Miracle-dz/UniFAR-FTUs huggingface dataset:
Robustness of Locally Differentially Private Graph Analysis Against Poisoning

Locally differentially private (LDP) graph analysis allows private analysis on a graph that is distributed across multiple users. However, such computations are vulnerable to data poisoning attacks where an adversary can skew the results by submitting malformed data. In this paper, we formally study the impact of poisoning attacks for graph degree estimation protocols under LDP. We make two key technical contributions. First, we observe LDP makes a protocol more vulnerable to poisoning -- the impact of poisoning is worse when the adversary can directly poison their (noisy) responses, rather than their input data. Second, we observe that graph data is naturally redundant -- every edge is shared between two users. Leveraging this data redundancy, we design robust degree estimation protocols under LDP that can significantly reduce the impact of data poisoning and compute degree estimates with high accuracy. We evaluate our proposed robust degree estimation protocols under poisoning attacks on real-world datasets to demonstrate their efficacy in practice.
Modeling the hourly consumption of electricity during period of power crisis

In this paper, we capture the dynamic behavior of hourly consumption of electricity during the period of power crisis ("dumsor'' period) in Ghana using two-state Markov switching autoregressive (MS-AR) and autoregressive (AR) models. Hourly data between the periods of January 1, 2014 and December 31, 2014 was obtained from the Ghana Grid company and used for the study. Using different information criteria, the MS(2)-AR(4) is selected as the optimal model to describe the dynamic behavior of electricity consumption during periods of power crisis in Ghana. The parameters of the MS(2)-AR(4) model are then estimated using the expectation-maximization algorithm. From the results, the likelihood of staying under a low electricity consumption regime is estimated to be 87%. The expected duration for a low electricity consumption regime is 7.8 hours, and the high electricity consumption regime is expected to last 2.3 hours before switching to the low demand regime. The proposed model is robust as compared to the autoregressive model because it effectively captures the dynamics of electricity demand over time through the peaks and significant fluctuations in consumption patterns. Similarly, the model can identify distinct regime changes linked to electricity consumption during periods of power crises.
Multidimensional hypergeometric functions and representation theory of Lie algebras and quantum groups

Construction of complexes calculating homology of the complement of a configuration construction of homology complexes for a discriminantal configuration algebraic interpretation of chain complexes of a discriminantal configuration quasi-isomorphism of two-sided Hochschild complexes to suitable one-sided Hochschild complexes bundle properties of a discriminantal configuration R-matrix for the two-sided complexes monodromy R-matrix operator as the canonical element, quantum doubles hypergeometric integrals KacMoody Lie algebras without Serre's relations and their doubles hypergeometric integrals of a discriminantal configuration resonances at infinity degenerations of discriminantal configurations remarks on homology groups of a configuration with coefficients in local systems.
Streaming Radiance Fields for 3D Video Synthesis

We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes. Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame. By exploiting the simple yet effective tuning strategy with narrow bands, the proposed method realizes a feasible framework for handling video sequences on-the-fly with high training efficiency. The storage overhead induced by using explicit grid representations can be significantly reduced through the use of model difference based compression. We also introduce an efficient strategy to further accelerate model optimization for each frame. Experiments on challenging video sequences demonstrate that our approach is capable of achieving a training speed of 15 seconds per-frame with competitive rendering quality, which attains $1000 \times$ speedup over the state-of-the-art implicit methods. Code is available at https://github.com/AlgoHunt/StreamRF.
The Exact Moments of a Ratio of Quadratic Forms in Normal Variables

The invention relates to sealing means for a rotary shaft which passes through a container wall, such as the casing of a machine, e.g., a pump which operates on water or slurries. A first face seal is provided between lightly contacting sealing surfaces of two rings one of which is secured to the shaft and the other to the wall. These sealing surfaces may be of hard material, e.g. steel, and serve as a barrier to solid material. A second face seal is then provided which acts as a barrier to liquid passing the first face seal and is comprised of co-operating relatively hard and soft sealing surfaces.
A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem

In this paper, we study a class of bilevel optimization problems, also known as simple bilevel optimization, where we minimize a smooth objective function over the optimal solution set of another convex constrained optimization problem. Several iterative methods have been developed for tackling this class of problems. Alas, their convergence guarantees are either asymptotic for the upper-level objective, or the convergence rates are slow and sub-optimal. To address this issue, in this paper, we introduce a novel bilevel optimization method that locally approximates the solution set of the lower-level problem via a cutting plane, and then runs a conditional gradient update to decrease the upper-level objective. When the upper-level objective is convex, we show that our method requires ${\mathcal{O}}(\max\{1/\epsilon_f,1/\epsilon_g\})$ iterations to find a solution that is $\epsilon_f$-optimal for the upper-level objective and $\epsilon_g$-optimal for the lower-level objective. Moreover, when the upper-level objective is non-convex, our method requires ${\mathcal{O}}(\max\{1/\epsilon_f^2,1/(\epsilon_f\epsilon_g)\})$ iterations to find an $(\epsilon_f,\epsilon_g)$-optimal solution. We also prove stronger convergence guarantees under the H\"olderian error bound assumption on the lower-level problem. To the best of our knowledge, our method achieves the best-known iteration complexity for the considered class of bilevel problems.
PREPRINT: Do OpenSSF Scorecard Practices Contribute to Fewer Vulnerabilities?

—Due to the ever-increasing security breaches, practitioners are motivated to produce more secure software. In the United States, the White House Ofﬁce released a memorandum on Executive Order (EO) 14028 that mandates organizations provide self-attestation of the use of secure software development practices. The OpenSSF Scorecard project allows practitioners to measure the use of software security practices automatically. However, little research has been done to determine whether the use of security practices improves package security, particularly which security practices have the biggest impact on security outcomes. The goal of this study is to assist practitioners and researchers making informed decisions on which security practices to adopt through the development of models between software security practice scores and security vulnerability counts. To that end, we developed ﬁve supervised machine learning models for npm and PyPI packages using the OpenSSF Score-cared security practices scores and aggregate security scores as predictors and the number of externally-reported vulnerabilities as a target variable. Our models found four security practices ( Maintained, Code Review, Branch Protection, and Security Policy) were the most important practices inﬂuencing vulnerability count. However, we had low R 2 (ranging from 9% to 12%) when we tested the models to predict vulnerability counts. Additionally, we observed that the number of reported vulnerabilities increased rather than reduced as the aggregate security score of the packages increased. Both ﬁndings indicate that additional factors may inﬂuence the package vulnerability count. Other factors, such as the scarcity of vulnerability data, time to implicate security practices vs time to detect vulnerabilities, and the need for more adequate scripts to detect security practices, may impede the data-driven studies to indicate that a practice can aid in the reduction of externally-reported vulnerabilities. We suggest that vulnerability count and security score data be reﬁned such that these measures may be used to provide actionable guidance on security practices.
Lieb—Thirring type bounds for perturbed Schrödinger operators with single-well potentials

We prove an upper bound on the sum of distances between eigenvalues of a perturbed Schrödinger operator H0 − V and the lowest eigenvalue of H0. Our results hold for operators H0 = −Δ − V0 in one dimension with single-well potentials. We rely on a variation of the well-known commutation method. In Pöschl–Teller and Coulomb cases, we are able to use explicit factorizations to establish improved bounds.
Leveraging Demonstrations with Latent Space Priors

Demonstrations provide insight into relevant state or action space regions, bearing great potential to boost the efficiency and practicality of reinforcement learning agents. In this work, we propose to leverage demonstration datasets by combining skill learning and sequence modeling. Starting with a learned joint latent space, we separately train a generative model of demonstration sequences and an accompanying low-level policy. The sequence model forms a latent space prior over plausible demonstration behaviors to accelerate learning of high-level policies. We show how to acquire such priors from state-only motion capture demonstrations and explore several methods for integrating them into policy learning on transfer tasks. Our experimental results confirm that latent space priors provide significant gains in learning speed and final performance. We benchmark our approach on a set of challenging sparse-reward environments with a complex, simulated humanoid, and on offline RL benchmarks for navigation and object manipulation. Videos, source code and pre-trained models are available at the corresponding project website at https://facebookresearch.github. io/latent-space-priors .
Autoregressive Structured Prediction with Language Models

Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks. However, there are many difficult design decisions to represent structures (e.g. tagged text, coreference chains) in a way such that they can be captured by PLMs. Prior work on structured prediction with PLMs typically flattens the structured output into a sequence, which limits the quality of structural information being learned and leads to inferior performance compared to classic discriminative models. In this work, we describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs, allowing in-structure dependencies to be learned without any loss. Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at, namely, named entity recognition, end-to-end relation extraction, and coreference resolution.
Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension

In this work, we focus on dialogue reading comprehension (DRC), a task extracting answer spans for questions from dialogues. Dialogue context modeling in DRC is tricky due to complex speaker information and noisy dialogue context. To solve the two problems, previous research proposes two self-supervised tasks respectively: guessing who a randomly masked speaker is according to the dialogue and predicting which utterance in the dialogue contains the answer. Although these tasks are effective, there are still urging problems: (1) randomly masking speakers regardless of the question cannot map the speaker mentioned in the question to the corresponding speaker in the dialogue, and ignores the speaker-centric nature of utterances. This leads to wrong answer extraction from utterances in unrelated interlocutors' scopes; (2) the single utterance prediction, preferring utterances similar to the question, is limited in finding answer-contained utterances not similar to the question. To alleviate these problems, we first propose a new key utterances extracting method. It performs prediction on the unit formed by several contiguous utterances, which can realize more answer-contained utterances. Based on utterances in the extracted units, we then propose Question-Interlocutor Scope Realized Graph (QuISG) modeling. As a graph constructed on the text of utterances, QuISG additionally involves the question and question-mentioning speaker names as nodes. To realize interlocutor scopes, speakers in the dialogue are connected with the words in their corresponding utterances. Experiments on the benchmarks show that our method can achieve better and competitive results against previous works.
Diversity Order Analysis for Quantized Constant Envelope Transmission

Quantized constant envelope (QCE) transmission is a popular and effective technique to reduce the hardware cost and improve the power efficiency of 5G and beyond systems equipped with large antenna arrays. It has been widely observed that the number of quantization levels has a substantial impact on the system performance. This paper aims to quantify the impact of the number of quantization levels on the system performance. Specifically, we consider a downlink single-user multiple-input-single-output (MISO) system with <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula>-phase shift keying (PSK) constellation under the Rayleigh fading channel. We first derive a novel bound on the system symbol error probability (SEP). Based on the derived SEP bound, we characterize the achievable diversity order of the quantized matched filter (MF) precoding strategy. Our results show that full diversity order can be achieved when the number of quantization levels <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula> is greater than the PSK constellation order <inline-formula><tex-math notation="LaTeX">$M,$</tex-math></inline-formula> i.e., <inline-formula><tex-math notation="LaTeX">$L>M$</tex-math></inline-formula>, only half diversity order is achievable when <inline-formula><tex-math notation="LaTeX">$L=M$</tex-math></inline-formula>, and the achievable diversity order is 0 when <inline-formula><tex-math notation="LaTeX">$L< M$</tex-math></inline-formula>. Simulation results verify our theoretical analysis.
Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using Wav2vec 2.0

Self-supervised learning approaches have lately achieved great success on a broad spectrum of machine learning problems. In the field of speech processing, one of the most successful recent self-supervised models is wav2vec 2.0. In this paper, we explore the effectiveness of this model on three basic speech classification tasks: speaker change detection, overlapped speech detection, and voice activity detection. First, we concentrate on only one task – speaker change detection – where our proposed system surpasses the previously reported results on four different corpora, and achieves comparable performance even when trained on out-of-domain data from an artificially designed dataset. Then we expand our approach to tackle all three tasks in a single multitask system with state-of-the-art performance on the AMI corpus. The implementation of the algorithms in this paper is publicly available at https://github.com/mkunes/w2v2_audioFrameClassification.
Channel-Aware Ordered Successive Relaying with Finite-Blocklength Coding

Successive relaying can improve the transmission rate by allowing the source and relays to transmit messages simultaneously, but it may cause severe inter-relay interference (IRI). IRI cancellation schemes have been proposed to mitigate IRI. However, interference cancellation methods have a high risk of error propagation, resulting in a severe transmission rate loss in finite blocklength regimes. Thus, jointly decoding for successive relaying with finite-blocklength coding (FBC) remains a challenge. In this paper, we present an optimized channel-aware ordered successive relaying protocol with finite-blocklength coding (CAO-SIR-FBC), which can recover the rate loss by carefully adapting the relay transmission order and rate. We analyze the average throughput of the CAO-SIR-FBC method, based on which a closed-form expression in a high signal-to-noise regime (SNR) is presented. Average throughput analysis and simulations show that CAO-SIR-FBC outperforms conventional two-timeslot half-duplex relaying in terms of spectral efficiency.
Parallel Gated Neural Network with Attention Mechanism for Speech Enhancement

