Reviewerly/exHarmony huggingface dataset:
Looping for Encryption Key Generation Over the Internet: A New Frontier in Physical Layer Security

Current key sharing techniques rely on the hardness of solving a solvable, but complex, mathematical problem. This entails, in Information Theoretical sense, the encryption key is not secret, it can be found by solving the underlying mathemat-ical problem. Sensitive data we encrypt today using traditional techniques can be recorded by malicious parties and be deciphered in the future whenever improved hacking techniques and supporting computing technology permit. Information Theory proves the existence of methods for sharing of encryption keys that are unconditionally secure, but does not show how to bring such theoretical results to practical use. One of the central information theoretical approaches to key sharing is based on exploiting common randomness. This theoretical result states that if two dependent random variables, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> and B, are available at Alice and Bob, then, by communicating through a public channel between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> and B, it is possible to securely share a key of size I(A;B). To bridge the gap between theory and practice, one needs a method to generate two sets of dependent random variables, one at Alice‘s side and the other one at the Bob’ side, as well as a method to extract two identical keys from these dependent random variables. This article presents a novel technique to achieve this goal over the Internet. Dependent random variables are generated by measuring packet travel times between Alice and Bob, and error-free key extraction from dependent random variables is realized by using a randomized Low Density Parity Check Code (LDPC). Through looping of packets between Alice and Bob, the mutual information between random variables is increased. Finally, methods are presented to measure the likelihood values required in decoding the underlying LDPC. It is shown that the key rate is approximately equal to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.5\log_{9}(4L^{2}/(4L-1))\approx 0.5\log_{2}(L)$</tex> where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> is the number of round trips (loops). Test results (based on measurements between distant nodes over the Internet) are presented, demonstrating the feasibility of the proposed technique. The proposed method is implemented entirely in software (through high-level programming, e.g., using C-language, at the application layer). This operation does not require modifying the underlying network.
Cost-Damage Analysis of Attack Trees

Attack trees (ATs) are a widely deployed modelling technique to categorize potential attacks on a system. An attacker of such a system aims at doing as much damage as possible, but might be limited by a cost budget. The maximum possible damage for a given cost budget is an important security metric of a system. In this paper, we find the maximum damage given a cost budget by modelling this problem with ATs, both in deterministic and probabilistic settings. We show that the general problem is NP-complete, and provide heuristics to solve it. For general ATs these are based on integer linear programming. However when the AT is tree-structured, then one can instead use a faster bottom-up approach. We also extend these methods to other problems related to the cost-damage tradeoff, such as the cost-damage Pareto front.
ECSM: An Ensembled Client Selection Mechanism for Efficient Federated Learning

This research paper proposes a multi-criteria client selection approach to enhance the efficiency of Federated Learning (FL). While the state-of-the-art client selection in FL mainly focuses on a single characteristic to determine a suitable client for the training process, a multi-criteria selection is needed to provide a more efficient FL system. We introduce the Ensembled Client Selection Mechanism (ECSM) as a novel approach to address this issue. The proposed approach takes into account client accuracy, reputation, and randomness to improve accuracy during the lower communication period. The study employs random client selection to prevent repetitive training and ensure model generalization. The results indicate that the proposed ECSM mechanism can improve FL performance by achieving the desired accuracy with fewer communication rounds. Specifically, the approach improves FL efficiency by 56% when tested on the FMNIST dataset compared to the baseline approach. These findings suggest that the ECSM mechanism can significantly enhance the efficiency of the FL process.
Inspired by art, physicists probe the science behind the dazzling colors of polarization

We introduce an intuitive particle-based model of time-symmetric quantum physics that gives a concrete description of what goes on in the time between projective measurements of a quantum system. The time-symmetry in this model means that we ...Physical interpretations of the time-symmetric formulation of quantum mechanics, due to Aharonov, Bergmann, and Lebowitz are discussed in terms of weak values. The most direct, yet somewhat naive, interpretation uses the time-symmetric formulation to ...
A New Digital Era for European Universities: Implementing Large-scale AV Projects for the Next Generation Digital Hybrid Classrooms - A quick-start guide from 0 to 10: Challenges, Benefits, Pitfalls

Many universities had started designing and implementing digital transformation plans before the outbreak of the COVID-19 pandemic, including Audio-Visual (AV) infrastructure for teaching and learning. The COVID-19 pandemic has accelerated the need for European universities to adapt their teaching methods and embrace new digital tools. Implementing large-scale AV projects for the next generation of digital hybrid classrooms has emerged as a promising solution. Focusing on the Aristotle University of Thessaloniki (AUTh) case, this paper examines the challenges, benefits, and things to avoid when implementing AV projects in European universities. The paper also presents a roadmap for universities to successfully implement AV projects from 0 to 10, considering the necessary steps, resources, and expertise required at each stage. The challenges of implementing AV projects in universities include the need for significant investment, the complexity of AV systems, technical expertise (e.g., experienced AV integrators), and the difficulty of adapting teaching methods to the new digital environment. However, the benefits of implementing AV projects are manifold, including enhanced student engagement and interactivity, improved learning outcomes, the ability to reach remote learners, and enhanced inclusiveness. To ensure the success of AV projects, universities must avoid common pitfalls such as underestimating the necessary resources and expertise, failing to involve faculty and students in the design process, and neglecting to provide adequate training and support for faculty and staff. In conclusion, implementing large-scale AV projects for the next generation of digital hybrid classrooms presents a unique opportunity for European universities to adopt modern teaching methods and better meet the needs of today's learners. With careful planning, investment, and collaboration among all stakeholders, universities can successfully implement AV projects and realize the benefits of a new digital era for higher education.
SupRB in the context of rule-based machine learning methods: A comparative study

Comprehending why an artificial intelligence–based agent makes certain predictions is often discussed as one of the central issues that needs to be solved in the near future to make such agents ubiquitously deployable. However, many common black-box machine learning models are hard to analyse. Recently, we proposed a new machine learning algorithm to construct concise sets of rules. This algorithm, SupRB, creates compact, interpretable and transparent models. One key feature that differentiates it from many similar previously proposed systems is that it optimizes the model selection tasks concerning rule discovery and rule set composition separately. Therefore, users can tailor a model's structure more easily towards fulfilling use-case specific explainability requirements. Keeping the model selection tasks separate during optimization allows for more clearly defined goals when compared to state-of-the-art systems and – importantly – facilitates independent rule fitnesses, making the training process easier to explain and the models easier to analyse post-hoc. In this paper, we extend previous benchmarking of SupRB, where it was compared with XCSF, a prominent rule set learning system, to also include Decision Trees (DTs) and Random Forests (RFs) in our analysis. We find that – as expected – SupRB performs comparable to XCSF, while allowing easier control of model structure and showing a substantially smaller sensitivity to random seeds and data splits, and it performs slightly worse than RFs, and better than DTs. Importantly, the overall model size was substantially smaller with SupRB, making it the easiest to interpret system while not falling significantly short in terms of prediction errors.
A Theoretical Analysis of Out-of-Distribution Detection in Multi-Label Classification

The ability to detect out-of-distribution (OOD) inputs is essential for safely deploying machine learning models in an open world. Most existing research on OOD detection, and more generally uncertainty quantification, has focused on multi-class classification. However, for many information retrieval (IR) applications, the classification of documents or images is by nature not multi-class but multi-label. This paper presents a pure theoretical analysis of the under-explored problem of OOD detection in multi-label classification using deep neural networks. First, we examine main existing approaches such as MSP (proposed in ICLR-2017) and MaxLogit (proposed in ICML-2022), and summarize them as different combinations of label-wise scoring and aggregation functions. Some existing methods are shown to be equivalent. Then, we prove that JointEnergy (proposed in NeurIPS-2021) is indeed the optimal probabilistic solution when the class labels are conditionally independent with each other for any given data sample. This provides a more rigorous explanation for the effectiveness of JointEnergy than the original joint-likelihood interpretation, and also reveals its reliance upon the assumption of label independence rather than the exploitation of label relationships as previously thought. Finally, we discuss potential future research directions in this area.
Takagi-Sugeno-Kang Fuzzy System Towards Label-scarce Incomplete Multi-View Data Classification

