Miracle-dz/UniFAR-FTUs huggingface dataset:
Hateful Memes Challenge: An Enhanced Multimodal Framework

Hateful Meme Challenge proposed by Facebook AI has attracted contestants around the world. The challenge focuses on detecting hateful speech in multimodal memes. Various state-of-the-art deep learning models have been applied to this problem and the performance on challenge's leaderboard has also been constantly improved. In this paper, we enhance the hateful detection framework, including utilizing Detectron for feature extraction, exploring different setups of VisualBERT and UNITER models with different loss functions, researching the association between the hateful memes and the sensitive text features, and finally building ensemble method to boost model performance. The AUROC of our fine-tuned VisualBERT, UNITER, and ensemble method achieves 0.765, 0.790, and 0.803 on the challenge's test set, respectively, which beats the baseline models. Our code is available at https://github.com/yatingtian/hateful-meme
Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from videos for MediaEval 2021

Sports video analysis is a prevalent research topic due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests up to analysis of athletes' performance. The Sports Video task is part of the MediaEval 2021 benchmark. This task tackles fine-grained action detection and classification from videos. The focus is on recordings of table tennis games. Running since 2019, the task has offered a classification challenge from untrimmed video recorded in natural conditions with known temporal boundaries for each stroke. This year, the dataset is extended and offers, in addition, a detection challenge from untrimmed videos without annotations. This work aims at creating tools for sports coaches and players in order to analyze sports performance. Movement analysis and player profiling may be built upon such technology to enrich the training experience of athletes and improve their performance.
Quantum arithmetic dynamics

We study dynamics of the Lattès maps in the complex plane in terms of the Cuntz-Krieger algebras associated to the endomorphisms of the non-commutative tori. In particular, it is shown that iterations of the Lattès maps can be reduced to the dynamics of the subshifts of finite type. Using such a reduction, we calculate the zeta function of the Lattès maps.
An ASP-based Approach to Answering Natural Language Questions for Texts

An approach based on answer set programming (ASP) is proposed in this paper for representing knowledge generated from natural language texts. Knowledge in a text is modeled using a Neo Davidsonian-like formalism, which is then represented as an answer set program. Relevant commonsense knowledge is additionally imported from resources such as WordNet and represented in ASP. The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system. This approach can facilitate many natural language tasks such as automated question answering, text summarization, and automated question generation. ASP-based representation of techniques such as default reasoning, hierarchical knowledge organization, preferences over defaults, etc., are used to model commonsense reasoning methods required to accomplish these tasks. In this paper, we describe the CASPR system that we have developed to automate the task of answering natural language questions given English text. CASPR can be regarded as a system that answers questions by “understanding” the text and has been tested on the SQuAD data set, with promising results.
Soft-switching flyback inverter with enhanced power decoupling for photovoltaic applications

A novel single-phase flyback inverter for photovoltaic applications is proposed to achieve low-frequency ripple current reduction on the DC busbar and to draw sinusoidal current into the AC grid. Based on capacitive idling techniques, the proposed circuit topology is derived from a single-ended primary-inductance converter and two-switch flyback inverter to obtain soft-switching operation for all of the active switches. Compared with a buck-boost inverter and other flyback inverter topologies for AC photovoltaic module systems, no extra active switches are used in the proposed inverter to realise both soft-switching and enhanced power decoupling with only four active switches. Peak-current mode control method is employed in the control schemes to ensure pure sinusoidal current with unity power factor on the AC grid. Laboratory experimental results based on a 500 W prototype are provided to verify the effectiveness of the proposed inverter.
Data-Driven Outage Restoration Time Prediction via Transfer Learning With Cluster Ensembles

This article develops a data-driven approach to accurately predict the restoration time of outages under different scales and factors. To achieve the goal, the proposed method consists of three stages. First, given the unprecedented amount of data collected by utilities, a SDESC method is proposed to decompose historical outage datasets, which enjoys good computational efficiency and scalability. Specifically, each outage sample is represented by a linear combination of a small number of selected dictionary samples using a density-based method. Then, the dictionary-based representation is utilized to perform the spectral analysis to group the data samples with similar features into the same subsets. In the second stage, a knowledge-transfer-added restoration time prediction model is trained for each subset by combining weather information and outage-related features. The transfer learning technology is introduced to deal with the underestimation problem caused by data imbalance in different subsets, thus improving the model performance. Furthermore, to connect unseen outages with the learned outage subsets, a t-distributed stochastic neighbor embedding-based strategy is applied. The proposed method fully builds on and is also tested on a large real-world outage dataset from a utility provider with a time span of six consecutive years. The numerical results validate that our method has high prediction accuracy while showing good stability against real-world data limitations.
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions

The potential of reinforcement learning (RL) to deliver aligned and performant agents is partially bottlenecked by the reward engineering problem. One alternative to heuristic trial-and-error is preference-based RL (PbRL), where a reward function is inferred from sparse human feedback. However, prior PbRL methods lack interpretability of the learned reward structure, which hampers the ability to assess robustness and alignment. We propose an online, active preference learning algorithm that constructs reward functions with the intrinsically interpretable, compositional structure of a tree. Using both synthetic and human-provided feedback, we demonstrate sample-efficient learning of tree-structured reward functions in several environments, then harness the enhanced interpretability to explore and debug for alignment.
Manifold learning via quantum dynamics

We introduce an algorithm for computing geodesics on sampled manifolds that relies on simulation of quantum dynamics on a graph embedding of the sampled data. Our approach exploits classic results in semiclassical analysis and the quantum-classical correspondence, and forms a basis for techniques to learn the manifold from which a dataset is sampled, and subsequently for nonlinear dimensionality reduction of high-dimensional datasets. We illustrate the new algorithm with data sampled from model manifolds and also by a clustering demonstration based on COVID-19 mobility data. Finally, our method reveals interesting connections between the discretization provided by data sampling and quantization.
ADJUST: a dictionary-based joint reconstruction and unmixing method for spectral tomography

Advances in multi-spectral detectors are causing a paradigm shift in x-ray computed tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the object of interest. If the materials and their spectral responses are known a priori, the image reconstruction step is rather straightforward. If they are not known, however, the maps as well as the responses need to be estimated jointly. A conventional workflow in spectral CT involves performing volume reconstruction followed by material decomposition, or vice versa. However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem. To resolve this issue, we propose ‘A Dictionary-based Joint reconstruction and Unmixing method for Spectral Tomography’ (ADJUST). Our formulation relies on forming a dictionary of spectral signatures of materials common in CT and prior knowledge of the number of materials present in an object. In particular, we decompose the spectral volume linearly in terms of spatial material maps, a spectral dictionary, and the indicator of materials for the dictionary elements. We propose a memory-efficient accelerated alternating proximal gradient method to find an approximate solution to the resulting bi-convex problem. From numerical demonstrations on several synthetic phantoms, we observe that ADJUST performs exceedingly well compared to other state-of-the-art methods. Additionally, we address the robustness of ADJUST against limited and noisy measurement patterns. The demonstration of the proposed approach on a spectral micro-CT dataset shows its potential for real-world applications. Code is available at https://github.com/mzeegers/ADJUST.
Phase‐mode‐based construction of a coupling matrix for uniform circular arrays with a center element

A rigorous circuit model with a limited number of parameters is presented to describe mutual coupling in uniform circular arrays (UCAs) with a center element. The model, which is based on a phase‐mode expansion, is applied to construct a coupling matrix in order to compensate for mutual coupling in this type of arrays. A sample dual‐band uniform circular array with center element illustrates the use of the coupling matrix for direction‐of‐arrival (DOA) estimation. It is explained in which cases mutual coupling is fully taken into account by the coupling matrix and in which cases some residual errors due to mutual coupling remain in the DOA. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 291–298, 2006; Published online in Wiley InterScience (www. interscience.wiley.com). DOI 10.1002/mop.21330
An Inference Approach To Question Answering Over Knowledge Graphs

Knowledge Graphs (KG) act as a great tool for holding distilled information from large natural language text corpora. The problem of natural language querying over knowledge graphs is essential for the human consumption of this information. This problem is typically addressed by converting the natural language query to a structured query and then firing the structured query on the KG. Direct answering models over knowledge graphs in literature are very few. The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph. In this work, we convert the problem of natural language querying over knowledge graphs to an inference problem over premise-hypothesis pairs. Using trained deep learning models for the converted proxy inferencing problem, we provide the solution for the original natural language querying problem. Our method achieves over 90% accuracy on MetaQA dataset, beating the existing state-of-the-art. We also propose a model for inferencing called Hierarchical Recurrent Path Encoder(HRPE). The inferencing models can be fine-tuned to be used across domains with less training data. Our approach does not require large domain-specific training data for querying on new knowledge graphs from different domains.
Trying an IP Over NDN Packet Gateway

