Reviewerly/exHarmony huggingface dataset:
A Hash Table Without Hash Functions, and How to Get the Most Out of Your Random Bits

This paper considers the basic question of how strong of a probabilistic guarantee can a hash table, storing $n(1+\Theta(1))\log n$-bit key/value pairs, offer? Past work on this question has been bottlenecked by limitations of the known families of hash functions: The only hash tables to achieve failure probabilities less than $1/2^{\text{polylog}n}$ require access to fully-random hash functions-if the same hash tables are implemented using the known explicit families of hash functions, their failure probabilities become $1/{poly}(n)$. To get around these obstacles, we show how to construct a randomized data structure that has the same guarantees as a hash table, but that avoids the direct use of hash functions. Building on this, we able to construct a hash table using $O(n)$ random bits that achieves failure probability $1/n^{n^{1-\varepsilon}}$ for an arbitrary positive constant $\varepsilon$. In fact, we show that this guarantee can even be achieved by a succinct dictionary, that is, by a dictionary that uses space within a $1+o(1)$ factor of the information-theoretic optimum. Finally we also construct a succinct hash table whose probabilistic guarantees fall on a different extreme, offering a failure probability of $1/{poly}(n)$ while using only $\tilde{O}(\log n)$ random bits. This latter result replicates a guarantee previously achieved by Dietzfelbinger et al., but with increased space efficiency and with several surprising technical components.
Fooling polynomials using invariant theory

We revisit the problem of constructing explicit pseudorandom generators that fool with error ϵ degree-d polynomials in n variables over the field F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</inf> , in the case of large q. Previous constructions either have seed length $\geq 2^{d}\log q$, and thus are only non-trivial when $d\lt \log n$, or else rely on a seminal reduction by Bogdanov (STOC 2005). This reduction yields seed length not less than $d^{4}\log n+\log q$ and requires fields of size $q\geq d^{6}/\epsilon^{2}$; and explicit generators meeting such bounds are known.Departing from Bogdanov’s reduction, we develop an algebraic analogue of the Bogdanov-Viola paradigm (FOCS 2007, SICOMP 2010) of summing generators for degree-one polynomials. Whereas previous analyses of the paradigm are restricted to degree $d\lt \log n$, we give a new analysis which handles large degrees. A main new idea is to show that the construction preserves indecomposability of polynomials. Apparently for the first time in the area, the proof uses invariant theory.Our approach in particular yields several new pseudorandom generators. In particular, for large enough fields we obtain seed length $O(d\log n+\log q)$ which is optimal up to constant factors. We also construct generators for fields of size as small as $O(d^{4})$. Further reducing the field size requires a significant change in techniques: Most or all generators for large-degree polynomials rely on Weil bounds; but such bounds are only applicable when $q\gt d^{4}$
Active Linear Regression for ℓ<sub>p</sub> Norms and Beyond

We study active sampling algorithms for linear regression, which aim to query only a small number of entries of a target vector and output a near minimizer to the objective function. For $\ell_{p}$ norm regression for any $ 0\lt p \lt \infty$, we give an algorithm based on Lewis weight sampling which outputs $\mathrm{a}(1+\epsilon)$-approximate solution using just $\tilde{O}(d/\epsilon^{2})$ queries to b for $p\in(0,1)$, $\tilde{O}$ $(d/\epsilon)$ queries for $p\in(1,2)$, and $\tilde{O}$ $(d^{p/2}/\epsilon^{p})$ queries for $p\in(2,\ \infty)$. For $p\in(0,2)$, our bounds are optimal up to logarithmic factors, thus settling the query complexity for this range of p. For $p\in(2,\ \infty)$, our dependence on d is optimal, while our dependence on $\epsilon$ is off by at most a single $\epsilon$ factor, up to logarithmic factors. Our result resolves an open question of Chen and Dereziński, who gave near optimal bounds for the $\ell_{1}$ norm, but required at least $d^{2}/\epsilon^{2}$ samples for $\ell_{p}$ regression with $p\in(1,2)$, and gave no bounds for $p\in(2,\ \infty)$ or $p\in(0,1)$. We also provide the first total sensitivity upper bound for loss functions with at most degree p polynomial growth. This improves a recent result of Tukan, Maalouf, and Feldman. By combining this with our techniques for $\ell_{p}$ regression, we obtain the first active regression algorithms for such loss functions, including the important cases of the Tukey and Huber losses. This answers another question of Chen and Dereziński. Our sensitivity bounds also give improvements to a variety of previous results using sensitivity sampling, including Orlicz norm subspace embeddings, robust subspace approximation, and dimension reduction for smoothed p-norms. Finally, our active sampling results give the first sublinear time algorithms for Kronecker product regression under every $\ell_{p}$ norm. Previous results required reading the entire b vector in the kernel feature space. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Extended abstract; full version available at https://arxiv.org/abs/2111.04888.
Classical Verification of Quantum Computations in Linear Time

