OpenRaiser/Intern-Atlas huggingface dataset:
N-dimensional nonlinear prediction with MLP
Venue: arXiv cs.LG 2022
Year: 2022

In this paper we propose a Non-Linear Predictive Vector quantizer (PVQ) for speech coding, based on Multi-Layer Perceptrons. With this scheme we have improved the results of our previous ADPCM coder with nonlinear prediction, and we have reduced the bit rate up to 1 bit per sample.
A Real-World Implementation of Unbiased Lift-based Bidding System
Venue: arXiv cs.IR 2022
Year: 2022

In display ad auctions of Real-Time Bid-ding (RTB), a typical Demand-Side Platform (DSP)bids based on the predicted probability of click and conversion right after an ad impression. Recent studies find such a strategy is suboptimal and propose a better bidding strategy named lift-based bidding.Lift-based bidding simply bids the price according to the lift effect of the ad impression and achieves maximization of target metrics such as sales. Despiteits superiority, lift-based bidding has not yet been widely accepted in the advertising industry. For one reason, lift-based bidding is less profitable for DSP providers under the current billing rule. Second, thepractical usefulness of lift-based bidding is not widely understood in the online advertising industry due to the lack of a comprehensive investigation of its impact.We here propose a practically-implementable lift-based bidding system that perfectly fits the current billing rules. We conduct extensive experiments usinga real-world advertising campaign and examine the performance under various settings. We find that lift-based bidding, especially unbiased lift-based bidding is most profitable for both DSP providers and advertisers. Our ablation study highlights that lift-based bidding has a good property for currently dominant first price auctions. The results will motivate the online
Combining Modular Skills in Multitask Learning
Venue: arXiv cs.LG 2022
Year: 2022

A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. In this work, we assume that each task is associated with a subset of latent discrete skills from a (potentially small) inventory. In turn, skills correspond to parameter-efficient (sparse / low-rank) model parameterisations. By jointly learning these and a task-skill allocation matrix, the network for each task is instantiated as the average of the parameters of active skills. To favour non-trivial soft partitions of skills across tasks, we experiment with a series of inductive biases, such as an Indian Buffet Process prior and a two-speed learning rate. We evaluate our latent-skill model on two main settings: 1) multitask reinforcement learning for grounded instruction following on 8 levels of the BabyAI platform; and 2) few-shot adaptation of pre-trained text-to-text generative models on CrossFit, a benchmark comprising 160 NLP tasks. We find that the modular design of a network significantly increases sample efficiency in reinforcement learning and few-shot generalisation in supervised learning, compared to baselines with fully shared, task-specific, or conditionally generated parameters where knowledge is entangled across tasks. In addition, we show how discrete skills help interpretability, as they yield an explicit hierarchy of tasks.
Implementation Technologies of an Advanced Cloud-based System for Distribution Operations
Venue: arXiv cs.DC 2022
Year: 2022

Today's era is characterized as the "digital transformation era". Digital processes and information systems are used in every aspect of social and business activity. The use of information technology over the internet is so extensive that we interact with it daily without even recognizing it. The technological advances can offer a plethora of improvements for the supply chain processes, especially in the field of distribution planning and execution. The scope of this paper is to present the technological content of an advanced routing and scheduling system for transportation and delivery of goods. The system focuses on the routing and scheduling problem in urban areas, as city logistics have become a complex environment for companies to deliver their goods. The presented system deals with both static and dynamic routing and scheduling problems. More specifically, the system can create initial routing plans based on orders, available vehicles, time windows, and traffic forecasting data. Afterwards, during the execution of the plans, the system can monitor the fleet, detect deviations from the original plans, and finally, perform rerouting operations when needed. After a brief presentation of the system's modules and functionality, the paper describes thoroughly the technologies used to develop the system. The technological elements of the system are integrated into a cloud environment offering a system that is easy to maintain and can effectively support logistics companies' distribution activities. The system is provided as a Software as a Service with data being maintained on a central host and processed on the cloud. Therefore, logistics companies that decide to implement it can achieve faster, more accurate and more cost-efficient distribution activities while ensuring better customer service.
Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks
Venue: arXiv cs.DC 2022
Year: 2022

