Reviewerly/exHarmony huggingface dataset:
Knowing Before Seeing: Incorporating Post-retrieval Information into Pre-retrieval Query Intention Classification

Query intention classification is crucial for modern search engine, which explicitly limits the search range to the suggested leaf categories where related search results can be retrieved accurately. Recent studies in the field of query intention classification mainly rely on various post-retrieval information, e.g., click-graphs, document categories and click-through logs. However, despite the promising results of such methods, they are not always valid when applied to real search engines. This mainly can be attributed to the unavailability of the post-retrieval information especially for 1) the pre-retrieval scenario and 2) processing the massive long-tail data that have never appeared. To address the problems , we introduce a unique Post-retrieval Information Fusion (PostIF) framework to incorporate post-retrieval information into a pre-retrieval model during training, instead of directly leveraging a straightforward post-retrieval model in both training and inference. The PostIF framework consists of two parts: an imitation module and an integration module. The imitation module learns to transform existing pre-retrieval information into pseudo post-retrieval information under the guidance of real post-retrieval information, and the integration module predicts the final result by integrating the pre-retrieval and the pseudo post-retrieval representation. We further design two specific methods (i.e., detective method, generative method) to implement the imitation module. Extensive experiments on real-world search logs have validated the proposed method in the pre-retrieval query intention classification task.
TGKT-Based Personalized Learning Path Recommendation with Reinforcement Learning

In recent years, emerging technologies represented by artificial intelligence have been widely applied in education, and online learning has transcended the limitations of time and space. How to recommend personalized learning paths for learners based on their characteristics has become a new issue. However, in the existing researches on personalized learning path recommendation, many researchers have not fully considered or only mined the initial personalized features of learners, and the process of learning path recommendations does not have good interpretability. In order to solve these issues, this paper proposes a model named TGKT-RL, in which we combine temporal convolutional network and graph attention network into the knowledge tracing model and use it as the environment of the reinforcement learning model. At the same time, we set learning goals for learners and adjust recommendation policy based on the states and rewards during the simulated learning process, ultimately recommending a personalized learning path. We conduct a series of experiments on two public knowledge tracing datasets, and the results show that our method achieves good performance and has good interpretability.
GMiRec: A Multi-image Visual Recommendation Model Based on a Gated Neural Network

Making use of visual perception in recommender systems is becoming more and more important. In the existing visual recommendation models (VRMs), the visual features of items are usually extracted based on the pre-trained convolutional neural network, and then combined with the non-visual features modeling to complete the prediction of users’ interest. There are two challenges in this field so far. First, most VRMs are developed around single-image items, and how to more effectively mine the visual features of multi-image items is seldom considered. Second, most models do not consider the distribution difference between the training datasets of the pre-trained model and the datasets for recommendation when extracting visual features based on the pre-training model, which may deepen the gap in the convolutional neural network’s understanding of image semantics on datasets. To address the above challenges, a Multi-image Visual Recommendation Model based on a Gated Neural Network (GMiRec) is proposed. It performs different forms of pooling operations on the visual features of multi-image items and uses the feed-forward neural network to realize the fusion of the multi-image visual information. In addition, a gated neural network taking item categories as input is designed to achieve supervised dimensionality reduction on the item visual features, which alleviates the problem of semantic gap. Experiments conducted on the Amazon datasets show that the proposed model is significantly improved compared with the existing models.
Critical Node Privacy Protection Based on Random Pruning of Critical Trees

With the popularity of social networks, the conflict between the heterogeneous social network data publishing and user privacy leakage is becoming very obvious. Especially for critical node users, if the critical node users suffer from background knowledge attacks during data publishing, it can not only lead to the privacy information leakage of the critical user but also lead to the privacy leakage of their friends. To address this issue, we propose a critical node privacy protection method based on random pruning of critical trees. First, we obtain the critical node candidate set by the degree centrality. Then, we calculate the candidate node’s global and local criticality to get the critical node set. Next, we extract the critical tree with the critical node as the root node. Finally, we design a critical tree privacy protection strategy based on random pruning. The experimental results show that the proposed method can balance the privacy and availability of critical nodes in the network data publishing.
Tri-State Circuits

