Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automated Approach for Privacy Leakage Identification in IoT Apps

This paper presents a fully automated static analysis approach and a tool, Taint-Things, for the identification of tainted flows in SmartThings IoT apps. Taint-Things accurately identifies all tainted flows reported by one of the state-of-the-art tools with at least 4 times improved performance. Our approach reports potential vulnerable tainted flows in a form of a concise security slice, where the relevant parts of the code are given with the lines affecting the sensitive information, which could provide security auditors with an effective and precise tool to pinpoint security issues in SmartThings apps under test. We also present and test ways to add precision to Taint-Things by adding extra sensitivities; we provide different approaches for flow, path and context sensitive analyses through modules that can be added to Taint-Things. We present experiments to evaluate Taint-Things by running it on a SmartThings app dataset as well as testing for precision and recall on a set generated by a mutation framework to see how much coverage is achieved without adding false positives. This shows an improvement in performance both in terms of speed up to 4 folds, as well as improving the precision avoiding false positives by providing a higher level of flow and path sensitivity analysis in comparison with one of state of the art tools.
OPTIMISING FUEL EFFICIENCY IN TRANSPORT FLEETS

It has been suggested that large savings in pollution and operating costs can be made by increasing the miles per gallon (MPG) of Large Goods Vehicles (LGVs). Work undertaken to date in this project has however identified key problems with data management when attempting to analyse fuel consumption. Methodologies to overcome these problems and ensure good quality data have been devised and are being implemented in a group of pilot companies. Once the quality of the data has been ensured then a ‘base line’ can be established, enabling the testing of a range of interventions, both technical and managerial in nature. An explanation is given as to why seasonal influences should be taken into account when testing any intervention and when examining data provided by suppliers of interventions.
A Survey on Image Aesthetic Assessment

Automatic image aesthetics assessment is a computer vision problem dealing with categorizing images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a review of the contemporary automatic image aesthetics assessment techniques. Many traditional hand-crafted and deep learning-based approaches are reviewed, and critical problem aspects are discussed, including why some features or models perform better than others and the limitations. A comparison of the quantitative results of different methods is also provided.
The 6-Ds of Creating AI-Enabled Systems

We are entering our tenth year of the current Artificial Intelligence (AI) spring, and, as with previous AI hype cycles, the threat of an AI winter looms. AI winters occurred because of ineffective approaches towards navigating the technology “valley of death. ” The 6-D framework provides an end-toend framework to successfully navigate this challenge. The 6-D framework starts with problem decomposition to identify potential AI solutions, and ends with considerations for deployment of AI-enabled systems. Each component of the 6-D framework and a precision medicine use case is described in this paper.
Confidence Guided Depth Completion Network

The paper proposes an image-guided depth completion method to estimate accurate dense depth maps with fast computation time. The proposed network has two-stage structure. The first stage predicts a first depth map. Then, the second stage further refines the first depth map using the confidence maps. The second stage consists of two layers, each of which focuses on different regions and generates a refined depth map and a confidence map. The final depth map is obtained by combining two depth maps from the second stage using the corresponding confidence maps. Compared with the top-ranked models on the KITTI depth completion online leaderboard, the proposed model shows much faster computation time and competitive performance.
Partial Encryption after Encoding for Security and Reliability in Data Systems

We consider the problem of secure and reliable communication over a noisy multipath network. Previous work considering a noiseless version of our problem proposed a hybrid universal network coding cryptosystem (HUNCC). By combining an information-theoretically secure encoder together with partial encryption, HUNCC is able to obtain security guarantees, even in the presence of an all-observing eavesdropper. In this paper, we propose a version of HUNCC for noisy channels (N-HUNCC). This modification requires four main novelties. First, we present a network coding construction which is jointly, individually secure and error-correcting. Second, we introduce a new security definition which is a computational analogue of individual security, which we call individual indistinguishability under chosen ciphertext attack (individual IND-CCA1), and show that N-HUNCC satisfies it. Third, we present a noise based decoder for N-HUNCC, which permits the decoding of the encoded-then-encrypted data. Finally, we discuss how to select parameters for N-HUNCC and its error-correcting capabilities.
Mental Stress Detection using Data from Wearable and Non-wearable Sensors: A Review

