OpenRaiser/Intern-Atlas huggingface dataset:
Over-the-Air Consensus for Distributed Vehicle Platooning Control (Extended version)
Venue: arXiv cs.IT 2022
Year: 2022

A distributed control of vehicle platooning is referred to as distributed consensus (DC) since many autonomous vehicles (AVs) reach a consensus to move as one body with the same velocity and inter-distance. For DC control to be stable, other AVs' real-time position information should be inputted to each AV's controller via vehicle-to-vehicle (V2V) communications. On the other hand, too many V2V links should be simultaneously established and frequently retrained, causing frequent packet loss and longer communication latency. We propose a novel DC algorithm called over-the-air consensus (AirCons), a joint communication-and-control design with two key features to overcome the above limitations. First, exploiting a wireless signal's superposition and broadcasting properties renders all AVs' signals to converge to a specific value proportional to participating AVs' average position without individual V2V channel information. Second, the estimated average position is used to control each AV's dynamics instead of each AV's individual position. Through analytic and numerical studies, the effectiveness of the proposed AirCons designed on the state-of-the-art New Radio architecture is verified by showing a $14.22\%$ control gain compared to the benchmark without the average position.
Re-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms
Venue: arXiv cs.NE 2022
Year: 2022

For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training the networks (adjusting the weights), designing network topology, optimizing global parameters, and selecting features. Here, we provide a systematic brief survey about applications of the EAs on the specific domain of the recurrent NNs named Reservoir Computing (RC). At the beginning of the 2000s, the RC paradigm appeared as a good option for employing recurrent NNs without dealing with the inconveniences of the training algorithms. RC models use a nonlinear dynamic system, with fixed recurrent neural network named the \textit{reservoir}, and learning process is restricted to adjusting a linear parametric function. %so the performance of learning is fast and precise. However, an RC model has several hyper-parameters, therefore EAs are helpful tools to figure out optimal RC architectures. We provide an overview of the results on the area, discuss novel advances, and we present our vision regarding the new trends and still open questions.
BCIM: Efficient Implementation of Binary Neural Network Based on Computation in Memory
Venue: arXiv cs.ET 2022
Year: 2022

Applications of Binary Neural Networks (BNNs) are promising for embedded systems with hard constraints on computing power. Contrary to conventional neural networks with the floating-point datatype, BNNs use binarized weights and activations which additionally reduces memory requirements. Memristors, emerging non-volatile memory devices, show great potential as the target implementation platform for BNNs by integrating storage and compute units. The energy and performance improvements are mainly due to 1) accelerating matrix-matrix multiplication as the main kernel for BNNs, 2) diminishing memory bottleneck in von-Neumann architectures, 3) and bringing massive parallelization. However, the efficiency of this hardware highly depends on how the network is mapped and executed on these devices. In this paper, we propose an efficient implementation of XNOR-based BNN to maximize parallelization while using a simple sensing scheme to generate activation values. Besides, a new mapping is introduced to minimize the overhead of data communication between convolution layers mapped to different memristor crossbars. This comes with extensive analytical and simulation-based analysis to evaluate the implication of different design choices considering the accuracy of the network. The results show that our approach achieves up to $10\times$ energy-saving and $100\times$ improvement in latency compared to the state-of-the-art in-memory hardware design.
Can one hear the position of nodes?
Venue: arXiv cs.SI 2022
Year: 2022

Wave propagation through nodes and links of a network forms the basis of spectral graph theory. Nevertheless, the sound emitted by nodes within the resonating chamber formed by a network are not well studied. The sound emitted by vibrations of individual nodes reflects the structure of the overall network topology but also the location of the node within the network. In this article, a sound recognition neural network is trained to infer centrality measures from the nodes' wave-forms. In addition to advancing network representation learning, sounds emitted by nodes are plausible in most cases. Auralization of the network topology may open new directions in arts, competing with network visualization.
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
Venue: arXiv cs.IR 2022
Year: 2022

