Reviewerly/exHarmony huggingface dataset:
Near real-time filtering of high precision borehole strainmeter signals for volcano surveillance

Volumetric strain signal recorded by the Sacks-Evertson strainmeter has a very high resolution and can measure small strain changes (down to 10–9) accompanying volcanic processes. However, different disturbing components perturb the recorded strain signal. These disturbances can mask ultra-small strain changes related to volcano activity and thus need to be filtered in order to accurately monitor volcano deformation. We developed the software STRALERT (STRain and wArning signaLs in nEar Real-Time) to provide both the recorded and the filtered strain signals in near real-time to the Surveillance Room of the “Istituto Nazionale di Geofisica e Vulcanologia – Osservatorio Etneo” which monitors the Etna activity. The software encloses a modified version of the code BAYTAP-G for filtering the incoming signal from the continuously running strainmeter at Etna. Thanks to the appropriate and robust signal filtering, STRALERT is capable to provide anefficient estimate of transient strain changes, which concur in tracing volcano deformation and detecting the onset and the conclusion of eruptive events. Our findings demonstrate the important contribution of STRALERT for volcano surveillance operations.
Experimental Demonstration of an Efficient Mach–Zehnder Modulator Bias Control for Quantum Key Distribution Systems

A Mach–Zehnder modulator (MZM) is necessary for implementing a decoy-state protocol in a practical quantum key distribution (QKD) system. However, an MZM bias control method optimized for QKD systems has been missing to date. In this study, we propose an MZM bias control method using N (≥2) diagnostic pulses. The proposed method can be efficiently applied to a QKD system without any additional hardware such as light sources or detectors. Furthermore, it does not reduce the key rate significantly because it uses time slots allocated to existing decoy pulses. We conducted an experimental demonstration of the proposed method in a field-deployed 1 × 3 QKD network and a laboratory test. It is shown that our method can maintain the MZM extinction ratio stably over 20 dB (bit error rate ≤1%), even in an actual network environment for a significant period. Consequently, we achieved successful QKD performances.
Seeking the Truth Beyond the Data. An Unsupervised Machine Learning Approach

Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The goal of this process is to provide a useful aid to the researcher that will help her/him to identify patterns among the data. Dealing with large databases, such patterns may not be easily detectable without the contribution of a clustering algorithm. This article provides a deep description of the most widely used clustering methodologies accompanied by useful presentations concerning suitable parameter selection and initializations. Simultaneously, this article not only represents a review highlighting the major elements of examined clustering techniques but emphasizes the comparison of these algorithms' clustering efficiency based on 3 datasets, revealing their existing weaknesses and capabilities through accuracy and complexity, during the confrontation of discrete and continuous observations. The produced results help us extract valuable conclusions about the appropriateness of the examined clustering techniques in accordance with the dataset's size.
Quantum Metropolis Solver: A Quantum Walks Approach to Optimization Problems

The efficient resolution of optimization problems is one of the key issues in today's industry. This task relies mainly on classical algorithms that present scalability problems and processing limitations. Quantum computing has emerged to challenge these types of problems. In this paper, we focus on the Metropolis-Hastings quantum algorithm that is based on quantum walks. We use this algorithm to build a quantum software tool called Quantum Metropolis Solver (QMS). We validate QMS with the N-Queen problem to show a potential quantum advantage in an example that can be easily extrapolated to an Artificial Intelligence domain. We carry out different simulations to validate the performance of QMS and its configuration.
Learnability of Competitive Threshold Models

Modeling the spread of social contagions is central to various applications in social computing. In this paper, we study the learnability of the competitive threshold model from a theoretical perspective. We demonstrate how competitive threshold models can be seamlessly simulated by artificial neural networks with finite VC dimensions, which enables analytical sample complexity and generalization bounds. Based on the proposed hypothesis space, we design efficient algorithms under the empirical risk minimization scheme. The theoretical insights are finally translated into practical and explainable modeling methods, the effectiveness of which is verified through a sanity check over a few synthetic and real datasets. The experimental results promisingly show that our method enjoys a decent performance without using excessive data points, outperforming off-the-shelf methods.
Graph Masked Autoencoder Enhanced Predictor for Neural Architecture Search

Performance estimation of neural architecture is a crucial component of neural architecture search (NAS). Meanwhile, neural predictor is a current mainstream performance estimation method. However, it is a challenging task to train the predictor with few architecture evaluations for efficient NAS. In this paper, we propose a graph masked autoencoder (GMAE) enhanced predictor, which can reduce the dependence on supervision data by self-supervised pre-training with untrained architectures. We compare our GMAE-enhanced predictor with existing predictors in different search spaces, and experimental results show that our predictor has high query utilization. Moreover, GMAE-enhanced predictor with different search strategies can discover competitive architectures in different search spaces. Code and supplementary materials are available at https://github.com/kunjing96/GMAENAS.git.
Automated Sifting of Stories from Simulated Storyworlds

