pure-Python libraries pyhf and funcX reduces the common problem in HEP analyses of performing statistical inference with binned models, that would traditionally take multiple hours and bespoke scheduling, to an on-demand (fitting) "function as a service" that can scalably execute across workers in just a few minutes, offering reduced time to insight and inference. We demonstrate execution of a scalable workflow using funcX to simultaneously fit 125 signal hypotheses from a published ATLAS search for new physics using pyhf with a wall time of under 3 minutes. We additionally show performance comparisons for other physics analyses with openly published probability models and argue for a blueprint of fitting as a service systems at HPC centers.
An Iterative Contextualization Algorithm with Second-Order Attention
Venue: arXiv cs.CL 2021
Year: 2021

Combining the representations of the words that make up a sentence into a cohesive whole is difficult, since it needs to account for the order of words, and to establish how the words present relate to each other. The solution we propose consists in iteratively adjusting the context. Our algorithm starts with a presumably erroneous value of the context, and adjusts this value with respect to the tokens at hand. In order to achieve this, representations of words are built combining their symbolic embedding with a positional encoding into single vectors. The algorithm then iteratively weighs and aggregates these vectors using our novel second-order attention mechanism. Our models report strong results in several well-known text classification tasks.
Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation
Venue: arXiv cs.CL 2021
Year: 2021

Linear embedding transformation has been shown to be effective for zero-shot cross-lingual transfer tasks and achieve surprisingly promising results. However, cross-lingual embedding space mapping is usually studied in static word-level embeddings, where a space transformation is derived by aligning representations of translation pairs that are referred from dictionaries. We move further from this line and investigate a contextual embedding alignment approach which is sense-level and dictionary-free. To enhance the quality of the mapping, we also provide a deep view of properties of contextual embeddings, i.e., anisotropy problem and its solution. Experiments on zero-shot dependency parsing through the concept-shared space built by our embedding transformation substantially outperform state-of-the-art methods using multilingual embeddings.
LQResNet: A Deep Neural Network Architecture for Learning Dynamic Processes
Venue: arXiv cs.LG 2021
Year: 2021

Mathematical modeling is an essential step, for example, to analyze the transient behavior of a dynamical process and to perform engineering studies such as optimization and control. With the help of first-principles and expert knowledge, a dynamic model can be built, but for complex dynamic processes, appearing, e.g., in biology, chemical plants, neuroscience, financial markets, this often remains an onerous task. Hence, data-driven modeling of the dynamics process becomes an attractive choice and is supported by the rapid advancement in sensor and measurement technology. A data-driven approach, namely operator inference framework, models a dynamic process, where a particular structure of the nonlinear term is assumed. In this work, we suggest combining the operator inference with certain deep neural network approaches to infer the unknown nonlinear dynamics of the system. The approach uses recent advancements in deep learning and possible prior knowledge of the process if possible. We also briefly discuss several extensions and advantages of the proposed methodology. We demonstrate that the proposed methodology accomplishes the desired tasks for dynamics processes encountered in neural dynamics and the glycolytic oscillator.
The Ultimate Weapon for Ultra-Broadband 6G: Digital Beamforming and Doubly Massive mmWave MIMO
Venue: arXiv cs.IT 2021
Year: 2021

The use of millimeter waves for wireless communications is one of the main technological innovations of 5G systems with respect to previous generations of cellular systems. Their consideration, however, has been up to now mainly restricted to the case in which analog or, at most, hybrid analog-digital beamforming structures were used, thus posing a limitation on the multiplexing capabilities and peak data rates that could be theoretically achieved at these frequencies. Recent progress in the field of electronics, however, has made the energy consumption of digital beamforming structures at least on par with that of analog beamforming, thus redeeming them from the ghetto they had been placed in over the last year. Digital beamforming, coupled with the use of large antenna arrays at both sides of the communication link, promises thus to be one of the secret weapons of future 6G networks, capable of unleashing unprecedented values of spectral and energy efficiency for ultra-broadband connectivity.
TaskAllocator: A Recommendation Approach for Role-based Tasks Allocation in Agile Software Development
Venue: arXiv cs.SE 2021
Year: 2021

In this paper, we propose a recommendation approach -- TaskAllocator -- in order to predict the assignment of incoming tasks to potential befitting roles. The proposed approach, identifying team roles rather than individual persons, allows project managers to perform better tasks allocation in case the individual developers are over-utilized or moved on to different roles/projects. We evaluated our approach on ten agile case study projects obtained from the Taiga.io repository. In order to determine the TaskAllocator's performance, we have conducted a benchmark study by comparing it with contemporary machine learning models. The applicability of the TaskAllocator was assessed through a plugin that can be integrated with JIRA and provides recommendations about suitable roles whenever a new task is added to the project. Lastly, the source code of the plugin and the dataset employed have been made public.
Reinforcement Learning with External Knowledge by using Logical Neural Networks
Venue: arXiv cs.AI 2021
Year: 2021

Conventional deep reinforcement learning methods are sample-inefficient and usually require a large number of training trials before convergence. Since such methods operate on an unconstrained action set, they can lead to useless actions. A recent neuro-symbolic framework called the Logical Neural Networks (LNNs) can simultaneously provide key-properties of both neural networks and symbolic logic. The LNNs functions as an end-to-end differentiable network that minimizes a novel contradiction loss to learn interpretable rules. In this paper, we utilize LNNs to define an inference graph using basic logical operations, such as AND and NOT, for faster convergence in reinforcement learning. Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide. Our results empirically demonstrate that our method converges faster compared to a model-free reinforcement learning method that doesn't have such logical constraints.
Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning
Venue: arXiv cs.CV 2021
Year: 2021

We present a neural optimization model trained with reinforcement learning to solve the coordinate ordering problem for sets of star glyphs. Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set. To find the optimal coordinate order for the given set, we train a neural network using reinforcement learning to reward orderings with high silhouette coefficients. The network consists of an encoder and a decoder with an attention mechanism. The encoder employs a recurrent neural network (RNN) to encode input shape and class information, while the decoder together with the attention mechanism employs another RNN to output a sequence with the new coordinate order. In addition, we introduce a neural network to efficiently estimate the similarity between shape context descriptors, which allows to speed up the computation of silhouette coefficients and thus the training of the axis ordering network. Two user studies demonstrate that the orders provided by our method are preferred by users for perceiving class separation. We tested our model on different settings to show its robustness and generalization abilities and demonstrate that it allows to order input sets with unseen data size, data dimension, or number of classes. We also demonstrate that our model can be adapted to coordinate ordering of other types of plots such as RadViz by replacing the proposed shape-aware silhouette coefficient with the corresponding quality metric to guide network training.