OpenRaiser/Intern-Atlas huggingface dataset:
Generating Connected, Simple, and Realistic Cyber Graphs for Smart Grids
Venue: arXiv cs.SI 2022
Year: 2022

Smart grids integrate communication systems with power networks to enable power grids operation and command through real-time data collection and control signals. Designing, analyzing, and simulating smart grid infrastructures as well as predicting the impact of power network failures strongly depend on the topologies of the underlying power network and communication system. Despite the substantial impact that the communication systems bring to smart grid operation, the topology of communication systems employed in smart grids was less studied. The power community lacks realistic generative communication system models that can be calibrated to match real-world data. To address this issue, this paper proposes a framework to generate the underlying topological graphs for the communication systems deployed in smart grids by mimicking the topology of real-world smart grids. In this regard, we have updated the Chung-Lu algorithm to guarantee the communication network connectivity and to match the degree distribution of a real-world smart grid rather than following an expected degree distribution. In addition, key characteristics of communication systems such as diameter, average shortest paths, clustering coefficients, assortativity, and spectral gap were taken into consideration to generate the most similar real-world communication network for smart grid studies. We believe that the proposed algorithm to generate realistic cyber graphs for smart grid studies will benefit the power community.
Toward Experience-Driven Traffic Management and Orchestration in Digital-Twin-Enabled 6G Networks
Venue: arXiv cs.NI 2022
Year: 2022

The envisioned 6G networks are expected to support extremely high data rates, low-latency, and radically new applications empowered by machine learning. The futuristic 6G networks require a novel framework that can be used to operate, manage, and optimize its underlying services such as ultra-reliable and low-latency communication, and Internet of everything. In recent years, artificial intelligence (AI) has demonstrated significant success in optimizing and designing networks. The AI-enabled traffic orchestration can dynamically organize different network architectures and slices to provide quality of experience considering the dynamic nature of the wireless communication network. In this paper, we propose a digital twin enabled network framework, empowered by AI to cater the variability and complexity of envisioned 6G networks, to provide smart resource management and intelligent service provisioning. Digital twin paves a way for achieving optimizing 6G services by creating a virtual representation of the 6G network along with its associated communication technologies (e.g., intelligent reflecting surfaces, terahertz and millimeter communication), computing systems (e.g., cloud computing and fog computing) with its associated algorithms (e.g., optimization and machine learning). We then discuss and review the existing AI-enabled traffic management and orchestration techniques and highlight future research directions and potential solutions in 6G networks.
Effects of Virtual Room Size and Objects on Relative Translation Gain Thresholds in Redirected Walking
Venue: arXiv cs.HC 2022
Year: 2022

This paper investigates how the size of virtual space and objects within it affect the threshold range of relative translation gains, a Redirected Walking (RDW) technique that scales the user's movement in virtual space in different ratios for the width and depth. While previous studies assert that a virtual room's size affects relative translation gain thresholds on account of the virtual horizon's location, additional research is needed to explore this assumption through a structured approach to visual perception in Virtual Reality (VR). We estimate the relative translation gain thresholds in six spatial conditions configured by three room sizes and the presence of virtual objects (3 X 2), which were set according to differing Angles of Declination (AoDs) between eye-gaze and the forward-gaze. Results show that both size and virtual objects significantly affect the threshold range, it being greater in the large-sized condition and furnished condition. This indicates that the effect of relative translation gains can be further increased by constructing a perceived virtual movable space that is even larger than the adjusted virtual movable space and placing objects in it. Our study can be applied to adjust virtual spaces in synchronizing heterogeneous spaces without coordinate distortion where real and virtual objects can be leveraged to create realistic mutual spaces.
Predicting Terrorist Attacks in the United States using Localized News Data
Venue: arXiv cs.LG 2022
Year: 2022

Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. Toward the end of better understanding and mitigating these attacks, we present a set of machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model--a Random Forest that learns from a novel variable-length moving average representation of the feature space--achieves area under the receiver operating characteristic scores $> .667$ on four of the five states that were impacted most by terrorism between 2015 and 2018. Our key findings include that modeling terrorism as a set of independent events, rather than as a continuous process, is a fruitful approach--especially when the events are sparse and dissimilar. Additionally, our results highlight the need for localized models that account for differences between locations. From a machine learning perspective, we found that the Random Forest model outperformed several deep models on our multimodal, noisy, and imbalanced data set, thus demonstrating the efficacy of our novel feature representation method in such a context. We also show that its predictions are relatively robust to time gaps between attacks and observed characteristics of the attacks. Finally, we analyze factors that limit model performance, which include a noisy feature space and small amount of available data. These contributions provide an important foundation for the use of machine learning in efforts against terrorism in the United States and beyond.
Coverage Path Planning for Robotic Quality Inspection with Control on Measurement Uncertainty
Venue: arXiv cs.RO 2022
Year: 2022

