Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Longitudinal Changes in Privacy Behavior of Android Applications

Privacy concerns have long been expressed around smart devices, and the concerns around Android apps have been studied by many past works. Over the past 10 years, we have crawled and scraped data for almost 1.9 million apps, and also stored the APKs for 135,536 of them. In this paper, we examine the trends in how Android apps have changed over time with respect to privacy and look at it from two perspectives: (1) how privacy behavior in apps have changed as they are updated over time, (2) how these changes can be accounted for when comparing third-party libraries and the app’s own internals. To study this, we examine the adoption of HTTPS, whether apps scan the device for other installed apps, the use of permissions for privacy-sensitive data, and the use of unique identifiers. We find that privacy-related behavior has improved with time as apps continue to receive updates, and that the third-party libraries used by apps are responsible for more issues with privacy. However, we observe that in the current state of Android apps, there has not been enough of an improvement in terms of privacy and many issues still need to be addressed.
Using maps to predict economic activity

We introduce a novel machine learning approach to leverage historical and contemporary maps to systematically predict economic statistics. Remote sensing data have been used as reliable proxies for local economic activity. However, they have only become available in recent years, thus limiting their applicability for long-term analysis. Historical maps, on the other hand, date back several decades. Our simple algorithm extracts meaningful features from the maps based on their color compositions. The grid-level population predictions by our approach outperform the conventional CNN-based predictions using raw map images. It also predicts population better than other approaches using night light satellite images or land cover classifications as the input for predictions. JEL Classifications: J11, N30, R11
The impact of students behaviour, their approach, emotions and problem difficulty level on the performance prediction, evaluation and overall learning process during online coding activities

Learning process while solving coding problems is quite complex to understand. It is extremely important to understand the skills which are required and gained during learning to code. As a first step to understand the students’ behaviour and approach during learning coding, two online coding assignments or competitions are conducted with a 1-hour time limit. A survey has been conducted at the end of each coding test and answers to different questions have been collected. In depth statistical analysis is done to understand the learning process while solving the coding problems. It involves lots of parameters including students’ behaviour, their approach and difficulty level of coding problems. The inclusion of mood and emotions related questions can improve overall prediction performance but difficulty level matters in the submission status prediction. Two coding assignments or competitions are analysed through in-depth research on 229 (first coding competition dataset) and 325 (second coding competition dataset) data points. The primary results are promising and these results give in depth insights about how learning to solve coding problems is affected by students’ behaviour, their approach, emotions and problem difficulty level.
A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings

Neural Machine Translation (NMT) models are strong enough to convey semantic and syntactic information from the source language to the target language. However, these models are suffering from the need for a large amount of data to learn the parameters. As a result, for languages with scarce data, these models are at risk of underperforming. We propose to augment attention based neural network with reordering information to alleviate the lack of data. This augmentation improves the translation quality for both English to Persian and Persian to English by up to 6% BLEU absolute over the baseline models.
Prosperity or Pollution? Mineral Mining and Regional Growth in Industrializing Japan

This study investigates the impact of coal mining on the regional economy in industrializing Japan. By linking the location information of mines with registration-and census-based statistics, I found that mines increased the local population via internal migration. This led to a local, structural shift from the agricultural sector to the mining, manufacturing, and service sectors, depending on sex. I also found that these eﬀects are heterogeneous with respect to the type of mines; coal mines are more likely to have stronger eﬀects than heavy metal mines. While coal mines increase the risk of fetal and infant deaths, the metal mines improve the health status of infants.
State Compression and Quantitative Assessment Model for Assessing Security Risks in the Oil and Gas Transmission Systems

The SCADA system is the foundation of the large-scale industrial control system. It is widely used in industries of petrochemistry, electric power, pipeline, etc. The natural gas SCADA system is among the critical infrastructure systems that have security issues related to trusted communications in transactions at the control system layer, and lack quantitative risk assessment and mitigation models. However, to guarantee the security of the Oil and Gas Transmission SCADA systems (OGTSS), there should be a holistic security system that considers the nature of these SCADA systems. In this paper, we augment our Security Awareness Framework (SAF) with two new contributions, (i) a Data Quantization and State Compression Approach (DQSCA) that improves the classification accuracy, speeds up the detection algorithm, and reduces the computational resource consumption. DQSCA reduces the size of processed data while preserving original key events and patterns within the datasets. (ii) A quantitative risk assessment model that carries out regular system information security evaluation and assessment on the SCADA system using a deductive process, where the topmost undesirable base attack event is postulated. Then, the ways for this event to occur are deduced. Our experiments denote that DQSCA has a low negative impact on the reduction of the detection accuracy (2.45% and 4.45%) while it reduces the detection time much (27.74% and 42.06%) for the Turnipseed’s and Gao’s datasets respectively. Furthermore, the mean absolute percentage error (MAPE) rate for the proposed risk assessment model is lower than the intrusion response system (Suricata) for the DOS, Response Injection, and Command Injection attacks by 59.80%, 73.72%, and 66.96% respectively.
Robust Sparse Recovery with Sparse Bernoulli matrices via Expanders

