Reviewerly/exHarmony huggingface dataset:
Specific Emitter Identification with Principal Component Analysis (PCA) Dimensionality Reduction and Convolutional Neural Network

The capability to identify and authenticate electronic devices through their physical intrinsic features has received considerable attention by the research community in recent years. This capability has been called in literature Specific emitter identification, radiometric identification or RF-DNA as a conceptual link to the physical identification of human being through the DNA. As in other fields of wireless communication, deep learning has been applied to implement such capability, where it has proven an excellent performance at the cost of the need of significant computing resources and time. To address the latter problem, this paper proposes a set of pre-processing steps based on time series segmentation, feature extraction and Principal Component Analysis (PCA) to reduce the data dimension given as an input to a Convolutional Neural Network (CNN) for classification. The proposed approach is able not only to reduce significantly the overall classification time, but it is also able to improve the classification accuracy in presence of noise in comparison to the baseline case where all the data is used.
Switchable Representation Learning Framework with Self-Compatibility

Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrain platforms leads to limited accuracy. It is expected to deploy models with different capacities adapting to the resource constraints, which requires features extracted by these models to be aligned in the metric space. The method to achieve feature alignments is called “compatible learning”. Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models. We propose a Switchable representation learning Framework with Self-Compatibility (SFSC). SFSC generates a series of compatible sub-models with different capacities through one training process. The optimization of sub-models faces gradients conflict, and we mitigate this problem from the perspective of the magnitude and direction. We adjust the priorities of sub-models dynamically through uncertainty estimation to co-optimize sub-models properly. Besides, the gradients with conflicting directions are projected to avoid mutual interference. SFSC achieves state-of-the-art performance on the evaluated datasets.
Logical Implications for Visual Question Answering Consistency

Despite considerable recent progress in Visual Question Answering (VQA) models, inconsistent or contradictory answers continue to cast doubt on their true reasoning capabilities. However, most proposed methods use indirect strategies or strong assumptions on pairs of questions and answers to enforce model consistency. Instead, we propose a novel strategy intended to improve model performance by directly reducing logical inconsistencies. To do this, we introduce a new consistency loss term that can be used by a wide range of the VQA models and which relies on knowing the logical relation between pairs of questions and answers. While such information is typically not available in VQA datasets, we propose to infer these logical relations using a dedicated language model and use these in our proposed consistency loss function. We conduct extensive experiments on the VQA Introspect and DME datasets and show that our method brings improvements to state-of-the-art VQA models while being robust across different architectures and settings.
LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision &amp; Language Models

Soft prompt learning has recently emerged as one of the methods of choice for adapting V&L models to a downstream task using a few training examples. However, current methods significantly overfit the training data, suffering from large accuracy degradation when tested on unseen classes from the same domain. To this end, in this paper, we make the following 4 contributions: (1) To alleviate base class overfitting, we propose a novel Language- Aware Soft Prompting (LASP) learning method by means of a text-to-text cross-entropy loss that maximizes the probability of the learned prompts to be correctly classified with respect to pre-defined hand-crafted textual prompts. (2) To increase the representation capacity of the prompts, we propose grouped LASP where each group of prompts is optimized with respect to a separate subset of textual prompts. (3) We identify a visual-language misalignment introduced by prompt learning and LASP, and more importantly, propose a re-calibration mechanism to address it. (4) We show that LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available, further increasing the robustness of the learned prompts. Through evaluations on 11 datasets, we show that our approach (a) significantly outperforms all prior works on soft prompting, and (b) matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets. Code will be made available here.
'AI-Teacher' Assistant System: A Smart Attendance and Participation tracking system for students

Students and teachers are connected in an orderly way to attain learning outcomes, which is the foundation of attendance systems. Academic institutions, however, encounter poor strategies for these parties' participation. In order to address the low attendance and participation among students and faculty in a university community, a university-wide ‘AI-Teacher’ system will be built in one of the educational institutions in the Gulf region. The system was prepared for by planning, gathering, modelling, and analyzing the requirements, designing the interfaces and databases, and then coding and testing. The AI-Teacher assistant system is approachable and simple to use. A warning is sent to parents after a student misses a predetermined number of classes, along with an automatic or manual attendance record, alerts, and notifications. Also, the application system, ‘AI-Teacher’ which works with iOS and Android devices, can improve connections between "students and instructor". The study concluded with some recommendations for further research and development.
On Properties of Doeblin Coefficients