Story sifting (or story recognition) allows for the exploration of events, stories, and patterns that emerge from simulated storyworlds. The goal of this work is to reduce the authoring burden for creating sifting queries. In this paper, we use the event traces of simulated storyworlds to create Dynamic Character Networks that track the changing relationship scores between characters in a simulation. These networks allow for the fortunes between any two characters to be plotted against time as a story arc. Similarity scores between story arcs from the simulation and a user’s query arc can be calculated using the Dynamic Time Warping algorithm. Events corresponding to the story arc that best matches the query arc can then be returned to the user, thus providing an intuitive means for users to sift a variety of stories without coding a search query. These components are implemented in our experimental prototype ARC SIFT. The results of a user study support our expectation that ARC SIFT is an intuitive and accurate tool that allows human users to sift stories out from a larger chronicle of events emerging from a simulated story world.
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent (Extended Abstract)

Expectation maximization (EM) is the default algorithm for fitting probabilistic models with missing or latent variables, yet we lack a full understanding of its non-asymptotic convergence properties. Previous works show results along the lines of “EM converges at least as fast as gradient descent” by assuming the conditions for the convergence of gradient descent apply. This approach is not only loose, in that it does not capture that EM can make more progress than a gradient step, but the assumptions fail to hold for textbook examples of EM like Gaussian mixtures. In this work, we show that for the common setting of exponential family distributions, viewing EM as a mirror descent algorithm leads to convergence rates in Kullback-Leibler (KL) divergence and how the KL divergence is related to first-order stationarity via Bregman divergences. In contrast to previous works, the analysis is invariant to the choice of parametrization and holds with minimal assumptions. We also show applications of these ideas to local linear (and superlinear) convergence rates, generalized EM, and non-exponential family distributions.
Knowledge, Attitude, and Barriers in Distance Education Using Smartphones among Pediatric Dentistry Master Degree students during COVID-19 Pandemic in Egypt: A Cross-Sectional Study

Objective: To assess the knowledge, attitude, and barriers in distance education using smartphones among Pediatric Dentistry Master Degree students during COVID-19 Pandemic in Egypt. Methods: A cross-sectional survey was performed by the distribution of a validated well-constructed online questionnaire consisting of five sections (21 questions). The five sections gathered information about demographic characteristics of the participants, the rate of smartphone utilization, the extent of smartphones utilization for knowledge purposes, the participants' attitudes toward smartphone utilization and barriers to smartphones utilization in distance education. Results: The results revealed that all the respondents had smartphones. 73.8% of the respondents used the internet more than 4 hours daily. 93.4% of the respondents preferred using their smartphone for study and research activities rather than using the library. 67.2% strongly agreed that the use of mobile tools is beneficial. 60.7% of the respondents disagreed that wireless access is available in all their locations. Conclusion: Smartphones provided an additional mean for dental education and knowledge acquisition during COVID-19 pandemic. Postgraduate dental students have adequate knowledge about dental mobile applications that can be used in dental education. Lack of internet availability in the different premises can be considered a possible barrier to smartphone technology utilization.
Adaptive Information Belief Space Planning

Reasoning about uncertainty is vital in many real-life autonomous systems. However, current state-of-the-art planning algorithms either cannot reason about uncertainty explicitly, or do so with high computational burden. Here, we focus on making informed decisions efficiently, using reward functions that explicitly deal with uncertainty. We formulate an approximation, namely an abstract observation model, that uses an aggregation scheme to alleviate computational costs. We derive bounds on the expected information-theoretic reward function and, as a consequence, on the value function. We then propose a method to refine aggregation to achieve identical action selection in a fraction of the computational time.
Evaluation Methods for Representation Learning: A Survey

Representation learning enables us to automatically extract generic feature representations from a dataset to solve another machine learning task. Recently, extracted feature representations by a representation learning algorithm and a simple predictor have exhibited state-of-the-art performance on several machine learning tasks. Despite its remarkable progress, there exist various ways to evaluate representation learning algorithms depending on the application because of the flexibility of representation learning. To understand the current applications of representation learning, we review evaluation methods of representation learning algorithms. On the basis of our evaluation survey, we also discuss the future direction of representation learning. The extended version, https://arxiv.org/abs/2204.08226, gives more detailed discussions and a survey on theoretical analyses.
Learning to Hash Naturally Sorts