In the quantum computation verification problem, a quantum server wants to convince a client that the output of evaluating a quantum circuit C is some result that it claims. This problem is considered very important both theoretically and practically in quantum computation [1], [2], [3]. The client is considered to be limited in computational power, and one desirable property is that the client can be completely classical, which leads to the classical verification of quantum computation (CVQC) problem. In terms of the time complexity of server-side quantum computations (which typically dominate the total time complexity of both the client and the server), the fastest single-server CVQC protocol so far has complexity $O(\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{y}(\kappa)|C|^{3})$ where $|C|$ is the size of the circuit to be verified and $\kappa$ is the security parameter, given by Mahadev [4]. This leads to a similar cubic time blowup in many existing protocols including multiparty quantum computation, zero knowledge and obfuscation [5], [6], [7], [8], [9], [10]. Considering the preciousness of quantum computation resources, this cubic complexity barrier could be a big obstacle for theoretical and practical development of protocols for these problems.In this work, by developing new techniques, we give a new CVQC protocol with complexity $O(\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{y}(\kappa)|C|)$ (in terms of the total time complexity of both the client and the server), which is significantly faster than existing protocols. Our protocol is secure in the quantum random oracle model [11] assuming the existence of noisy trapdoor claw-free functions [12], which are both extensively used assumptions in quantum cryptography. Along the way, we also give a new classical channel remote state preparation protocol for states in $\displaystyle \left\{|+\theta\rangle=\frac{1}{\sqrt{2}}(|0\rangle+e^{\mathrm{i}\theta\pi/4}|1\rangle)\ :\ \theta\in\left\{0,1\cdots 7\right\}\right\}$, another basic primitive in quantum cryptography. Our protocol allows for parallel verifiable preparation of L independently random states in this form (up to a constant overall error and a possibly unbounded server-side simulator), and runs in only O(poly($\kappa$)L) time and constant rounds; for comparison, existing works (even for possibly simpler state families) all require very large or unestimated time and round complexities [13], [14], [15], [16].
Integration and Evaluation of an Immersive Virtual Platform

The virtual reality (VR) industry has tried to enhance VR immersion, a perception of being physically present in a non-physical environment. Sound, simulators, and surrounding environments can be synergistically augmented together to enhance immersion in VR. In this paper, we first integrate an immersive virtual platform that builds on a moving vehicle simulator in an immersive virtual environment. The simulator is integrated to have efficient localization with inexpensive sensors and to produce the loosely synchronous motion according to dynamically changing VR simulations and contents. The virtual environment called an immersive room is designed to provide 360-degree videos experience and deliver VR contents without a head-mounted display (HMD). In addition, we investigate the quality of experience (QoE) via a comparative survey study between the proposed immersive virtual platform and the conventional VR using an HMD in terms of immersion and motion sickness. Through the human-subject study with the newly designed questionnaire, we confirm that the general QoE is improved in terms of immersion and motion sickness by using the proposed immersive virtual platform. To verify which factors in the loosely synchronous motion are effective in improving QoE, the components of the motion control are ablated to show their individual effects. We provide the statistical analysis to support our hypotheses that for the proposed VR platform, participants with the loosely synchronous motion are more immersed and less likely to feel motion sickness than those without the synchronous motion in both the immersive room and VR with HMD.
PI-Link: A Ground-Truth Dataset of Links Between Pull-Requests and Issues in GitHub

GitHub hosts Git repositories and provides issues-tracking services to provide a better collaboration environment for software developers. Issues and Pull-Requests are frequently used in GitHub to discuss and review the software requirements (new features, bugs, etc.) and software solutions (source code, test cases, etc.) respectively. The links between Issues and their corresponding Pull-Requests comprise valuable information to keep tracking current development as well as documenting knowledge for future development. Considering a large number of links, such information can be used to train machine learning models for several purposes such as feature location, bug prediction and localization, recommendation systems and documentation generation. To the best of our knowledge, no dataset has been proposed as a ground-truth of links between Issues and Pull-Requests. In this paper, we propose, PI-Link, a new significant and reliable ground-truth dataset composed of 50369 links that explicitly connect 34732 Issues with 50369 Pull-Requests. These links are automatically extracted from all (907,139) Android projects in GitHub created between January 1, 2011 and January 1, 2021. To better organize and store the collected data, we propose a metamodel based on the concepts of Issues and Pull Requests. Moreover, we analyze the relationships between Issues and their linked Pull Requests based on four features related to their titles, bodies, labels and comments. The selected features are analyzed in terms of their lengths and similarities based on three lexical and one semantic similarity metrics. The results showed promising similarities between Issues and their linked PRs at the lexical and semantic levels. In addition, some feature similarities are sensitive to the text length, whereas other feature similarities are sensitive to the term frequency.
Two-Person Graph Convolutional Network for Skeleton-Based Human Interaction Recognition