Doeblin coefficients are a classical tool to study the ergodicity of Markov chains. Propelled by recent works on contraction coefficients of strong data processing inequalities, we investigate whether Doeblin coefficients also exhibit some of the notable properties of canonical contraction coefficients. Specifically, we present various new structural and geometric properties of Doeblin coefficients. Then, by establishing an extremal coupling characterization, we show that Doeblin coefficients generalize the well-known total variation (TV) distance to a multi-way divergence, enabling us to measure the distance between multiple distributions rather than just two. We also demonstrate that Doeblin coefficients exhibit contraction properties over Bayesian networks similar to other canonical contraction coefficients. Finally, we discuss how Doeblin coefficients can be used to define a new rule for fusion of probability mass functions.
TrustGAT: Sparse Trust Data Mining with Graph Attention for Mobile Social Networks

Trust-aware recommendation plays an essential role in alleviating information overload by exploiting social relationships among users to build recommendation systems. However, recommendation systems in mobile social networks suffer from the sparse trust problem, which severely affects the reliability of trust propagation and the accuracy of the recommendation. The flourishing graph neural networks have revitalized Trust-aware Recommendation Systems. Therefore, we propose a sparse trust data mining method based on graph attention networks (TrustGAT) to mine potential trust information between entities in large-scale mobile social networks. First, the potential relationships between the trustors and the trustees are simulated in sparse data, and thereof an adaptive trust network is built. On this basis, features of trust-related information and items are learned in multi-head attention modules to enable the scheme stability. In addition, the implicit influence of trust is introduced to augment network node representation. Empirical results on three public benchmarks show that TrustGAT can make recommendation for users rapidly and accurately as well as alleviate the sparse trust problem effectively.
Bias Mimicking: A Simple Sampling Approach for Bias Mitigation

Prior work has shown that Visual Recognition datasets frequently underrepresent bias groups <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> (e.g. Female) within class labels <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Y$</tex> (e.g. Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address this problem require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Alternatively, data sampling baselines from the class imbalance literature (e.g. Undersampling, Upweighting), which can often be implemented in a single line of code and often have no hyperparameters, offer a cheaper and more efficient solution. However, these methods suffer from significant shortcomings. For example, Undersampling drops a significant part of the input distribution per epoch while Oversampling repeats samples, causing overfitting. To address these shortcomings, we introduce a new class-conditioned sampling method: Bias Mimicking. The method is based on the observation that if a class <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c$</tex> bias distribution, i.e. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{D}(B\vert Y=c)$</tex> is mimicked across every <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c^{\prime}\neq c$</tex> , then <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Y$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> are statistically independent. Using this notion, BM, through a novel training procedure, ensures that the model is exposed to the entire distribution per epoch without repeating samples. Consequently, Bias Mimicking improves underrepresented groups' accuracy of sampling methods by 3% over four benchmarks while maintaining and sometimes improving performance over nonsampling methods. Code: https://github.com/mqraitem/Bias-Mimicking
Differentially-Private Collaborative Online Personalized Mean Estimation

We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy. Two privacy mechanisms are proposed and we provide a theoretical convergence analysis of the proposed algorithm for any bounded unknown distributions on the agents’ data. Numerical results show that for a considered scenario the proposed approach converges much faster than a fully local approach where agents do not share data, and performs comparably to ideal performance where all data is public. This illustrates the benefit of private collaboration in an online setting.
Schrödinger Mechanisms: Optimal Differential Privacy Mechanisms for Small Sensitivity

We consider the problem of designing optimal differential privacy mechanisms with a favorable privacy-utility tradeoff in the limit of a large number n of compositions (i.e., sequential queries). Here, utility is measured by the average distance between the mechanism's input and output, evaluated by a cost function c. We show that if n is sufficiently large and the sensitivities of all queries are small, then the optimal additive noise mechanism has probability density function fully characterized by the ground-state eigenfunction of the Schrödinger operator with potential c. This leads to a family of optimal mechanisms, dubbed the Schrödinger mechanisms, depending on the choice of the cost function. Instantiating this result, we demonstrate that for c(x) = x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> the Gaussian mechanism is optimal, and for c(x) = |x|, the optimal mechanism is obtained by the Airy function, thereby leading to the Airy mechanism.
One-Shot Bounds on State Generation using Correlated Resources and Local Encoders