Learning to hash pictures a list-wise sorting problem. Its testing metrics, e.g., mean-average precision, count on a sorted candidate list ordered by pair-wise code similarity. However, scarcely does one train a deep hashing model with the sorted results end-to-end because of the non-differentiable nature of the sorting operation. This inconsistency in the objectives of training and test may lead to sub-optimal performance since the training loss often fails to reflect the actual retrieval metric. In this paper, we tackle this problem by introducing Naturally-Sorted Hashing (NSH). We sort the Hamming distances of samples' hash codes and accordingly gather their latent representations for self-supervised training. Thanks to the recent advances in differentiable sorting approximations, the hash head receives gradients from the sorter so that the hash encoder can be optimized along with the training procedure. Additionally, we describe a novel Sorted Noise-Contrastive Estimation (SortedNCE) loss that selectively picks positive and negative samples for contrastive learning, which allows NSH to mine data semantic relations during training in an unsupervised manner. Our extensive experiments show the proposed NSH model significantly outperforms the existing unsupervised hashing methods on three benchmarked datasets.
Automated Program Analysis: Revisiting Precondition Inference through Constraint Acquisition

Program annotations under the form of function pre/postconditions are crucial for many software engineering and program verification applications. Unfortunately, such annotations are rarely available and must be retrofit by hand. In this paper, we explore how Constraint Acquisition (CA), a learning framework from Constraint Programming, can be leveraged to automatically infer program preconditions in a black-box manner, from input-output observations. We propose PreCA, the first ever framework based on active constraint acquisition dedicated to infer memory-related preconditions. PreCA overpasses prior techniques based on program analysis and formal methods, offering well-identified guarantees and returning more precise results in practice.
PPT: Backdoor Attacks on Pre-trained Models via Poisoned Prompt Tuning

Recently, prompt tuning has shown remarkable performance as a new learning paradigm, which freezes pre-trained language models (PLMs) and only tunes some soft prompts. A fixed PLM only needs to be loaded with different prompts to adapt different downstream tasks. However, the prompts associated with PLMs may be added with some malicious behaviors, such as backdoors. The victim model will be implanted with a backdoor by using the poisoned prompt. In this paper, we propose to obtain the poisoned prompt for PLMs and corresponding downstream tasks by prompt tuning. We name this Poisoned Prompt Tuning method "PPT". The poisoned prompt can lead a shortcut between the specific trigger word and the target label word to be created for the PLM. So the attacker can simply manipulate the prediction of the entire model by just a small prompt. Our experiments on various text classification tasks show that PPT can achieve a 99% attack success rate with almost no accuracy sacrificed on original task. We hope this work can raise the awareness of the possible security threats hidden in the prompt.
Learning Graph-based Residual Aggregation Network for Group Activity Recognition

Group activity recognition aims to understand the overall behavior performed by a group of people. Recently, some graph-based methods have made progress by learning the relation graphs among multiple persons. However, the differences between an individual and others play an important role in identifying confusable group activities, which have not been elaborately explored by previous methods. In this paper, a novel Graph-based Residual AggregatIon Network (GRAIN) is proposed to model the differences among all persons of the whole group, which is end-to-end trainable. Specifically, a new local residual relation module is explicitly proposed to capture the local spatiotemporal differences of relevant persons, which is further combined with the multi-graph relation networks. Moreover, a weighted aggregation strategy is devised to adaptively select multi-level spatiotemporal features from the appearance-level information to high level relations. Finally, our model is capable of extracting a comprehensive representation and inferring the group activity in an end-to-end manner. The experimental results on two popular benchmarks for group activity recognition clearly demonstrate the superior performance of our method in comparison with the state-of-the-art methods.
Inverting 43-step MD4 via Cube-and-Conquer

MD4 is a prominent cryptographic hash function proposed in 1990. The full version consists of 48 steps and produces a hash of size 128 bits given a message of an arbitrary finite size. In 2007, its truncated 39-step version was inverted via reducing to SAT and applying a CDCL solver. Since that time, several attempts have been made but the 40-step version still remains unbroken. In this study, 40-, 41-, 42-, and 43-step versions of MD4 are successfully inverted. The problems are reduced to SAT and solved via the Cube-and-Conquer approach. Two algorithms are proposed for this purpose. The first one generates inversion problems for MD4 by adding special constraints. The second one is aimed at finding a proper threshold for the cubing phase of Cube-and-Conquer. While the first algorithm is focused on inverting MD4 and similar cryptographic hash functions, the second one is not area specific and so is applicable to a variety of classes of hard SAT instances.
Automated Synthesis of Mechanisms

Mechanism Design aims to design a game so that a desirable outcome is reached regardless of agents' self-interests. In this paper, we show how this problem can be rephrased as a synthesis problem, where mechanisms are automatically synthesized from a partial or complete specification in a high-level logical language. We show that Quantitative Strategy Logic is a perfect candidate for specifying mechanisms as it can express complex strategic and quantitative properties. We solve automated mechanism design in two cases: when the number of actions is bounded, and when agents play in turn.
Self-supervised Learning and Adaptation for Single Image Dehazing

Existing deep image dehazing methods usually depend on supervised learning with a large number of hazy-clean image pairs which are expensive or difficult to collect. Moreover, dehazing performance of the learned model may deteriorate significantly when the training hazy-clean image pairs are insufficient and are different from real hazy images in applications. In this paper, we show that exploiting large scale training set and adapting to real hazy images are two critical issues in learning effective deep dehazing models. Under the depth guidance estimated by a well-trained depth estimation network, we leverage the conventional atmospheric scattering model to generate massive hazy-clean image pairs for the self-supervised pre-training of dehazing network. Furthermore, self-supervised adaptation is presented to adapt pre-trained network to real hazy images. Learning without forgetting strategy is also deployed in self-supervised adaptation by combining self-supervision and model adaptation via contrastive learning. Experiments show that our proposed method performs favorably against the state-of-the-art methods, and is quite efficient, i.e., handling a 4K image in 23 ms. The codes are available at https://github.com/DongLiangSXU/SLAdehazing.
Plausibility Reasoning via Projected Answer Set Counting - A Hybrid Approach

Answer set programming is a form of declarative programming widely used to solve difficult search problems. Probabilistic applications however require to go beyond simple search for one solution and need counting. One such application is plausibility reasoning, which provides more fine-grained reasoning mode between simple brave and cautious reasoning. When modeling with ASP, we oftentimes introduce auxiliary atoms in the program. If these atoms are functionally independent of the atoms of interest, we need to hide the auxiliary atoms and project the count to the atoms of interest resulting in the problem projected answer set counting. In practice, counting becomes quickly infeasible with standard systems such as clasp. In this paper, we present a novel hybrid approach for plausibility reasoning under projections, thereby relying on projected answer set counting as basis. Our approach combines existing systems with fast dynamic programming, which in our experiments shows advantages over existing ASP systems.
On the Complexity of Enumerating Prime Implicants from Decision-DNNF Circuits

We consider the problem Enum·IP of enumerating prime implicants of Boolean functions represented by decision decomposable negation normal form (dec-DNNF) circuits. We study Enum·IP from dec-DNNF within the framework of enumeration complexity and prove that it is in OutputP, the class of output polynomial enumeration problems, and more precisely in IncP, the class of polynomial incremental time enumeration problems. We then focus on two closely related, but seemingly harder, enumeration problems where further restrictions are put on the prime implicants to be generated. In the first problem, one is only interested in prime implicants representing subset-minimal abductive explanations, a notion much investigated in AI for more than thirty years. In the second problem, the target is prime implicants representing sufficient reasons, a recent yet important notion in the emerging field of eXplainable AI, since they aim to explain predictions achieved by machine learning classifiers. We provide evidence showing that enumerating specific prime implicants corresponding to subset-minimal abductive explanations or to sufficient reasons is not in OutputP.
The Egocentric Logic of Preferences

The paper studies preferences of agents about other agents in a social network. It proposes a logical system that captures the properties of such preferences, called "likes". The system can express nested constructions "agent likes humbled people", "agent likes those who like humbled people", etc. The main technical results are a model checking algorithm and a sound, complete, and decidable axiomatization of the proposed system.
AttExplainer: Explain Transformer via Attention by Reinforcement Learning

Transformer and its variants, built based on attention mechanisms, have recently achieved remarkable performance in many NLP tasks. Most existing works on Transformer explanation tend to reveal and utilize the attention matrix with human subjective intuitions in a qualitative manner. However, the huge size of dimensions directly challenges these methods to quantitatively analyze the attention matrix. Therefore, in this paper, we propose a novel reinforcement learning (RL) based framework for Transformer explanation via attention matrix, namely AttExplainer. The RL agent learns to perform step-by-step masking operations by observing the change in attention matrices. We have adapted our method to two scenarios, perturbation-based model explanation and text adversarial attack. Experiments on three widely used text classification benchmarks validate the effectiveness of the proposed method compared to state-of-the-art baselines. Additional studies show that our method is highly transferable and consistent with human intuition. The code of this paper is available at https://github.com/niuzaisheng/AttExplainer .