We introduce tri-state circuits (TSCs). TSCs form a natural model of computation that, to our knowledge, has not been considered by theorists. The model captures a surprising combination of simplicity and power. TSCs are simple in that they allow only three wire values (0, 1, and undefined – $$\mathcal {Z}$$ ) and three types of fan-in two gates; they are powerful in that their statically placed gates fire (execute) eagerly as their inputs become defined, implying orders of execution that depend on input. This behavior is sufficient to efficiently evaluate RAM programs. We construct a TSC that emulates T steps of any RAM program and that has only $$O(T \cdot \log ^3 T \cdot \log \log T)$$ gates. Contrast this with the reduction from RAM to Boolean circuits, where the best approach scans all of memory on each access, incurring quadratic cost. We connect TSCs with Garbled Circuits (GC). TSCs capture the power of garbling far better than Boolean Circuits, offering a more expressive model of computation that leaves per-gate cost essentially unchanged. As an important application, we construct authenticated Garbled RAM (GRAM), enabling constant-round maliciously-secure 2PC of RAM programs. Let $$\lambda $$ denote the security parameter. We extend authenticated garbling to TSCs; by simply plugging in our TSC-based RAM, we obtain authenticated GRAM running at cost $$O(T \cdot \log ^3 T \cdot \log \log T \cdot \lambda )$$ , outperforming all prior work, including prior semi-honest GRAM. We also give semi-honest garbling of TSCs from a one-way function (OWF). This yields OWF-based GRAM at cost $$O(T \cdot \log ^3 T \cdot \log \log T \cdot \lambda )$$ , outperforming the best prior OWF-based GRAM by more than factor $$\lambda $$ .
Non-interactive Universal Arguments

In 2002, Barak and Goldreich introduced the notion of a universal argument and constructed an interactive universal argument for non-deterministic computations based on polynomially hard collision-resistant hash functions. Since then, and especially in recent years, there have been tremendous developments in the construction of non-interactive succinct arguments for deterministic computations under standard hardness assumptions. However, the constructed succinct arguments can be proven universal only under sub-exponential assumptions. Assuming polynomially hard fully homomorphic encryption and a widely believed worst-case complexity assumption, we prove a general lifting theorem showing that all existing non-interactive succinct arguments can be made universal. The required complexity assumption is that non-uniformity does not allow arbitrary polynomial speedup. In the setting of uniform adversaries, this extra assumption is not needed.
Dual-Dimensional Refinement of Knowledge Graph Embedding Representation

Knowledge graph representation learning aims to embed kn-owledge facts into a continuous vector space, enabling models to capture semantic connections within and between triples. However, existing methods primarily focus on a single dimension of entities or relations, limiting their ability to learn knowledge facts. To address this issue, this paper proposes a dual-dimension refined representation model. At the entity level, we perform residual semantic stratification of entities based on modulus and phase information. At the relation level, we introduce an adaptive direction mapping property, allowing entities to have different mapping directions in different relations, and employ negative sampling to further enhance the model’s ability to refine relations. Experimental results show that our model exhibits outstanding link prediction performance on datasets such as WN18RR, FB15k-237, and UMLS. Through validation experiments, we substantiate our assumptions and analyses regarding datasets and model capabilities, thereby addressing the interpretability shortcomings of existing embedding models on underperforming datasets.
Practical-Time Related-Key Attack on GOST with Secret S-Boxes

The block cipher GOST 28147-89 was the Russian Federation encryption standard for over 20 years, and is still one of its two standard block ciphers. GOST is a 32-round Feistel construction, whose security benefits from the fact that the S-boxes used in the design are kept secret. In the last 10 years, several attacks on the full 32-round GOST were presented. However, they all assume that the S-boxes are known. When the S-boxes are secret, all published attacks either target a small number of rounds, or apply for small sets of weak keys. In this paper we present the first practical-time attack on GOST with secret S-boxes. The attack works in the related-key model and is faster than all previous attacks in this model which assume that the S-boxes are known. The complexity of the attack is less than $$2^{27}$$ encryptions. It was fully verified, and runs in a few seconds on a PC. The attack is based on a novel type of related-key differentials of GOST, inspired by local collisions. Our new technique may be applicable to certain GOST-based hash functions as well. To demonstrate this, we show how to find a collision on a Davies-Meyer construction based on GOST with an arbitrary initial value, in less than $$2^{10}$$ hash function evaluations.
MAGNN-GC: Multi-head Attentive Graph Neural Networks with Global Context for Session-Based Recommendation

Session-based recommendation aims to predict the final preference of anonymous users based on their current session and global context. However, integrating high-dimensional information such as the item features of the current session and global context can lead to insufficient information fusion and imbalanced positive and negative samples during model training. To address these issues, we propose Multi-Head Attentive Graph Neural Networks with Global Context for Session-based Recommendation (MAGNN-GC). We construct global and local session graphs based on all historical session sequences to fully represent the complex transition relationships between items in the session. We use graph convolutional networks to capture the item features in the global context and graph attention networks to capture the item features of the current session. We also integrate the position information of session items with the learned global-level and local-level item embeddings using the multi-head attention mechanism. Additionally, we use the focal loss as a loss function to adjust sample weights and address the problem of imbalanced positive and negative samples during model training. Our experiments on three real-world datasets consistently show the superior performance of MAGNN-GC over state-of-the-art methods.
Reusable Secure Computation in the Plain Model

