Reviewerly/exHarmony huggingface dataset:
Bidirectional Multi-channel Semantic Interaction Model of Labels and Texts for Text Classification

Text classification, aiming for discovering corresponding relationships between labels and texts, is a pivotal task in Natural Language Processing (NLP). The existing joint text-label models help input texts to establish early global category semantic awareness via label embedding techniques, but they cannot simultaneously capture literal and semantic relationships between texts and labels. It may lead models to ignore obvious clues or semantic relations on different cognitive levels. In this paper, we propose a Bidirectional Multi-channel semantic Interaction model (BMI) to handle both explicit and implicit category semantics in texts for text classification. On the explicit semantic level, BMI designs a word representation similarity match channel for shallow interaction to get rid of semantic mismatch based on assumptions that words have different meanings under the same context. On the implicit semantic level, BMI provides a novel attended attention mechanism over texts and labels for deep interaction to model bidirectional text explanation for labels and label guidance for texts. Furthermore, a gated residual mechanism is employed to obtain core information of labels to improve efficiency. Experiments on benchmark datasets show that BMI achieves competitive results over 15 strong baseline methods, especially in the case of short texts.
Original or Fake? How to Understand the Digital Artworks’ Value in the Blockchain

The recent Blockchain technology for recording and storing data opened a cultural scenario which prefigures unexplored frontiers. It is precisely in this frontier territory that a revolution in the digital art sector has come to life, represented by the introduction and dissemination of non-fungible tokens (NFTs) as certificates of ownership/authorship of a digital work. NFTs redefine the concepts of artwork ownership/authorship, authenticity and value. Supported by the Blockchain structure, which is in fact permanent and unchangeable, these certificates are inviolable, unassailable and indestructible, offering a type of guarantee never experienced before. As any new technologies, there are challenges that users face: 1) avoiding fraud (i.e. digital artworks that are not original, but mere copies of the original) 2) estimating the real value of a NFT connected to a digital artwork. The paper proposes a predictive tool, NFT price Oracle, as a solution for the Blockchain users that want to be sure they are purchasing an authentic digital artwork and wish to understand the value of the artwork (and thus how much to pay for the NFT associated with the artwork) in the Blockchain.
Augmented Topic-Specific Summarization for Domain Dialogue Text

This paper describes HW-TSC’s submission to the NLPCC 2022 dialogue text summarization task. We convert it into a sub-summary generation and a topic detection task. A sequence-to-sequence model Transformer is adopted as the foundational structure of our generation model. An ensemble topic detection model is used to filter uninformative summaries. On the other hand, we utilize multiple data processing and data augmentation methods to improve the effectiveness of the system. A constrained search method is used to construct generation model’s training pairs between sub-dialogues and sub-summaries. Multiple role-centric training data augmentation strategies are used to enhance both the generation model and the topic detection model. Our experiments demonstrate the effectiveness of these methods. Finally, we rank first with the highest ROUGE score of 51.764 in the test evaluation.
An Interactive Fusion Model for Hierarchical Multi-label Text Classification

Scientific research literature usually has multi-level labels, and there are often dependencies between multi-level labels. It is crucial for the model to learn and integrate the information between multi-level labels for the hierarchical multi-label text classification (HMTC) of scientific research literature texts. Therefore, for the HMTC task in the scientific research literature, we use the pre-trained language model SciBERT trained on scientific texts. And we introduce a shared TextCNN layer in our multi-task learning architecture to learn the dependency information between labels at each level. Then the hierarchical feature information is fused and propagated from top to bottom according to the task level. We conduct ablation experiments on the dependency information interaction module and the hierarchical information fusion propagation module. Experimental results on the NLPCC2022 SharedTask5 Track1 dataset demonstrate the effectiveness of our model, and we rank 4th place in the task.
An Image Super Resolution Algorithm Using Reparameterization

At present, the residual-based deep network model mainly adopts a multi-branch structure, but this structure increases the memory access cost, and the reasoning time is not as good as that of a single-branch network. In this work, we propose a re-parameterized construction structure based on EDSR, namely Re-parameterized Deep Super-Resolution (RepEDSR). The training and inference processes are decoupled to speed up inference without sacrificing performance. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm outperforms existing methods in terms of speed and accuracy.
Order vs. Chaos: Multi-trunk Classifier for Side-Channel Attack