While FPGA accelerator boards and their respective high-level design tools are maturing, there is still a lack of multi-FPGA applications, libraries, and not least, benchmarks and reference implementations towards sustained HPC usage of these devices. As in the early days of GPUs in HPC, for workloads that can reasonably be decoupled into loosely coupled working sets, multi-accelerator support can be achieved by using standard communication interfaces like MPI on the host side. However, for performance and productivity, some applications can profit from a tighter coupling of the accelerators. FPGAs offer unique opportunities here when extending the dataflow characteristics to their communication ininterfaces. In this work, we extend the HPCC FPGA benchmark suite by multi-FPGA support and three missing benchmarks that particularly characterize or stress inter-device communication: b_eff, PTRANS, and LINPACK. With all benchmarks implemented for current boards with Intel and Xilinx FPGAs, we established a baseline for multi-FPGA performance. Additionally, for the communication-centric benchmarks, we explored the potential of direct FPGA-to-FPGA communication with a circuit-switched inter-FPGA network that is currently only available for one of the boards. The evaluation with parallel execution on up to 26 FPGA boards makes use of one of the largest academic FPGA installations.
How Well Do My Results Generalize Now? The External Validity of Online Privacy and Security Surveys
Venue: arXiv cs.HC 2022
Year: 2022

Privacy and security researchers often rely on data collected through online crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) and Prolific. Prior work -- which used data collected in the United States between 2013 and 2017 -- found that MTurk responses regarding security and privacy were generally representative for people under 50 or with some college education. However, the landscape of online crowdsourcing has changed significantly over the last five years, with the rise of Prolific as a major platform and the increasing presence of bots. This work attempts to replicate the prior results about the external validity of online privacy and security surveys. We conduct an online survey on MTurk (n=800), a gender-balanced survey on Prolific (n=800), and a representative survey on Prolific (n=800) and compare the responses to a probabilistic survey conducted by the Pew Research Center (n=4272). We find that MTurk response quality has degraded over the last five years, and our results do not replicate the earlier finding about the generalizability of MTurk responses. By contrast, we find that data collected through Prolific is generally representative for questions about user perceptions and experiences, but not for questions about security and privacy knowledge. We also evaluate the impact of Prolific settings, attention check questions, and statistical methods on the external validity of online surveys, and we develop recommendations about best practices for conducting online privacy and security surveys.
A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs
Venue: arXiv cs.LG 2022
Year: 2022

We propose a novel robust decentralized graph clustering algorithm that is provably equivalent to the popular spectral clustering approach. Our proposed method uses the existing wave equation clustering algorithm that is based on propagating waves through the graph. However, instead of using a fast Fourier transform (FFT) computation at every node, our proposed approach exploits the Koopman operator framework. Specifically, we show that propagating waves in the graph followed by a local dynamic mode decomposition (DMD) computation at every node is capable of retrieving the eigenvalues and the local eigenvector components of the graph Laplacian, thereby providing local cluster assignments for all nodes. We demonstrate that the DMD computation is more robust than the existing FFT based approach and requires 20 times fewer steps of the wave equation to accurately recover the clustering information and reduces the relative error by orders of magnitude. We demonstrate the decentralized approach on a range of graph clustering problems.
Piezoelectric Strain FET (PeFET) based Non-Volatile Memories
Venue: arXiv cs.ET 2022
Year: 2022

We propose non-volatile memory (NVM) designs based on Piezoelectric Strain FET (PeFET) utilizing a piezoelectric/ferroelectric (PE/FE such as PZT) coupled with 2D Transition Metal Dichalcogenide (2D-TMD such as MoS2) transistor. The proposed NVMs store bit information in the form of polarization (P) of the FE/PE, use electric-field driven P-switching for write and employ piezoelectricity induced dynamic bandgap modulation of 2D-TMD channel for bit sensing. We analyze PeFET with COMSOL based 3D modeling showing that the circuit-driven optimization of PeFET geometry is essential to achieve effective hammer-and-nail effect and adequate bandgap modulation for NVM read. Our results show that distinguishability of binary states to up to 11X is achieved in PeFETs.We propose various flavors of PeFET NVMs, namely (a) high density (HD) NVM featuring a compact access-transistor-less bit-cell, (b) 1T-1PeFET NVM with segmented architecture, targeted for optimized write energy and latency and (c) cross-coupled (CC) NVM offering a trade-off between area and latency.PeFET NVMs offer up to 7X smaller cell area, 66% lower write energy, 87% lower read energy and 44% faster read compared to 2D-FET SRAM. This comes at the cost of high write latency in PeFET NVMs, which can be minimized by virtue of optimized PE geometry.
MRI-GAN: A Generalized Approach to Detect DeepFakes using Perceptual Image Assessment
Venue: arXiv cs.CV 2022
Year: 2022