Graph convolutional networks (GCNs) have been the predominant methods in skeleton-based human action recognition, including human-human interaction recognition. However, when dealing with interaction sequences, current GCN-based methods simply split the two-person skeleton into two discrete graphs and perform graph convolution separately as done for single-person action classification. Such operations ignore rich interactive information and hinder effective spatial inter-body relationship modeling. To overcome the above shortcoming, we introduce a novel unified two-person graph to represent inter-body and intra-body correlations between joints. Experiments show accuracy improvements in recognizing both interactions and individual actions when utilizing the proposed two-person graph topology. In addition, We design several graph labeling strategies to supervise the model to learn discriminant spatial-temporal interactive features. Finally, we propose a two-person graph convolutional network (2P-GCN). Our model achieves state-of-the-art results on four benchmarks of three interaction datasets: SBU, interaction subsets of NTU-RGB+D and NTU-RGB+D 120.
A 7-Gbps SCA-Resistant Multiplicative-Masked AES Engine in Intel 4 CMOS

A multiplicative masked advanced encryption standard (AES)-128/-256 engine with measured side-channel resistance to correlation power and electromagnetic (EM) attacks in Intel 4 CMOS process is presented. While conventional additive masking offers significant improvements in minimum-time-to-disclosure (MTD) for the extracted key bytes, mask compensations in non-linear Sboxes incur >100% area overheads. Multiplicative masking provides a simpler computation of non-linear inverse operation by converting the inputs from an additive to a multiplicative domain. However, multiplicative masked AES designs suffer from zero-value attacks, where “0” valued inputs on Sbox bytes exhibit distinct power signatures compared to a random input byte. The AES engine implements dual-rail zero-value attack detection and mitigation circuits to counteract zero-valued input Sbox bytes. Low-overhead mask conversion and multiplicative Sbox datapath circuits enable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula> and 50% reduction in area and performance overheads, respectively. The countermeasure enables 34000–40 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$000\times $ </tex-math></inline-formula> improvements in measured MTD against correlation power and EM attacks compared to an unprotected AES implementation while limiting the area and performance overheads to 65% and 4%, respectively.
<i>iGniter:</i> Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, <i>spatial sharing</i> of GPUs among co-located DNN inference workloads becomes increasingly compelling. However, GPU sharing inevitably brings <i>severe performance interference</i> among co-located inference workloads, as motivated by an empirical measurement study of DNN inference on EC2 GPU instances. While existing works on guaranteeing inference performance service level objectives (SLOs) focus on either <i>temporal sharing</i> of GPUs or <i>reactive</i> GPU resource scaling and inference migration techniques, how to <i>proactively</i> mitigate such severe performance interference has received comparatively little attention. In this paper, we propose <i>iGniter</i> , an <i>interference-aware</i> GPU resource provisioning framework for cost-efficiently achieving predictable DNN inference in the cloud. <i>iGniter</i> is comprised of two key components: (1) a <i>lightweight</i> DNN inference performance model, which leverages the system and workload metrics that are practically accessible to capture the performance interference; (2) A <i>cost-efficient</i> GPU resource provisioning strategy that <i>jointly</i> optimizes the GPU resource allocation and adaptive batching based on our inference performance model, with the aim of achieving predictable performance of DNN inference workloads. We implement a prototype of <i>iGniter</i> based on the NVIDIA Triton inference server hosted on EC2 GPU instances. Extensive prototype experiments on four representative DNN models and datasets demonstrate that <i>iGniter</i> can guarantee the performance SLOs of DNN inference workloads with practically acceptable runtime overhead, while saving the monetary cost by up to <inline-formula><tex-math notation="LaTeX">$25\%$</tex-math></inline-formula> in comparison to the state-of-the-art GPU resource provisioning strategies.
An Indoor Multi-Environment Sensor System Based on Intelligent Edge Computing