The optical scanning gauges mounted on the robots are commonly used in quality inspection, such as verifying the dimensional specification of sheet structures. Coverage path planning (CPP) significantly influences the accuracy and efficiency of robotic quality inspection. Traditional CPP strategies focus on minimizing the number of viewpoints or traveling distance of robots under the condition of full coverage inspection. The measurement uncertainty when collecting the scanning data is less considered in the free-form surface inspection. To address this problem, a novel CPP method with the optimal viewpoint sampling strategy is proposed to incorporate the measurement uncertainty of key measurement points (MPs) into free-form surface inspection. At first, the feasible ranges of measurement uncertainty are calculated based on the tolerance specifications of the MPs. The initial feasible viewpoint set is generated considering the measurement uncertainty and the visibility of MPs. Then, the inspection cost function is built to evaluate the number of selected viewpoints and the average measurement uncertainty in the field of views (FOVs) of all the selected viewpoints. Afterward, an enhanced rapidly-exploring random tree (RRT*) algorithm is proposed for viewpoint sampling using the inspection cost function and CPP optimization. Case studies, including simulation tests and inspection experiments, have been conducted to evaluate the effectiveness of the proposed method. Results show that the scanning precision of key MPs is significantly improved compared with the benchmark method.
Weighted Sum Rate Maximization of the mmWave Cell-Free MIMO Downlink Relying on Hybrid Precoding
Venue: arXiv cs.IT 2022
Year: 2022

The cell-free MIMO concept relying on hybrid precoding constitutes an innovative technique capable of dramatically increasing the network capacity of millimeter-wave (mmWave) communication systems. It dispenses with the cell boundary of conventional multi-cell MIMO systems, while drastically reducing the power consumption by limiting the number of radio frequency (RF) chains at the access points (APs). In this paper, we aim for maximizing the weighted sum rate (WSR) of mmWave cell-free MIMO systems by conceiving a low-complexity hybrid precoding algorithm. We formulate the WSR optimization problem subject to the transmit power constraint for each AP and the constant-modulus constraint for the phase shifters of the analog precoders. A block coordinate descent (BCD) algorithm is proposed for iteratively solving the problem. In each iteration, the classic Lagrangian multiplier method and the penalty dual decomposition (PDD) method are combined for obtaining near-optimal hybrid analog/digital precoding matrices. Furthermore, we extend our proposed algorithm for deriving closed-form expressions for the precoders of fully digital cell-free MIMO systems. Moreover, we present the convergency analysis and complexity analysis of our proposed method. Finally, our simulation results demonstrate the superiority of the algorithms proposed for both fully digital and hybrid precoding matrices.
An Efficient and Adaptive Granular-ball Generation Method in Classification Problem
Venue: arXiv cs.LG 2022
Year: 2022

Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. This paper proposes a method for accelerating the granular-ball generation using the division to replace $k$-means. It can greatly improve the efficiency of granular-ball generation while ensuring the accuracy similar to the existing method. Besides, a new adaptive method for the granular-ball generation is proposed by considering granular-ball's overlap eliminating and some other factors. This makes the granular-ball generation process of parameter-free and completely adaptive in the true sense. In addition, this paper first provides the mathematical models for the granular-ball covering. The experimental results on some real data sets demonstrate that the proposed two granular-ball generation methods have similar accuracies with the existing method while adaptiveness or acceleration is realized.
Diffix Elm: Simple Diffix
Venue: arXiv cs.CR 2022
Year: 2022

Historically, strong data anonymization requires substantial domain expertise and custom design for the given data set and use case. Diffix is an anonymization framework designed to make strong data anonymization available to non-experts. This paper describes Diffix Elm, a version of Diffix that is very easy to use at the expense of query features. We describe Diffix Elm, and show that it provides strong anonymity based on the General Data Protection Regulation (GDPR) criteria. This document is the third version of Diffix Elm. The second version added ceiling, round, and bucket\_width functions (in addition to floor). This document adds the ability to protect multiple different kinds of protected entities (a feature not found in earlier versions of Diffix). It also adds counting distinct values for any column (rather than only the AID column).
Blockchain software patterns for the design of decentralized applications: A systematic literature review
Venue: arXiv cs.SE 2022
Year: 2022

