OpenRaiser/Intern-Atlas huggingface dataset:
An Efficient HTN to STRIPS Encoding for Concurrent Plans
Venue: arXiv cs.AI 2022
Year: 2022

The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems in terms of decompositions of tasks into subtaks. Many techniques have been proposed to solve such hierarchical planning problems. A particular technique is to encode hierarchical planning problems as classical STRIPS planning problems. One advantage of this technique is to benefit directly from the constant improvements made by STRIPS planners. However, there are still few effective and expressive encodings. In this paper, we present a new HTN to STRIPS encoding allowing to generate concurrent plans. We show experimentally that this encoding outperforms previous approaches on hierarchical IPC benchmarks.
A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus
Venue: arXiv cs.DC 2022
Year: 2022

In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners. In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload, thus the computation power of miners will not purely be spent on the hash puzzle. In this way, the hardware and energy will support the blockchain service and deep learning training simultaneously. While the incentive of miners is to earn tokens, individual miners are motivated to join mining pools to become more competitive. In this paper, we are the first to demonstrate a mining pool solution for novel consensuses based on deep learning. The mining pool manager partitions the full searching space into subspaces and all miners are scheduled to collaborate on the Neural Architecture Search (NAS) tasks in the assigned subspace. Experiments demonstrate that the performance of this type of mining pool is more competitive than an individual miner. Due to the uncertainty of miners' behaviors, the mining pool manager checks the standard deviation of the performance of high reward miners and prepares backup miners to ensure the completion of the tasks of high reward miners.
Cloud Computing -- Everything As A Service
Venue: arXiv cs.DC 2022
Year: 2022

Compute infrastructure hosted by a cloud provider allows an application to scale without limit. The application developer no longer needs to worry about the up-front investment in a server farm provisioned for a worst-case load scenario. However, managing cloud deployments requires a sophisticated framework that can autoscale the infrastructure and guarantee the up-time of running container images. This paper surveys existing research addressing the management and orchestration of cloud deployments as well as the modelling framework to abstract away the low-level details of the host infrastructure. We investigate blockchain distributed ledgers, quantum computing and Internet of Things application stacks to show how they can utilize cloud deployments.
Generalized Nash Equilibrium Models for Asymmetric, Non-cooperative Games on Line Graphs: Application to Water Resource Systems
Venue: arXiv cs.GT 2022
Year: 2022

This paper investigates the game theory of resource-allocation situations where the "first come, first serve" heuristic creates inequitable, asymmetric benefits to the players. Specifically, this problem is formulated as a Generalized Nash Equilibrium Model where the players are arranged sequentially along a directed line graph. The goal of the model is to reduce the asymmetric benefits among the players using a policy instrument. It serves as a more realistic, alternative approach to the line-graph models considered in the cooperative game-theoretic literature. An application-oriented formulation is also developed for water resource systems. The players in this model are utilities who withdraw water and are arranged along a river basin from upstream to downstream. This model is applied to a stylized, three-node model as well as a test bed in the Duck River Basin in Tennessee, USA. Based on the results, a non-cooperative, water-release market can be an acceptable policy instrument according to metrics traditionally used in cooperative game theory
TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose Estimation
Venue: arXiv cs.CV 2022
Year: 2022

3D hand pose estimation methods have made significant progress recently. However, the estimation accuracy is often far from sufficient for specific real-world applications, and thus there is significant room for improvement. This paper proposes TriHorn-Net, a novel model that uses specific innovations to improve hand pose estimation accuracy on depth images. The first innovation is the decomposition of the 3D hand pose estimation into the estimation of 2D joint locations in the depth image space (UV), and the estimation of their corresponding depths aided by two complementary attention maps. This decomposition prevents depth estimation, which is a more difficult task, from interfering with the UV estimations at both the prediction and feature levels. The second innovation is PixDropout, which is, to the best of our knowledge, the first appearance-based data augmentation method for hand depth images. Experimental results demonstrate that the proposed model outperforms the state-of-the-art methods on three public benchmark datasets. Our implementation is available at https://github.com/mrezaei92/TriHorn-Net.
Potentials and Limits of Using Preconfigured Spatial Beams as Bandwidth Resources: Beam Selection vs Beam Aggregation
Venue: arXiv cs.IT 2022
Year: 2022