Fuzzy systems have shown a powerful ability in multi-view learning. However, most existing multi-view fuzzy systems require a large amount of labeled and complete multi-view data, which is a demanding requirement for real-world applications. To address these challenges, a novel transductive semi-supervised incomplete multi-view TSK fuzzy system (SSIMV_TSK) is proposed. First, to deal with the scarcity of labeled data, we introduce transductive learning, in which pseudo labels is assigned for unlabeled data. Then, to handle the incomplete multi-view data simultaneously, we integrate missing view imputation, pseudo label learning, and fuzzy system training as a single process to enable mutual learning. Furthermore, two new mechanisms – bidirectional structural preservation of instances and labels, and the adaptive multiple alignment collaborative learning – are proposed to further improve the robustness. Compared with the existing methods, the highlights of the proposed SSIMV_TSK are the ability to handle label scarcity and incomplete multi-view data simultaneously, the integration of missing view imputation and modeling as one process to make the imputed views more pertinent for classification; and the introduction of two new mechanisms to improve the quality of the learned pseudo labels and the imputed views. Experimental studies show that SSIMV_TSK significantly outperforms the state-of-the-art methods.
Exploring the Representation Power of SPLADE Models

The SPLADE (SParse Lexical AnD Expansion) model is a highly effective approach to learned sparse retrieval, where documents are represented by term impact scores derived from large language models. During training, SPLADE applies regularization to ensure postings lists are kept sparse --- with the aim of mimicking the properties of natural term distributions --- allowing efficient and effective lexical matching and ranking. However, we hypothesize that SPLADE may encode additional signals into common postings lists to further improve effectiveness. To explore this idea, we perform a number of empirical analyses where we re-train SPLADE with different, controlled vocabularies and measure how effective it is at ranking passages. Our findings suggest that SPLADE can effectively encode useful ranking signals in documents even when the vocabulary is constrained to terms that are not traditionally useful for ranking, such as stopwords or even random words.
Neural adaptive IoT streaming analytics with RL-Adapt

IoT stream processing is an emerging technology that plays a crucial role in enabling time-critical IoT applications, which often demand high accuracy and low latency. For example, Augmented Reality (AR) applications require high object detection precision and object localization precision, as well as low latency. Note that the exact definition of accuracy varies depending on the specific application Existing stream processing engines are insufficient to meet these requirements, since they could not integrate and respond timely to variable network conditions in the dynamic wireless environment. Recent efforts focusing on adaptive streaming support user-specified policies to adapt to the variable network conditions. However, existing works suffer from manual policies or simplified models of the deployment environment which limit their ability to achieve optimal performance across a broad set of network conditions and quality of experience (QoE) objectives. In this paper, we present a Reinforcement Learning-based Adaptive streaming system (RL-Adapt) that is capable of generating adaption policies using RL-strategy and providing declarative APIs for efficient development. RL-Adapt trains a neural network model that can automatically select the optimal policy based on the observed network conditions. RL-Adapt does not rely on pre-defined models or assumptions about the environment. Instead, it learns to make decisions solely through observations of the resulting performance of past decisions. We implemented RL-Adapt and evaluated its performance extensively in three representative real-world IoT applications. Our results show that RL-Adapt outperforms the state-of-the-art schemes in terms of QoE metrics.
KALE: Using a K-Sparse Projector for Lexical Expansion

Recent research has proposed retrieval approaches based on sparse representations and inverted indexes, with terms produced by neural language models and leveraging the advantages from both neural retrieval and lexical matching. This paper proposes KALE, a new lightweight method of this family that uses a small model with a k-sparse projector to convert dense representations into a sparse set of entries from a latent vocabulary. The KALE vocabulary captures semantic concepts than perform well when used in isolation, and perform better when extending the original lexical vocabulary, this way improving first-stage retrieval accuracy. Experiments with the MSMARCOv1 passage retrieval dataset, the TREC Deep Learning dataset, and BEIR datasets, examined the effectiveness of KALE under varying conditions. Results show that the KALE terms can replace the original lexical vocabulary, with gains in accuracy and efficiency. Combining KALE with the original lexical vocabulary, or with other learned terms, can further improve retrieval accuracy with only a modest increase in computational cost.
Few-shot node classification on attributed networks based on deep metric learning for Cyber–Physical–Social Services

In Cyber–Physical–Social Systems (CPSS), the interactions among various entities form complex graphs. Many tasks can be formulated as instances of node classification. Node classification on graphs has attracted increasing research interest. However, the performance of existing graph neural networks for few-shot node classification has not achieved satisfactory results due to the limitation of the number of labeled instances. Therefore, we propose a graph-weighted prototype scaling network (GWPSN) based on deep metric learning for addressing the graph few-shot node classification problem. Specifically, we first extract node representations for the attributed graph via the simplifying graph convolutional network. At the same time, learning the importance of each node in the attributed graph is used to aggregate class prototypes. Finally, the class of the test node can be predicted by comparing the scaled metric distance between the test node and the class prototype. Experiments indicate that GWPSN can achieve superior performance on three real-world datasets and thus can provide enhanced few-shot classification services for CPSS.
Seven definitions of bipartite bound entanglement

