Miracle-dz/UniFAR-FTUs huggingface dataset:
Users Comprehension and Behaviour Study on Android Permissions

The Android Market has become the main source of applications (apps) download for Android based devices. The majority of users trust that the apps that they downloaded are safe and trustworthy. However, it is not always the case since a large numbers of apps contain several unnecessary permissions that will potentially provide threats to the users’ privacy and security by stealing their important data, and also offer services that will cost money to the users. The main objective of this paper is to investigate the level of knowledge, understanding and behaviour of the users towards these permissions. The results obtained show that the awareness regarding these permissions among the users is still low and they need to be warned against the potential threats of these permissions to ensure that they can make a more informed decisions whether to install the apps or not.
On a wider class of prior distributions for graphical models

Gaussian graphical models are useful tools for conditional independence structure inference of multivariate random variables. Unfortunately, Bayesian inference of latent graph structures is challenging due to exponential growth of $\mathcal{G}_n$ , the set of all graphs in n vertices. One approach that has been proposed to tackle this problem is to limit search to subsets of $\mathcal{G}_n$ . In this paper we study subsets that are vector subspaces with the cycle space $\mathcal{C}_n$ as the main example. We propose a novel prior on $\mathcal{C}_n$ based on linear combinations of cycle basis elements and present its theoretical properties. Using this prior, we implement a Markov chain Monte Carlo algorithm, and show that (i) posterior edge inclusion estimates computed with our technique are comparable to estimates from the standard technique despite searching a smaller graph space, and (ii) the vector space perspective enables straightforward implementation of MCMC algorithms.
Number Entity Recognition

Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. Though numbers are typically not accounted for distinctly in most NLP tasks, there is still an underlying amount of numeracy already exhibited by NLP models. In this work, we attempt to tap this potential of state-of-the-art NLP models and transfer their ability to boost performance in related tasks. Our proposed classiﬁcation of numbers into entities helps NLP models perform well on several tasks, including a handcrafted Fill-In-The-Blank (FITB) task and on question answering using joint embeddings, outperforming the BERT and RoBERTa baseline classiﬁcation.
Towards Operationalising Responsible AI: An Empirical Study

—While artiﬁcial intelligence (AI) has great poten- tial to transform many industries, there are concerns about its ability to make decisions in a responsible way. Many AI ethics guidelines and principles have been recently proposed by governments and various organisations, covering areas such as privacy, accountability, safety, reliability, transparency, explainability, contestability, and fairness. However, such principles are typically high-level and do not provide tangible guidance on how to design and develop responsible AI systems. To address this shortcoming, we present an empirical study involving interviews with 21 scientists and engineers, designed to gain insight into practitioners’ perceptions of AI ethics principles, their possible implementation, and the trade-offs between the principles. The salient ﬁndings cover four aspects of AI system development: (i) overall development process, (ii) requirements engineering, (iii) design and implementation, (iv) deployment and operation.
SubGraph Networks based Entity Alignment for Cross-lingual Knowledge Graph

Entity alignment is the task of ﬁnding entities represent-ing the same real-world object in two knowledge graphs(KGs). Cross-lingual knowledge graph entity alignment aims to discover the cross-lingual links in the multi-language KGs, which is of great signiﬁcance to the NLP applications and multi-language KGs fusion. In the task of aligning cross-language knowledge graphs, the structures of the two graphs are very similar, and the equivalent entities often have the same subgraph structure characteristics. The traditional GCN method neglects to obtain structural features through representative parts of the original graph and the use of adjacency matrix is not enough to eﬀectively represent the structural features of the graph. In this paper, we introduce the subgraph network (SGN) method into the GCN-based cross-lingual KG entity alignment method. In the method, we extracted the ﬁrst-order subgraphs of the KGs to expand the structural features of the original graph to enhance the representation ability of the entity embedding and improve the alignment accuracy. Experiments show that the proposed method outperforms the state-of-the-art GCN-based method.
Cascading failure model and robustness of heterogeneous interdependent combat network