There is a revived interest in applying machine learning techniques for side-channel attacks, focusing on utilizing advancements in deep learning techniques. Most of the recent research work focuses on using a discriminative-learning-based classifier approach for profiled attacks, which we henceforth denote as a standard classifier approach. The standard classifier learns the intermediate target value in the training phase using a training loss function designed with classification accuracy. At the same time, the performance metric used for reporting results on a real attack dataset is generally key guessing entropy. Although the standard classifiers are popular, they severely suffer from low classification accuracy (almost close to random guessing accuracy) on the attack and validation dataset. This also poses a problem in model selection with early stopping, and most of the literature does model selection at some arbitrary number of training epochs. This raises the concern that the standard classifier approach is ill-posed for the side-channel attack task, and it motivated us to investigate alternative ways of performing a side-channel attack. This paper will introduce a novel multi-trunk binary classifier (MTOvC) approach as an alternative to a standard classifier. It exhibits good validation and attack dataset accuracies, suggesting that the resulting loss function is more suitable for the side-channel attack task. Moreover, good validation accuracies allow us to perform sensible model selection with early stopping in the case of multi-trunk classifiers.
PEPEC: Precomputed ECC Points Embedded in Certificates and Verified by CT Log Servers

Elliptic curve cryptography (ECC) is respected in public key infrastructures (PKIs) due to its high performance and small key size. However, for some client devices with limited computing resources, signature verification and key agreement using the ECC public key is computationally difficult which mainly due to the complexity of scalar multiplication. The window non-adjacent form algorithm can be used to improve the performance of the elliptic curve public key operation, which is combining the window method with the Non-adjacent form representation (w-NAF). Compared with fixed-point scalar multiplication using the offline precomputed table, for the unknown-point multiplication of ECC public key operation, a pre-computed table needs to be generated online. In this paper, a novel efficient certificate scheme called PEPEC (Precomputed ECC Points Embedded in Certificates) is proposed to integrate the w-NAF into CT (Certificate Transparency) which is a trusted enhancement for PKI. By using the PEPEC certificate, the client can improve the performance of the public key operation by more than 10%, with the offline-generated precomputed table of the w-NAF algorithm. The correctness of the precomputed table is provided by CT. Our PEPEC certificate is compatible with the existing standardized PKI system. The client can select the optimal window size of the w-NAF algorithm according to the current situation of computing resources to improve the performance of the public key operation.
Efficient Software Implementation of GMT6-672 and GMT8-542 Pairing-Friendly Curves for a 128-Bit Security Level

A Bilinear pairing on an elliptic curve defined over a finite field provides an attractive prospect for designing cryptographic schemes with various functionalities. An elliptic curve over which a computationally efficient bilinear pairing can be defined is called a “pairing-friendly curve”. Finding families of pairing-friendly curves with sufficient anticipated bit security has attracted significant research attention. For example, the Barreto-Neahrig (BN) and Barreto-Lynn-Scott (BLS) curves, are existing curves of this type. However, there is a need for alternatives to back up these already evaluated curves. In 2020 Guillevic, Masson, and Thomé (GMT) proposed pairing-friendly curves with embedding degrees 5 to 8 range. GMTk denotes curves with an embedding degree k. A composite k is preferred from the efficiency viewpoint. However, to the best of the GMT6 and GMT8 curves have been reported in the literature. In this paper, novel field-towering methods using two types of extension method and constructions are developed. These methods are applied to efficiently implement and analyze the bilinear pairings based on the GMT6 curve over a 672-bit prime field and the GMT8 curve over a 542-bit prime field. The pairing-computation times of our developed software evaluated using an Intel Core i7-8700 (@4.3 GHz Turbo Boost on) is computer are 0.987 ms and 1.12 ms for GMT6-672 and GMT8-542, respectively indicating the practicality of these curves.
UniqueChain: Achieving (Near) Optimal Transaction Settlement Time via Single Leader Election

Leader election in most existing blockchain consensus protocols is probabilistic (i.e. based on proof-of-work or proof-of-stake) and results in the differences in honest parties’ selection of local leaders, which further leads to the inconsistency among honest parties’ local chains and high latency in terms of transaction settlement time. In this work, we study the impact of probabilistic leader election on transaction settlement time and propose a new structure of two-chain blockchain, called UniqueChain, which, for the first time, presents a general way to achieve single leader election under the mildly adaptive adversary. Precisely, UniqueChain provides the honest parties with a same and unique local leader. To showcase the usability of UniqueChain, we apply it in the proof-of-stake setting to obtain a provably secure blockchain consensus protocol $$\pi _{\textit{uc}}$$ . Specifically, $$\pi _{\textit{uc}}$$ achieves the property of uniqueness and exhibits (near) optimal transaction settlement time.
Vulnerability Detection for Smart Contract via Backward Bayesian Active Learning

