Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Analysis of SMS Scam Detection Systems

The short message service (SMS) was introduced a generation ago to the mobile phone users. They make up the world's oldest large-scale network, with billions of users and therefore attracts a lot of fraud. Due to the convergence of mobile network with internet, SMS based scams can potentially compromise the security of internet services as well. In this study, we present a new SMS scam dataset consisting of 153,551 SMSes. This dataset that we will release publicly for research purposes represents the largest publicly-available SMS scam dataset. We evaluate and compare the performance achieved by several established machine learning methods on the new dataset, ranging from shallow machine learning approaches to deep neural networks to syntactic and semantic feature models. We then study the existing models from an adversarial viewpoint by assessing its robustness against different level of adversarial manipulation. This perspective consolidates the current state of the art in SMS Spam filtering, highlights the limitations and the opportunities to improve the existing approaches.
Identifying the Impact of User Reviews on App Updates: An Exploratory Study on App Release Notes

Release planning for mobile apps has recently become an area of active research. Prior research concentrated on app analysis based on app release notes in App Store, or tracking user reviews to support app evolution with issue trackers. However, as a platform for development teams to communicate with users, Apple Store has not been studied for detecting the relevance between release notes and user reviews. In this paper, we introduce RoseMatcher , an automatic approach to match relevant user reviews with app release notes, and identify matched pairs with high conﬁdence. We collected 944 release notes and 1,046,862 user reviews from 5 mobile apps in the Apple App Store as research data, and evaluated the eﬀectiveness and accuracy of RoseMatcher . Our evaluation shows that RoseMatcher can reach a hit ratio of 0.718 for identifying relevant matched pairs. We further conducted manual labelling and content analysis on 984 relevant matched pairs, and deﬁned 8 roles user reviews play in app update according to the relationship between release notes and user reviews in the relevant matched pairs. The study results show that release notes tend to respond and solve feature requests, bug reports, and complaints raised in user reviews, while user reviews also tend to give positive, negative, and constructive feedback on app updates. Additionally, in the time dimension, the relevant reviews of release notes tend to be posed in a small period of time before and after the release of release notes. In the matched pairs, the time interval between the post time of release notes and user reviews reaches a maximum of three years and an average of one year. These ﬁndings indicate that the development teams do adopt user reviews when updating apps, and users show their interest in app release notes.
POGD: Gradient Descent with New Stochastic Rules

There introduce Particle Optimized Gradient Descent (POGD), an algorithm based on the gradient descent but integrates the particle swarm optimization (PSO) principle to achieve the iteration. From the experiments, this algorithm has adaptive learning ability. The experiments in this paper mainly focus on the training speed to reach the target value and the ability to prevent the local minimum. The experiments in this paper are achieved by the convolutional neural network (CNN) image classification on the MNIST and cifar-10 datasets.
Towards Practical Explainability with Cluster Descriptors

With the rapid development of machine learning, improving its explainability has become a crucial research goal. We study the problem of making the clusters more explainable by investigating the cluster descriptors. Given a set of objects $S$, a clustering of these objects $\pi$, and a set of tags $T$ that have not participated in the clustering algorithm. Each object in $S$ is associated with a subset of $T$. The goal is to find a representative set of tags for each cluster, referred to as the cluster descriptors, with the constraint that these descriptors we find are pairwise disjoint, and the total size of all the descriptors is minimized. In general, this problem is NP-hard. We propose a novel explainability model that reinforces the previous models in such a way that tags that do not contribute to explainability and do not sufficiently distinguish between clusters are not added to the optimal descriptors. The proposed model is formulated as a quadratic unconstrained binary optimization problem which makes it suitable for solving on modern optimization hardware accelerators. We experimentally demonstrate how a proposed explainability model can be solved on specialized hardware for accelerating combinatorial optimization, the Fujitsu Digital Annealer, and use real-life Twitter and PubMed datasets for use cases. Reproducibility materials: Link
Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes

In real life, the decoration of 3D indoor scenes through designing furniture layout provides a rich experience for people. In this paper, we explore the furniture layout task as a Markov decision process (MDP) in virtual reality, which is solved by hierarchical reinforcement learning (HRL). The goal is to produce a proper two-furniture layout in the virtual reality of the indoor scenes. In particular, we first design a simulation environment and introduce the HRL formulation for a two-furniture layout. We then apply a hierarchical actor-critic algorithm with curriculum learning to solve the MDP. We conduct our experiments on a large-scale real-world interior layout dataset that contains industrial designs from professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts as compared with the state-of-art models.
Differentiable Self-Adaptive Learning Rate

Learning rate adaptation is a popular topic in machine learning. Gradient Descent trains neural nerwork with a fixed learning rate. Learning rate adaptation is proposed to accelerate the training process through adjusting the step size in the training session. Famous works include Momentum, Adam and Hypergradient. Hypergradient is the most special one. Hypergradient achieved adaptation by calculating the derivative of learning rate with respect to cost function and utilizing gradient descent for learning rate. However, Hypergradient is still not perfect. In practice, Hypergradient fail to decrease training loss after learning rate adaptation with a large probability. Apart from that, evidence has been found that Hypergradient are not suitable for dealing with large datesets in the form of minibatch training. Most unfortunately, Hypergradient always fails to get a good accuracy on the validation dataset although it could reduce training loss to a very tiny value. To solve Hypergradient's problems, we propose a novel adaptation algorithm, where learning rate is parameter specific and internal structured. We conduct extensive experiments on multiple network models and datasets compared with various benchmark optimizers. It is shown that our algorithm can achieve faster and higher qualified convergence than those state-of-art optimizers.
Dodona: Learn to Code with a Virtual Co-teacher that Supports Active Learning

Dodona (dodona.ugent.be) is an intelligent tutoring system for learning computer programming, statistics and data science. It bridges the gap between assessment and learning by providing real-time data and feedback to help students learn better, teachers teach better and educational technology become more effective. We show how Dodona can be used as a virtual co-teacher to stimulate active learning and support challenge-based education in open and collaborative learning environments. We also highlight some of the opportunities and challenges we have faced in practice. Dodona is free to use and has more than 50 thousand registered users across many educational and research institutions, including 15 thousand new users in the last year. Dodona's source code is available on GitHub under the permissive MIT open-source license.
Fast Approximation of the Generalized Sliced-Wasserstein Distance

Generalized sliced Wasserstein distance is a variant of sliced Wasserstein distance that exploits the power of non-linear projection through a given defining function to better capture the complex structures of the probability distributions. Similar to sliced Wasserstein distance, generalized sliced Wasserstein is defined as an expectation over random projections which can be approximated by the Monte Carlo method. However, the complexity of that approximation can be expensive in high-dimensional settings. To that end, we propose to form deterministic and fast approximations of the generalized sliced Wasserstein distance by using the concentration of random projections when the defining functions are polynomial function, circular function, and neural network type function. Our approximations hinge upon an important result that one-dimensional projections of a high-dimensional random vector are approximately Gaussian.
SML: Enhance the Network Smoothness with Skip Meta Logit for CTR Prediction

In light of the smoothness property brought by skip connections in ResNet, this paper proposed the Skip Logit to introduce the skip connection mechanism that fits arbitrary DNN dimensions and embraces similar properties to ResNet. Meta Tanh Normalization (MTN) is designed to learn variance information and stabilize the training process. With these delicate designs, our Skip Meta Logit (SML) brought incremental boosts to the performance of extensive SOTA ctr prediction models on two real-world datasets. In the meantime, we prove that the optimization landscape of arbitrarily deep skip logit networks has no spurious local optima. Finally, SML can be easily added to building blocks and has delivered offline accuracy and online business metrics gains on app ads learning to rank systems at TikTok.
Radiation Transport Two-temperature GRMHD Simulations of Warped Accretion Disks

In many black hole (BH) systems, the accretion disk is expected to be misaligned with respect to the BH spin axis. If the scale height of the disk is much smaller than the misalignment angle, the spin of the BH can tear the disk into multiple, independently precessing “sub-disks. ” This is most likely to happen during outbursts in black hole X-Ray binaries (BHXRBs) and in active galactic nuclei (AGNs) accreting above a few percent of the Eddington limit, because the disk becomes razor-thin. Disk tearing has the potential to explain variability phenomena including quasi-periodic oscillations in BHXRBs and changing-look phenomena in AGNs. Here, we present the first radiative two-temperature general relativistic magnetohydrodynamic (GRMHD) simulation of a strongly tilted (65°) accretion disk around an M BH = 10 M ⊙ BH, which tears and precesses. This leads to luminosity swings between a few percent and 50% of the Eddington limit on sub-viscous timescales. Surprisingly, even where the disk is radiation-pressure-dominated, the accretion disk is thermally stable over t ≳ 14,000 r g /c. This suggests warps play an important role in stabilizing the disk against thermal collapse. The disk forms two nozzle shocks perpendicular to the line of nodes where the scale height of the disk decreases tenfold and the electron temperature reaches T e ∼ 108–109 K. In addition, optically thin gas crossing the tear between the inner and outer disk gets heated to T e ∼ 108 K. This suggests that warped disks may emit a Comptonized spectrum that deviates substantially from idealized models.
Exact algorithms for graph-theoretic frequency assignment problems

Many real-life problems have very natural graph-theoretic models. One of such problems is the frequency assignment problem. It asks for an assignment of channels of frequency to transmitters in a broadcast network, so that no pair of transmitters interfere with each other. Depending on a way how we de ne the interference, this leads to many well-studied graph theoretic problems. For example, an L(2, 1)-labeling of a graphG is a mapping from the vertex set of G to the set of non-negative integers, such that the labels assigned to adjacent vertices di er by at least 2 and labels assigned to vertices with a common neighbor are di erent. The span of such a labeling is the maximum label used and the L(2, 1)-labeling problem asks for the minimum span of an L(2, 1)-labeling of G. Other models considered in this context are the graph coloring problem, the channel assignment problem, the T -coloring problem etc. Since all the problems mentioned above are NP-hard, there is little hope to solve them exactly in a polynomial time. Thus in this dissertation we are mostly interested in designing exact algorithms, whose complexities are bounded by exponential functions of the size of the input. We describe and analyze a general framework, which can be used to solve some type of graph labeling problems. More precisely, we design an algorithm, which solves the so-called generalized list T -coloring problem, which is a common generalization of all the problems mentioned above. We also show how to improve the complexity analysis if the input graph has some speci c structure. Then we show how a re ned version of this general algorithm works for the L(2, 1)-labeling problem. The time and space complexity of our re ned algorithm is O(2.6488). We also present how to adapt the general algorithm to obtain algorithms
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentation

The recent advances in reinforcement learning have led to e ﬀ ective methods able to obtain above human-level performances in very complex environments. However, once solved, these environments become less valuable, and new challenges with di ﬀ erent or more complex scenarios are needed to support research advances. This work presents DIAMBRA Arena, a new platform for reinforcement learning research and experimentation, featuring a collection of high-quality environments exposing a Python API fully compliant with OpenAI Gym standard. They are episodic tasks with discrete actions and observations composed by raw pixels plus additional numerical values, all supporting both single player and two players mode, allowing to work on standard reinforcement learning, competitive multi-agent, human-agent competition, self-play, human-in-the-loop training and imitation learning. Software capabilities are demonstrated by successfully training multiple deep reinforcement learning agents with proximal policy optimization obtaining human-like behavior. Results conﬁrm the utility of DIAMBRA Arena as a reinforcement learning research tool, providing environments designed to study some of the most challenging topics in the ﬁeld.
Recurrence and Transience for Branching Random Walks in an iid Random Environment

We give three different criteria for transience of a Branching Markov Chain. These conditions enable us to give a classification of Branching Random Walks in Random Environment (BRWRE) on Cayley Graphs in recurrence and transience. This classification is stated explicitly for BRWRE on $\Z^d. $ Furthermore, we emphasize the interplay between Branching Markov Chains and the spectral radius. We prove properties of the spectral radius of the Random Walk in Random Environment with the help of appropriate Branching Markov Chains.
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking

Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors. Recent researches start from the pretrained knowledge of language models and take multimodal information into CSC models to improve the performance. However, they overlook the rich knowledge in the dictionary, the reference book where one can learn how one character should be pronounced, written, and used. In this paper, we propose the LEAD framework, which renders the CSC model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning. LEAD first constructs positive and negative samples according to the knowledge of character phonetics, glyphs, and definitions in the dictionary. Then a unified contrastive learning-based training scheme is employed to refine the representations of the CSC models. Extensive experiments and detailed analyses on the SIGHAN benchmark datasets demonstrate the effectiveness of our proposed methods.
Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval

Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information. As instances from different modalities can often provide complementary information describing the underlying concept, we propose a cross-attention framework for Vision Transformers (XModalViT) that fuses modality-specific information instead of discarding them. Our framework first maps paired datapoints from the individual photo and sketch modalities to fused representations that unify information from both modalities. We then decouple the input space of the aforementioned modality fusion network into independent encoders of the individual modalities via contrastive and relational cross-modal knowledge distillation. Such encoders can then be applied to downstream tasks like cross-modal retrieval. We demonstrate the expressive capacity of the learned representations by performing a wide range of experiments and achieving state-of-the-art results on three fine-grained sketch-based image retrieval benchmarks: Shoe-V2, Chair-V2 and Sketchy. Implementation is available at https://github.com/abhrac/xmodal-vit.
Analysis of Master Vein Attacks on Finger Vein Recognition Systems

Finger vein recognition (FVR) systems have been commercially used, especially in ATMs, for customer verification. Thus, it is essential to measure their robustness against various attack methods, especially when a handcrafted FVR system is used without any countermeasure methods. In this paper, we are the first in the literature to introduce master vein attacks in which we craft a vein-looking image so that it can falsely match with as many identities as possible by the FVR systems. We present two methods for generating master veins for use in attacking these systems. The first uses an adaptation of the latent variable evolution algorithm with a proposed generative model (a multi-stage combination of β-VAE and WGAN-GP models). The second uses an adversarial machine learning attack method to attack a strong surrogate CNN-based recognition system. The two methods can be easily combined to boost their attack ability. Experimental results demonstrated that the proposed methods alone and together achieved false acceptance rates up to 73.29% and 88.79%, respectively, against Miura’s hand-crafted FVR system. We also point out that Miura’s system is easily compromised by non-vein-looking samples generated by a WGAN-GP model with false acceptance rates up to 94.21%. The results raise the alarm about the robustness of such systems and suggest that master vein attacks should be considered an important security measure.
Evaluation Metrics for Object Detection for Autonomous Systems

This paper studies the evaluation of learning-based object detection models in conjunction with model-checking of formal specifications defined on an abstract model of an autonomous system and its environment. In particular, we define two metrics -- \emph{proposition-labeled} and \emph{class-labeled} confusion matrices -- for evaluating object detection, and we incorporate these metrics to compute the satisfaction probability of system-level safety requirements. While confusion matrices have been effective for comparative evaluation of classification and object detection models, our framework fills two key gaps. First, we relate the performance of object detection to formal requirements defined over downstream high-level planning tasks. In particular, we provide empirical results that show that the choice of a good object detection algorithm, with respect to formal requirements on the overall system, significantly depends on the downstream planning and control design. Secondly, unlike the traditional confusion matrix, our metrics account for variations in performance with respect to the distance between the ego and the object being detected. We demonstrate this framework on a car-pedestrian example by computing the satisfaction probabilities for safety requirements formalized in Linear Temporal Logic (LTL).
Factors influencing patient delay in individuals with obstructive sleep apnoea: a study based on an integrated model

Abstract Background Obstructive sleep apnoea (OSA) is the most common sleep-related breathing disorder, yet it remains undiagnosed in a large proportion of adults. Objective This study aims to investigate the status of patient delay and provider delay in OSA patients and examine related factors affecting patient delay in OSA individuals in China. Methods A cross-sectional design was conducted on a sample of 309 OSA patients (aged from 18 to 76, median age of 47 years, 84.8% male) in Northeast China. Participants were required to complete the sociodemographic questionnaire, the symptom characteristics questionnaire, the help-seeking attitude scale (HSAS), the social support rating scale and the chronic disease self-efficacy scales (CDSES) to test the hypothesis. Binary logistic regression analysis was conducted to explore factors that account for the patient delay. Results The median patient delay among OAS patients in this study was 22 months, the median provider delay was one month, and the median total delay was 26 months. As shown by multivariate analysis results, patients who have snored for over 6 years (OR = 3.377, 95%CI: 1.175–9.702) were more likely to experience prolonged patient delays. Per capita monthly family income above 3000 RMB (OR = 0.172, 95%CI: 0.052–0.571), taking up residence in cities or towns (OR = 0.484, 95%CI: 0.248–0.946), higher self-recognition of the disease (OR = 0.793, 95%CI: 0.647–0.972), higher objective support (OR = 0.825, 95%CI: 0.739–0.921) and stronger self-efficacy (OR = 0.674, 95%CI: 0.525–0.867) were significantly associated with shorter patient delays. Conclusion Patient delay is common in Chinese OSA patients. The upstream factors affecting the patient delay in individuals with OSA include income, place of residence, and objective support; midstream factors include self-recognition of the disease and self-efficacy; downstream factors include years of snoring. KEY MESSAGES Despite being a high-prevalence disease, many obstructive sleep apnoea (OSA) patients are not clearly diagnosed and treated. The factors affecting the delay in seeking medical treatment in individuals with OSA included income, place of residence, objective support, self-recognition of the disease, self-efficacy and years of snoring. Investigations into OSA patients’ care-seeking behaviours can better reflect the secondary prevention of OSA, and it is crucial to pay attention to the delayed phase of patients.