Reviewerly/exHarmony huggingface dataset:
Leveraging Large Language Models for Automated Dialogue Analysis

Developing high-performing dialogue systems benefits from the automatic identification of undesirable behaviors in system responses. However, detecting such behaviors remains challenging, as it draws on a breadth of general knowledge and understanding of conversational practices. Although recent research has focused on building specialized classifiers for detecting specific dialogue behaviors, the behavior coverage is still incomplete and there is a lack of testing on real-world human-bot interactions. This paper investigates the ability of a state-of-the-art large language model (LLM), ChatGPT-3.5, to perform dialogue behavior detection for nine categories in real human-bot dialogues. We aim to assess whether ChatGPT can match specialized models and approximate human performance, thereby reducing the cost of behavior detection tasks. Our findings reveal that neither specialized models nor ChatGPT have yet achieved satisfactory results for this task, falling short of human performance. Nevertheless, ChatGPT shows promising potential and often outperforms specialized detection models. We conclude with an in-depth examination of the prevalent shortcomings of ChatGPT, offering guidance for future research to enhance LLM capabilities.
An Open-Domain Avatar Chatbot by Exploiting a Large Language Model

With the ambition to create avatars capable of human-level casual conversation, we developed an open-domain avatar chatbot, situated in a virtual reality environment, that employs a large language model (LLM). Introducing the LLM posed several challenges for multimodal integration, such as developing techniques to align diverse outputs and avatar control, as well as addressing the issue of slow generation speed. To address these challenges, we integrated various external modules into our system. Our system is based on the award-winning model from the Dialogue System Live Competition 5. Through this work, we hope to stimulate discussions within the research community about the potential and challenges of multimodal dialogue systems enhanced with LLMs.
Investigating Explicitation of Discourse Connectives in Translation using Automatic Annotations

Discourse relations have different patterns of marking across different languages. As a result, discourse connectives are often added, omitted, or rephrased in translation. Prior work has shown a tendency for explicitation of discourse connectives, but such work was conducted using restricted sample sizes due to difficulty of connective identification and alignment. The current study exploits automatic methods to facilitate a large-scale study of connectives in English and German parallel texts. Our results based on over 300 types and 18000 instances of aligned connectives and an empirical approach to compare the cross-lingual specificity gap provide strong evidence of the Explicitation Hypothesis. We conclude that discourse relations are indeed more explicit in translation than texts written originally in the same language. Automatic annotations allow us to carry out translation studies of discourse relations on a large scale. Our methodology using relative entropy to study the specificity of connectives also provides more fine-grained insights into translation patterns.
Dialogue Response Generation Using Completion of Omitted Predicate Arguments Based on Zero Anaphora Resolution

Human conversation attempts to build common ground consisting of shared beliefs, knowledge, and perceptions that form the premise for understanding utterances. Recent deep learning-based dialogue systems use human dialogue data to train a mapping from a dialogue history to responses, but common ground not directly expressed in words makes it difficult to generate coherent responses by learning statistical patterns alone. We propose Dialogue Completion using Zero Anaphora Resolution (DCZAR), a framework that explicitly completes omitted information in the dialogue history and generates responses from the completed dialogue history. In this study, we conducted automatic and human evaluations by applying several pretraining methods and datasets in Japanese in various combinations. Experimental results show that the DCZAR framework contributes to the generation of more coherent and engaging responses.
Vehicle Re-Identification Based on Unsupervised Domain Adaptation by Incremental Generation of Pseudo-Labels

The main goal of vehicle re-identification (ReID) is to associate the same vehicle identity in different cameras. This is a challenging task due to variations in light, viewpoints or occlusions; in particular, vehicles present a large intra-class variability and a small inter-class variability. In ReID, the samples in the test sets belong to identities that have not been seen during training. To reduce the domain gap between train and test sets, this work explores unsupervised domain adaptation (UDA) generating automatically pseudo-labels from the testing data, which are used to fine-tune the ReID models. Specifically, the pseudo-labels are obtained by clustering using different hyperparameters and incrementally due to retraining the model a number of times per hyperparameter with the generated pseudo-labels. The ReID system is evaluated in CityFlow-ReID-v2 dataset.
Active Supervision: Human in the Loop

After the learning process, certain types of images may not be modeled correctly because they were not well represented in the training set. These failures can then be compensated for by collecting more images from the real-world and incorporating them into the learning process – an expensive process known as “active learning”. The proposed twist, called active supervision, uses the model itself to change the existing images in the direction where the boundary is less defined and requests feedback from the user on how the new image should be labeled. Experiments in the context of class imbalance show the technique is able to increase model performance in rare classes. Active human supervision helps provide crucial information to the model during training that the training set lacks.
Graph Embedding of Almost Constant Large Graphs