Distributed source simulation is the task where two (or more) parties share some correlated randomness and use local operations and no communication to convert this into some target correlation. Wyner’s seminal result showed that asymptotically the rate of uniform shared randomness needed for this task is given by a mutual information induced measure, now referred to as Wyner’s common information. In this work we characterize the quantum version of this task in the one-shot setting using the smooth entropy framework and one-shot operational quantities. We further establish asymptotic equipartition properties for our correlation measures. We also introduce entanglement versions of the distributed source simulation task and determine bounds in this setting via quantum embezzling.
How can objects help action recognition?

Current state-of-the-art video models process a video clip as a long sequence of spatio-temporal tokens. However, they do not explicitly model objects, their interactions across the video, and instead process all the tokens in the video. In this paper, we investigate how we can use knowledge of objects to design better video models, namely to process fewer tokens and to improve recognition accuracy. This is in contrast to prior works which either drop tokens at the cost of accuracy, or increase accuracy whilst also increasing the computation required. First, we propose an object-guided token sampling strategy that enables us to retain a small fraction of the input tokens with minimal impact on accuracy. And second, we propose an object-aware attention module that enriches our feature representation with object information and improves overall accuracy. Our resulting model, ObjectViViT, achieves better performance when using fewer tokens than strong baselines. In particular, we match our baseline with 30%, 40%, and 60% of the input tokens on SomethingElse, Something-something v2, and Epic-Kitchens, respectively. When we use Object-ViViT to process the same number of tokens as our baseline, we improve by 0.6 to 4.2 points on these datasets.
Subfield Attack on NTRU by using symmetric function map

We describe a subfield attack for NTRU problem by using the symmetric function map S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> , which is a generalization of results presented by Albrecht, Bai and Ducas [1] and Cheon, Jeong and [2]. At first, we prove that S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> with an appropriate k can fits for more cases compared with previous related works while maintaining the same or better efficiency. Then we show the subfield attack has its advantage compared with the subring attack described in [3]. At last we present a method to make the subfield attack algorithm more efficient.
Zero-Shot Model Diagnosis

When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluate deep learning models is to build a labeled test set with attributes of interest and assess how well it performs. However, creating a balanced test set (i.e., one that is uniformly sampled over all the important traits) is often time-consuming, expensive, and prone to mistakes. The question we try to address is: can we evaluate the sensitivity of deep learning models to arbitrary visual attributes without an annotated test set? This paper argues the case that Zero-shot Model Diagnosis (ZOOM) is possible without the need for a test set nor labeling. To avoid the need for test sets, our system relies on a generative model and CLIP. The key idea is enabling the user to select a set of prompts (relevant to the problem) and our system will automatically search for semantic counterfactual images (i.e., synthesized images that flip the prediction in the case of a binary classifier) using the generative model. We evaluate several visual tasks (classification, key-point detection, and segmentation) in multiple visual domains to demonstrate the viability of our methodology. Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.
Hierarchical Reinforcement Learning-based Mapless Navigation with Predictive Exploration Worthiness

Hierarchical reinforcement learning (HRL) is a promising approach for complex mapless navigation tasks by decomposing the task into a hierarchy of subtasks. However, selecting appropriate subgoals is challenging. Existing methods predominantly rely on sensory inputs, which may contain inadequate information or excessive redundancy. Inspired by the cognitive processes underpinning human navigation, our aim is to enable the robot to leverage both ‘intrinsic and extrinsic factors’ to make informed decisions regarding subgoal selection. In this work, we propose a novel HRL-based mapless navigation framework. Specifically, we introduce a predictive module, named Predictive Exploration Worthiness (PEW), into the high-level (HL) decision-making policy. The hypothesis is that the worthiness of an area for further exploration is related to obstacle spatial distribution, such as the area of free space and the distribution of obstacles. The PEW is introduced as a compact representation for obstacle spatial distribution. Additionally, to incorporate ‘intrinsic factors’ in the subgoal selection process, a penalty element is introduced in the HL reward function, allowing the robot to take into account the capabilities of the low-level policy when selecting subgoals. Our method exhibits significant improvements in success rate when tested in unseen environments.
Blockchain for Ridesharing: A Systematic Literature Review