With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry. Even though progress on improving model design has been rapid in research, we argue that many advances fail to translate into practice because of two limiting assumptions. First, most approaches focus on a transductive learning setting which cannot handle unseen users or items and second, many existing methods are developed for static settings that cannot incorporate new data as it becomes available. We argue that these are largely impractical assumptions on real-world platforms where new user interactions happen in real time. In this survey paper, we formalize both concepts and contextualize recommender systems work from the last six years. We then discuss why and how future work should move towards inductive learning and incremental updates for recommendation model design and evaluation. In addition, we present best practices and fundamental open challenges for future research.
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations. Many SOTA methods fuse different sources of information (user, item, knowledge graph, tags, etc.) into a graph and use Graph Neural Networks to introduce the auxiliary information through the message passing paradigm. In this work, we seek an alternative framework that is light and effective through self-supervised learning across different sources of information, particularly for the commonly accessible item tag information. We use a self-supervision signal to pair users with the auxiliary information associated with the items they have interacted with before. To achieve the pairing, we create a proxy training task. For a given item, the model predicts the correct pairing between the representations obtained from the users that have interacted with this item and the assigned tags. This design provides an efficient solution, using the auxiliary information directly to enhance the quality of user and item embeddings. User behavior in recommendation systems is driven by the complex interactions of many factors behind the decision-making processes. To make the pairing process more fine-grained and avoid embedding collapse, we propose an intent-aware self-supervised pairing process where we split the user embeddings into multiple sub-embedding vectors. Each sub-embedding vector captures a specific user intent via self-supervised alignment with a particular cluster of tags. We integrate our designed framework with various recommendation models, demonstrating its flexibility and compatibility. Through comparison with numerous SOTA methods on seven real-world datasets, we show that our method can achieve better performance while requiring less training time. This indicates the potential of applying our approach on web-scale datasets.
The BlackParrot BedRock Cache Coherence System
Venue: arXiv cs.AR 2022
Year: 2022

This paper presents BP-BedRock, the open-source cache coherence protocol and system implemented within the BlackParrot 64-bit RISC-V multicore processor. BP-BedRock implements the BedRock directory-based MOESIF cache coherence protocol and includes two different open-source coherence protocol engines, one FSM-based and the other microcode programmable. Both coherence engines support coherent uncacheable access to cacheable memory and L1-based atomic read-modify-write operations. Fitted within the BlackParrot multicore, BP-BedRock has been silicon validated in a GlobalFoundries 12nm FinFET process and FPGA validated with both coherence engines in 8-core configurations, booting Linux and running off the shelf benchmarks. After describing BP-BedRock and the design of the two coherence engines, we study their performance by analyzing processing occupancy and running the Splash-3 benchmarks on the 8-core FPGA implementations. Careful design and coherence-specific ISA extensions enable the programmable controller to achieve performance within 1% of the fixed-function FSM controller on average (2.3% worst-case) as demonstrated on our FPGA test system. Analysis shows that the programmable coherence engine increases die area by only 4% in an ASIC process and increases logic utilization by only 6.3% on FPGA with one additional block RAM added per core.
STAR: A Session-Based Time-Aware Recommender System
Venue: arXiv cs.IR 2022
Year: 2022

Session-Based Recommenders (SBRs) aim to predict users' next preferences regard to their previous interactions in sessions while there is no historical information about them. Modern SBRs utilize deep neural networks to map users' current interest(s) during an ongoing session to a latent space so that their next preference can be predicted. Although state-of-art SBR models achieve satisfactory results, most focus on studying the sequence of events inside sessions while ignoring temporal details of those events. In this paper, we examine the potential of session temporal information in enhancing the performance of SBRs, conceivably by reflecting the momentary interests of anonymous users or their mindset shifts during sessions. We propose the STAR framework, which utilizes the time intervals between events within sessions to construct more informative representations for items and sessions. Our mechanism revises session representation by embedding time intervals without employing discretization. Empirical results on Yoochoose and Diginetica datasets show that the suggested method outperforms the state-of-the-art baseline models in Recall and MRR criteria.
Fast formation and assembly for spline-based 3D fictitious domain methods
Venue: arXiv cs.CE 2022
Year: 2022

Standard finite element methods employ an element-wise assembly strategy. The element's contribution to the system matrix is formed by a loop over quadrature points. This concept is also used in fictitious domain methods, which perform simulations on a simple tensor-product background mesh cut by a boundary representation that defines the domain of interest. Considering such $d$-dimensional background meshes based on splines of degree $p$ with maximal smoothness, $C^{p-1}$, the cost of setting up the system matrix is $\mathcal{O}\left(p^{3d}\right)$ per degree of freedom. Alternative assembly and formation techniques can significantly reduce this cost. In particular, the combination of (1) sum factorization, (2) weighted quadrature, and (3) row-based assembly yields a cost of $\mathcal{O}\left(p^{d+1}\right)$ for non-cut background meshes. However, applying this fast approach to cut background meshes is an open challenge since they do not have a tensor-product structure. This work presents techniques that allow the treatment of cut background meshes and thus the application of fast formation and assembly to fictitious domain methods. First, a discontinuous version of weighted quadrature is presented, which introduces a discontinuity into a cut test function's support. The cut region can be treated separately from the non-cut counterpart; the latter can be assembled by the fast concepts. A three-dimensional example investigates the accuracy and efficiency of the proposed concept and demonstrates its speed-up compared to conventional formation and assembly.
FinTech for Social Good: A Research Agenda from NLP Perspective
Venue: arXiv cs.CY 2022
Year: 2022