This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using data from wearable and non-wearable sensors. In particular, wearable sensor-based methods commonly use data from electroencephalography, electrocardiogram, galvanic skin response, electromyography, electrodermal activity, heart rate, heart rate variability, and photoplethysmography both individually and in multimodal fusion strategies. Whereas, methods based on non-wearable sensors include strategies such as analyzing pupil dilation and speech, smartphone data, eye movement, body posture, and thermal imaging. Whenever a stressful situation is encountered by an individual, physiological, physical, or behavioral changes are induced which help in coping with the challenge at hand. A wide range of studies has attempted to establish a relationship between these stressful situations and the response of human beings by using different kinds of psychological, physiological, physical, and behavioral measures. Inspired by the lack of availability of a definitive verdict about the relationship of human stress with these different kinds of markers, a detailed survey about human stress detection methods is conducted in this paper. In particular, we explore how stress detection methods can benefit from artificial intelligence utilizing relevant data from various sources. This review will prove to be a reference document that would Muhammad Majid m.majid@uettaxila.edu. pk 1 Department of Computer Engineering, University of Engineering and Technology Taxila, 47050, Pakistan. 2 Department of Software Engineering, University of Engineering and Technology Taxila, 47050, Pakistan. ar X iv :2 20 2. 03 03 3v 1 [ cs . H C ] 7 F eb 2 02 2 2 Aamir Arsalan1 et al. provide guidelines for future research enabling effective detection of human stress conditions.
A practical algorithm to minimize the overall error in FEM computations

Using the standard finite element method (FEM) to solve general partial differential equations, the round-off error is found to be proportional to $N^{\beta_{\rm R}}$, with $N$ the number of degrees of freedom (DoFs) and $\beta_{\rm R}$ a coefficient. A method which uses a few cheap numerical experiments is proposed to determine the coefficient of proportionality and $\beta_{\rm R}$ in various space dimensions and FEM packages. Using the coefficients obtained above, the strategy put forward in \cite{liu386balancing} for predicting the highest achievable accuracy $E_{\rm min}$ and the associated optimal number of DoFs $N_{\rm opt}$ for specific problems is extended to general problems. This strategy allows predicting $E_{\rm min}$ accurately for general problems, with the CPU time for obtaining the solution with the highest accuracy $E_{\rm min}$ typically reduced by 60\%--90\%.
Autocorrelation, Wigner and Ambiguity Transforms on Polygons for Coherent Radiation Rendering

Simulating the radar illumination of large scenes generally relies on a geometric model of light transport which largely ignores prominent wave effects. This can be remedied through coherence ray-tracing, but this requires the Wigner transform of the aperture. This diffraction function has been historically difficult to generate, and is relevant in the fields of optics, holography, synchrotron-radiation, quantum systems and radar. In this paper we provide the Wigner transform of arbitrary polygons through geometric transforms and the Stokes Fourier transform; and display its use in Monte-Carlo rendering.
A Temporal-Difference Approach to Policy Gradient Estimation

The policy gradient theorem (Sutton et al., 2000) prescribes the usage of a cumulative discounted state distribution under the target policy to approximate the gradient. Most algorithms based on this theorem, in practice, break this assumption, introducing a distribution shift that can cause the convergence to poor solutions. In this paper, we propose a new approach of reconstructing the policy gradient from the start state without requiring a particular sampling strategy. The policy gradient calculation in this form can be simplified in terms of a gradient critic, which can be recursively estimated due to a new Bellman equation of gradients. By using temporal-difference updates of the gradient critic from an off-policy data stream, we develop the first estimator that sidesteps the distribution shift issue in a model-free way. We prove that, under certain realizability conditions, our estimator is unbiased regardless of the sampling strategy. We empirically show that our technique achieves a superior bias-variance trade-off and performance in presence of off-policy samples.
Grassmann Stein Variational Gradient Descent

Stein variational gradient descent (SVGD) is a deterministic particle inference algorithm that provides an efficient alternative to Markov chain Monte Carlo. However, SVGD has been found to suffer from variance underestimation when the dimensionality of the target distribution is high. Recent developments have advocated projecting both the score function and the data onto real lines to sidestep this issue, although this can severely overestimate the epistemic (model) uncertainty. In this work, we propose Grassmann Stein variational gradient descent (GSVGD) as an alternative approach, which permits projections onto arbitrary dimensional subspaces. Compared with other variants of SVGD that rely on dimensionality reduction, GSVGD updates the projectors simultaneously for the score function and the data, and the optimal projectors are determined through a coupled Grassmann-valued diffusion process which explores favourable subspaces. Both our theoretical and experimental results suggest that GSVGD enjoys efficient state-space exploration in high-dimensional problems that have an intrinsic low-dimensional structure.
Auto-Lambda: Disentangling Dynamic Task Relationships

Understanding the structure of multiple related tasks allows for multi-task learning to improve the generalisation ability of one or all of them. However, it usually requires training each pairwise combination of tasks together in order to capture task relationships, at an extremely high computational cost. In this work, we learn task relationships via an automated weighting framework, named Auto-Lambda. Unlike previous methods where task relationships are assumed to be fixed, Auto-Lambda is a gradient-based meta learning framework which explores continuous, dynamic task relationships via task-specific weightings, and can optimise any choice of combination of tasks through the formulation of a meta-loss; where the validation loss automatically influences task weightings throughout training. We apply the proposed framework to both multi-task and auxiliary learning problems in computer vision and robotics, and show that Auto-Lambda achieves state-of-the-art performance, even when compared to optimisation strategies designed specifically for each problem and data domain. Finally, we observe that Auto-Lambda can discover interesting learning behaviors, leading to new insights in multi-task learning. Code is available at https://github.com/lorenmt/auto-lambda.
GhostTalk: Interactive Attack on Smartphone Voice System Through Power Line

Inaudible voice command injection is one of the most threatening attacks towards voice assistants. Existing attacks aim at injecting the attack signals over the air, but they require the access to the authorized user's voice for activating the voice assistants. Moreover, the effectiveness of the attacks can be greatly deteriorated in a noisy environment. In this paper, we explore a new type of channel, the power line side-channel, to launch the inaudible voice command injection. By injecting the audio signals over the power line through a modified charging cable, the attack becomes more resilient against various environmental factors and liveness detection models. Meanwhile, the smartphone audio output can be eavesdropped through the modified cable, enabling a highly-interactive attack. To exploit the power line side-channel, we present GhostTalk, a new hidden voice attack that is capable of injecting and eavesdropping simultaneously. Via a quick modification of the power bank cables, the attackers could launch interactive attacks by remotely making a phone call or capturing private information from the voice assistants. GhostTalk overcomes the challenge of bypassing the speaker verification system by stealthily triggering a switch component to simulate the press button on the headphone. In case when the smartphones are charged by an unaltered standard cable, we discover that it is possible to recover the audio signal from smartphone loudspeakers by monitoring the charging current on the power line. To demonstrate the feasibility, we design GhostTalk-SC, an adaptive eavesdropper system targeting smartphones charged in the public USB ports. To correctly recognize the private information in the audio, GhostTalk-SC carefully extracts audio spectra and integrates a neural network model to classify spoken digits in the speech.
Dataset Condensation with Contrastive Signals

Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation (DC), methods can achieve state-of-the-art performance when applied to data-efficient learning tasks. However, in this study, we prove that the existing DC methods can perform worse than the random selection method when task-irrelevant information forms a significant part of the training dataset. We attribute this to the lack of participation of the contrastive signals between the classes resulting from the class-wise gradient matching strategy. To address this problem, we propose Dataset Condensation with Contrastive signals (DCC) by modifying the loss function to enable the DC methods to effectively capture the differences between classes. In addition, we analyze the new loss function in terms of training dynamics by tracking the kernel velocity. Furthermore, we introduce a bi-level warm-up strategy to stabilize the optimization. Our experimental results indicate that while the existing methods are ineffective for fine-grained image classification tasks, the proposed method can successfully generate informative synthetic datasets for the same tasks. Moreover, we demonstrate that the proposed method outperforms the baselines even on benchmark datasets such as SVHN, CIFAR-10, and CIFAR-100. Finally, we demonstrate the high applicability of the proposed method by applying it to continual learning tasks.
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

Investigating active learning, we focus on the relation between the number of labeled examples (budget size), and suitable querying strategies. Our theoretical analysis shows a behavior reminiscent of phase transition: typical examples are best queried when the budget is low, while unrepresentative examples are best queried when the budget is large. Combined evidence shows that a similar phenomenon occurs in common classification models. Accordingly, we propose TypiClust -- a deep active learning strategy suited for low budgets. In a comparative empirical investigation of supervised learning, using a variety of architectures and image datasets, TypiClust outperforms all other active learning strategies in the low-budget regime. Using TypiClust in the semi-supervised framework, performance gets an even more significant boost. In particular, state-of-the-art semi-supervised methods trained on CIFAR-10 with 10 labeled examples selected by TypiClust, reach 93.2% accuracy -- an improvement of 39.4% over random selection. Code is available at https://github.com/avihu111/TypiClust.
Locally Differentially Private Distributed Deep Learning via Knowledge Distillation

Deep learning often requires a large amount of data. In real-world applications, e.g., healthcare applications, the data collected by a single organization (e.g., hospital) is often limited, and the majority of massive and diverse data is often segregated across multiple organizations. As such, it motivates the researchers to conduct distributed deep learning, where the data user would like to build DL models using the data segregated across multiple different data owners. However, this could lead to severe privacy concerns due to the sensitive nature of the data, thus the data owners would be hesitant and reluctant to participate. We propose LDP-DL, a privacy-preserving distributed deep learning framework via local differential privacy and knowledge distillation, where each data owner learns a teacher model using its own (local) private dataset, and the data user learns a student model to mimic the output of the ensemble of the teacher models. In the experimental evaluation, a comprehensive comparison has been made among our proposed approach (i.e., LDP-DL), DP-SGD, PATE and DP-FL, using three popular deep learning benchmark datasets (i.e., CIFAR10, MNIST and FashionMNIST). The experimental results show that LDP-DL consistently outperforms the other competitors in terms of privacy budget and model accuracy.
Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities

Future wireless services will focus on improving the quality of life by enabling various applications, such as extended reality, brain-computer interaction, and healthcare. These applications will have diverse performance requirements (e.g., user-defined quality of experience metrics, latency, and reliability) which will be challenging to be fulfilled by existing wireless systems. To meet the diverse requirements of the emerging applications, the concept of digital twins has been recently proposed. A digital twin uses a virtual representation along with security-related technologies (e.g., blockchain), communication technologies (e.g., 6G), computing technologies (e.g., edge computing), and machine learning, so as to enable the smart applications. In this tutorial, we present a comprehensive overview on digital twins for wireless systems. First, we present the fundamental concepts (i.e., design aspects, high-level architecture, and frameworks) of digital twins for wireless systems. Second, a comprehensive taxonomy is devised for two aspects, namely, twins for wireless and wireless for twins. For the twins for wireless aspect, we consider issues related to design of twin objects, physical devices, and interface, as well as prototyping, deployment trends, incentive mechanism, isolation of twins, and decoupling. For the wireless for twins aspect, we consider issues related to accessing twin objects, security and privacy, and air interface design are considered. Finally, open research challenges and opportunities are discussed.
Classification on Sentence Embeddings for LegalAssistance

Legal proceedings take plenty of time and also cost a lot. The lawyers have to do a lot of work in order to identify the different sections of prior cases and statutes. The paper tries to solve the first tasks in AILA2021 (Artificial Intelligence for Legal Assistance) that will be held in FIRE2021 (Forum for Information Retrieval Evaluation). The task is to semantically segment the document into different assigned one of the 7 predefined labels or"rhetorical roles. "The paper uses BERT to obtain the sentence embeddings from a sentence, and then a linear classifier is used to output the final prediction. The experiments show that when more weightage is assigned to the class with the highest frequency, the results are better than those when more weightage is given to the class with a lower frequency. In task 1, the team legalNLP obtained a F1 score of 0.22.
Fröhlich electron-phonon interaction and LO-TO splitting in doped semiconductors

We develop a method to compute the electron-phonon interaction and the phonon frequencies in doped 3-dimensional semiconductors. The present formalism goes beyond the state-of-the-art description of the Fröhlich-like vertex and of the long-range non-analytical part of the dynamical matrix, taking into account the change of the inverse dielectric function −1(q) in presence of free carriers at finite temperature. The method is computationally fast thanks to the use of interpolation based on maximally localized Wannier functions. We apply our approach to cubic silicon carbide 3C-SiC, where we find that in presence of doping the Fröhlich coupling and the LO-TO splitting are strongly reduced, thereby influencing observable properties such as the electronic lifetime.
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity

Creating reinforcement learning (RL) agents that are capable of accepting and leveraging task-specific knowledge from humans has been long identified as a possible strategy for developing scalable approaches for solving long-horizon problems. While previous works have looked at the possibility of using symbolic models along with RL approaches, they tend to assume that the high-level action models are executable at low level and the fluents can exclusively characterize all desirable MDP states. Symbolic models of real world tasks are however often incomplete. To this end, we introduce Approximate Symbolic-Model Guided Reinforcement Learning, wherein we will formalize the relationship between the symbolic model and the underlying MDP that will allow us to characterize the incompleteness of the symbolic model. We will use these models to extract high-level landmarks that will be used to decompose the task. At the low level, we learn a set of diverse policies for each possible task subgoal identified by the landmark, which are then stitched together. We evaluate our system by testing on three different benchmark domains and show how even with incomplete symbolic model information, our approach is able to discover the task structure and efficiently guide the RL agent towards the goal.