Deep learning algorithm are increasingly used for speech enhancement (SE). In supervised methods, global and local information is required for accurate spectral mapping. A key restriction is often poor capture of key contextual information. To leverage long-term for target speakers and compensate distortions of cleaned speech, this paper adopts a sequence-to-sequence (S2S) mapping structure and proposes a novel monaural speech enhancement system, consisting of a Feature Extraction Block (FEB), a Compensation Enhancement Block (ComEB) and a Mask Block (MB). In the FEB a U-net block is used to extract abstract features using complex-valued spectra with one path to suppress the background noise in the magnitude domain using masking methods and the MB takes magnitude features from the FEB and compensates the lost complex-domain features produced from ComEB to restore the final cleaned speech. Experiments are conducted on the Librispeech dataset and results show that the proposed model obtains a good performance in terms of ESTOI and PESQ scores.
DEMIS: A Threat Model for Selectively Encrypted Visual Surveillance Data

The monitoring of individuals/objects has become increasingly possible in recent years due to the convenience of integrated cameras in many devices. Due to the important moments or activities of people captured by these devices, it has made it a great asset for attackers to launch attacks against by exploiting the weaknesses in these devices. Different studies proposed naïve/selective encryption of the captured visual data for safety but despite the encryption, an attacker can still access or manipulate such data. This paper proposed a novel threat model, DEMIS which helps analyse the threats against such encrypted videos. The paper also examines the attack vectors that can be used for threats and the mitigation that will reduce or prevent the attack. For experiments, firstly the data set is generated by applying selective encryption on the Regions-of-interests (ROI) of the tested videos using the image segmentation technique and Chacha20 cipher. Secondly, different types of attacks, such as inverse, lowercase, uppercase, random insertion, and malleability attacks were simulated in experiments to show the effects of the attacks, the risk matrix, and the severity of these attacks. Our developed data set with the original, selective encrypted, and attacked videos are available on git-repository for future researchers.
WebCrack: Dynamic Dictionary Adjustment for Web Weak Password Detection based on Blasting Response Event Discrimination

The feature diversity of different web systems in page elements, submission contents and return information makes it difficult to detect weak password automatically. To solve this problem, multifactor correlation detection method—as integrated in the DBKER algorithm—is proposed to achieve automatic detection of web weak passwords and universal passwords. It generates password dictionaries based on PCFG algorithm, proposes to judge blasting result via 4 steps with traditional static keyword features and dynamic page feature information. Then the blasting failure events are discriminated and the usernames are blasted based on response time. Thereafter the weak password dictionary is dynamically adjusted according to the hints provided by the response failure page. Based on the algorithm, this paper implements a detection system named WebCrack. Experimental results of two blasting tests on DedeCMS and Discuz! systems as well as a random backend test show that the proposed method can detect weak passwords and universal passwords of various web systems with an average accuracy rate of about 93.75%, providing security advisories for users' password settings with strong practicability. Keywords—web security, weak password detection, universal password, automatic login, dynamic dictionary adjustment
The Championship Simulator: Architectural Simulation for Education and Competition

Recent years have seen a dramatic increase in the microarchitectural complexity of processors. This increase in complexity presents a twofold challenge for the field of computer architecture. First, no individual architect can fully comprehend the complexity of the entire microarchitecture of the core. This leads to increasingly specialized architects, who treat parts of the core outside their particular expertise as black boxes. Second, with increasing complexity, the field becomes decreasingly accessible to new students of the field. When learning core microarchitecture, new students must first learn the big picture of how the system works in order to understand how the pieces all fit together. The tools used to study microarchitecture experience a similar struggle. As with the microarchitectures they simulate, an increase in complexity reduces accessibility to new users. In this work, we present ChampSim. ChampSim uses a modular design and configurable structure to achieve a low barrier to entry into the field of microarchitecural simulation. ChampSim has shown itself to be useful in multiple areas of research, competition, and education. In this way, we seek to promote access and inclusion despite the increasing complexity of the field of computer architecture.
Analyzing Multi-Task Learning for Abstractive Text Summarization

Despite the recent success of multi-task learning and pre-finetuning for natural language understanding, few works have studied the effects of task families on abstractive text summarization. Task families are a form of task grouping during the pre-finetuning stage to learn common skills, such as reading comprehension. To close this gap, we analyze the influence of multi-task learning strategies using task families for the English abstractive text summarization task. We group tasks into one of three strategies, i.e., sequential, simultaneous, and continual multi-task learning, and evaluate trained models through two downstream tasks. We find that certain combinations of task families (e.g., advanced reading comprehension and natural language inference) positively impact downstream performance. Further, we find that choice and combinations of task families influence downstream performance more than the training scheme, supporting the use of task families for abstractive text