Even though the TCP/IP architecture has served the Internet quite satisfactorily during its more than forty years of lifespan, there are doubts about whether this host-centric paradigm is well suited for the communication patterns of modern applications. These applications mainly need to get information pieces without being bothered about where they are located in the network. Additionally, the lack of both in-built security mechanisms and proper global multicast support may also be a symptom of fundamental problems with the classic architecture. Proponents of the novel Information Centric Networking (ICN) paradigm assert that the replacement of the TCP/IP architecture with one based on the information itself could be the ideal solution to all these problems. However, if such a replacement ever takes place, it will need the help of some transition mechanisms, for instance, for the transmission of legacy IP traffic on top of the new ICN based networks. In this paper we design and optimize such an open source IP over ICN transition application using the Named Data Networking (NDN) proposal as our target ICN network. We performed several tests that confirm that our prototype is able to transparently transport IP traffic across a pure NDN network with negligible packets losses, low jitter, and good performance.
Task-oriented Dialogue Systems: performance vs. quality-optima, a review

Task-oriented dialogue systems (TODS) – designed to assist users to achieve a goal – are continuing to rise in popularity as various industries find ways to effectively harness their capabilities, saving both time and money. However, even state-of-the-art TODS have not yet reached their full potential. TODS typically have a primary design focus on completing the task at hand, so the metric of task-resolution should take priority. Other conversational quality attributes that may point to the success, or otherwise, of the dialogue, are usually ignored. This can harm the interactions between the human and the dialogue system leaving the user dissatisfied or frustrated. This paper explores the role of conversational quality attributes within dialogue systems, looking at if, how, and where they are utilised, and examining their correlation with the performance of the dialogue system.
Ab initio
 investigation of H-bond disordering in δ-AlOOH

$\delta$-AlOOH ($\delta$) is a high-pressure hydrous phase that participates in the deep geological water cycle. At 0 GPa, $\delta$ has asymmetric hydrogen bonds (H-bonds). Under pressure, it exhibits H-bond disordering, tunneling, and finally, H-bond symmetrization at ~18 GPa. This study investigates these 300 K pressure-induced state changes in $\delta$ with ab initio calculations. H-bond disordering in $\delta$ was modeled using supercell multi-configuration quasiharmonic calculations. We examine: (a) energy barriers for proton jumps, (b) the pressure dependence of phonon frequencies, (c) 300 K compressibility, (d) neutron diffraction pattern anomalies, and (e) compare ab initio bond lengths with measured ones. Such thorough and systematic comparisons indicate that: (a) proton"disorder"has a restricted meaning when applied to $\delta$. Nevertheless, H-bonds are disordered between 0 and 8 GPa, and a gradual change in H-bond configuration results in enhanced compressibility. (b) several structural and vibrational anomalies at ~8 GPa are consistent with the disappearance of a particular (HOC-12) H-bond configuration and its change into another one (HOC-11*). (c) between 8-11 GPa, H-bond configuration (HOC-11*) is generally ordered, at least in short- to mid-range scale. (d) between 11.5-18 GPa, H-bond lengths approach a critical value that impedes compression, resulting in decreased compressibility. In this pressure range, especially approaching H-bond symmetrization at ~18 GPa, anharmonicity and tunneling should play an essential role in the proton dynamics. Further simulations accounting for these effects are desirable to clarify the protons' state in this pressure range.
ANUBIS: a provenance graph-based framework for advanced persistent threat detection

We present ANUBIS, a highly effective machine learning-based APT detection system. Our design philosophy for ANUBIS involves two principal components. Firstly, we intend ANUBIS to be effectively utilized by cyber-response teams. Therefore, prediction explainability is one of the main focuses of ANUBIS design. Secondly, ANUBIS uses system provenance graphs to capture causality and thereby achieves high detection performance. At the core of the predictive capability of ANUBIS, there is a Bayesian Neural Network that can tell how confident it is in its predictions. We evaluate ANUBIS against a recent APT dataset (DARPA OpTC) and show that ANUBIS can detect malicious activity akin to APT campaigns with high accuracy. Moreover, ANUBIS learns about high-level patterns that allow it to explain its predictions to threat analysts. The high predictive performance with explainable attack story reconstruction makes ANUBIS an effective tool to use for enterprise cyber defense.
The PUEVA Inventory: A Toolkit to Evaluate the Personality, Usability and Enjoyability of Voice Agents

The proliferation of voice agents in consumer devices requires new tools for evaluating these systems beyond their technical functionality. This paper presents a toolkit for the evaluation of Voice User Interfaces (VUIs) with the intention of measuring the crucial factors of subjective enjoyment in the user experience. The PUEVA toolkit was constructed using a meta-analysis of existing literature, structured N=20 and semi-structured N=18 interviews and a within subjects lab study. The resulting questionnaire contains 35 items that represent 12 scales in three categories: (1) Personality (2) Usablity and (3) Enjoyability. The PUEVA Toolkit moves us towards the capacity to evaluate and compare subjective, joyful experiences in between-subject as well as within-subject research designs.
Siegel Zeros, Twin Primes, Goldbach’s Conjecture, and Primes in Short Intervals

We study the distribution of prime numbers under the unlikely assumption that Siegel zeros exist. In particular, we prove for $$ \begin{align*} & \sum_{n \leq X} \Lambda(n) \Lambda(\pm n+h) \end{align*}$$an asymptotic formula that holds uniformly for $h = O(X)$. Such an asymptotic formula has been previously obtained only for fixed $h$ in which case our result quantitatively improves those of Heath-Brown (1983) and Tao and Teräväinen (2021). Since our main theorems work also for large $h$, we can derive new results concerning connections between Siegel zeros and the Goldbach conjecture and between Siegel zeros and primes in almost all very short intervals.
Automatic Termination for Hyperparameter Optimization

Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget, it is hard to pre-specify an optimal value in advance. In this work, we propose an effective and intuitive termination criterion for BO that automatically stops the procedure if it is sufficiently close to the global optimum. Our key insight is that the discrepancy between the true objective (predictive performance on test data) and the computable target (validation performance) suggests stopping once the suboptimality in optimizing the target is dominated by the statistical estimation error. Across an extensive range of real-world HPO problems and baselines, we show that our termination criterion achieves a better trade-off between the test performance and optimization time. Additionally, we find that overfitting may occur in the context of HPO, which is arguably an overlooked problem in the literature, and show how our termination criterion helps to mitigate this phenomenon on both small and large datasets.
The Representation Jensen-Rényi Divergence

We introduce a divergence measure between data distributions based on operators in reproducing kernel Hilbert spaces defined by kernels. The empirical estimator of the divergence is computed using the eigenvalues of positive definite Gram matrices that are obtained by evaluating the kernel over pairs of data points. The new measure shares similar properties to Jensen-Shannon divergence. Convergence of the proposed estimators follows from concentration results based on the difference between the ordered spectrum of the Gram matrices and the integral operators associated with the population quantities. The proposed measure of divergence avoids the estimation of the probability distribution underlying the data. Numerical experiments involving comparing distributions and applications to sampling unbalanced data for classification show that the proposed divergence can achieve state of the art results.
A simple scheme for unity power-factor rectification for high frequency AC buses

A simple scheme is proposed for offline unity power factor rectification for high-frequency AC buses (20 kHz). A bandpass filter of the series-resonant type, centered at the line frequency, is inserted between the line and the full-wave rectified load. The Q=Z/sub 0//R/sub L/ formed by the load and the characteristic impedance of the tank circuit determines the power factor, the boundary between continuous and discontinuous conduction modes, the peak stresses, and the transient response of the rectifier. It is shown that for Q>2/ pi the rectifier operates in continuous conduction mode and the output voltage is independent of the load. Also, it is shown that for Q>2 the line current is nearly sinusoidal with less than 5% third-harmonic distortion and the power factor is essentially unity. An increase in Q causes an increase in the peak voltages of the tank circuit and a slower transient response of the rectifier circuit. The DC, small-signal, and transient analyses of the rectifier circuit are carried out, and the results are in good agreement with simulation and experimental results. >
Performance of Portland Limestone Cements

Portland limestone cements (PLCs) have recently been approved as a part of the ASTM C595/AASHTO M240 specifications. These cements are designed to enable more sustainable concrete production by replacing up to 15% of the clinker with interground limestone particles. The PLCs represent a potential method to reduce the carbon dioxide embodied in built infrastructure and extend the life of limestone quarries. This paper presents a comparison of the performance of three commercially available PLCs meeting the ASTM C595/AASHTO M240 specifications and three ordinary portland cements (OPCs) made from the same cement clinkers. An additional OPC was blended with limestone that had two mean particle sizes. One OPC and PLC were used with a Class C fly ash. Each of these cementitious systems was used to produce typical concrete paving mixtures with the performance of these materials being quantified through a series of standardized tests. The results of the study show that the mechanical properties of PLCs have negligible changes as they relate to design practices and implementation. The early-age volume changes of a PLC ground to levels consistent with achieving similar 28-day strengths were shown to be similar to that of the corresponding OPC, whereas PLCs that were ground significantly finer might lead to increased early-age shrinkage. The transport properties show behavior that is ±30% of the conventional OPC, whereas the results for PLC systems containing fly ash show a synergistic effect with improved performance. The overall results of this study show that PLCs conforming to ASTM C595/AASHTO M240 could achieve similar performance as the OPC they would replace.