OpenRaiser/Intern-Atlas huggingface dataset:
Reconfiguring Wireless Environment via Intelligent Surfaces for 6G: Reflection, Modulation, and Security
Venue: arXiv cs.IT 2022
Year: 2022

Reconfigurable intelligent surface (RIS) has been recognized as an essential enabling technique for the sixth-generation (6G) mobile communication network. Specifically, an RIS is comprised of a large number of small and low-cost reflecting elements whose parameters are dynamically adjustable with a programmable controller. Each of these elements can effectively reflect a phase-shifted version of the incident electromagnetic wave. By adjusting the wave phases in real time, the propagation environment of the reflected signals can be dynamically reconfigured to enhance communication reliability, boost transmission rate, expand cellular coverage, and strengthen communication security. In this paper, we provide an overview on RIS-assisted wireless communications. Specifically, we elaborate on the state-of-the-art enabling techniques of RISs as well as their corresponding substantial benefits from the perspectives of RIS reflection and RIS modulation. With these benefits, we envision the integration of RIS into emerging applications for 6G. In addition, communication security is of unprecedented importance in the 6G network with ubiquitous wireless services in multifarious verticals and areas. We highlight potential contributions of RIS to physical-layer security in terms of secrecy rate and secrecy outage probability, exemplified by a typical case study from both theoretical and numerical aspects. Finally, we discuss challenges and opportunities on the deployment of RISs in practice to motivate future research.
A Review of Federated Learning in Energy Systems
Venue: arXiv cs.CR 2022
Year: 2022

With increasing concerns for data privacy and ownership, recent years have witnessed a paradigm shift in machine learning (ML). An emerging paradigm, federated learning (FL), has gained great attention and has become a novel design for machine learning implementations. FL enables the ML model training at data silos under the coordination of a central server, eliminating communication overhead and without sharing raw data. In this paper, we conduct a review of the FL paradigm and, in particular, compare the types, the network structures, and the global model aggregation methods. Then, we conducted a comprehensive review of FL applications in the energy domain (refer to the smart grid in this paper). We provide a thematic classification of FL to address a variety of energy-related problems, including demand response, identification, prediction, and federated optimizations. We describe the taxonomy in detail and conclude with a discussion of various aspects, including challenges, opportunities, and limitations in its energy informatics applications, such as energy system modeling and design, privacy, and evolution.
Gender Representation in Brazilian Computer Science Conferences
Venue: arXiv cs.DL 2022
Year: 2022

This study presents an automated bibliometric analysis of 6569 research papers published in thirteen Brazilian Computer Science Society (SBC) conferences from 1999 to 2021. Our primary goal was to gather data to understand the gender representation in publications in the field of Computer Science. We applied a systematic assignment of gender to 23.573 listed papers authorships, finding that the gender gap for women is significant, with female authors being under-represented in all years of the study.
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models
Venue: arXiv cs.AI 2022
Year: 2022

Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) tasks such as link prediction; i.e., answering (h; p; ?) or (?; p; t) queries. Such models are usually evaluated with averaged metrics on a held-out test set. While useful for tracking progress, averaged single-score metrics cannot reveal what exactly a model has learned -- or failed to learn. To address this issue, we propose KGxBoard: an interactive framework for performing fine-grained evaluation on meaningful subsets of the data, each of which tests individual and interpretable capabilities of a KGC model. In our experiments, we highlight the findings that we discovered with the use of KGxBoard, which would have been impossible to detect with standard averaged single-score metrics.
CrossA11y: Identifying Video Accessibility Issues via Cross-modal Grounding
Venue: arXiv cs.HC 2022
Year: 2022

Authors make their videos visually accessible by adding audio descriptions (AD), and auditorily accessible by adding closed captions (CC). However, creating AD and CC is challenging and tedious, especially for non-professional describers and captioners, due to the difficulty of identifying accessibility problems in videos. A video author will have to watch the video through and manually check for inaccessible information frame-by-frame, for both visual and auditory modalities. In this paper, we present CrossA11y, a system that helps authors efficiently detect and address visual and auditory accessibility issues in videos. Using cross-modal grounding analysis, CrossA11y automatically measures accessibility of visual and audio segments in a video by checking for modality asymmetries. CrossA11y then displays these segments and surfaces visual and audio accessibility issues in a unified interface, making it intuitive to locate, review, script AD/CC in-place, and preview the described and captioned video immediately. We demonstrate the effectiveness of CrossA11y through a lab study with 11 participants, comparing to existing baseline.
Strong XOR Lemma for Communication with Bounded Rounds
Venue: arXiv cs.CC 2022
Year: 2022

