Miracle-dz/UniFAR-FTUs huggingface dataset:
Macroprogramming: Concepts, State of the Art, and Opportunities of Macroscopic Behaviour Modelling

Macroprogramming refers to the theory and practice of expressing the macro(scopic) behaviour of a collective system using a single program. Macroprogramming approaches are motivated by the need of effectively capturing global/system-level aspects and the collective behaviour of multiple computational components, while abstracting over low-level details. Previously, this programming style had been primarily adopted to describe the data-processing logic in sensor networks; recently, research forums on spatial computing, collective systems, and the Internet of Things have provided renewed interest in macro approaches. However, related contributions are still fragmented and lack conceptual consistency. Therefore, to foster principled research, an integrated view of the field is provided, together with opportunities and challenges.
Tree-based Search Graph for Approximate Nearest Neighbor Search

Nearest neighbor search supports important applications in many domains, such as database, machine learning, computer vision. Since the computational cost for accurate search is too high, the community turned to the research of approximate nearest neighbor search (ANNS). Among them, graph-based algorithm is one of the most important branches. Research by Fu et al. shows that the algorithms based on Monotonic Search Network (MSNET), such as NSG and NSSG, have achieved the state-of-the-art search performance in efficiency. The MSNET is dedicated to achieving monotonic search with minimal out-degree of nodes to pursue high efficiency. However, the current MSNET designs did not optimize the probability of the monotonic search, and the lower bound of the probability is only 50%. If they fail in monotonic search stage, they have to suffer tremendous backtracking cost to achieve the required accuracy. This will cause performance problems in search efficiency. To address this problem, we propose (r,p)-MSNET, which achieves guaranteed probability on monotonic search. Due to the high building complexity of a strict (r,p)-MSNET, we propose TBSG, which is an approximation with low complexity. Experiment conducted on four million-scaled datasets show that TBSG outperforms existing state-of-the-art graph-based algorithms in search efficiency. Our code has been released on Github.
Using Wi-Fi Signal as Sensing Medium: Passive Radar, Channel State Information and Followups

The idea of exploiting the Wi-Fi bursts as the medium for sensing purposes, particularly for the human targets in the indoor environment, was cultivated in both radar and computer science communities and it has became a noticeable research genre with cross-disciplinary impact in security, healthcare, human-machine interaction etc.This article comparatively introduces passive radar based and channel state information (CSI) based approaches. For each means, the primary design principles, signal processing and representative applications scenarios are shown. At last, some opportunities and challenges of Wi-Fi sensing are pointed out for the sake of stepping closer to the practitioners and end-users.
A Retrospective and Futurespective of Rowhammer Attacks and Defenses on DRAM

Rowhammer has drawn much attention from both academia and industry in the last few years as rowhammer exploitation poses severe consequences to system security. Since the first comprehensive study of rowhammer in 2014, a number of rowhammer attacks have been demonstrated against ubiquitous dynamic random access memory (DRAM)-based commodity systems to cause denial-of-service, gain privilege escalation, leak sensitive information or degrade DNN model inference accuracy. Accordingly, numerous software defenses have been proposed to protect legacy systems while hardware defenses aim to protect next-generation DRAM-based systems. In this paper, we systematize rowhammer attacks and defenses with a focus on DRAM. Particularly, we characterize rowhammer attacks comprehensively, shedding lights on possible new attack vectors that have not yet been explored. We further summarize and classify existing software defenses, from which new defense strategies are identified and worth future exploring. We also categorize proposed hardware defenses from both industry and academia and summarize their limitations. In particular, most industrial solutions have turned out to be ineffective against rowhammer while on-die ECC's susceptibility to rowhammer calls for a comprehensive study. Our work is to inspire the software-security community to identify new rowhammer attack vectors while present novel defense solutions against them in legacy systems. More importantly, both software and hardware security communities should work together to develop more effective and practical defense solutions.
Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models

Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompanying methodology—borrowing ideas from statistical physics and computational chemistry—for inferring the posterior distribution of latent diffusion paths and model parameters, given observations of the process. Joint configurations of the underlying process noise and of parameters, mapping onto diffusion paths consistent with observations, form an implicitly defined manifold. Then, by making use of a constrained Hamiltonian Monte Carlo algorithm on the embedded manifold, we are able to perform computationally efficient inference for a class of discretely observed diffusion models. Critically, in contrast with other approaches proposed in the literature, our methodology is highly automated, requiring minimal user intervention and applying alike in a range of settings, including: elliptic or hypo‐elliptic systems; observations with or without noise; linear or non‐linear observation operators. Exploiting Markovianity, we propose a variant of the method with complexity that scales linearly in the resolution of path discretisation and the number of observation times. Python code reproducing the results is available at http://doi.org/10.5281/zenodo.5796148.
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI

This paper quantiﬁes the quality of heatmap-based eXplainable AI (XAI) methods w.r. t image classiﬁcation problem. Here, a heatmap is considered desirable if it improves the probability of predicting the correct classes. Diﬀerent XAI heatmap-based methods are empirically shown to improve classiﬁcation conﬁdence to diﬀerent extents depending on the datasets, e.g. Saliency works best on ImageNet and Deconvolution on Chest X-Ray Pneumonia dataset. The novelty includes a new gap distribution that shows a stark diﬀerence between correct and wrong predictions. Finally, the generative augmentative explanation is introduced, a method to generate heatmaps capable of improving predictive conﬁdence to a high level.
Towards Interactive Reinforcement Learning with Intrinsic Feedback

Reinforcement learning (RL) and brain-computer interfaces (BCI) have experienced significant growth over the past decade. With rising interest in human-in-the-loop (HITL), incorporating human input with RL algorithms has given rise to the sub-field of interactive RL. Adjacently, the field of BCI has long been interested in extracting informative brain signals from neural activity for use in human-computer interactions. A key link between these fields lies in the interpretation of neural activity as feedback such that interactive RL approaches can be employed. We denote this new and emerging medium of feedback as intrinsic feedback. Despite intrinsic feedback's ability to be conveyed automatically and even unconsciously, proper exploration surrounding this key link has largely gone unaddressed by both communities. Thus, to help facilitate a deeper understanding and a more effective utilization, we provide a tutorial-style review covering the motivations, approaches, and open problems of intrinsic feedback and its foundational concepts.
Streaming Enumeration on Nested Documents

Some of the most relevant document schemas used online, such as XML and JSON, have a nested format. In the last decade, the task of extracting data from nested documents over streams has become especially relevant. We focus on the streaming evaluation of queries with outputs of varied sizes over nested documents. We model queries of this kind as Visibly Pushdown Transducers (VPT), a computational model that extends visibly pushdown automata with outputs and has the same expressive power as MSO over nested documents. Since processing a document through a VPT can generate a massive number of results, we are interested in reading the input in a streaming fashion and enumerating the outputs one after another as efficiently as possible, namely, with constant-delay. This paper presents an algorithm that enumerates these elements with constant-delay after processing the document stream in a single pass. Furthermore, we show that this algorithm is worst-case optimal in terms of update-time per symbol and memory usage.
Autonomous and Adaptive Navigation for Terrestrial-Aerial Bimodal Vehicles

Terrestrial-aerial bimodal vehicles bloom in both academia and industry because they incorporate both the high mobility of aerial vehicles and the long endurance of ground vehicles. However, there are no representative works on the autonomous navigation of this class of vehicles. To this end, we present an autonomous and adaptive navigation framework that brings complete autonomy to this class of vehicles. The framework mainly includes 1) a hierarchical motion planner that generates safe and low-power terrestrial-aerial trajectories in unknown environments and 2) a unified motion controller which dynamically adjusts energy consumption in terrestrial locomotion. Extensive real-world experiments and benchmark comparisons are conducted on a customized robot platform to validate the proposed frameworks robustness and performance. During the tests, the robot safely traverses complex environments with terrestrial-aerial integrated mobility, and achieves 7 energy savings in terrestrial locomotion. Finally, we will release our code and hardware configuration for the reference of the community.
J an 2 02 2 The van der Waals-like Phase Transition in the FRW Universe