The networked combat system-of-system (CSOS) is the trend of combat development with the innovation of technology. The achievement of combat eﬀectiveness requires CSOS to have a good ability to deal with external interference. From the perspective of complex networks, the modeling of CSOS is carried out, and the robustness of the combat network is explored based on this. Firstly, a more realistic double-layer heterogeneous interdependent combat network model is established. Then, the conditional group dependency situation is considered to design failure rules for dependent failure, and the coupling relation between the double-layer subnets is analysed for cascading failure. Furthermore, the initial load and capacity of the node are deﬁned, respectively, as well as the load redistribution strategy and the status judgment rules for the cascading failure model. Simulation experiments are carried out by changing the attack modes and diﬀerent parameters, and the results show that the robustness of the combat network can be eﬀectively improved by improving the tolerance limit of one-way dependency of the functional net, the node capacity of the functional net and the tolerance of the overload state. The conclusions of this paper can provide a useful reference for the network structure optimization and network security protection in military ﬁeld. the robustness of the combat network. The simulation results show that the robustness of the combat network can be eﬀectively improved by improving the tolerance limit of one-way dependency of the functional net, the node capacity of the functional net and the tolerance of the overload state. When the attack intensity remains
Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction

Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction. However, existing approaches for MNER and MRE usually suffer from error sensitivity when irrelevant object images incorporated in texts. To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance. Specifically, we regard visual representation as pluggable visual prefix to guide the textual representation for error insensitive forecasting decision. We further propose a dynamic gated aggregation strategy to achieve hierarchical multi-scaled visual features as visual prefix for fusion. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method, and achieve state-of-the-art performance. Code is available in https://github.com/zjunlp/HVPNeT.
Communication Compression for Decentralized Learning With Operator Splitting Methods

In decentralized learning, operator splitting methods using a primal-dual formulation (e.g., Edge-Consensus Learning (ECL)) have been shown to be robust to heterogeneous data and have attracted significant attention in recent years. However, in the ECL, a node needs to exchange dual variables with its neighbors. These exchanges incur significant communication costs. For the Gossip-based algorithms, many compression methods have been proposed, but these Gossip-based algorithms do not perform well when the data distribution held by each node is statistically heterogeneous. In this work, we propose a novel framework of the compression methods for the ECL, called the Communication Compressed ECL (C-ECL). Specifically, we reformulate the update formulas of the ECL and propose to compress the update values of the dual variables. We demonstrate experimentally that the C-ECL can achieve a nearly equivalent performance with fewer parameter exchanges than the ECL. Moreover, we demonstrate that the C-ECL is more robust to heterogeneous data than the Gossip-based algorithms.
Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling

In this work, we are dedicated to multi-target active object tracking (AOT), where there are multiple targets as well as multiple cameras in the environment. The goal is maximize the overall target coverage of all cameras. Previous work makes a strong assumption that each camera is fixed in a location and only allowed to rotate, which limits its application. In this work, we relax the setting by allowing all cameras to both move along the boundary lines and rotate. In our setting, the action space becomes much larger, which leads to much higher computational complexity to identify the optimal action. To this end, we propose to leverage the action selection from multi-agent reinforcement learning (MARL) network to prune the search tree of Monte Carlo Tree Search (MCTS) method, so as to find the optimal action more efficiently. Besides, we model the motion of the targets to predict the future position of the targets, which makes a better estimation of the future environment state in the MCTS process. We establish a multi-target 2D environment to simulate the sports games, and experimental results demonstrate that our method can effectively improve the target coverage.
Can We Run in Parallel? Automating Loop Parallelization for TornadoVM

With the advent of multi-core systems, GPUs and FPGAs, loop parallelization has become a promising way to speed-up program execution. In order to stay up with time, various performance-oriented programming languages provide a multitude of constructs to allow programmers to write parallelizable loops. Correspondingly, researchers have developed techniques to automatically parallelize loops that do not carry dependences across iterations, and/or call pure functions. However, in managed languages with platform-independent runtimes such as Java, it is practically infeasible to perform complex dependence analysis during JIT compilation. In this paper, we propose AutoTornado, a first of its kind static+JIT loop parallelizer for Java programs that parallelizes loops for heterogeneous architectures using TornadoVM (a Graal-based VM that supports insertion of @Parallel constructs for loop parallelization). AutoTornado performs sophisticated dependence and purity analysis of Java programs statically, in the Soot framework, to generate constraints encoding conditions under which a given loop can be parallelized. The generated constraints are then fed to the Z3 theorem prover (which we have integrated with Soot) to annotate canonical for loops that can be parallelized using the @Parallel construct. We have also added runtime support in TornadoVM to use static analysis results for loop parallelization. Our evaluation over several standard parallelization kernels shows that AutoTornado correctly parallelizes 61.3% of manually parallelizable loops, with an efficient static analysis and a near-zero runtime overhead. To the best of our knowledge, AutoTornado is not only the first tool that performs program-analysis based parallelization for a real-world JVM, but also the first to integrate Z3 with Soot for loop parallelization.
Sub-nanotesla sensitivity at the nanoscale with a single spin