A software pattern is a reusable solution to address a commonly occurring problem within a given context when designing software. Using patterns is a common practice for software architects to ensure software quality. Many pattern collections have been proposed for a large number of application domains. However, because of the technology's recentness, there are only a few available collections with a lack of extensive testing in industrial blockchain applications. It is also difficult for software architects to adequately apply blockchain patterns in their applications, as it requires deep knowledge of blockchain technology. Through a systematic literature review, this paper has identified 120 unique blockchain-related patterns and proposes a pattern taxonomy composed of multiple categories, built from the extracted pattern collection. The purpose of this collection is to map, classify, and describe all available patterns across the literature to help readers make adequate decisions regarding blockchain pattern selection. This study also shows potential applications of those patterns and identifies the relationships between blockchain patterns and other non-blockchain software patterns.
Enlightening Flash Storage to Stream Writes by Objects
Venue: arXiv cs.DB 2022
Year: 2022

For a write request, today flash storage cannot distinguish the logical object it comes from. In such object-oblivious flash devices, concurrent writes from different objects are simply packed in their arrival order to flash memory blocks; hence objects with different lifetimes are multiplexed onto the same flash blocks. This multiplexing incurs write amplification, worsening the performance. Tackling the multiplexing problem, we propose a novel interface for flash storage, FlashAlloc. It is used to pass the logical address ranges of logical objects to the flash storage and thus enlighten the storage to stream writes by objects. The object-aware flash storage can de-multiplex writes from different objects with distinct deathtimes into per-object dedicated flash blocks. Given that popular data stores separate writes using objects (e.g., SSTables in RocksDB), we can achieve, unlike the existing solutions, transparent write streaming just by calling FlashAlloc upon object creation. Our experimental results using an open-source SSD prototype demonstrate that FlashAlloc can reduce write amplification factor (WAF) in RocksDB, F2FS, and MySQL by 1.5, 2.5, and 0.3, respectively and thus improve throughput by 2x, 1.8x, and 1.2x, respectively. In particular, FlashAlloc will mitigate the interference among multitenants. When RocksDB and MySQL were run together on the same SSD, FlashAlloc decreased WAF from 4.2 to 2.5 and doubled their throughputs.
Topic Modeling on Podcast Short-Text Metadata
Venue: arXiv cs.IR 2022
Year: 2022

Podcasts have emerged as a massively consumed online content, notably due to wider accessibility of production means and scaled distribution through large streaming platforms. Categorization systems and information access technologies typically use topics as the primary way to organize or navigate podcast collections. However, annotating podcasts with topics is still quite problematic because the assigned editorial genres are broad, heterogeneous or misleading, or because of data challenges (e.g. short metadata text, noisy transcripts). Here, we assess the feasibility to discover relevant topics from podcast metadata, titles and descriptions, using topic modeling techniques for short text. We also propose a new strategy to leverage named entities (NEs), often present in podcast metadata, in a Non-negative Matrix Factorization (NMF) topic modeling framework. Our experiments on two existing datasets from Spotify and iTunes and Deezer, a new dataset from an online service providing a catalog of podcasts, show that our proposed document representation, NEiCE, leads to improved topic coherence over the baselines. We release the code for experimental reproducibility of the results.
Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?
Venue: arXiv cs.LG 2022
Year: 2022

In practice, it is very demanding and sometimes impossible to collect datasets of tagged data large enough to successfully train a machine learning model, and one possible solution to this problem is transfer learning. This study aims to assess how transferable are the features between different domains of time series data and under which conditions. The effects of transfer learning are observed in terms of predictive performance of the models and their convergence rate during training. In our experiment, we use reduced data sets of 1,500 and 9,000 data instances to mimic real world conditions. Using the same scaled-down datasets, we trained two sets of machine learning models: those that were trained with transfer learning and those that were trained from scratch. Four machine learning models were used for the experiment. Transfer of knowledge was performed within the same domain of application (seismology), as well as between mutually different domains of application (seismology, speech, medicine, finance). We observe the predictive performance of the models and the convergence rate during the training. In order to confirm the validity of the obtained results, we repeated the experiments seven times and applied statistical tests to confirm the significance of the results. The general conclusion of our study is that transfer learning is very likely to either increase or not negatively affect the predictive performance of the model or its convergence rate. The collected data is analysed in more details to determine which source and target domains are compatible for transfer of knowledge. We also analyse the effect of target dataset size and the selection of model and its hyperparameters on the effects of transfer learning.
ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming
Venue: arXiv cs.MM 2022
Year: 2022