Consider the standard setting of two-party computation where the sender has a secret function f and the receiver has a secret input x and the output f(x) is delivered to the receiver at the end of the protocol. Let us consider the unidirectional message model where only one party speaks in each round. In this setting, Katz and Ostrovsky (Crypto 2004) showed that at least four rounds of interaction between the parties are needed in the plain model (i.e., no trusted setup) if the simulator uses the adversary in a black-box way (a.k.a. black-box simulation). Suppose the sender and the receiver would like to run multiple sequential iterations of the secure computation protocol on possibly different inputs. For each of these iterations, do the parties need to start the protocol from scratch and exchange four messages? In this work, we explore the possibility of amortizing the round complexity or in other words, reusing a certain number of rounds of the secure computation protocol in the plain model. We obtain the following results.
Subspace Clustering with Feature Grouping for Categorical Data

In clustering analysis, data clusters are usually associated with feature subsets rather than the whole space. Therefore, soft subspace clustering devote to find the corresponding subspace for each cluster by assigning weight to the features. However, since categorical data is qualitative rather than quantitative data, the study of subspace clustering for categorical data is relatively rare and challenging. Therefore, this paper presents a new two-step subspace clustering algorithm for categorical data. Firstly, an initial clustering method is proposed to obtain a reliable initial cluster structure, based on which, the feature-to-cluster groups are constructed by utilizing intrinsic relationships between features. Subsequently, the local and global clustering are defined to learn the local cluster relations between data objects and achieve the final clustering results, respectively. Experimental results on benchmark datasets demonstrate the effectiveness of proposed method.
Dual Channel Knowledge Graph Embedding with Ontology Guided Data Augmentation

Current knowledge graph completion suffers from two major issues: data sparsity and false negatives. To address these challenges, we propose an ontology-guided joint embedding framework that utilizes dual data augmentation channels and a joint loss function to learn embeddings of knowledge graphs. Our approach spontaneously generates positive and negative instances from two distinct ontology axiom sets, leading to improved completion rates for originally sparse knowledge graphs while also producing true-negative samples. Additionally, we propose two novel metrics for evaluating a model’s reasoning capabilities in predicting relations or links using KG and ontology data, thus avoiding incorrect predictions. Empirical results demonstrate that our framework outperforms existing models in most tasks and datasets, with significantly better performance in many cases for reasoning capability evaluation metrics.
Non-interactive Zero-Knowledge from Non-interactive Batch Arguments

Zero-knowledge and succinctness are two important properties that arise in the study of non-interactive arguments. Previously, Kitagawa et al. (TCC 2020) showed how to obtain a non-interactive zero-knowledge (NIZK) argument for $$\textsf{NP}$$ from a succinct non-interactive argument (SNARG) for $$\textsf{NP}$$ . In particular, their work demonstrates how to leverage the succinctness property from an argument system and transform it into a zero-knowledge property. In this work, we study a similar question of leveraging succinctness for zero-knowledge. Our starting point is a batch argument for $$\textsf{NP}$$ , a primitive that allows a prover to convince a verifier of T $$\textsf{NP}$$ statements $$x_1, \ldots , x_T$$ with a proof whose size scales sublinearly with T. Unlike SNARGs for $$\textsf{NP}$$ , batch arguments for $$\textsf{NP}$$ can be built from group-based assumptions in both pairing and pairing-free groups and from lattice-based assumptions. The challenge with batch arguments is that the proof size is only amortized over the number of instances, but can still encode full information about the witness to a small number of instances. We show how to combine a batch argument for $$\textsf{NP}$$ with a local pseudorandom generator (i.e., a pseudorandom generator where each output bit only depends on a small number of input bits) and a dual-mode commitment scheme to obtain a NIZK for $$\textsf{NP}$$ . Our work provides a new generic approach of realizing zero-knowledge from succinctness and highlights a new connection between succinctness and zero-knowledge.
RTAD-TP: Real-Time Anomaly Detection Algorithm for Univariate Time Series Data Based on Two-Parameter Estimation

The anomaly detection of univariate time series data is becoming increasingly important, as early detection of anomalies is valuable in practical applications. However, due to the continuous influx of data streams and the dynamic changes in data patterns, real-time anomaly detection still poses challenges. Algorithms such as SPOT, DSPOT, and FluxEV are efficient unsupervised anomaly detection algorithms for data streams, but their detection performance still needs to be improved when processing large-scale data streams. To address this, we propose the Real-Time Anomaly Detection Algorithm for Univariate Time Series Data Based on Two-Parameter Estimation (RTAD-TP), a fast and effective unsupervised real-time anomaly detection algorithm. We calculate the residual between the current value and the predicted value using Exponential Moving Average (EMA), and apply extreme value distribution to determine the threshold for the residual. In addition, we use two-parameter estimation to improve the speed and accuracy of parameter estimation in automatic thresholding, addressing the limitations of SPOT, DSPOT, and FluxEV. Experimental results show that the RTAD-TP algorithm has better detection performance than the baseline algorithm.
One-Message Secure Reductions: On the Cost of Converting Correlations

Correlated secret randomness is a useful resource for secure computation protocols, often enabling dramatic speedups compared to protocols in the plain model. This has motivated a line of work on identifying and securely generating useful correlations. Different kinds of correlations can vary greatly in terms of usefulness and ease of generation. While there has been major progress on efficiently generating oblivious transfer (OT) correlations, other useful kinds of correlations are much more costly to generate. Thus, it is highly desirable to develop efficient techniques for securely converting copies of a given source correlation into copies of a given target correlation, especially when the former are cheaper to generate than the latter. In this work, we initiate a systematic study of such conversions that only involve a single uni-directional message. We refer to such a conversion as a one-message secure reduction (OMSR). Recent works (Agarwal et al., Eurocrypt 2022; Khorasgani et al., Eurocrypt 2022) studied a similar problem when no communication is allowed; this setting is quite restrictive, however, with few non-trivial conversions being feasible. The OMSR setting substantially expands the scope of feasible results, allowing for direct applications to existing MPC protocols. We obtain the following positive and negative results.
Conf-UNet: A Model for Speculation on Unknown Oracle Bone Characters

Oracle Bone Characters (OBC) are the oldest developed pictographs in China, having been created over 3000 years ago. As a result of their antiquity and the scarcity of relevant historical sources, identifying and interpreting OBC has been an ongoing challenge for scholars of oracle bone characters. Large Seal Script evolved from OBC, and retains many of its features, making the linking of these two scripts an urgent task. The traditional method of textual study and interpretation can be time-consuming, requires a high degree of professionalism, and demands significant material and human resources. To address these issues, we propose the Conf-UNet model, a deep learning approach that incorporates a U-net combined with a multi-head self-attention mechanism. This model was used to link Large Seal Script with unidentified oracle bone characters to reduce the workload and provide linguists with a reliable method to speculate the sealed characters that correlate with unidentified OBC. The proposed model also enables linguists to use deep learning to research the evolution of pictographs. Experiments on the HWOBC-A dataset demonstrate that our model outperforms other models on this task of identifying oracle bone characters.
Impact of Employee Competence and Behaviour on Banking Customer Trust: Empirical Study in Saudi Arabia

The main purpose of the study was to find out the impact of employee competence and employee behaviour on banking customers’ attitude towards banking and trust in the banking system. 300 responses were collected from banking customers by administering structured questionnaire developed for the purpose and translated into arabic language. The results of the study showed that employee competence positive relationship with employee behaviour, and customer attitudes, however it has no significant relationship with the banking customer trust. Employee behaviour has statistically significant relationship with attitude and trust. Customer’s attitude influence trust of the banking customer. The study makes significant contribution to the literature. The findings of the study are important for the banks to improve and enhance banking customer’s attitudes and trust through employee competence and behaviour.
Universal Amplification of KDM Security: From 1-Key Circular to Multi-Key KDM

An encryption scheme is Key Dependent Message (KDM) secure if it is safe to encrypt messages that can arbitrarily depend on the secret keys themselves. In this work, we show how to upgrade essentially the weakest form of KDM security into the strongest one. In particular, we assume the existence of a symmetric-key bit-encryption that is circular-secure in the 1-key setting, meaning that it maintains security even if one can encrypt individual bits of a single secret key under itself. We also rely on a standard CPA-secure public-key encryption. We construct a public-key encryption scheme that is KDM secure for general functions (of a-priori bounded circuit size) in the multi-key setting, meaning that it maintains security even if one can encrypt arbitrary functions of arbitrarily many secret keys under each of the public keys. As a special case, the latter guarantees security in the presence of arbitrary length key cycles. Prior work already showed how to amplify n-key circular to n-key KDM security for general functions. Therefore, the main novelty of our work is to upgrade from 1-key to n-key security for arbitrary n. As an independently interesting feature of our result, our construction does not need to know the actual specification of the underlying 1-key circular secure scheme, and we only rely on the existence of some such scheme in the proof of security. In particular, we present a universal construction of a multi-key KDM-secure encryption that is secure as long as some 1-key circular-secure scheme exists. While this feature is similar in spirit to Levin’s universal construction of one-way functions, the way we achieve it is quite different technically, and does not come with the same “galactic inefficiency”.