Smart contract is a piece of program code running on the blockchain, which aims to realize trusted transactions without third parties. In recent years, smart contract vulnerabilities emerge one after another, resulting in huge economic losses. Machine learning technology is widely used in smart contract vulnerability detection. It is common that model training in machine learning often requires a large amount of labeled data while the unlabeled data in the current field is very rich and acquiring labels is extremely difficult. As a result, it takes a lot of manpower and time to label a vulnerability, and it is challenging to perform effective smart contract vulnerability detection. To tackle this problem, we propose BwdBAL, a novel framework for smart contract vulnerability detection that combines Bayesian Active Learning (BAL) and a backward noise removal method. We use BAL to remove the impact of model uncertainty on uncertainty sampling in active learning. During the backward process, we clean up the noise in the labeled dataset to reduce the negative influence on the classification model. We evaluate BwdBAL on 8 vulnerabilities about 4929 smart contracts with four performance indicators. The experimental results show that BwdBAL outperforms two baseline methods: conventional machine learning-enabled classification method and one-way active learning method.
Leaky Blinders: Information Leakage in Mobile VPNs

In the mobile domain, VPN applications promise an additional layer of protection for wireless connections. They offer users the choice to improve the security of their connections, however, we only have very limited knowledge about the technical implications that the shift from desktop to mobile applications brings. In this work, we conduct a quantitative analysis of selected Android VPNs and demonstrate how all of them leak packets during an active tunnel. We conduct these measurements for different phones and in varying use case scenarios, including the comparison of Wi-Fi and 4G connections, to get a better understanding of how the mobile setting influences the security of a VPN. While we observe leaks in all combinations, some settings particularly cause the transmission of thousands of unprotected packets. We further conduct a series of case studies to provide some first insights on the causes for the observed leakage.
SG-SRNs: Superpixel-Guided Scene Representation Networks

Recently, Scene Representation Networks (SRNs) have attracted increasing attention in computer vision, due to their continuous and light-weight scene representation ability. However, SRNs generally perform poorly on low-texture image regions. Addressing this problem, we propose superpixel-guided scene representation networks in this paper, called SG-SRNs, consisting of a backbone module (SRNs), a superpixel segmentation module, and a superpixel regularization module. In the proposed method, except for the novel view synthesis task, the task of representation-aware superpixel segmentation mask generation is realized by the proposed superpixel segmentation module. Then, the superpixel regularization module utilizes the superpixel segmentation mask to guide the backbone to be learned in a locally smooth way, and optimizes the scene representations of the local regions to indirectly alleviate the structure distortion of low-texture regions in a self-supervised manner. Extensive experimental results on both our constructed datasets and the public Synthetic-NeRF dataset demonstrated that the proposed SG-SRNs achieved a significantly better 3D structure representing performance.
POSTER: Enabling User-Accountable Mechanisms in Decision Systems

Decision systems are at the core of our democratic and meritocratic processes. Systems for voting, procurement, grant management, and competitive examinations all rest on submission, evaluation, and ranking. Computer assistance is a critical part of modern decision systems and so are cybersecurity challenges. As decision systems get increasingly complex, the classic approach of enforcing security through fail-safe mechanisms preventing cybersecurity attacks becomes infeasible. A recent trend in cybersecurity is to disincentivize potential attacks by using deterrence-based mechanisms that make stakeholders accountable for their actions. However, using such mechanisms requires knowledge of the underlying technology, which is not accessible to all people. This poster looks at ways to extend decision systems with user-accountable mechanisms enabling users to verify correct executions and provide dispute resolution capabilities by combining cryptographic techniques for human senses with advanced cryptographic protocols. If successful, this line of work will provide novel ways to secure decision systems by creating disincentivizing mechanisms that are accessible to any human user.
Efficient Verifiable Boolean Range Query for Light Clients on Blockchain Database