In this paper, we treat the Friedmann-Robertson-Walker (FRW) universe as a thermodynamic system and study its thermodynamic properties especially the van der Waals (vdW)-like (or P V ) phase transition. The thermodynamic pressure P is, almost for the first time, defined by the work density W introduced by Hayward. We further take the general relativity (GR) and the 4dimensional Einstein-Gauss-Bonnet (4D EGB) gravity as two examples to write down the equations of state P = P (V, T ). We find that in GR, there is no vdW-like phase transition for the FRW universe, while in the 4D EGB gravity the FRW universe can have vdW-like phase transition, which to our best knowledge was never found previously in the literature of the FRW thermodynamics. We also find that the corresponding critical exponents are the same as those in the vdW system and the mean field theory, so they satisfy the scaling laws.
Machines&Influence: An Information Systems Lens

Policymakers face a broader challenge of how to view AI capabilities today and where does society stand in terms of those capabilities. This paper surveys AI capabilities and tackles this very issue, exploring it in context of political security in digitally networked societies. We extend the ideas of Information Management to better understand contemporary AI systems as part of a larger and more complex information system. Comprehensively reviewing AI capabilities and contemporary man-machine interactions, we undertake conceptual development to suggest that better information management could allow states to more optimally offset the risks of AI enabled influence and better utilise the emerging capabilities which these systems have to offer to policymakers and political institutions across the world. Hopefully this long essay will actuate further debates and discussions over these ideas, and prove to be a useful contribution towards governing the future of AI.
Measuring Mothers’ Warmth: Naïve Observers, Trained Coders, and Self-Reports

SYNOPSIS Objective. The current research evaluated whether thin slicing, which involves brief observations of others’ behaviors, is useful in understanding maternal warmth in the context of mother-child interaction. Design. The extent to which naïve observers’ ratings based on brief observations correspond to other reports of maternal warmth as well as the predictive validity of such ratings were evaluated in 158 pairs of European American mothers and their early adolescent children engaged in a set of problem-solving activities in the laboratory. Naïve (untrained) observers provided ratings of mothers’ warmth based on the first 5 min of the dyadic interaction. Trained coders rated mothers’ behaviors based on the first 5 min as well as the full 15 min video using a behavioral coding system. Mothers and children reported on mothers’ warmth using surveys. Results. Naïve observers’ ratings were associated with trained coders’ ratings, but not with child or mother reports. Child reports of mothers’ warmth were associated with both subjective and objective measures of their academic functioning. Naïve observers’ ratings of mothers’ warmth were associated with an objective measure of children’s academic functioning. Conclusions. The correspondence between naïve observers’ and trained coders’ reports of mothers’ warmth warrants further investigation into the conditions under which less time- and labor-intensive methods, such as thin slicing, can be applied to assess parents’ expressive behaviors.
Cognitive and behavioral modernity in Homo erectus: skull globularity and hominin brain evolution

Abstract In this article we provide evidence that evolutionary pressures altered the cranial base and the mastoid region of the temporal bone more than the calvaria in the transition from H. erectus to H. sapiens. This process seems to have resulted in the evolution of more globular skull shape – but not as a result of expansion of the brain in the parietal regions but of reduction of the cranial base and the mastoid region relative to the parietals. Consequently, we argue that expansion of the parietals seems to be unrelated to brain evolution, but is more a by-product of reduction in other regions of the skull, reduction that may be related to dietary factors. Additionally, these findings suggest that cognitive and behavioural modernity may not necessarily be dependent on brain shape. Also, it cannot be attributed to the change in brain size because H. erectus and modern human cranial capacities overlap substantially. Consequently, we suggest H. erectus possessed the full suite of cognitive adaptations characteristic of modern humans without possessing a globular skull with flared parietals. Our results also support the theory that paedomorphic morphogenesis of the skull was important in the transition from H. erectus to H. sapiens and that such changes may be related to both dietary factors and social evolution.
Integrated Tick Management in the Upper Midwest: Impact of Invasive Vegetation Removal and Host-targeted Acaricides on Ixodes scapularis Infestation and Borrelia burgdorferi Prevalence of Small Mammals

Integration of tick management strategies has been suggested to overcome ecological variation in tick, host, pathogen, and habitat, yet empirical evidence assessing combined treatment effect on blacklegged ticks, Ixodes scapularis Say, is limited. In this 5-year study (2014-2018) we tested whether combining two methods targeting tick/mammal interactions could reduce juvenile I. scapularis parasitism of two small mammal species, Peromyscus leucopus Rafinesque and Tamias striatus Linnaeus. Infection of small mammals with Borrelia burgdorferi was used to evaluate host exposure to feeding ticks. Using a factorial design, removal of invasive vegetation (Amur honeysuckle, Lonicera maackii Ruprecht and common buckthorn, Rhamnus cathartica Linnaeus) was coupled with deployments of permethrin-treated cotton nesting materials (tick tubes) and evaluated against control sites. Removal of invasive vegetation resulted in lower captures of T. striatus suggesting that treatment impacted reservoir activity in the plots. Deployments of permethrin-treated cotton were effective at reducing the frequency of juvenile I. scapularis parasitism of P. leucopus by 91% across the study compared to controls. However, tick tubes did not offer consistent protection against mouse exposure to B. burgdorferi exposure. An additive negative effect was detected for juvenile tick intensity on P. leucopus when tick tubes were combined with invasive vegetation removal. We conclude that integration of these two methods provides very limited benefit and that permethrin treatment alone offers the best option for reducing I. scapularis infestation on P. leucopus.
A Finite-Volume Approach to Modeling Ice Accretion

In this work we present a novel, generalized, multiscale physics, unstructured finite-volume, CFD approach for simulating ice accretion on aircraft. A multi-physics solver that evaluates the (1) air flow, (2) droplet trajectories, (3) surface-liquid flow, (4) solidification, and (5) computes the deformed ice shape, is presented. Initial results show promise in the developed methods and solvers, that are expected to later be extended for future rotorcraft ice-accretion analysis. Initial validation cases are presented for the various components of the solver, and compare reasonable well with LEWICE and experiments for simple geometries. This initial capability displays a capability that could be extended, in future efforts, with more detailed models and provide ice shapes of similar quality as the current methodologies, while providing a capability that extends to more complex configurations such as rotorcraft.
A Hybrid PSO-GA for Extractive Text Summarization

In extractive summarization, searching for the most salient sentences to generate summaries can be resolved by applying meta-heuristic optimization algorithms including genetic algorithms (GAs), particle swarm optimization (PSO), etc. In these approaches, the best solution, i.e. the expected summary, is found by optimizing an objective function, taking into account the features of document and/or sentence. However, these traditional algorithms alone may suffer from a weak local search capability and slow convergence speed. To this end, this paper proposes a novel hybrid GA-PSO algorithm, namely PSOGA-BKSum, for single document text summarization based on a scoring feature subset and updating the candidate toward the best solution technique. The experiment results on the two common extractive datasets, DUC2001 and DUC 2002, have shown that PSOGA-BKSum out-performs some state-of-the-art works on all three ROUGE point metrics. PSOGA-BKSum has considerably progressed on DUC2001 dataset, from 4.3 to 9.7% higher (i.e. 8.5 to 21.9% relatively) than the best ROUGE-1 score of previous un-∗
Starting a new treebank? Go SUD!