ABSTRACT High-sensitivity detection of the microscopic magnetic field is essential in many fields. Good sensitivity and high spatial resolution are mutually contradictory in measurement, which is quantified by the energy resolution limit. Here we report that a sensitivity of 0.5 nT/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\sqrt{\rm Hz}$\end{document} at the nanoscale is achieved experimentally by using nitrogen-vacancy defects in diamond with depths of tens of nanometers. The achieved sensitivity is substantially enhanced by integrating with multiple quantum techniques, including real-time-feedback initialization, dynamical decoupling with shaped pulses and repetitive readout via quantum logic. Our magnetic sensors will shed new light on searching new physics beyond the standard model, investigating microscopic magnetic phenomena in condensed matters, and detection of life activities at the sub-cellular scale.
Temperature-Gradient-Driven Magnetic Skyrmion Motion

The static and dynamic properties of skyrmions have recently received increased attention due to the potential application of skyrmions as information carriers and for unconventional computing. While the current-driven dynamics has been explored deeply, both theoretically and experimentally, the theory of temperature gradient-induced dynamics - Skyrmion-Caloritronics - is still at its early stages of development. Here, we move the topic forward by identifying the role of entropic torques due to the temperature dependence of magnetic parameters. Our results show that, skyrmions move towards higher temperatures in single-layer ferromagnets with interfacial Dzyaloshinski-Moriya interactions, whereas, in multilayers, they move to lower temperatures. We analytically and numerically demonstrate that the opposite behaviors are due to different scaling relations of the material parameters as well as a non-negligible magnetostatic field gradient in multilayers. We also find a spatially dependent skyrmion Hall angle in multilayers hosting hybrid skyrmions due to variations of the thickness dependent chirality as
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality

The machine translation (MT) task is typically formulated as that of returning a single translation for an input segment. However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended target audience, characteristics of the speaker, or even the relationship between speakers. Specific problems arise when dealing with honorifics, particularly translating from English into languages with formality markers. For example, the sentence"Are you sure? "can be translated in German as"Sind Sie sich sicher? "(formal register) or"Bist du dir sicher? "(informal). Using wrong or inconsistent tone may be perceived as inappropriate or jarring for users of certain cultures and demographics. This work addresses the problem of learning to control target language attributes, in this case formality, from a small amount of labeled contrastive data. We introduce an annotated dataset (CoCoA-MT) and an associated evaluation metric for training and evaluating formality-controlled MT models for six diverse target languages. We show that we can train formality-controlled models by fine-tuning on labeled contrastive data, achieving high accuracy (82% in-domain and 73% out-of-domain) while maintaining overall quality.
Determination of class-specific variables in nonparametric multiple-class classification

As technology advanced, collecting data via automatic collection devices become popular, thus we commonly face data sets with lengthy variables, especially when these data sets are collected without specific research goals beforehand. It has been pointed out in the literature that the difficulty of high-dimensional classification problems is intrinsically caused by too many noise variables useless for reducing classification error, which offer less benefits for decision-making, and increase complexity, and confusion in model-interpretation. A good variable selection strategy is therefore a must for using such kinds of data well; especially when we expect to use their results for the succeeding applications/studies, where the model-interpretation ability is essential. hus, the conventional classification measures, such as accuracy, sensitivity, precision, cannot be the only performance tasks. In this paper, we propose a probability-based nonparametric multiple-class classification method, and integrate it with the ability of identifying high impact variables for individual class such that we can have more information about its classification rule and the character of each class as well. The proposed method can have its prediction power approximately equal to that of the Bayes rule, and still retains the ability of"model-interpretation. "We report the asymptotic properties of the proposed method, and use both synthesized and real data sets to illustrate its properties under different classification situations. We also separately discuss the variable identification, and training sample size determination, and summarize those procedures as algorithms such that users can easily implement them with different computing languages.
The R-process Alliance: A Nearly Complete R-process Abundance Template Derived from Ultraviolet Spectroscopy of the R-process-enhanced Metal-poor Star HD 222925