The use of blockchain technologies allows to shift ridesharing services from centralized to decentralized systems. In order to emphasize the blockchain's role in ridesharing and ride-hailing services and how it could boost the efficiency of these systems, we conduct a systematic literature review using the PRISMA method. For each selected paper, we discussed its key contribution and the main issue/challenge solved by blockchain. We then classified papers according to the type of blockchain used and implementation details. According to our sample of publications, most proposed solutions have been limited to using blockchain as a traceable, auditable, and tamper-proof ledger for completed rides and hadn’t implemented the whole process of ridesharing.
POI Recommendation by Learning Short-, Long- and Mid-Term Preferences through GNN

Recommender systems nowadays are commonly used in various platforms to provide information based on user preferences. In POI recommendation, systems can generally learn the users' short-term and long-term preferences, which are based on sessions and global information. Existing systems, however, usually overlook the mid-term information, which may contain important indications of user preferences. In this work, we propose a session-based POI recommender system based on Graph Neural Network (GNN). In contrast to existing work, our model can learn short-term, long-term, and mid-term preferences at the same time. In order to learn the mid-term item representation, we construct a week graph and process it by a GAT-based graph model. We further use a gate fusion to integrate three temporal dimensions to obtain the hybrid item representation. We conduct experiments with a real-world POI visiting dataset, and the evaluation results show that our model outperforms compared state-of-art models. By adding mid-term information, the prediction accuracy can be improved by 5% compared to the best baseline.
On the Gibbs Exponential Mechanism and Private Synthetic Data Generation

We study the Gibbs exponential mechanism and its use in generating private synthetic data. We first present bounds on the expected utility of the Gibbs and generalized Gibbs mechanisms based on their privacy parameters. Next, we provide two notions of privacy and functionals of utility with respect to which the Gibbs mechanism is provably optimal among all mechanisms. These rely on a valuable property of Gibbs distributions relating them to relative entropy, called the Gibbs variational principle, and its extension to Rényi divergences and generalized moments. Finally, we study how to use the Gibbs mechanism to generate synthetic data privately. Combining known results in empirical process theory with the privacy-utility tradeoff results of this paper, we derive bounds on the utility of the Gibbs mechanism as a function of the size of the synthetic database.
Extracting Insights from Big Source Code Repositories with Automatic Clustering of Projects by File Names and Types

Software project delivery requires a set of related activities to be conducted. The output of these activities form a collection of unstructured data such as specifications, requirements, manuals, source code and packaging files which is stored in configuration management systems. Software repositories are infrastructures to support project management activities and can be composed with several systems that include code change management, bug tracking, code review, build system, release binaries, wikis, forums, etc. This large and variable data collection provides opportunities for text mining tasks which further can be utilized for software delivery or business intelligence related goals. The proposed approach uses machine learning methods to inspect large software repositories and classifies the results to propose insights to project managers as an aide to make strategic business decisions. In the present work every software project is described by a certain representation (vector or set of vectors), which is constructed from names and types of the files from project content. These representations are used to find clusters of similar projects in big code repositories and highlight specific properties of the found groups: most used names and types of the file.
Design of MADDPG Capture Algorithm for Multiple UAV Cooperation

Based on the background of Multiple Unmanned Aerial Vehicle (Multi-UAV) game with limited attack and defense capabilities and communication range, a game model is established to study the capture and attack problem of a red UAV team against its blue target UAV in this paper. Under the Actor-Critic framework of multi-agent deep deterministic policy gradient (MADDPG) algorithm, the original MADDPG algorithm is improved according to the characteristics of the game scenario to solve the problem of UAVs’ number attenuation and sparse rewards. The simulation results shows that the algorithm designed in this paper has improved the convergence speed, learning efficiency and stability. The sub-networks make the algorithm more suitable for the game scenario where the number of UAVs is dynamically declining.