Making our research results positively impact on society and environment is one of the goals our community has been pursuing recently. Although financial technology (FinTech) is one of the popular application fields, we notice that there is no discussion on how NLP can help in FinTech for the social good. When mentioning FinTech for social good, people are talking about financial inclusion and green finance. However, the role of NLP in these directions only gets limited discussions. To fill this gap, this paper shares our idea of how we can use NLP in FinTech for social good. We hope readers can rethink the relationship between finance and NLP based on our sharing, and further join us in improving the financial literacy of individual investors and improving the supports for impact investment.
Blockchain Technology to Secure Bluetooth
Venue: arXiv cs.CR 2022
Year: 2022

Bluetooth is a communication technology used to wirelessly exchange data between devices. In the last few years there have been found a great number of security vulnerabilities, and adversaries are taking advantage of them causing harm and significant loss. Numerous system security updates have been approved and installed in order to sort out security holes and bugs, and prevent attacks that could expose personal or other valuable information. But those updates are not sufficient and appropriate and new bugs keep showing up. In Bluetooth technology, pairing is identified as the step where most bugs are found and most attacks target this particular process part of Bluetooth. A new technology that has been proved bulletproof when it comes to security and the exchange of sensitive information is Blockchain. Blockchain technology is promising to be incorporated well in a network of smart devices, and secure an Internet of Things (IoT), where Bluetooth technology is being extensively used. This work presents a vulnerability discovered in Bluetooth pairing process, and proposes a Blockchain solution approach to secure pairing and mitigate this vulnerability. The paper first introduces the Bluetooth technology and delves into how Blockchain technology can be a solution to certain security problems. Then a solution approach shows how Blockchain can be integrated and implemented to ensure the required level of security. Certain attack incidents on Bluetooth vulnerable points are examined and discussion and conclusions give the extension of the security related problems.
Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification
Venue: arXiv cs.CV 2022
Year: 2022

The current paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data is continuous. Our objective is to develop an end-to-end pipeline for frame-by-frame annotation of naturalistic driving studies videos into various driving events including stop and lane keeping events, lane changes, left-right turning movements, and horizontal curve maneuvers. To address the time series segmentation problem, the study developed an Energy Maximization Algorithm (EMA) capable of extracting driving events of varying durations and frequencies from continuous signal data. To reduce overfitting and false alarm rates, heuristic algorithms were used to classify events with highly variable patterns such as stops and lane-keeping. To classify segmented driving events, four machine learning models were implemented, and their accuracy and transferability were assessed over multiple data sources. The duration of events extracted by EMA were comparable to actual events, with accuracies ranging from 59.30% (left lane change) to 85.60% (lane-keeping). Additionally, the overall accuracy of the 1D-convolutional neural network model was 98.99%, followed by the Long-short-term-memory model at 97.75%, then random forest model at 97.71%, and the support vector machine model at 97.65%. These model accuracies where consistent across different data sources. The study concludes that implementing a segmentation-classification pipeline significantly improves both the accuracy for driver maneuver detection and transferability of shallow and deep ML models across diverse datasets.
Speech-to-Speech Translation For A Real-world Unwritten Language
Venue: arXiv cs.CL 2022
Year: 2022

We study speech-to-speech translation (S2ST) that translates speech from one language into another language and focuses on building systems to support languages without standard text writing systems. We use English-Taiwanese Hokkien as a case study, and present an end-to-end solution from training data collection, modeling choices to benchmark dataset release. First, we present efforts on creating human annotated data, automatically mining data from large unlabeled speech datasets, and adopting pseudo-labeling to produce weakly supervised data. On the modeling, we take advantage of recent advances in applying self-supervised discrete representations as target for prediction in S2ST and show the effectiveness of leveraging additional text supervision from Mandarin, a language similar to Hokkien, in model training. Finally, we release an S2ST benchmark set to facilitate future research in this field. The demo can be found at https://huggingface.co/spaces/facebook/Hokkien_Translation .
On Measuring Cognition and Cognitive Augmentation
Venue: arXiv cs.HC 2022
Year: 2022