The paper brings to the fore some advantages to ﬁrst develop a new treebank in Surface-Syntactic Universal Dependencies (SUD) annotation scheme, even if the goal is to obtain a UD treebank. Theoretical beneﬁts of SUD are presented, as well as UD-compatible SUD innovations. The two-way UD , SUD conversion is explained, as well as the possibility to customize the conversion for a given language. The paper concludes by a practical guide for the development of a SUD treebank.
A Dependency Treebank for Classical Arabic Poetry

This paper introduces the first syntactically annotated corpus for Classical Arabic poetry, a morphologically rich ancient Arabic text. The paper describes how the dependency treebank was prepared, focusing on some issues dealing with Classical Arabic poems in which syntactic constructions require special attention. We also present the results of the baseline experiments on Classical Arabic poetry dependency parsing with this treebank.
Is Old French tougher to parse?

Medieval French is known to be relatively hard to parse, with several possible sources of confusion for automatic parsers, among which its flexible word order and its graphical and syntactic variation, both synchronically and diachronically. In this work, we study in particular the influence of word order, by comparing the performances of two state-of-the-art syntactic parsers trained and evaluated on two treebanks: the Syntactic Reference Corpus of Medieval French (SRCMF), a treebank of Old French (9th—13th century) and the Google Stanford Dependency treebank of contemporary French.
Bootstrapping Role and Reference Grammar Treebanks via Universal Dependencies

We describe ud2rrg, a rule-based approach for converting UD trees to Role and Reference Grammar (RRG) structures. Our conversion method aims at facilitating the annotation of multilingual RRG treebanks. ud2rrg uses general and language-speciﬁc conversion rules. In order to evaluate ud2rrg, we approximate the subsequent annotation effort via measures of tree edit distance. Our evaluation, based on English, German, French, Russian, and Farsi, shows that the ud2rrg transformation of UD-parsed data constitutes a highly useful starting point for multilingual RRG treebanking. Once a sufﬁcient amount of data has been annotated in this way, the automatic conversion can be replaced by a statistical parser trained on that data for an even better starting point.
ViVQA: Vietnamese Visual Question Answering

Visual question answering (VQA) is a hot topic that has recently drew the attention of researchers from domains as diverse as natural language processing and computer vision. In spite of the fact that numerous VQA datasets and models have been fundamentally considered in English, there are no related works in Vietnamese VQA. In this article, we have created ViVQA, a new dataset for Vi etnamese V isual Q uestion A nswering consisting of 10,328 images and 15,000 question-answer pairs in Vietnamese for evaluating Viet-namese VQA models. On the ViQA dataset, we also propose a system that uses the Hierarchical Co-Attention Model and achieves Accuracy, WUPS 0.9, and WUPS 0.0 of 0.3496, 0.4513, and 0.7786, respectively. Our system beats the two baseline models (LSTM and BiLSTM) on the ViQA dataset. These findings are optimistic for the development of visual question-answering systems in Vietnam. Finally, we discuss future prospects for ViVQA models in order to improve performance. Our dataset 1 is available freely for research purposes.
Noise and Vibration Characteristics of Permanent-Magnet Synchronous Motors Using Electromagnetic and Structural Analyses

This paper provides a detailed finding of the mechanics of vibration in permanent magnet (PM) synchronous motors due to electromagnetic (EM) origins. Several fractional-slot PM topologies are investigated to quantify the vibration phenomenon that is influenced by motor slot/pole and winding configurations. This paper also sorts out the vibration mode order that is responsible for deformation and the resulting excitation frequency. A finite-element-based cosimulation in the EM and structural domain is used to find the radial forces and, hence, the displacement, to quantify the vibration/noise performance of these selected motors. The displacement and frequency are converted to the sound pressure level to show the relative differences in the noise levels among these motor topologies. The selected motors were experimentally tested to verify the theoretical findings.