In this paper, we prove a strong XOR lemma for bounded-round two-player randomized communication. For a function $f:\mathcal{X}\times \mathcal{Y}\rightarrow\{0,1\}$, the $n$-fold XOR function $f^{\oplus n}:\mathcal{X}^n\times \mathcal{Y}^n\rightarrow\{0,1\}$ maps $n$ input pairs $(X_1,\ldots,X_n,Y_1,\ldots,Y_n)$ to the XOR of the $n$ output bits $f(X_1,Y_1)\oplus \cdots \oplus f(X_n, Y_n)$. We prove that if every $r$-round communication protocols that computes $f$ with probability $2/3$ uses at least $C$ bits of communication, then any $r$-round protocol that computes $f^{\oplus n}$ with probability $1/2+\exp(-O(n))$ must use $n\cdot \left(r^{-O(r)}\cdot C-1\right)$ bits. When $r$ is a constant and $C$ is sufficiently large, this is $Ω(n\cdot C)$ bits. It matches the communication cost and the success probability of the trivial protocol that computes the $n$ bits $f(X_i,Y_i)$ independently and outputs their XOR, up to a constant factor in $n$. A similar XOR lemma has been proved for $f$ whose communication lower bound can be obtained via bounding the discrepancy [Shaltiel'03]. By the equivalence between the discrepancy and the correlation with $2$-bit communication protocols [Viola-Wigderson'08], our new XOR lemma implies the previous result.
Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis
Venue: arXiv cs.AR 2022
Year: 2022

Graphics processing units (GPUs) are now considered the leading hardware to accelerate general-purpose workloads such as AI, data analytics, and HPC. Over the last decade, researchers have focused on demystifying and evaluating the microarchitecture features of various GPU architectures beyond what vendors reveal. This line of work is necessary to understand the hardware better and build more efficient workloads and applications. Many works have studied the recent Nvidia architectures, such as Volta and Turing, comparing them to their successor, Ampere. However, some microarchitecture features, such as the clock cycles for the different instructions, have not been extensively studied for the Ampere architecture. In this paper, we study the clock cycles per instructions with various data types found in the instruction-set architecture (ISA) of Nvidia GPUs. Using microbenchmarks, we measure the clock cycles for PTX ISA instructions and their SASS ISA instructions counterpart. we further calculate the clock cycle needed to access each memory unit. We also demystify the new version of the tensor core unit found in the Ampere architecture by using the WMMA API and measuring its clock cycles per instruction and throughput for the different data types and input shapes. The results found in this work should guide software developers and hardware architects. Furthermore, the clock cycles per instructions are widely used by performance modeling simulators and tools to model and predict the performance of the hardware.
Investigating the Requirements for Building a Blockchain Simulator for IoT Applications
Venue: arXiv cs.CR 2022
Year: 2022

The pervasiveness of the Internet of Things (IoT) has enabled the administration of a large number of intelligent devices. However, IoT is based on centralised models, which introduce a number of problems, such as a single point of failure and security risks. Blockchain may offer a viable option for addressing these concerns. Practically, both blockchain and IoT are complex technologies posing further challenges in assessing application performance. The availability of a reliable simulation environment for Blockchain based IoT applications would be a major aid in the development and evaluation of such applications. Our research has found that currently there are no simulators with a comprehensive set of features, for the development and evaluation of blockchain based IoT applications, which is the main motivation for our work. The purpose of this study is to gather the opinions of experts regarding the creation of a simulation environment for IoT based blockchain applications. To do this, we utilise two separate investigations. First, a questionnaire is developed to ensure that the development of such simulation software would be of significant use. Second, interviews with participants are performed to gain their perspectives on the primary issues they face with blockchain-based IoT applications. In addition, the interviews focused on collecting the perspectives of participants on how blockchain may improve IoT and how to identify blockchain's applicability in IoT. Our findings demonstrate that the participants had a great deal of confidence in blockchain to resolve IoT issues. However, they lack the tools necessary to assess this concept. This highlights their requirement for a simulator to analyse the integration of blockchain and IoT.
Accelerating Monte-Carlo Tree Search on CPU-FPGA Heterogeneous Platform
Venue: arXiv cs.DC 2022
Year: 2022

Monte Carlo Tree Search (MCTS) methods have achieved great success in many Artificial Intelligence (AI) benchmarks. The in-tree operations become a critical performance bottleneck in realizing parallel MCTS on CPUs. In this work, we develop a scalable CPU-FPGA system for Tree-Parallel MCTS. We propose a novel decomposition and mapping of MCTS data structure and computation onto CPU and FPGA to reduce communication and coordination. High scalability of our system is achieved by encapsulating in-tree operations in an SRAM-based FPGA accelerator. To lower the high data access latency and inter-worker synchronization overheads, we develop several hardware optimizations. We show that by using our accelerator, we obtain up to $35\times$ speedup for in-tree operations, and $3\times$ higher overall system throughput. Our CPU-FPGA system also achieves superior scalability wrt number of parallel workers than state-of-the-art parallel MCTS implementations on CPU.
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis
Venue: arXiv cs.LG 2022
Year: 2022

In dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients. Federated learning (FL) can use decentralized data to train models while keeping data local. Existing FL methods assume all the data have labels. However, medical data often comes without full labels due to high labeling costs. Self-supervised learning (SSL) methods, contrastive learning (CL) and masked autoencoders (MAE), can leverage the unlabeled data to pre-train models, followed by fine-tuning with limited labels. However, combining SSL and FL has unique challenges. For example, CL requires diverse data but each device only has limited data. For MAE, while Vision Transformer (ViT) based MAE has higher accuracy over CNNs in centralized learning, MAE's performance in FL with unlabeled data has not been investigated. Besides, the ViT synchronization between the server and clients is different from traditional CNNs. Therefore, special synchronization methods need to be designed. In this work, we propose two federated self-supervised learning frameworks for dermatological disease diagnosis with limited labels. The first one features lower computation costs, suitable for mobile devices. The second one features high accuracy and fits high-performance servers. Based on CL, we proposed federated contrastive learning with feature sharing (FedCLF). Features are shared for diverse contrastive information without sharing raw data for privacy. Based on MAE, we proposed FedMAE. Knowledge split separates the global and local knowledge learned from each client. Only global knowledge is aggregated for higher generalization performance. Experiments on dermatological disease datasets show superior accuracy of the proposed frameworks over state-of-the-arts.
ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels
Venue: arXiv cs.LG 2022
Year: 2022

Existing works on anomaly detection (AD) rely on clean labels from human annotators that are expensive to acquire in practice. In this work, we propose a method to leverage weak/noisy labels (e.g., risk scores generated by machine rules for detecting malware) that are cheaper to obtain for anomaly detection. Specifically, we propose ADMoE, the first framework for anomaly detection algorithms to learn from noisy labels. In a nutshell, ADMoE leverages mixture-of-experts (MoE) architecture to encourage specialized and scalable learning from multiple noisy sources. It captures the similarities among noisy labels by sharing most model parameters, while encouraging specialization by building "expert" sub-networks. To further juice out the signals from noisy labels, ADMoE uses them as input features to facilitate expert learning. Extensive results on eight datasets (including a proprietary enterprise security dataset) demonstrate the effectiveness of ADMoE, where it brings up to 34% performance improvement over not using it. Also, it outperforms a total of 13 leading baselines with equivalent network parameters and FLOPS. Notably, ADMoE is model-agnostic to enable any neural network-based detection methods to handle noisy labels, where we showcase its results on both multiple-layer perceptron (MLP) and the leading AD method DeepSAD.
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey
Venue: arXiv cs.CV 2022
Year: 2022

State-of-the-art deep learning models are often trained with a large amount of costly labeled training data. However, requiring exhaustive manual annotations may degrade the model's generalizability in the limited-label regime. Semi-supervised learning and unsupervised learning offer promising paradigms to learn from an abundance of unlabeled visual data. Recent progress in these paradigms has indicated the strong benefits of leveraging unlabeled data to improve model generalization and provide better model initialization. In this survey, we review the recent advanced deep learning algorithms on semi-supervised learning (SSL) and unsupervised learning (UL) for visual recognition from a unified perspective. To offer a holistic understanding of the state-of-the-art in these areas, we propose a unified taxonomy. We categorize existing representative SSL and UL with comprehensive and insightful analysis to highlight their design rationales in different learning scenarios and applications in different computer vision tasks. Lastly, we discuss the emerging trends and open challenges in SSL and UL to shed light on future critical research directions.
$\fracρ{1-ε}$-approximate pure Nash equilibria algorithms for weighted congestion games and their runtimes
Venue: arXiv cs.GT 2022
Year: 2022

This paper concerns computing approximate pure Nash equilibria in weighted congestion games, which has been shown to be PLS-complete. With the help of $\hatΨ$-game and approximate potential functions, we propose two algorithms based on best response dynamics, and prove that they efficiently compute $\fracρ{1-ε}$-approximate pure Nash equilibria for $ρ= d!$ and $ρ=\frac{2\cdot W\cdot(d+1)}{2\cdot W+d+1}\le {d + 1}$, respectively, when the weighted congestion game has polynomial latency functions of degree at most $d \ge 1$ and players' weights are bounded from above by a constant $W \ge 1$. This improves the recent work of Feldotto et al.[2017] and Giannakopoulos et al. [2022] that showed efficient algorithms for computing $d^{d+o(d)}$-approximate pure Nash equilibria.
K-Order Graph-oriented Transformer with GraAttention for 3D Pose and Shape Estimation
Venue: arXiv cs.CV 2022
Year: 2022