This letter studies how to use spatial beams pre-configured in a legacy spatial division multiple access (SDMA) network as bandwidth resources via the implementation of non-orthogonal multiple access (NOMA). Two different beam management schemes, namely beam selection and beam aggregation, are developed to improve the overall system throughput without consuming extra spectrum or changing the performance of the legacy network. Analytical and simulation results are presented to show that the two schemes realize different tradeoffs between system performance and complexity.
Edge Security: Challenges and Issues
Venue: arXiv cs.CR 2022
Year: 2022

Edge computing is a paradigm that shifts data processing services to the network edge, where data are generated. While such an architecture provides faster processing and response, among other benefits, it also raises critical security issues and challenges that must be addressed. This paper discusses the security threats and vulnerabilities emerging from the edge network architecture spanning from the hardware layer to the system layer. We further discuss privacy and regulatory compliance challenges in such networks. Finally, we argue the need for a holistic approach to analyze edge network security posture, which must consider knowledge from each layer.
Understanding Narratives through Dimensions of Analogy
Venue: arXiv cs.AI 2022
Year: 2022

Analogical reasoning is a powerful qualitative reasoning tool that enables humans to connect two situations, and to generalize their knowledge from familiar to novel situations. Cognitive Science research provides valuable insights into the richness and complexity of analogical reasoning, together with implementations of expressive analogical reasoners with limited scalability. Modern scalable AI techniques with the potential to reason by analogy have been only applied to the special case of proportional analogy, and not to understanding higher-order analogies. In this paper, we aim to bridge the gap by: 1) formalizing six dimensions of analogy based on mature insights from Cognitive Science research, 2) annotating a corpus of fables with each of these dimensions, and 3) defining four tasks with increasing complexity that enable scalable evaluation of AI techniques. Experiments with language models and neuro-symbolic AI reasoners on these tasks reveal that state-of-the-art methods can be applied to reason by analogy with a limited success, motivating the need for further research towards comprehensive and scalable analogical reasoning by AI. We make all our code and data available.
Measuring Representational Harms in Image Captioning
Venue: arXiv cs.CY 2022
Year: 2022

Previous work has largely considered the fairness of image captioning systems through the underspecified lens of "bias." In contrast, we present a set of techniques for measuring five types of representational harms, as well as the resulting measurements obtained for two of the most popular image captioning datasets using a state-of-the-art image captioning system. Our goal was not to audit this image captioning system, but rather to develop normatively grounded measurement techniques, in turn providing an opportunity to reflect on the many challenges involved. We propose multiple measurement techniques for each type of harm. We argue that by doing so, we are better able to capture the multi-faceted nature of each type of harm, in turn improving the (collective) validity of the resulting measurements. Throughout, we discuss the assumptions underlying our measurement approach and point out when they do not hold.
Streaming non-autoregressive model for any-to-many voice conversion
Venue: arXiv cs.SD 2022
Year: 2022

Voice conversion models have developed for decades, and current mainstream research focuses on non-streaming voice conversion. However, streaming voice conversion is more suitable for practical application scenarios than non-streaming voice conversion. In this paper, we propose a streaming any-to-many voice conversion based on fully non-autoregressive model, which includes a streaming transformer based acoustic model and a streaming vocoder. Streaming transformer based acoustic model is composed of a pre-trained encoder from streaming end-to-end based automatic speech recognition model and a decoder modified on FastSpeech blocks. Streaming vocoder is designed for streaming task with pseudo quadrature mirror filter bank and causal convolution. Experimental results show that the proposed method achieves significant performance both in latency and conversion quality and can be real-time on CPU and GPU.
Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlook
Venue: arXiv cs.DB 2022
Year: 2022

Knowledge management systems (KMS) are in high demand for industrial researchers, chemical or research enterprises, or evidence-based decision making. However, existing systems have limitations in categorizing and organizing paper insights or relationships. Traditional databases are usually disjoint with logging systems, which limit its utility in generating concise, collated overviews. In this work, we briefly survey existing approaches of this problem space and propose a unified framework that utilizes relational databases to log hierarchical information to facilitate the research and writing process, or generate useful knowledge from references or insights from connected concepts. Our framework of bidirectional knowledge management system (BKMS) enables novel functionalities encompassing improved hierarchical note-taking, AI-assisted brainstorming, and multi-directional relationships. Potential applications include managing inventories and changes for manufacture or research enterprises, or generating analytic reports with evidence-based decision making.
A Continuous Risk Assessment Methodology for Cloud Infrastructures
Venue: arXiv cs.CR 2022
Year: 2022

Cloud systems are dynamic environments which make it difficult to keep track of security risks that resources are exposed to. Traditionally, risk assessment is conducted for individual assets to evaluate existing threats; their results, however, are quickly outdated in such a dynamic environment. In this paper, we propose an adaptation of the traditional risk assessment methodology for cloud infrastructures which loosely couples manual, in-depth analyses with continuous, automatic application of their results. These two parts are linked by a novel threat profile definition that allows to reusably describe configuration weaknesses based on properties that are common across assets and cloud providers. This way, threats can be identified automatically for all resources that exhibit the same properties, including new and modified ones. We also present a prototype implementation which automatically evaluates an infrastructure as code template of a cloud system against a set of threat profiles, and we evaluate its performance. Our methodology not only enables organizations to reuse their threat analysis results, but also to collaborate on their development, e.g. with the public community. To that end, we propose an initial open-source repository of threat profiles.
Cautious Learning of Multiattribute Preferences
Venue: arXiv cs.AI 2022
Year: 2022

This paper is dedicated to a cautious learning methodology for predicting preferences between alternatives characterized by binary attributes (formally, each alternative is seen as a subset of attributes). By "cautious", we mean that the model learned to represent the multi-attribute preferences is general enough to be compatible with any strict weak order on the alternatives, and that we allow ourselves not to predict some preferences if the data collected are not compatible with a reliable prediction. A predicted preference will be considered reliable if all the simplest models (following Occam's razor principle) explaining the training data agree on it. Predictions are based on an ordinal dominance relation between alternatives [Fishburn and LaValle, 1996]. The dominance relation relies on an uncertainty set encompassing the possible values of the parameters of the multi-attribute utility function. Numerical tests are provided to evaluate the richness and the reliability of the predictions made.
Indexing Temporal Relations for Range-Duration Queries
Venue: arXiv cs.DB 2022
Year: 2022

Temporal information plays a crucial role in many database applications, however support for queries on such data is limited. We present an index structure, termed RD-index, to support range-duration queries over interval timestamped relations, which constrain both the range of the tuples' positions on the timeline and their duration. RD-index is a grid structure in the two-dimensional space, representing the position on the timeline and the duration of timestamps, respectively. Instead of using a regular grid, we consider the data distribution for the construction of the grid in order to ensure that each grid cell contains approximately the same number of intervals. RD-index features provable bounds on the running time of all the operations, allow for a simple implementation, and supports very predictable query performance. We benchmark our solution on a variety of datasets and query workloads, investigating both the query rate and the behavior of the individual queries. The results show that RD-index performs better than the baselines on range-duration queries, for which it is explicitly designed. Furthermore, it outperforms specialized indexes also on workloads containing queries constraining either only the duration or the range.
Investigating Multi-Feature Selection and Ensembling for Audio Classification
Venue: arXiv cs.SD 2022
Year: 2022

Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some interesting patterns--particularly in classification of audio data. For better performance of audio classification, feature selection and combination play a key role as they have the potential to make or break the performance of any DL model. To investigate this role, we conduct an extensive evaluation of the performance of several cutting-edge DL models (i.e., Convolutional Neural Network, EfficientNet, MobileNet, Supper Vector Machine and Multi-Perceptron) with various state-of-the-art audio features (i.e., Mel Spectrogram, Mel Frequency Cepstral Coefficients, and Zero Crossing Rate) either independently or as a combination (i.e., through ensembling) on three different datasets (i.e., Free Spoken Digits Dataset, Audio Urdu Digits Dataset, and Audio Gujarati Digits Dataset). Overall, results suggest feature selection depends on both the dataset and the model. However, feature combinations should be restricted to the only features that already achieve good performances when used individually (i.e., mostly Mel Spectrogram, Mel Frequency Cepstral Coefficients). Such feature combination/ensembling enabled us to outperform the previous state-of-the-art results irrespective of our choice of DL model.
Body Gesture Recognition to Control a Social Robot
Venue: arXiv cs.RO 2022
Year: 2022

In this work, we propose a gesture based language to allow humans to interact with robots using their body in a natural way. We have created a new gesture detection model using neural networks and a custom dataset of humans performing a set of body gestures to train our network. Furthermore, we compare body gesture communication with other communication channels to acknowledge the importance of adding this knowledge to robots. The presented approach is extensively validated in diverse simulations and real-life experiments with non-trained volunteers. This attains remarkable results and shows that it is a valuable framework for social robotics applications, such as human robot collaboration or human-robot interaction.
Learn to Adapt: Robust Drift Detection in Security Domain
Venue: arXiv cs.CR 2022
Year: 2022