In some machine learning applications, graphs tend to be composed of a large number of tiny almost constant sub-structures. The current embedding methods are not prepared for this type of graphs and thus, their representational power tends to be very low. Our aim is to define a new graph embedding, called GraphFingerprint, that considers this specific type of graphs. The three-dimensional characterisation of a chemical metal-oxide nanocompound easily fits in these types of graphs, which nodes are atoms and edges are their bonds. Our graph embedding method has been used to predict the toxicity of these nanocompounds, achieving a high accuracy compared to other embedding methods.
Explaining Semantic Text Similarity in Knowledge Graphs

In this paper we explore the application of text similarity for building text-rich knowledge graphs, where nodes describe concepts that relate semantically to each other. Semantic text similarity is a basic task in natural language processing (NLP) that aims at measuring the semantic relatedness of two texts. Transformer-based encoders like BERT combined with techniques like contrastive learning are currently the state-of-the-art methods in the literature. However, these methods act as black boxes where the similarity score between two texts cannot be directly explained from their components (e.g., words or sentences). In this work, we propose a method for similarity explainability for texts that are semantically connected to each other in a knowledge graph. To demonstrate the usefulness of this method, we use the Agenda 2030 which consists of a graph of sustainable development goals (SDGs), their subgoals and the indicators proposed for their achievement. Experiments carried out on this dataset show that the proposed explanations not only provide us with explanations about the computed similarity score but also they allow us to improve the accuracy of the predicted links between concepts.
Hierarchical Attribute-Based Encryption Scheme Supporting Computing Outsourcing and Time-Limited Access in Edge Computing

With the rapid increase of user data and traffic, the traditional attribute encryption scheme based on the central cloud will cause the bottleneck of computing performance. And user's access privilege and ciphertext in the existing scheme is not limited by the time duration and the number of attempts, which could be brute force attack. We propose a solution to support computing outsourcing and time-limited access in edge computing. Edge nodes can shorten data transmission distances and eliminate latency issues. To solve the central cloud performance problem during encryption and decryption, massive and complex computing is considered outsource to edge nodes. And set valid time for the ciphertext and the user key, which avoid their permanent validity and significantly improve data security. In addition, all attributes are divided into attribute trees. According to the hierarchical relationship between attributes, we judge the user's access privilege. Finally, we give security proof, performance cost, functional comparison of the scheme.
Multi-intent Description of Keyword Expansion for Code Search

To address the issue of discrepancies between online query data and offline training data in code search research, we propose a novel code search model called multi intent description keyword extension-based code search (MDKE-CS). Our model utilizes offline training data to expand query data, thereby mitigating the impact of insufficient query data and intention differences between training and query data on search results. Furthermore, we construct a multi-intention description keyword vocabulary library based on developers, searchers, and discussants from the StackOverflow Q&A library to further expand the query. To evaluate the effectiveness of MDKE-CS in code search tasks, we conducted comparative experimental analyses using two baseline models, DeepCS and UNIF, as well as WordNet and BM25 extension methods. Our experimental results demonstrate that MDKE-CS outperforms the baseline models in terms of R@1, R@5, R@10, and MRR values.
History Based Incremental Singular Value Decomposition for Background Initialization and Foreground Segmentation

Background initialization is an essential step for both hand-crafted and deep learning foreground segmentation approaches. In this paper, we propose a low-rank approximation algorithm that effectively handles the challenge caused by Stationary Foreground Objects (SFOs) on both offline and online bases. The proposed algorithm employs different incremental decomposition mechanisms that control the contribution of earlier and current frames in the overall covariance of the processed video. The proposed algorithm is able to identify the type of the detected SFO, whether it is an abandoned or removed object. Moreover, a background-updating mechanism is introduced to feed the proper background to learning models that are pretrained for foreground segmentation. The experimental results demonstrate the effectiveness of both proposed mechanisms: the SFO identification and the background initialization.
Novelty Detection in Human-Machine Interaction Through a Multimodal Approach

As the interest in robots continues to grow across various domains, including healthcare, construction and education, it becomes crucial to prioritize improving user experience and fostering seamless interaction. These human-machine interactions (HMI) are often impersonal. Our proposal, built upon previous work in the field, aims to use biometric data of individuals to detect whether a person has been encountered before. Since many models depend on a threshold set, an optimization method using a genetic algorithm was proposed. The novelty detection is made through a multimodal approach using both voice and facial images from the individuals, although the unimodal approaches of just each single cue were also tested. To assess the effectiveness of the proposed system, we conducted comprehensive experiments on three diverse datasets, namely VoxCeleb, Mobio and AveRobot, each possessing distinct characteristics and complexities. By examining the impact of data quality on model performance, we gained valuable insights into the effectiveness of the proposed solution. Our approach outperformed several conventional novelty detection methods, yielding superior and therefore promising results.
Condition Invariance for Autonomous Driving by Adversarial Learning