We propose a novel attention-based 2D-to-3D pose estimation network for graph-structured data, named KOG-Transformer, and a 3D pose-to-shape estimation network for hand data, named GASE-Net. Previous 3D pose estimation methods have focused on various modifications to the graph convolution kernel, such as abandoning weight sharing or increasing the receptive field. Some of these methods employ attention-based non-local modules as auxiliary modules. In order to better model the relationship between nodes in graph-structured data and fuse the information of different neighbor nodes in a differentiated way, we make targeted modifications to the attention module and propose two modules designed for graph-structured data, graph relative positional encoding multi-head self-attention (GR-MSA) and K-order graph-oriented multi-head self-attention (KOG-MSA). By stacking GR-MSA and KOG-MSA, we propose a novel network KOG-Transformer for 2D-to-3D pose estimation. Furthermore, we propose a network for shape estimation on hand data, called GraAttention shape estimation network (GASE-Net), which takes a 3D pose as input and gradually models the shape of the hand from sparse to dense. We have empirically shown the superiority of KOG-Transformer through extensive experiments. Experimental results show that KOG-Transformer significantly outperforms the previous state-of-the-art methods on the benchmark dataset Human3.6M. We evaluate the effect of GASE-Net on two public available hand datasets, ObMan and InterHand2.6M. GASE-Net can predict the corresponding shape for input pose with strong generalization ability.
Time-to-Green predictions for fully-actuated signal control systems with supervised learning
Venue: arXiv cs.LG 2022
Year: 2022

Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications.
Appraisal of a Random Bit Generator Utilizing Smartphone Sensors as Entropy Source
Venue: arXiv cs.CR 2022
Year: 2022

We aim to access entropy sources available within smartphones in order to construct and evaluate a random number generator which is competitive in comparison with existing and proven random number generators. A prototype utilizing the herein proposed algorithm shall generate data that can be tested against the Statistical Test Suit provided by NIST. Although our initial intention of using cosmic radiation failed, we were able to extract randomness from incoming video and audio sources. We found that it is possible to access these sources of entropy utilizing sensors from smartphones resulting in 15 out of 15 successful passed tests within the Statistical Test Suit. We also found that wrong methods of sensor data collection using our prototype eventually generates weak random numbers and fails NIST's Statistical Test Suit. Finally, we suggest that in order to reach the initial goal of providing a smartphone-based true nondeterministic random number generator the detection of muons shall be researched.
Matching Theory-based Recommender Systems in Online Dating
Venue: arXiv cs.IR 2022
Year: 2022

Online dating platforms provide people with the opportunity to find a partner. Recommender systems in online dating platforms suggest one side of users to the other side of users. We discuss the potential interactions between reciprocal recommender systems (RRSs) and matching theory. We present our ongoing project to deploy a matching theory-based recommender system (MTRS) in a real-world online dating platform.
Efficient Data-Driven Network Functions
Venue: arXiv cs.NI 2022
Year: 2022

Cloud environments require dynamic and adaptive networking policies. It is preferred to use heuristics over advanced learning algorithms in Virtual Network Functions (VNFs) in production becuase of high-performance constraints. This paper proposes Aquarius to passively yet efficiently gather observations and enable the use of machine learning to collect, infer, and supply accurate networking state information-without incurring additional signalling and management overhead. This paper illustrates the use of Aquarius with a traffic classifier, an autoscaling system, and a load balancer-and demonstrates the use of three different machine learning paradigms-unsupervised, supervised, and reinforcement learning, within Aquarius, for inferring network state. Testbed evaluations show that Aquarius increases network state visibility and brings notable performance gains with low overhead.
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations
Venue: arXiv cs.HC 2022
Year: 2022

Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with "black-box" models that are unable to present the detected patterns in a human-readable way. Technical developments recently led to eXplainable Artificial Intelligence (XAI) techniques that aim to open such black-boxes and enable humans to gain new insights from detected patterns. We investigated the application of XAI in an area where specific insights can have a significant effect on consumer behaviour, namely electricity use. Knowing that specific feedback on individuals' electricity consumption triggers resource conservation, we created five visualizations with ML and XAI methods from electricity consumption time series for highly personalized feedback, considering existing domain-specific design knowledge. Our experimental evaluation with 152 participants showed that humans can assimilate the pattern displayed by XAI visualizations, but such visualizations should follow known visualization patterns to be well-understood by users.