As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive bitrate (ABR) algorithms with a better understanding of network behavior and access to radio and player metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming with Machine Learning. ECAS-ML focuses on managing the tradeoff among bitrate, segment switches, and stalls to achieve a higher quality of experience (QoE). For that purpose, we use machine learning techniques to analyze radio throughput traces and predict the best parameters of our algorithm to achieve better performance. The results show that ECAS-ML outperforms other client-based and edge-based ABR algorithms.
Transmission Scheme, Detection and Power Allocation for Uplink User Cooperation with NOMA and RSMA
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we propose two novel cooperative-non-orthogonal-multiple-access (C-NOMA) and cooperative-rate-splitting-multiple-access (C-RSMA) schemes for uplink user cooperation. At the first mini-slot of these schemes, each user transmits its signal and receives the transmitted signal of the other user in full-duplex mode, and at the second mini-slot, each user relays the other user's message with amplify-and-forward (AF) protocol. At both schemes, to achieve better spectral efficiency, users transmit signals in the non-orthogonal mode in both mini-slots. In C-RSMA, we also apply the rate-splitting method in which the message of each user is divided into two streams. In the proposed detection schemes for C-NOMA and C-RSMA, we apply a combination of maximum-ratio-combining (MRC) and successive-interference-cancellation (SIC). Then, we derive the achievable rates for C-NOMA and C-RSMA, and formulate two optimization problems to maximize the minimum rate of two users by considering the proportional fairness coefficient. We propose two power allocation algorithms based on successive-convex-approximation (SCA) and geometric-programming (GP) to solve these non-convex problems. Next, we derive the asymptotic outage probability of the proposed C-NOMA and C-RSMA schemes, and prove that they achieve diversity order of two. Finally, the above-mentioned performance is confirmed by simulations.
Virtual Elastic Objects
Venue: arXiv cs.CV 2022
Year: 2022

We present Virtual Elastic Objects (VEOs): virtual objects that not only look like their real-world counterparts but also behave like them, even when subject to novel interactions. Achieving this presents multiple challenges: not only do objects have to be captured including the physical forces acting on them, then faithfully reconstructed and rendered, but also plausible material parameters found and simulated. To create VEOs, we built a multi-view capture system that captures objects under the influence of a compressed air stream. Building on recent advances in model-free, dynamic Neural Radiance Fields, we reconstruct the objects and corresponding deformation fields. We propose to use a differentiable, particle-based simulator to use these deformation fields to find representative material parameters, which enable us to run new simulations. To render simulated objects, we devise a method for integrating the simulation results with Neural Radiance Fields. The resulting method is applicable to a wide range of scenarios: it can handle objects composed of inhomogeneous material, with very different shapes, and it can simulate interactions with other virtual objects. We present our results using a newly collected dataset of 12 objects under a variety of force fields, which will be shared with the community.
Revelation of Task Difficulty in AI-aided Education
Venue: arXiv cs.HC 2022
Year: 2022

When a student is asked to perform a given task, her subjective estimate of the difficulty of that task has a strong influence on her performance. There exists a rich literature on the impact of perceived task difficulty on performance and motivation. Yet, there is another topic that is closely related to the subject of the influence of perceived task difficulty that did not receive any attention in previous research - the influence of revealing the true difficulty of a task to the student. This paper investigates the impact of revealing the task difficulty on the student's performance, motivation, self-efficacy and subjective task value via an experiment in which workers are asked to solve matchstick riddles. Furthermore, we discuss how the experiment results might be relevant for AI-aided education. Specifically, we elaborate on the question of how a student's learning experience might be improved by supporting her with two types of AI systems: an AI system that predicts task difficulty and an AI system that determines when task difficulty should be revealed and when not.
Grassmannian Shape Representations for Aerodynamic Applications
Venue: arXiv cs.GR 2022
Year: 2022

Airfoil shape design is a classical problem in engineering and manufacturing. Our motivation is to combine principled physics-based considerations for the shape design problem with modern computational techniques informed by a data-driven approach. Traditional analyses of airfoil shapes emphasize a flow-based sensitivity to deformations which can be represented generally by affine transformations (rotation, scaling, shearing, translation). We present a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data, (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes.