Reviewerly/exHarmony huggingface dataset:
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks

Designing deep networks robust to adversarial examples remains an open problem. Recently, it was shown that adversaries relying on only top-1 feedback (i.e., the hard-label) from an image classification model can arbitrarily shift an image towards an intended target prediction. Likewise, these hard-label adversaries enjoy performance comparable to first-order adversaries relying on the full model gradient. It was also shown in the gradient-level setting that regular adversarial examples leave the data manifold, while their on-manifold counterparts are in fact generalization errors. In this paper, we argue that query efficiency in the hard-label setting is also connected to an adversary's traversal through the data manifold. To explain this behavior, we propose an information-theoretic argument based on a noisy manifold distance oracle, which leaks manifold information through the adversary's distribution of gradient estimates. Through numerical experiments of manifold-gradient mutual information, we show this behavior acts as a function of the effective problem dimensionality. On high-dimensional real-world datasets and multiple hard-label attacks using dimension reduction, we observe the same behavior to produce samples closer to the data manifold. This can result in up to 10x decrease in the manifold distance measure, regardless of the model robustness. Our results suggest that our variant of hard-label attack can find a higher concentration of generalization errors than previous techniques, leading to improved worst-case analysis for model designers.
Sniper Backdoor: Single Client Targeted Backdoor Attack in Federated Learning

Federated Learning (FL) enables collaborative training of Deep Learning (DL) models where the data is retained locally.Like DL, FL has severe security weaknesses that the attackers can exploit, e.g., model inversion and backdoor attacks.Model inversion attacks reconstruct the data from the training datasets, whereas backdoors misclassify only classes containing specific properties, e.g., a pixel pattern.Backdoors are prominent in FL and aim to poison every client model, while model inversion attacks can target even a single client.This paper introduces a novel technique to allow backdoor attacks to be client-targeted, compromising a single client while the rest remain unchanged.The attack takes advantage of stateof-the-art model inversion and backdoor attacks.Precisely, we leverage a Generative Adversarial Network to perform the model inversion.Afterward, we shadow-train the FL network, in which, using a Siamese Neural Network, we can identify, target, and backdoor the victim's model.Our attack has been validated using the MNIST, F-MNIST, EMNIST, and CIFAR-100 datasets under different settings-achieving up to 99% accuracy on both source (clean) and target (backdoor) classes and against state-of-the-art defenses, e.g., Neural Cleanse, opening a novel threat model to be considered in the future.
Deep Learning based Detection of Mobility Aids using YOLOv5

Mobility impairments and difficulties are a problem faced by a significant portion of the population. The statistics on differently abled people and the mobility aids can benefit a great deal for governmental institutions in areas such as policy-making and infrastructure development. This paper proposes an automated process for the detection and identification of people who use mobility aids. The YOLO (You Only Look Once) algorithm is used to train the dataset. The performance metrics and the feasibility of the algorithm are being discussed. The primary aim of this paper is to help improve the facilities made for differently people in public places by the detection and analysis of the different kinds of mobility aids that people use and how it affects movement in a public place. The model detects and classifies each person in the image as differently abled or not, and if found to be differently abled, it finds out the type of the mobility aid used and the number of differently abled people in each class as well.
A Motion Control Method For Agent Based On Dyna-Q Algorithm

Motion control, i.e. the path planning, in the unknown environment of the agent is a general interest for scholars and how to increase the efficiency of motion control is the most important point. For that, this paper puts forward a motion control method based on Dyna-Q algorithm. The motion control steps with reinforcement learning algorithm is designed and for increase the learning efficiency, a Dyna-Q framework is utilized. The experiment results show that, the method this paper proposed has more fast convergence efficiency and is more suitable for the unknown and dynamic environment to the compared methods.
Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the goal of an adversary is to infer distributional information about the training data. So far, research on distribution inference has focused on demonstrating successful attacks, with little attention given to identifying the potential causes of the leakage and to proposing mitigations. To bridge this gap, as our main contribution, we theoretically and empirically analyze the sources of information leakage that allows an adversary to perpetrate distribution inference attacks. We identify three sources of leakage: (1) memorizing specific information about the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{E}[Y\vert X]$</tex> (expected label given the feature values) of interest to the adversary, (2) wrong inductive bias of the model, and (3) finiteness of the training data. Next, based on our analysis, we propose principled mitigation techniques against distribution inference attacks. Specifically, we demonstrate that causal learning techniques are more resilient to a particular type of distribution inference risk termed distributional membership inference than associative learning methods. And lastly, we present a formalization of distribution inference that allows for reasoning about more general adversaries than was previously possible.
ModelPred: A Framework for Predicting Trained Model from Training Data

In this work, we propose ModelPred, a framework that helps to understand the impact of changes in training data on a trained model. This is critical for building trust in various stages of a machine learning pipeline: from cleaning poor-quality samples and tracking important ones to be collected during data preparation, to calibrating uncertainty of model prediction, to interpreting why certain behaviors of a model emerge during deployment. Specifically, ModelPred learns a parameterized function that takes a dataset S as the input and predicts the model obtained by training on S. Our work differs from the recent work of Datamodels [1] as we aim for predicting the trained model parameters directly instead of the trained model behaviors. We demonstrate that a neural network-based set function class is capable of learning the complex relationships between the training data and model parameters. We introduce novel global and local regularization techniques to prevent overfitting and we rigorously characterize the expressive power of neural networks (NN) in approximating the end-to-end training process. Through extensive empirical investigations, we show that ModelPred enables a variety of applications that boost the interpretability and accountability of machine learning (ML), such as data valuation, data selection, memorization quantification, and model calibration.
Mobile Application for Children to Learn BISINDO Sign Language

Sign language is the most commonly used communication medium for people who are deaf and mute. By using body movements, they can convey meaning through visual cues, not vocal cues. This type of language is learned almost exdusively by people who are deaf or dumb and those who interact with them on a daily basis. Most people do not learn it because sign language is not part of basic education, and the lack of need to use it, especially from a young age. Most of the spoken languages in the world usually have sign language variants according to the language of their respective countries. In this study, a mobile application is developed for children to learn Indonesian sign language where the process model is designed using use case diagrams, and the database design is modeled with class diagrams. As for the implementation, the Android Studio software is used for the application, and the MySQLdatabase is for database storage.
Multiple transformation function estimation for image enhancement

Most deep learning-based image enhancement algorithms have been developed based on the image-to-image translation approach, in which enhancement processes are difficult to interpret. In this paper, we propose a novel interpretable image enhancement algorithm that estimates multiple transformation functions to describe complex color mapping. First, we develop a histogram-based multiple transformation function estimation network (HMTF-Net) to estimate multiple transformation functions by exploiting both the spatial and statistical information of the input images. Second, we estimate pixel-wise weight maps, which indicate the contribution of each transformation function at each pixel, based on the local structures of the input image and the transformed images obtained by each transformation function. Finally, we obtain the enhanced image as the weighted sum of the transformed images using the estimated weight maps. Extensive experiments confirm the effectiveness of the proposed approach and demonstrate that the proposed algorithm outperforms state-of-the-art image enhancement algorithms for different image enhancement tasks.
Fully context-aware image inpainting with a learned semantic pyramid

Restoring reasonable and realistic content for arbitrary missing regions in images is an important yet challenging task. Although recent image inpainting models have made significant progress in generating vivid visual details, they can still lead to texture blurring or structural distortions due to contextual ambiguity when dealing with more complex scenes. To address this issue, we propose the Semantic Pyramid Network (SPN) motivated by the idea that learning multi-scale semantic priors from specific pretext tasks can greatly benefit the recovery of locally missing content in images. SPN consists of two components. First, it distills semantic priors from a pretext model into a multi-scale feature pyramid, achieving a consistent understanding of the global context and local structures. Within the prior learner, we present an optional module for variational inference to realize probabilistic image inpainting driven by various learned priors. The second component of SPN is a fully context-aware image generator, which adaptively and progressively refines low-level visual representations at multiple scales with the (stochastic) prior pyramid. We train the prior learner and the image generator as a unified model without any post-processing. Our approach achieves the state of the art on multiple datasets, including Places2, Paris StreetView, CelebA, and CelebA-HQ, under both deterministic and probabilistic inpainting setups.
Hot topic detection in central bankers’ speeches

Analysis of central bankers’ statements is essential to understand current economic conditions and predict future market trends. The most popular method used for this purpose – qualitative monitoring and evaluation (QME) – is based on the input of domain experts, which is expensive and subjective by nature. Several researchers have attempted to use text mining techniques as alternatives. However, they have primarily focused on aligning textual information with macroeconomic indicators, rather than directly extracting keywords. The primary aim of this study is to identify the possibility of automatically detecting potential financial risk factors by applying text mining techniques to central bankers’ speeches. We propose a text mining framework to extract risk factors ex ante by detecting hot topics in speeches made by chairs of the Federal Reserve System. In the framework, we use a simple and effective unsupervised keyword-scoring method,1 which treats bigrams as keywords and incorporates the temporal importance of keywords by estimating the “emergence” of a term at a certain time period relative to previous time periods. In-depth analysis through extensive experiments was conducted to compare risk factor detection performance using eight existing methods Including statistical approaches and recent pretrained language model-based approaches. Experimental results demonstrate that our proposed method adopting a statistical approach effectively captures potential risk factors in central bankers’ speeches. Application of the proposed framework to manuscripts of speeches made between 1997 and 20192 revealed that the recurrence of the terms Such as “east asia” “information technology” and “subprime mortgage” which describe the Asian financial crisis Dot-com bubble And global financial crisis Respectively Could have been detected prior to the onset of the respective crises. Furthermore The quantitative evaluation revealed that the frequency of appearance of the phrase “subprime mortgage” increased by more than 400% in speeches made during the second quarter of 2007 as compared to the previous quarter Whereas the number of the New York times news articles containing the word “subprime mortgage” only increased by approximately 0%. our results show that central bankers’ speeches can be used as an appropriate data source for the ex ante identification of market risk factors. We anticipate that this study will contribute to broadening the understanding of trends in hot topics in the financial market Monitoring potential risk factors And developing preemptive response plans to deal with impending financial crises.
Photon-added squeezing-enhanced coherent state and its nonclassical and non-Gaussian properties

In this paper, a novel state called photon-added squeezing-enhanced coherent state (PASECS) is introduced and studied by adding m photons to the squeezing-enhanced coherent state. This state contains the non-Gaussian character and nonclassical properties by examining the negative values of the Wigner function. The nonclassical features of the PASECS are investigated based on the second-order correlation function, sub-Poissonian statistics, Q-function, and the degree of squeezing. The results show that by changing photon addition and squeezing-enhanced parameter r, the nonclassical properties are demonstrated stronger in the novel state than in the normal squeezed coherent state.
Tackling the elephant in the language classroom: introducing machine translation literacy in a Swiss language centre

Abstract University students, especially language learners, have increasingly been using machine translation (MT) systems in the last decade and for all kinds of texts, including homework, assignments and exams. This ubiquity does not translate into visibility as few teachers address the subject in class. Several researchers have shown that MT systems, while technically very easy to access and use, are not always employed in a critical manner. They have therefore suggested that users should develop MT literacy skills. As part of a larger Swiss project on digital literacy in university contexts (DigLit), an action research project at the University of Neuchâtel Language Centre (UniNE LC) seeks to investigate whether delivering a 20-min presentation about machine translation in all L2 classes (French, German, English) at the beginning of the semester was sufficient to foster minimal MT literacy in language learners. All LC students were surveyed at the end of the semester. These survey results were compared with those from a survey of Swiss university students carried out in spring 2021 as part of the DigLit project. These results have allowed us to monitor and enhance the teaching of MT literacy skills in our LC.
Intelligent Online Shopping using ML-based Product Comparison Engine

A relatively recent technique for gathering data online is known as scraping. The automated process involves the exploration of e-commerce websites and obtaining certain data, such as pricing, reviews, quality features, etc. Sentiment analysis of product attributes on e-commerce platforms, such as pricing, reviews, quality, etc., can substantially increase user satisfaction in the e-commerce industry. It continues to be difficult to envision precise sentiment analysis. This research work summarizes the product comparison website that implements intelligent web scraping. The website has a processing model that uses the Machine Learning (ML)-based product comparison engine. The simulation analysis for model training and testing uses the GitHub dataset and the Support Vector Machine (SVM) algorithm. The SVM classifies the product from various websites based on the rank value, assigned based on the selected features of the product. The proposed model is compared with other ML algorithms such as Decision Tree, Naïve Bayes, and Random Forest. The comparative analysis shows that the SVM algorithm can classify the best products with the highest accuracy ratio of 94.71%over other algorithms.
Movie Recommendation using Collaborative filtering and Content-based Filtering Approach

A recommendation system searches for content that would be Interesting to a specific person. Its goal is to build customer trust by offering content that is relevant which will increase the time spent of a digital platform. Additionally, this will also enhance user experience. The main objective of this strategy is to combine a content-based and collaborative approach. The proposed system can recommend a variety of movies to customers based on their interests or the box office performance of the movies. Collaborative filtering seeks to consider user opinions of various videos in order to suggest the best video to each user based on their past rankings and the views of other users, who share similar interests.
Performance evaluation of DFT based speckle reduction framework for synthetic aperture radar (SAR) images at different frequencies and image regions

Polarimetric Synthetic Aperture Radar (PolSAR) images are widely used for remote sensing and geoscience applications. However, the coherent processing of radar signals in PolSAR imaging leads to the presence of speckle noise, which can significantly degrade image quality and limit the accuracy of subsequent analyses. To address this issue, speckle reduction frameworks are often applied to PolSAR images to reduce the noise level and enhance image quality. In this paper, the performance of Discrete Fourier Transform (DFT) based speckle reduction framework is evaluated on different bands (L, C, and P band) against various evaluation metrics like CV, SD, SNR, ENL, SSI and SMPI. The proposed framework is evaluated by comparing filtered and unfiltered images across different parameters, such as mean, standard deviation, coefficient of variation, equivalent number of looks (ENL), variance, and signal-to-noise ratio (SNR) on all three bands for the diagonal elements (T11, T22, and T33) of T3 matrix. These metrics provide a comprehensive evaluation of the proposed framework's ability to (i) smoothen homogeneous regions, (ii) preserve contours, and (iii) retain polarimetric information. The framework's ability to reduce speckle noise and improve image quality is demonstrated through a significant reduction in standard deviation, coefficient of variation, and improvement in SNR and ENL values. The proposed framework successfully preserved polarimetric information while effectively suppressing speckle noise. These results suggest that the proposed framework could be a valuable tool for improving PolSAR image quality and enhancing subsequent processing of PolSAR data.
Table-Balancing Cooperative Robot Based on Deep Reinforcement Learning

Reinforcement learning is one of the artificial intelligence methods that enable robots to judge and operate situations on their own by learning to perform tasks. Previous reinforcement learning research has mainly focused on tasks performed by individual robots; however, everyday tasks, such as balancing tables, often require cooperation between two individuals to avoid injury when moving. In this research, we propose a deep reinforcement learning-based technique for robots to perform a table-balancing task in cooperation with a human. The cooperative robot proposed in this paper recognizes human behavior to balance the table. This recognition is achieved by utilizing the robot's camera to take an image of the state of the table, then the table-balance action is performed afterward. Deep Q-network (DQN) is a deep reinforcement learning technology applied to cooperative robots. As a result of learning table balancing, on average, the cooperative robot showed a 90% optimal policy convergence rate in 20 runs of training with optimal hyperparameters applied to DQN-based techniques. In the H/W experiment, the trained DQN-based robot achieved an operation precision of 90%, thus verifying its excellent performance.
Multiple hypotheses based motion compensation for learned video compression

Recently, learned video compression has attracted copious research attention. However, among the existing methods, the motion used for alignment is limited to one hypothesis only, leading to inaccurate motion estimation, especially for the complicated scenes with complex movements. Motivated by multiple hypotheses philosophy in traditional video compression, we develop the multiple hypotheses based motion compensation for the learned video compression, in an effort to enhance the motion compensation efficiency by providing diverse hypotheses with efficient temporal information fusion. In particular, the multiple hypotheses module which produces multiple motions and warped features for mining sufficient temporal information, is proposed to provide various hypotheses inferences from the reference frame. To utilize these hypotheses more copiously, the hypotheses attention module is adopted by introducing the channel-wised squeeze-and-excitation layer and the multi-scale network. In addition, the context combination is employed to fuse the weighted hypotheses to generate effective contexts with powerful temporal priors. Finally, the valid contexts are used for promoting the compression efficiency by merging weighted warped features. Extensive experiments show that the proposed method can significantly improve the rate-distortion performance of learned video compression. Compared with the state-of-the-art method for end-to-end video compression, over 13% bit rate reductions on average in terms of PSNR and MS-SSIM can be achieved.
Protecting the fidelity of a pulsed-driven harmonic oscillator

The temporal fidelity of a non-dissipative quantised harmonic oscillator (QHO) driven by two types of pulsed laser; namely, rectangular and train of n-chirped Gaussian pulses is investigated. The computational results are presented for two initial states of the HO, namely, the superposition of coherent states (Schrödinger cat states type) and squeezed coherent state. Quantum interference between the two component states |α〉 and |−α〉 of the initial cat state in the non-resonance case induces oscillatory behaviour in the fidelity. The case of initial squeezed coherent state shows profound maximum bounds of the fidelity or lesser maximum peaks, depending on the pulse shape and other system parameters (squeezing, chirp and pulse number parameters).