DeepFakes are synthetic videos generated by swapping a face of an original image with the face of somebody else. In this paper, we describe our work to develop general, deep learning-based models to classify DeepFake content. We propose a novel framework for using Generative Adversarial Network (GAN)-based models, we call MRI-GAN, that utilizes perceptual differences in images to detect synthesized videos. We test our MRI-GAN approach and a plain-frames-based model using the DeepFake Detection Challenge Dataset. Our plain frames-based-model achieves 91% test accuracy and a model which uses our MRI-GAN framework with Structural Similarity Index Measurement (SSIM) for the perceptual differences achieves 74% test accuracy. The results of MRI-GAN are preliminary and may be improved further by modifying the choice of loss function, tuning hyper-parameters, or by using a more advanced perceptual similarity metric.
$ \text{T}^3 $OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area
Venue: arXiv cs.AI 2022
Year: 2022

Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and propose a Transformer-based Time and Team Reinforcement Learning scheme ($ \text{T}^3 $OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on decentralized partially observed Markov decision processes (Dec-POMDP) to instantiate this problem. Second, by introducing and modifying the transformer-based observation sequence, QMIX is redefined to adapt to the complicated road structure, restricted moving spaces and constrained observations, so as to control vehicles to pursue the target combining the vehicle's observations. Third, a multi-intersection urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed $ \text{T}^3 $OMVP scheme achieves significant improvements relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is available at https://github.com/pipihaiziguai/T3OMVP.
Layer Adaptive Deep Neural Networks for Out-of-distribution Detection
Venue: arXiv cs.LG 2022
Year: 2022

During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels. While features at different layers could summarize the important factors of the inputs at varying levels, modern out-of-distribution (OOD) detection methods mostly focus on utilizing their ending layer features. In this paper, we proposed a novel layer-adaptive OOD detection framework (LA-OOD) for DNNs that can fully utilize the intermediate layers' outputs. Specifically, instead of training a unified OOD detector at a fixed ending layer, we train multiple One-Class SVM OOD detectors simultaneously at the intermediate layers to exploit the full spectrum characteristics encoded at varying depths of DNNs. We develop a simple yet effective layer-adaptive policy to identify the best layer for detecting each potential OOD example. LA-OOD can be applied to any existing DNNs and does not require access to OOD samples during the training. Using three DNNs of varying depth and architectures, our experiments demonstrate that LA-OOD is robust against OODs of varying complexity and can outperform state-of-the-art competitors by a large margin on some real-world datasets.
RMBR: A Regularized Minimum Bayes Risk Reranking Framework for Machine Translation
Venue: arXiv cs.CL 2022
Year: 2022

Beam search is the most widely used decoding method for neural machine translation (NMT). In practice, the top-1 candidate with the highest log-probability among the n candidates is selected as the preferred one. However, this top-1 candidate may not be the best overall translation among the n-best list. Recently, Minimum Bayes Risk (MBR) decoding has been proposed to improve the quality for NMT, which seeks for a consensus translation that is closest on average to other candidates from the n-best list. We argue that MBR still suffers from the following problems: The utility function only considers the lexical-level similarity between candidates; The expected utility considers the entire n-best list which is time-consuming and inadequate candidates in the tail list may hurt the performance; Only the relationship between candidates is considered. To solve these issues, we design a regularized MBR reranking framework (RMBR), which considers semantic-based similarity and computes the expected utility for each candidate by truncating the list. We expect the proposed framework to further consider the translation quality and model uncertainty of each candidate. Thus the proposed quality regularizer and uncertainty regularizer are incorporated into the framework. Extensive experiments on multiple translation tasks demonstrate the effectiveness of our method.
Private Convex Optimization via Exponential Mechanism
Venue: arXiv cs.DS 2022
Year: 2022

In this paper, we study private optimization problems for non-smooth convex functions $F(x)=\mathbb{E}_i f_i(x)$ on $\mathbb{R}^d$. We show that modifying the exponential mechanism by adding an $\ell_2^2$ regularizer to $F(x)$ and sampling from $π(x)\propto \exp(-k(F(x)+μ\|x\|_2^2/2))$ recovers both the known optimal empirical risk and population loss under $(ε,δ)$-DP. Furthermore, we show how to implement this mechanism using $\widetilde{O}(n \min(d, n))$ queries to $f_i(x)$ for the DP-SCO where $n$ is the number of samples/users and $d$ is the ambient dimension. We also give a (nearly) matching lower bound $\widetildeΩ(n \min(d, n))$ on the number of evaluation queries. Our results utilize the following tools that are of independent interest: (1) We prove Gaussian Differential Privacy (GDP) of the exponential mechanism if the loss function is strongly convex and the perturbation is Lipschitz. Our privacy bound is \emph{optimal} as it includes the privacy of Gaussian mechanism as a special case and is proved using the isoperimetric inequality for strongly log-concave measures. (2) We show how to sample from $\exp(-F(x)-μ\|x\|^2_2/2)$ for $G$-Lipschitz $F$ with $η$ error in total variation (TV) distance using $\widetilde{O}((G^2/μ) \log^2(d/η))$ unbiased queries to $F(x)$. This is the first sampler whose query complexity has \emph{polylogarithmic dependence} on both dimension $d$ and accuracy $η$.
Temporal Perceiver: A General Architecture for Arbitrary Boundary Detection
Venue: arXiv cs.CV 2022
Year: 2022