We are at the beginning of a new age in which artificial entities will perform significant amounts of high-level cognitive processing rivaling and even surpassing human thinking. The future belongs to those who can best collaborate with artificial cognitive entities achieving a high degree of cognitive augmenta-tion. However, we currently lack theoretically grounded fundamental metrics able to describe human or artificial cognition much less augmented and combined cognition. How do we measure thinking, cognition, information, and knowledge in an implementation-independent way? How can we tell how much thinking an artificial entity does and how much is done by a human? How can we measure the combined and possible even emergent effect of humans working together with intelligent artificial entities? These are some of the challenges for research-ers in this field. We first define a cognitive process as the transformation of data, information, knowledge, and wisdom. We then review several existing and emerging information metrics based on entropy, processing effort, quantum physics, emergent capacity, and human concept learning. We then discuss how these fail to answer the above questions and provide guidelines for future re-search.
Investigating co-occurrences of MITRE ATT\&CK Techniques
Venue: arXiv cs.CR 2022
Year: 2022

Cyberattacks use adversarial techniques to bypass system defenses, persist, and eventually breach systems. The MITRE ATT\&CK framework catalogs a set of adversarial techniques and maps between adversaries and their used techniques and tactics. Understanding how adversaries deploy techniques in conjunction is pivotal for learning adversary behavior, hunting potential threats, and formulating a proactive defense. The goal of this research is to aid cybersecurity practitioners and researchers in choosing detection and mitigation strategies through co-occurrence analysis of adversarial techniques reported in MITRE ATT&CK. We collect the adversarial techniques of 115 cybercrime groups and 484 malware from the MITRE ATT\&CK. We apply association rule mining and network analysis to investigate how adversarial techniques co-occur. We identify that adversaries pair T1059: Command and scripting interface and T1105: Ingress tool transfer techniques with a relatively large number of ATT\&CK techniques. We also identify adversaries using the T1082: System Information Discovery technique to determine their next course of action. We observe adversaries deploy the highest number of techniques from the TA0005: Defense evasion and TA0007: Discovery tactics. Based on our findings on co-occurrence, we identify six detection, six mitigation strategies, and twelve adversary behaviors. We urge defenders to prioritize primarily the detection of TA0007: Discovery and mitigation of TA0005: Defense evasion techniques. Overall, this study approximates how adversaries leverage techniques based on publicly reported documents. We advocate organizations investigate adversarial techniques in their environment and make the findings available for a more precise and actionable understanding.
Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms
Venue: arXiv cs.LG 2022
Year: 2022

We describe the current content moderation strategy employed by Meta to remove policy-violating content from its platforms. Meta relies on both handcrafted and learned risk models to flag potentially violating content for human review. Our approach aggregates these risk models into a single ranking score, calibrating them to prioritize more reliable risk models. A key challenge is that violation trends change over time, affecting which risk models are most reliable. Our system additionally handles production challenges such as changing risk models and novel risk models. We use a contextual bandit to update the calibration in response to such trends. Our approach increases Meta's top-line metric for measuring the effectiveness of its content moderation strategy by 13%.
Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Tests
Venue: arXiv cs.LG 2022
Year: 2022

Learning causal structure is useful in many areas of artificial intelligence, including planning, robotics, and explanation. Constraint-based structure learning algorithms such as PC use conditional independence (CI) tests to infer causal structure. Traditionally, constraint-based algorithms perform CI tests with a preference for smaller-sized conditioning sets, partially because the statistical power of conventional CI tests declines rapidly as the size of the conditioning set increases. However, many modern conditional independence tests are model-based, and these tests use well-regularized models that maintain statistical power even with very large conditioning sets. This suggests an intriguing new strategy for constraint-based algorithms which may result in a reduction of the total number of CI tests performed: Test variable pairs with large conditioning sets first, as a pre-processing step that finds some conditional independencies quickly, before moving on to the more conventional strategy that favors small conditioning sets. We propose such a pre-processing step for the PC algorithm which relies on performing CI tests on a few randomly selected large conditioning sets. We perform an empirical analysis on directed acyclic graphs (DAGs) that correspond to real-world systems and both empirical and theoretical analyses for Erdős-Renyi DAGs. Our results show that Pre-Processing Plus PC (P3PC) performs far fewer CI tests than the original PC algorithm, between 0.5% to 36%, and often less than 10%, of the CI tests that the PC algorithm alone performs. The efficiency gains are particularly significant for the DAGs corresponding to real-world systems.