Deploying robust machine learning models has to account for concept drifts arising due to the dynamically changing and non-stationary nature of data. Addressing drifts is particularly imperative in the security domain due to the ever-evolving threat landscape and lack of sufficiently labeled training data at the deployment time leading to performance degradation. Recently proposed concept drift detection methods in literature tackle this problem by identifying the changes in feature/data distributions and periodically retraining the models to learn new concepts. While these types of strategies should absolutely be conducted when possible, they are not robust towards attacker-induced drifts and suffer from a delay in detecting new attacks. We aim to address these shortcomings in this work. we propose a robust drift detector that not only identifies drifted samples but also discovers new classes as they arrive in an on-line fashion. We evaluate the proposed method with two security-relevant data sets -- network intrusion data set released in 2018 and APT Command and Control dataset combined with web categorization data. Our evaluation shows that our drifting detection method is not only highly accurate but also robust towards adversarial drifts and discovers new classes from drifted samples.
Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning
Venue: arXiv cs.LG 2022
Year: 2022

This paper proposes using communication pipelining to enhance the wireless spectrum utilization efficiency and convergence speed of federated learning in mobile edge computing applications. Due to limited wireless sub-channels, a subset of the total clients is scheduled in each iteration of federated learning algorithms. On the other hand, the scheduled clients wait for the slowest client to finish its computation. We propose to first cluster the clients based on the time they need per iteration to compute the local gradients of the federated learning model. Then, we schedule a mixture of clients from all clusters to send their local updates in a pipelined manner. In this way, instead of just waiting for the slower clients to finish their computation, more clients can participate in each iteration. While the time duration of a single iteration does not change, the proposed method can significantly reduce the number of required iterations to achieve a target accuracy. We provide a generic formulation for optimal client clustering under different settings, and we analytically derive an efficient algorithm for obtaining the optimal solution. We also provide numerical results to demonstrate the gains of the proposed method for different datasets and deep learning architectures.
Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Object detection and semantic segmentation with the 3D lidar point cloud data require expensive annotation. We propose a data augmentation method that takes advantage of already annotated data multiple times. We propose an augmentation framework that reuses real data, automatically finds suitable placements in the scene to be augmented, and handles occlusions explicitly. Due to the usage of the real data, the scan points of newly inserted objects in augmentation sustain the physical characteristics of the lidar, such as intensity and raydrop. The pipeline proves competitive in training top-performing models for 3D object detection and semantic segmentation. The new augmentation provides a significant performance gain in rare and essential classes, notably 6.65% average precision gain for "Hard" pedestrian class in KITTI object detection or 2.14 mean IoU gain in the SemanticKITTI segmentation challenge over the state of the art.
Searching Entangled Program Spaces
Venue: arXiv cs.PL 2022
Year: 2022

Many problem domains, including program synthesis and rewrite-based optimization, require searching astronomically large spaces of programs. Existing approaches often rely on building specialized data structures -- version-space algebras, finite tree automata, or e-graphs -- to compactly represent these programs. To find a compact representation, existing data structures exploit independence of subterms; they blow up when the choices of subterms are entangled. We introduce equality-constrained tree automata (ECTAs), a generalization of the three aforementioned data structures that can efficiently represent large spaces of programs with entangled subterms. We present efficient algorithms for extracting programs from ECTAs, implemented in a performant Haskell library, \texttt{ecta}. Using \texttt{ecta} we construct \textsc{Hectare}, a type-driven program synthesizer for Haskell. \textsc{Hectare} significantly outperforms a state-of-the-art synthesizer Hoogle+ -- providing an average speedup of 8x -- despite its implementation being an order of magnitude smaller.
TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models
Venue: arXiv cs.CL 2022
Year: 2022

Transferring knowledge from one domain to another is of practical importance for many tasks in natural language processing, especially when the amount of available data in the target domain is limited. In this work, we propose a novel few-shot approach to domain adaptation in the context of Named Entity Recognition (NER). We propose a two-step approach consisting of a variable base module and a template module that leverages the knowledge captured in pre-trained language models with the help of simple descriptive patterns. Our approach is simple yet versatile and can be applied in few-shot and zero-shot settings. Evaluating our lightweight approach across a number of different datasets shows that it can boost the performance of state-of-the-art baselines by 2-5% F1-score.