Object detection is a crucial task in autonomous driving, where domain shift between the training and the test set is one of the main reasons behind the poor performance of a detector when deployed. Some erroneous priors may be learned from the training set, therefore a model must be invariant to conditions that might promote such priors. To tackle this problem, we propose an adversarial learning framework consisting of an encoder, an object-detector, and a condition-classifier. The encoder is trained to deceive the condition-classifier and aid the object-detector as much as possible throughout the learning stage, in order to obtain highly discriminative features. Experiments showed that this framework is not very competitive regarding the trade-off between precision and recall, but it does improve the ability of the model to detect smaller objects and some object classes.
Self-supervised Monocular Depth Estimation on Unseen Synthetic Cameras

Monocular depth estimation is a critical task in computer vision, and self-supervised deep learning methods have achieved remarkable results in recent years. However, these models often struggle on camera generalization, i.e. at sequences captured by unseen cameras. To address this challenge, we present a new public custom dataset created using the CARLA simulator [4], consisting of three video sequences recorded by five different cameras with varying focal distances. This dataset has been created due to the absence of public datasets containing identical sequences captured by different cameras. Additionally, it is proposed in this paper the use of adversarial training to improve the models’ robustness to intrinsic camera parameter changes, enabling accurate depth estimation regardless of the recording camera. The results of our proposed architecture are compared with a baseline model, hence being evaluated the effectiveness of adversarial training and demonstrating its potential benefits both on our synthetic dataset and on the KITTI benchmark [8] as the reference dataset to evaluate depth estimation.
Phishing Scam Detection for Ethereum Based on Community Enhanced Graph Convolutional Networks

Blockchain technology has garnered a lot of interest recently, but it has also become a breeding ground for various network crimes. Cryptocurrency, for example, has suffered losses due to network phishing scams, posing a serious threat to the security of blockchain ecosystem transactions. To create a favorable investment environment, we propose a community-enhanced phishing scam detection model in this paper. We approach network phishing detection as a graph classification task and introduce a network phishing detection graph neural network framework. Firstly, we construct an Ethereum transaction network and extract transaction subgraphs, and corresponding content features from it. Based on this, we propose a community-enhanced graph convolutional network (GCN)-based detection model. It extracts more reasonable node representations in the GCN neighborhoods and explores the advanced semantics of the graph by defining community structure and measuring the similarity of nodes in the community. This distinguishes normal accounts from phishing accounts. Experiments on different large-scale real-data sets of Ethereum consistently demonstrate that our proposed model performs better than related methods.
ADGCN: A Weakly Supervised Framework for Anomaly Detection in Social Networks

Detecting abnormal users in social networks is crucial for protecting user privacy and preventing criminal activities. However, existing graph learning methods have limitations. Unsupervised methods focus on topological anomalies and may overlook user characteristics, while supervised methods require costly data annotations. To address these challenges, we propose a weakly supervised framework called Anomaly Detection Graph Convolutional Network (ADGCN). Our model includes three modules: information-preserving compression of user features, collaborative mining of global and local graph information, and multi-view weakly supervised classification. We demonstrate that ADGCN generates high-quality user representations using minimal labeled data and achieves state-of-the-art performance on two real-world social network datasets. Ablation experiments and performance analyses show the feasibility and effectiveness of our approach in practical scenarios.
Rogue-Instance Security for Batch Knowledge Proofs

We propose a new notion of knowledge soundness, denoted rogue-instance security, for interactive and non-interactive batch knowledge proofs. Our notion, inspired by the standard notion of rogue-key security for multi-signature schemes, considers a setting in which a malicious prover is provided with an honestly-generated instance $$\mathbb {x}_1$$ , and may then be able to maliciously generate related “rogue” instances $$\mathbb {x}_2,\ldots ,\mathbb {x}_{k}$$ for convincing a verifier in a batch knowledge proof of corresponding witnesses $$\mathbb {w}_1,\ldots ,\mathbb {w}_{k}$$ for all k instances – without actually having knowledge of the witness $$\mathbb {w}_1$$ corresponding to the honestly-generated instance. This setting provides a powerful security guarantee for batch versions of a wide variety of practically-relevant protocols, such as Schnorr’s protocol and similar ones. We present a highly-efficient generic construction of a batch proof-of-knowledge applicable to any algebraic Sigma protocols. The algebraic property refers to a homomorphic structure of the underlying group and includes Schnorr’s protocol and others. We provide an almost tight security analysis for our generic batch protocol, which significantly improves upon the previously known security bounds even for the specific case of batch Schnorr protocol. We extend our results beyond algebraic Sigma protocols. We analyze the rogue-instance security of a general batch protocol with plus-one special soundness (a generalization of standard special soundness) and achieve improved security bounds in the generic case. Our results use a particular type of high-moment assumptions introduced by Rotem and Segev (CRYPTO 2021). These assumptions consider the hardness of a relation against algorithms with bounded expected running time. Although Rotem and Segev introduced these assumptions, they did not provide evidence to support their hardness. To substantiate and validate the high-moment assumptions, we present a new framework for assessing the concrete hardness of cryptographic problems against oracle algorithms with bounded expected runtime. Our framework covers generic models, including the generic group model, random oracle model, and more. Utilizing our framework, we achieve the first hardness result for these high-moment assumptions. In particular, we establish the second-moment hardness of the discrete-logarithm problem against expected-time algorithms in the generic group model.
Composable Long-Term Security with Rewinding

We circumvent these impossibility results with new techniques, enabling rewinding-based simulation in a way that universal composability is achieved. This allows us to construct a long-term-secure composable commitment scheme in the CRS-hybrid model, which is provably impossible in the notion of Müller-Quade and Unruh. We base our construction on a statistically hiding commitment scheme in the CRS-hybrid model with CCA-like properties. To provide a CCA oracle, we cannot rely on super-polynomial extraction techniques and instead extract the value committed to via rewinding. To this end, we incorporate rewinding-based commitment extraction into the UC framework via a helper in analogy to Canetti, Lin and Pass (FOCS 2010), allowing both adversary and environment to extract statistically hiding commitments. Our new framework provides the first setting in which a commitment scheme that is both statistically hiding and universally composable can be constructed from standard polynomial-time hardness assumptions and a CRS only. We also prove that our CCA oracle is k-robust extractable. This asserts that extraction is possible without rewinding a concurrently executed k-round protocol. Consequently any k-round (standard) UC-secure protocol remains secure in the presence of our helper. Finally, we prove that building long-term-secure oblivious transfer (and thus general two-party computations) from long-term-revealing setups remains impossible in our setting. Still, our long-term-secure commitment scheme suffices for natural applications, such as long-term secure and composable (commit-and-prove) zero-knowledge arguments of knowledge.
Round-Robin is Optimal: Lower Bounds for Group Action Based Protocols

An hard homogeneous space (HHS) is a finite group acting on a set with the group action being hard to invert and the set lacking any algebraic structure. As such HHS could potentially replace finite groups where the discrete logarithm is hard for building cryptographic primitives and protocols in a post-quantum world. Threshold HHS-based primitives typically require parties to compute the group action of a secret-shared input on a public set element. On one hand this could be done through generic MPC techniques, although they incur in prohibitive costs due to the high complexity of circuits evaluating group actions known to date. On the other hand round-robin protocols only require black box usage of the HHS. However these are highly sequential procedures, taking as many rounds as parties involved. The high round complexity appears to be inherent due the lack of homomorphic properties in HHS, yet no lower bounds were known so far. In this work we formally show that round-robin protocols are optimal. In other words, any at least passively secure distributed computation of a group action making black-box use of an HHS must take a number of rounds greater or equal to the threshold parameter. We furthermore study fair protocols in which all users receive the output in the same round (unlike plain round-robin), and prove communication and computation lower bounds of $$\varOmega (n \log _2 n)$$ for n parties. Our results are proven in Shoup’s Generic Action Model (GAM), and hold regardless of the underlying computational assumptions.
Text Spotting of Electrical Diagram Based on Improved PP-OCRv3

The text detection and recognition plays an important role in automatic management of electrical diagrams. However, the images of electrical diagrams often have high resolution, and the format of the text in them is also unique and densely distributed. These factors make the general-purpose text spotting models unable to detect and recognize the text effectively. In this paper, we propose a text spotting model based on improved PP-OCRv3 to achieve better performance on text spotting of electrical diagrams. Firstly, a region re-segmentation module based on pixel line clustering is designed to correct detection errors on irregularly shaped text containing vertical and horizontal characters. Secondly, an improved BiFPN module with channel attention and depthwise separable convolution is introduced during text feature extracting to improve the robustness of input images with different scales. Finally, a character re-identification module based on region extension and cutting is added during the text recognition to reduce the adverse effects of simple and dense character on the model. The experimental results show that our model has better performance than the state-of-the-art (SOTA) methods on the electrical diagrams data sets.