Abstract An entangled state is bound entangled if one cannot combine any number of copies of the state to a maximally entangled state by using only local operations and classical communication. If one formalizes this notion of bound entanglement, one arrives immediately at four different definitions. In addition, at least three more definitions are commonly used in the literature, in particular in the very first paper on bound entanglement. Here we review critical distillation protocols and we examine how different results from quantum information theory interact in order to prove that all seven definitions are eventually equivalent. Our self-contained analysis unifies and extends previous results scattered in the literature and reveals details of the structure of bound entanglement.
Application of large language model in intelligent Q&amp;A of digital government

LLM (Large Language Model) is developing rapidly today, and it is very important to use LLM to help existing question answering systems improve. The government question answering system is of great value in improving government administrative efficiency. Existing intelligent questions and answers mostly use the combination of keyword matching and machine learning. But keyword matching is difficult to understand complex and multi-round dialogue questions, resulting in limited quality of reply content. Based on machine learning method, it has further improved the understanding of semantics, but because the understanding of words in the field of government affairs is too difficult and professional; and the semantic recognition of colloquial questions is difficult, the performance is not even as good as the question answering system based on keyword matching. This paper uses the large language model as a tool to help understand user questions, and integrates it into the existing government question answering system. On the premise of effectively utilizing the advantages of the large language model, it avoids its defects. And it is demonstrated by experiments that the above system has a great improvement compared with the previous method.
Location Excitation Network for Multi-modal 3D Object Detection

Multi-modal 3D object detection has achieved remarkable success in the field of autonomous driving. Existing multi-modal methods rely on LiDAR and camera sensors to obtain point cloud and image data for multi-modal fusion. But for most methods, establishing stable and robust interaction relationships and reasonable fusion mechanisms across different modal features continues to pose a challenge. To address these issues, this paper proposes a Location Excitation Network (LEN) that deeply leverages the location information of multi-modal features in a unified space. First, cross-modal spatial correlations are established based on the location information of features, followed by cross-attention to achieve complementary advantages between different modal features. Additionally, we incorporate the relative distance information of different categories of objects to compensate for distance at the feature level, thereby enhancing the overall detection accuracy. Our experiments demonstrate that our method surpasses the previous state-of-the-art approaches on the nuScenes validation set.
Detect Advanced Persistent Threat In Graph-Level Using Competitive AutoEncoder

APT(Advanced Persistent Threat) are known for their stealth, persistence, and sophistication, often carried out by skilled and well-funded attackers. To detect and investigate APT attacks at the host-level, scholars have extensively studied the provenance graph, which preserves the complete attack context. However, existing methods have impractical requirements for data collection and threshold setting, severely impacting their real-world performance. In this paper, we propose GCA (Graph Competitive Autoencoder), a GNN-based graph-level anomaly detection system. We first extract entities and their interactions from host logs to construct a provenance graph. Subsequently, we utilize GNN (Graph Neural Network) as a graph encoder to obtain graph-level embeddings. In the anomaly detection part, we use the competitive autoencoder and mutual information maximization to identify the attack graph. With this model, we can conduct graph-level anomaly detection even when the training data is partially contaminated, without manually setting the threshold. We deploy and evaluate our model on the public dataset StreamSpot. Our results show that GCA outperforms existing methods in terms of accuracy and practicality.
Multi-Label Classification for Postoperative Complication Prediction

Accurate postoperative risk prediction is crucial for improving patient treatment outcomes and survival rates. Traditional research in this area has focused on predicting single postoperative complications through binary classification models, which deviates from the reality where doctors need to simultaneously evaluate multiple postoperative risks. This paper proposes to treat the postoperative complication prediction task as a multi-label classification problem and uses a sequence generation model to address the challenges posed by interdependent and correlated labels. Specifically, we use the SGM model as the base model and add a scheduled sampling module to address the exposure bias issue. Experimental results show that our model outperforms traditional models and deep learning sequence generation models in predicting postoperative complications as a multi-label classification problem.