We present a nearly complete rapid neutron-capture process (r-process) chemical inventory of the metal-poor ([Fe/H] = −1.46 ± 0.10) r-process-enhanced ([Eu/Fe] = +1.32 ± 0.08) halo star HD 222925. This abundance set is the most complete for any object beyond the solar system, with a total of 63 metals detected and seven with upper limits. It comprises 42 elements from 31 ≤ Z ≤ 90, including elements rarely detected in r-process-enhanced stars, such as Ga, Ge, As, Se, Cd, In, Sn, Sb, Te, W, Re, Os, Ir, Pt, and Au. We derive these abundances from an analysis of 404 absorption lines in ultraviolet spectra collected using the Space Telescope Imaging Spectrograph on the Hubble Space Telescope and previously analyzed optical spectra. A series of appendices discusses the atomic data and quality of fits for these lines. The r-process elements from Ba to Pb, including all elements at the third r-process peak, exhibit remarkable agreement with the solar r-process residuals, with a standard deviation of the differences of only 0.08 dex (17%). In contrast, deviations among the lighter elements from Ga to Te span nearly 1.4 dex, and they show distinct trends from Ga to Se, Nb through Cd, and In through Te. The r-process contribution to Ga, Ge, and As is small, and Se is the lightest element whose production is dominated by the r-process. The lanthanide fraction, log X La = −1.39 ± 0.09, is typical for r-process-enhanced stars and higher than that of the kilonova from the GW170817 neutron-star merger event. We advocate adopting this pattern as an alternative to the solar r-process-element residuals when confronting future theoretical models of heavy-element nucleosynthesis with observations.
On Designing Data Models for Energy Feature Stores

—The digitization of the energy infrastructure enables new, data driven, applications often supported by machine learning models. However, domain speciﬁc data transformations, pre- processing and management in modern data driven pipelines is yet to be addressed. In this paper we perform a ﬁrst time study on data models, energy feature engineering and feature management solutions for developing ML-based energy applications. We ﬁrst propose a taxonomy for designing data models suitable for energy applications, analyze feature engineering techniques able to transform the data model into features suitable for ML model training and ﬁnally also analyze available designs for feature stores. Using a short-term forecasting dataset, we show the beneﬁts of designing richer data models and engineering the features on the performance of the resulting models. Finally, we benchmark three complementary feature management solutions, including an open-source feature store.
Fingerprint Template Invertibility: Minutiae vs. Deep Templates

Much of the success of fingerprint recognition is attributed to minutiae-based fingerprint representation. It was believed that minutiae templates could not be inverted to obtain a high fidelity fingerprint image, but this assumption has been shown to be false. The success of deep learning has resulted in alternative fingerprint representations (embeddings), in the hope that they might offer better recognition accuracy as well as non-invertibility of deep network-based templates. We evaluate whether deep fingerprint templates suffer from the same reconstruction attacks as the minutiae templates. We show that while a deep template can be inverted to produce a fingerprint image that could be matched to its source image, deep templates are more resistant to reconstruction attacks than minutiae templates. In particular, reconstructed fingerprint images from minutiae templates yield a TAR of about 100.0% (98.3%) @ FAR of 0.01% for type-I (type-II) attacks using a state-of-the-art commercial fingerprint matcher, when tested on NIST SD4. The corresponding attack performance for reconstructed fingerprint images from deep templates using the same commercial matcher yields a TAR of less than 1% for both type-I and type-II attacks; however, when the reconstructed images are matched using the same deep network, they achieve a TAR of 85.95% (68.10%) for type-I (type-II) attacks. Furthermore, what is missing from previous fingerprint template inversion studies is an evaluation of the black-box attack performance, which we perform using 3 different state-of-the-art fingerprint matchers. We conclude that fingerprint images generated by inverting minutiae templates are highly susceptible to both white-box and black-box attack evaluations, while fingerprint images generated by deep templates are resistant to black-box evaluations and comparatively less susceptible to white-box evaluations.
Towards Self-Adaptive Peer-to-Peer Monitoring for Fog Environments

Monitoring is a critical component in fog environments: it promptly provides insights about the behavior of systems, reveals Service Level Agreements (SLAs) violations, enables the autonomous orchestration of services and platforms, calls for the intervention of operators, and triggers self-healing actions. In such environments, monitoring solutions have to cope with the heterogeneity of the devices and platforms present in the Fog, the limited resources available at the edge of the network, and the high dynamism of the whole Cloud-to-Thing continuum. This paper addresses the challenge of accurately and efficiently monitoring the Fog with a self-adaptive peer-to-peer (P2P) monitoring solution that can opportunistically adjust its behavior according to the collected data exploiting a lightweight rule-based expert system.Empirical results show that adaptation can improve monitoring accuracy, while reducing network and power consumption at the cost of higher memory consumption. CCS CONCEPTS • Computer systems organization → Self-organizing autonomic computing; Peer-to-peer architectures; • Information systems → Expert systems.