Monitoring and predicting the environment in an indoor space plays an important role in securing big data and detecting abnormal conditions in the industrial environment and living space. This study proposes an indoor multi-environment sensor system based on intelligent edge computing that collects and predicts environmental data. The system collects data using 14 types of environmental sensors and object detection technology models and implements a model that predicts indoor air quality based on the bi-directional LSTM network. The trained model shows high performance in predicting indoor air quality (IAQ) factors, such as CO2, PM2.5, and total volatile organic compounds (TVOC). The indoor multi-environment sensor system based on intelligent edge computing is available for data collection and environmental prediction in various spaces without restrictions on specific locations. This study proposes an integrated approach with various functions by applying edge computing to indoor environment monitoring. We verify the proposed system through various experiments.
Quasar Identification Using Multivariate Probability Density Estimated from Nonparametric Conditional Probabilities

Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE). A novel density estimator recently developed by Farmer and Jacobs offers an alternative high-throughput automated approach to univariate nonparametric density estimation based on maximum entropy and order statistics, improving accuracy over univariate KDE. This article presents an extension of the single variable case to multiple variables. The univariate estimator is used to recursively calculate a product array of one-dimensional conditional probabilities. In combination with interpolation methods, a complete joint probability density estimate is generated for multiple variables. Good accuracy and speed performance in synthetic data are demonstrated by a numerical study using known distributions over a range of sample sizes from 100 to 106 for two to six variables. Performance in terms of speed and accuracy is compared to KDE. The multivariate density estimate developed here tends to perform better as the number of samples and/or variables increases. As an example application, measurements are analyzed over five filters of photometric data from the Sloan Digital Sky Survey Data Release 17. The multivariate estimation is used to form the basis for a binary classifier that distinguishes quasars from galaxies and stars with up to 94% accuracy.
Session-Based Recommendations for e-Commerce with Graph-Based Data Modeling

Conventional recommendation methods such as collaborative filtering cannot be applied when long-term user models are not available. In this paper, we propose two session-based recommendation methods for anonymous browsing in a generic e-commerce framework. We represent the data using a graph where items are connected to sessions and to each other based on the order of appearance or their co-occurrence. In the first approach, called Hierarchical Sequence Probability (HSP), recommendations are produced using the probabilities of items’ appearances on certain structures in the graph. Specifically, given a current item during a session, to create a list of recommended next items, we first compute the probabilities of all possible sequential triplets ending in each candidate’s next item, then of all candidate item pairs, and finally of the proposed item. In our second method, called Recurrent Item Co-occurrence (RIC), we generate the recommendation list based on a weighted score produced by a linear recurrent mechanism using the co-occurrence probabilities between the current item and all items. We compared our approaches with three state-of-the-art Graph Neural Network (GNN) models using four session-based datasets one of which contains data collected by us from a leather apparel e-shop. In terms of recommendation effectiveness, our methods compete favorably on a number of datasets while the time to generate the graph and produce the recommendations is significantly lower.
Diagnosis for the refrigerant undercharge fault of chiller using deep belief network enhanced extreme learning machine

HVAC systems accumulate large amounts of historical data in their long-term operation process, and the use of big data has become one of the effective tools for fault diagnosis. As a common fault of chiller in HVAC system, refrigerant undercharge fault is chosen for analysis in this paper. Firstly, the experimental data of normal operation and refrigerant undercharge fault of chillers in an industrial park are collected through the IoT intelligent terminal, and the fault sensitivity characteristics of the experimental data are evaluated. Combined with expert knowledge, 23 features were selected as the input of the model. Then, combining the advantages of deep learning and machine learning, a hybrid model of refrigerant undercharge fault diagnosis for chillers using Deep Belief Network (DBN) enhanced Extreme Learning Machine (ELM) is proposed, where the parameters of the hybrid model are optimized by Particle Swarm Optimization (PSO). Finally, the experimental results of the real chiller are shown that the hybrid model can maintain a high diagnosis accuracy in the case of the number of training data set is small and has the highest accuracy and robustness compared with models such as ELM, Support Vector Machines (SVM) and K-Nearest Neighbor (KNN). The comprehensive accuracy rate of the model reaches 99.86%, and it has better data analysis ability, which can be well applied in practical applications.
Optimizing User Engagement Through Adaptive Ad Sequencing

This paper develops an offline reinforcement learning framework that identifies and evaluate the ad sequencing policy that optimizes user engagement.
Stabilizer quantum codes defined by trace-depending polynomials

Quantum error-correcting codes with good parameters can be constructed by evaluating polynomials at the roots of the polynomial trace [18]. In this paper, we propose to evaluate polynomials at the roots of trace-depending polynomials (given by a constant plus the trace of a polynomial) and show that this procedure gives rise to stabilizer quantum error-correcting codes with a wider range of lengths than in [18] and with excellent parameters. Namely, we are able to provide new binary records according to [21] and non-binary codes improving the ones available in the literature.