Sparse binary matrices are of great interest in the field of compressed sensing. This class of matrices make possible to perform signal recovery with lower storage costs and faster decoding algorithms. In particular, random matrices formed by i.i. d Bernoulli $p$ random variables are of practical relevance in the context of nonnegative sparse recovery. In this work, we investigate the robust nullspace property of sparse Bernoulli $p$ matrices. Previous results in the literature establish that such matrices can accurately recover $n$-dimensional $s$-sparse vectors with $m=O\left (\frac{s}{c(p)}\log\frac{en}{s}\right )$ measurements, where $c(p) \le p$ is a constant that only depends on the parameter $p$. These results suggest that, when $p$ vanishes, the sparse Bernoulli matrix requires considerably more measurements than the minimal necessary achieved by the standard isotropic subgaussian designs. We show that this is not true. Our main result characterizes, for a wide range of levels sparsity $s$, the smallest $p$ such that it is possible to perform sparse recovery with the minimal number of measurements. We also provide matching lower bounds to establish the optimality of our results.
Mining and Classifying Privacy and Data Protection Requirements in Issue Reports

Digital and physical footprints are a trail of user activities collected over the use of software applications and systems. As software becomes ubiquitous, protecting user privacy has become challenging. With the increase of user privacy awareness and advent of privacy regulations and policies, there is an emerging need to implement software systems that enhance the protection of personal data processing. However, existing data protection and privacy regulations provide key principles in high-level, making it difficult for software engineers to design and implement privacy-aware systems. In this paper, we develop a taxonomy that provides a comprehensive set of privacy requirements based on four well-established personal data protection regulations and privacy frameworks, the General Data Protection Regulation (GDPR), ISO/IEC 29100, Thailand Personal Data Protection Act (Thailand PDPA) and Asia-Pacific Economic Cooperation (APEC) privacy framework. These requirements are extracted, refined and classified into a level that can be used to map with issue reports. We have also performed a study on how two large open-source software projects (Google Chrome and Moodle) address the privacy requirements in our taxonomy through mining their issue reports. The paper discusses how the collected issues were classified, and presents the findings and insights generated from our study. Mining and classifying privacy requirements in issue reports can help organisations be aware of their state of compliance by identifying privacy requirements that have not been addressed in their software projects. The taxonomy can also trace back to regulations, standards and frameworks that the software projects have not complied with based on the identified privacy requirements.
Scaling Limits of Random Walks, Harmonic Profiles, and Stationary Non-Equilibrium States in Lipschitz Domains

We consider the open symmetric exclusion (SEP) and inclusion (SIP) processes on a bounded Lipschitz domain $\Omega$, with both fast and slow boundary. For the random walks on $\Omega$ dual to SEP/SIP we establish: a functional-CLT-type convergence to the Brownian motion on $\Omega$ with either Neumann (slow boundary), Dirichlet (fast boundary), or Robin (at criticality) boundary conditions; the discrete-to-continuum convergence of the corresponding harmonic profiles. As a consequence, we rigorously derive the hydrodynamic and hydrostatic limits for SEP/SIP on $\Omega$, and analyze their stationary non-equilibrium fluctuations. All scaling limit results for SEP/SIP concern finite-dimensional distribution convergence only, as our duality techniques do not require to establish tightness for the fields associated to the particle systems.
IoT Security Challenges and Mitigations: An Introduction

The use of IoT in society is perhaps already ubiquitous, with a vast attack surface offering multiple opportunities for malicious actors. This short paper first presents an introduction to IoT and its security issues, including an overview of IoT layer models and topologies, IoT standardisation efforts and protocols. The focus then moves to IoT vulnerabilities and specific suggestions for mitigations. This work's intended audience are those relatively new to IoT though with existing network-related knowledge. It is concluded that device resource constraints and a lack of IoT standards are significant issues. Research opportunities exist to develop efficient IoT IDS and energy-saving cryptography techniques lightweight enough to reasonably deploy. The need for standardised protocols and channel-based security solutions is clear, underpinned by legislative directives to ensure high standards that prevent cost-cutting on the device manufacturing side.
A CW complex homotopy equivalent to spaces of locally convex curves

Locally convex (or nondegenerate) curves in the sphere Sn (or the projective space) have been studied for several reasons, including the study of linear ordinary differential equations of order n + 1. Taking Frenet frames allows us to obtain corresponding curves Γ in the group Spinn+1; recall that Π : Spinn+1 → Flagn+1 is the universal cover of the space of flags. Determining the homotopy type of spaces of such curves Γ with prescribed initial and final points appears to be a hard problem. Due to known results, we may focus on Ln, the space of (sufficiently smooth) locally convex curves Γ : [0, 1] → Spinn+1 with Γ(0) = 1 and Π(Γ(1)) = Π(1). Convex curves form a contractible connected component of Ln; there are 2n+1 other components, corresponding to non convex curves, one for each endpoint. The homotopy type of Ln has so far been determined only for n = 2 (the case n = 1 is trivial). This paper is a step towards solving the problem for larger values of n. The itinerary of a locally convex curve Γ : [0, 1]→ Spinn+1 belongs to Wn, the set of finite words in the alphabet Sn+1 r {e}. The itinerary of a curve lists the non open Bruhat cells crossed by the curve. Itineraries yield a stratification of the space Ln. We construct a CW complex Dn which is a kind of dual of Ln under this stratification: the construction is similar to Poincaré duality. The CW complex Dn is homotopy equivalent to Ln. The cells of Dn are naturally labeled by words in Wn so that Dn is infinite but locally finite. Explicit glueing instructions are described for lower dimensions. As an application, we describe an open subset Yn ⊂ Ln, a union of strata of Ln. In each non convex component of Ln, the intersection with Yn is connected and dense. Most connected components of Ln are contained in Yn. For n > 3, in the other components the complement of Yn has codimension at least 2. We prove that Yn is homotopically equivalent to the disjoint union of 2n+1 copies of Ω Spinn+1. In particular, for all n ≥ 2, all connected components of Ln are simply connected. 1 ar X iv :2 11 2. 14 53 9v 2 [ m at h. G T ] 1 7 Ju n 20 22
An AGM Approach to Revising Preferences

We look at preference change arising out of an interaction between two elements: the first is an initial preference ranking encoding a pre-existing attitude; the second element is new preference information signaling input from an authoritative source, which may come into conflict with the initial preference. The aim is to adjust the initial preference and bring it in line with the new preference, without having to give up more information than necessary. We model this process using the formal machinery of belief change, along the lines of the well-known AGM approach. We propose a set of fundamental rationality postulates, and derive the main results of the paper: a set of representation theorems showing that preference change according to these postulates can be rationalized as a choice function guided by a ranking on the comparisons in the initial preference order. We conclude by presenting operators satisfying our proposed postulates. Our approach thus allows us to situate preference revision within the larger family of belief change operators.
Mode stability and shallow quasinormal modes of Kerr-de Sitter black holes away from extremality

A Kerr-de Sitter black hole is a solution $(M,g_{\Lambda,\mathfrak{m},\mathfrak{a}})$ of the Einstein vacuum equations with cosmological constant $\Lambda>0$. It describes a black hole with mass $\mathfrak{m}>0$ and specific angular momentum $\mathfrak{a}\in\mathbb{R}$. We show that for any $\epsilon>0$ there exists $\delta>0$ so that mode stability holds for the linear scalar wave equation $\Box_{g_{\Lambda,\mathfrak{m},\mathfrak{a}}}\phi=0$ when $|\mathfrak{a}/\mathfrak{m}|\in[0,1-\epsilon]$ and $\Lambda\mathfrak{m}^2<\delta$. In fact, we show that all quasinormal modes $\sigma$ in any fixed half space $\Im\sigma>-C\sqrt\Lambda$ are equal to $0$ or $-i\sqrt{\Lambda/3}(n+o(1))$, $n\in\mathbb{N}$, as $\Lambda\mathfrak{m}^2\searrow 0$. We give an analogous description of quasinormal modes for the Klein-Gordon equation. We regard a Kerr-de Sitter black hole with small $\Lambda\mathfrak{m}^2$ as a singular perturbation either of a Kerr black hole with the same angular momentum-to-mass ratio, or of de Sitter spacetime without any black hole present. We use the mode stability of subextremal Kerr black holes, proved by Whiting and Shlapentokh-Rothman, as a black box; the quasinormal modes described by our main result are perturbations of those of de Sitter space. Our proof is based on careful uniform a priori estimates, in a variety of asymptotic regimes, for the spectral family and its de Sitter and Kerr model problems in the singular limit $\Lambda\mathfrak{m}^2\searrow 0$.
DDPG car-following model with real-world human driving experience in CARLA

In the autonomous driving ﬁeld, the fusion of human knowledge into Deep Rein-forcement Learning (DRL) is often based on the human demonstration recorded in the simulated environment. This limits the generalization and the feasibility of application in real-world trafﬁc. We proposed a two-stage DRL method, that learns from real-world human driving to achieve performance that is superior to the pure DRL agent. Training a DRL agent is done within a framework for CARLA with Robot Operating System (ROS). For evaluation, we designed different real-world driving scenarios to compare the proposed two-stage DRL agent with the pure DRL agent. After extracting the ’good’ behavior from the human driver, such as anticipation in a signalized intersection, the agent becomes more efﬁcient and drives safer, which makes this autonomous agent more adapt to Human-Robot Interaction (HRI) trafﬁc.
Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems

Channel decoding, channel detection, channel assessment, and resource management for wireless multiple-input multiple-output (MIMO) systems are all examples of problems where machine learning (ML) can be successfully applied. In this paper, we study several ML approaches to solve the problem of estimating the spectral efficiency (SE) value for a certain precoding scheme, preferably in the shortest possible time. The best results in terms of mean average percentage error (MAPE) are obtained with gradient boosting over sorted features, while linear models demonstrate worse prediction quality. Neural networks perform similarly to gradient boosting, but they are more resourceand time-consuming because of hyperparameter tuning and frequent retraining. We investigate the practical applicability of the proposed algorithms in a wide range of scenarios generated by the Quadriga simulator. In almost all scenarios, the MAPE achieved using gradient boosting and neural networks is less than 10%.
Ice jam flood forecasting: Hay River, N.W.T.

Much of the town of Hay River, N.W. T. , is located on the low-lying land of the Hay River delta, and is subject to severe ice jam floods every decade or so. As a first line of defence against these floods, it was proposed that an ice jam flood forecast procedure be developed. The major components of the study included a review of historical flood data, resident interviews, field surveys, and observations of the delta ice regime. It was found that a 1–2 day forecast of discharge in Hay River can be directly determined from discharges measured at a Water Survey of Canada gauging station upstream. From this and from an understanding of the breakup ice regime developed from the study as well as the water level–discharge relations determined for ice jams at three locations in the delta, it was possible to develop a first-generation ice jam flood forecasting procedure that gave a 1–2 day warning of high water at each of the three locations. The procedure was evaluated against the breakup events of 1988 and 1989 .
Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles

Self-driving cars and autonomous driving research has been receiving considerable attention as major promising prospects in modern artificial intelligence applications. According to the evolution of advanced driver assistance system (ADAS), the design of self-driving vehicle and autonomous driving systems becomes complicated and safety-critical. In general, the intelligent system simultaneously and efficiently activates ADAS functions. Therefore, it is essential to consider reliable ADAS function coordination to control the driving system, safely. In order to deal with this issue, this paper proposes a randomized adversarial imitation learning (RAIL) algorithm. The RAIL is a novel derivative-free imitation learning method for autonomous driving with various ADAS functions coordination; and thus it imitates the operation of decision maker that controls autonomous driving with various ADAS functions. The proposed method is able to train the decision maker that deals with the LIDAR data and controls the autonomous driving in multi-lane complex highway environments. The simulation-based evaluation verifies that the proposed method achieves desired performance.
Query Suggestion for Click-Absent Queries in Enterprise Search

Creating alternative queries, also known as query suggestion, has been proved to be helpful on improving users' search experience. Owing to the suggestions, users could retrieve their information need more quickly and accurately. In many scenarios, these suggestions could be generated from the click-through logs by establishing a bipartite graph of the clicked query-document pairs. Most of the existing methods focused on click-existing queries which possess clicked information in the search logs, to suggest related queries using the co-clicked documents. In this paper, we propose a simple yet effective query suggestion method particularly for click-absent queries by ensuring semantic consistency without utilising any additional resources. Our experimental results show that the proposed technique generates comparatively good suggestions for click-absent queries on a real bilingual enterprise search log.
The effect of time factor upon the stability of output parameters of electrohydrodynamic converters

The necessity of carrying out service time tests of electrohydrodynamic converters (EHDC) is substantiated. The description of the installation is given as well as the methods and conditions of the experiment. The experimental research results for a two-stage EHD-converter of liner ortoaxial type (lattice-lattice) are presented in the kerosene medium under dynamic (maximum consumption) and static (zero consumption) conditions. The total operating time of the EHD-converter estimated as a system of conductors of the first and second type, in which at least to one electrode pair (stage) a high voltage is applied, was over the order of 40000 hours. The dependences of the parameters (H is head in mm of kerosene column, and I is the current flowing through the collector in /spl mu/A) upon LOG-time are illustrated with plots. The obtained results gives an optimistic prediction for the long and continuous utilization of EHDC in high-voltage equipment filled by an insulating liquid.
BLDC Motor Control Algorithm for Industrial Applications Using a General Purpose Processor

Electrical motors are an integral part of industrial plants with no less than 5 billion motors built world wide every year. The demand for low-cost brushless DC (BLDC) motors has increased in industrial applications. This paper presents a BLDC motor control algorithm for low-cost motor drive applications using general purpose microcontrollers which have only one on-chip timer. This paper describes how to realize pulse width modulation (PWM) signals with general input/output (I/O) ports to control a three-phase permanent magnet brushless DC motor using the timer interrupt on MSP430F1232.