Generic Boundary Detection (GBD) aims at locating the general boundaries that divide videos into semantically coherent and taxonomy-free units, and could serve as an important pre-processing step for long-form video understanding. Previous works often separately handle these different types of generic boundaries with specific designs of deep networks from simple CNN to LSTM. Instead, in this paper, we present Temporal Perceiver, a general architecture with Transformer, offering a unified solution to the detection of arbitrary generic boundaries, ranging from shot-level, event-level, to scene-level GBDs. The core design is to introduce a small set of latent feature queries as anchors to compress the redundant video input into a fixed dimension via cross-attention blocks. Thanks to this fixed number of latent units, it greatly reduces the quadratic complexity of attention operation to a linear form of input frames. Specifically, to explicitly leverage the temporal structure of videos, we construct two types of latent feature queries: boundary queries and context queries, which handle the semantic incoherence and coherence accordingly. Moreover, to guide the learning of latent feature queries, we propose an alignment loss on the cross-attention maps to explicitly encourage the boundary queries to attend on the top boundary candidates. Finally, we present a sparse detection head on the compressed representation, and directly output the final boundary detection results without any post-processing module. We test our Temporal Perceiver on a variety of GBD benchmarks. Our method obtains the state-of-the-art results on all benchmarks with RGB single-stream features: SoccerNet-v2 (81.9% avg-mAP), Kinetics-GEBD (86.0% avg-f1), TAPOS (73.2% avg-f1), MovieScenes (51.9% AP and 53.1% Miou) and MovieNet (53.3% AP and 53.2% Miou), demonstrating the generalization ability of our Temporal Perceiver.
Counting stars: a survey on flexible Skyline Query approaches
Venue: arXiv cs.DB 2022
Year: 2022

Nowadays, as the quantity of data to process began to rise, so did the need for a method to discern what pieces of information could be useful for the user; in response, researchers focused their efforts on improving the already existing ranking methods or creating new ones starting from them. This survey will be presented a small list of some of the most known and/or most recent solutions proposed, with some possible applications for them, concerning a state of the art restricted to around the last ten years, comparing their performance with the traditional one top-k and skyline queries.
MIRRAX: A Reconfigurable Robot for Limited Access Environments
Venue: arXiv cs.RO 2022
Year: 2022

The development of mobile robot platforms for inspection has gained traction in recent years with the rapid advancement in hardware and software. However, conventional mobile robots are unable to address the challenge of operating in extreme environments where the robot is required to traverse narrow gaps in highly cluttered areas with restricted access. This paper presents MIRRAX, a robot that has been designed to meet these challenges with the capability of re-configuring itself to both access restricted environments through narrow ports and navigate through tightly spaced obstacles. Controllers for the robot are detailed, along with an analysis on the controllability of the robot given the use of Mecanum wheels in a variable configuration. Characterisation on the robot's performance identified suitable configurations for operating in narrow environments. The minimum lateral footprint width achievable for stable configuration ($<2^\text{o}$~roll) was 0.19~m. Experimental validation of the robot's controllability shows good agreement with the theoretical analysis. A further series of experiments shows the feasibility of the robot in addressing the challenges above: the capability to reconfigure itself for restricted entry through ports as small as 150mm diameter, and navigating through cluttered environments. The paper also presents results from a deployment in a Magnox facility at the Sellafield nuclear site in the UK - the first robot to ever do so, for remote inspection and mapping.
Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores
Venue: arXiv cs.CL 2022
Year: 2022

Automated essay scoring plays an important role in judging students' language abilities in education. Traditional approaches use handcrafted features to score and are time-consuming and complicated. Recently, neural network approaches have improved performance without any feature engineering. Unlike other natural language processing tasks, only a small number of datasets are publicly available for automated essay scoring, and the size of the dataset is not sufficiently large. Considering that the performance of a neural network is closely related to the size of the dataset, the lack of data limits the performance improvement of the automated essay scoring model. In this paper, we proposed a method to increase the number of essay-score pairs using back-translation and score adjustment and applied it to the Automated Student Assessment Prize dataset for augmentation. We evaluated the effectiveness of the augmented data using models from prior work. In addition, performance was evaluated in a model using long short-term memory, which is widely used for automated essay scoring. The performance of the models was improved by using augmented data to train the models.