Blockchain allows clients to query and verify any transactions, which requires the clients to maintain the entire blockchain database locally. This approach is inadvisable because the blockchain database is an append-only ledger and incurs significant maintenance overhead. Very recently, blockchain light client has attracted considerable concerns, which relies on a third party (i.e., a full node) to perform query processing and verification. However, the dishonest full node may return an incorrect and incomplete result of the query requests. Therefore, it remains a challenging issue to achieve secure, efficient, and rich verifiable queries for light clients. In this paper, we propose an efficient verifiable Boolean range query scheme for light clients on the blockchain database. Firstly, we design a new authenticated data structure, polynomial commitment B $$^+$$ -tree (PCB-tree), which efficiently ensures the correctness and completeness of Boolean range queries for blockchain light clients. Secondly, we provide a tunable trade-off between query time and communication overhead by autonomously setting the fanout size of the PCB-tree. Moreover, our scheme can support batch processing to reduce query complexity and proof size. Finally, security analysis and performance evaluation show that our proposed scheme is secure and practical.
An On-Device Machine Reading Comprehension Model with Adaptive Fast Inference

Pretrained language models (PrLMs) have been widely used in machine reading comprehension (MRC) tasks. Although PrLMs can learn powerful representations from large-scale corpora, it introduces the problem of model redundancy, which severely restricts the application of mobile devices. Recent studies have suggested the use of early exit (EE) methods for the fast inference of PrLMs. However, there are two limitations to the application of existing EE methods. First, different Transformer layers have different capabilities for learning semantic features. Thus, the lower layers may behave differently from the higher layers. As a result, the earlier the exit from the model, the lower its performance. Second, a greedy search is applied to the classifiers exiting either the start or the end position. However, the start and end positions may also be related. Choosing only one best candidate might be suitable for the current step, but it may be a suboptimal choice in global inference. To address these issues, this study proposes an on-device MRC model with fast inference based on an EE strategy. To ensure that the lower layers have similar abilities as the higher layers, we propose the use of self-distillation to transfer knowledge from the higher layers to the lower ones. Furthermore, a dynamic programming strategy was applied to choose the optimal confidence combination of both the start and end classifiers to better select the exit layers in the inference stage. Experimental results on both SQuAD 1.1 and 2.0 show that the inference of the proposed model is accelerated at the minimum cost of performance loss, thereby outperforming previous EE models.
Performance Enhancement of the Unbalanced Text Classification Problem Through a Modified Chi Square-Based Feature Selection Technique

This paper proposes a modified chi square-based feature selection algorithm in conjunction with a random vector functional link network-based text classifier for improving the classification performance of multi-labeled text documents with unbalanced class distributions. In the proposed feature selection method, maximum features are selected from classes that have a great deal of training and testing documents as an improvement towards original chi-square method. On two benchmark datasets that are multi-labeled, multi-class, and unbalanced, a comparison of the model with three conventional selection techniques such as chi-square, term frequency-inverse document frequency, and mutual information is accumulated for assessing its effectiveness. Additionally, the proposed model is compared with four different classifiers. In the study, it was found that the proposed model performs better in terms of precision, recall, f-measure, and hamming losses and is able to select the majority of true positive documents despite an unbalanced class distribution for both the datasets.
Network Access Control for Government

In the digital era, governments are considered high-value targets, chased not only by financially motivated criminals, but also to commit acts of espionage, terrorism, and warfare. One of the most important technologies developed is network access control (NAC), which implements policy-based access control to handle entry to the network. This research focuses on the application of network access control in government institutions and the numerous benefits of its adoption. Exploring the challenges of the government to adopt a budget that places cybersecurity as a priority will allow analyzing if the solutions offered in the market are achievable for big cities as well as for small cities. This paper explores the budgets of six cities, and three NAC solutions are offered to help the government increase its cybersecurity and comply with regulations.
Coarse-to-Fine Retriever for Better Open-Domain Question Answering

The retriever-reader framework has been widely used in open-domain question answering with great success. Current studies show that better retrieval can greatly improve the performance of final answer extraction and may replace the reader stage. Considering the limited computing resources and the great progress that has been made in reading comprehension, we continue to use the retriever-reader framework and focus on efficient retrieval. In this paper, we propose a new coarse-to-fine retrieval method to take away the semantic noise left by coarse-grained filtering. In particular, we join a fine-grained retriever after the passages generated by the coarse-grained retriever, making all sentences in the passage match more closely with the question. Meanwhile, we use contrastive learning to construct dense vector representation for fine-grained retriever. Experiments on the QA dataset show that our model outperforms the most mainstream model greatly by about 11.7% which importantly gets more out of the operation of retriever.
BART-Reader: Predicting Relations Between Entities via Reading Their Document-Level Context Information

Document-level relation extraction (Doc-RE) aims to classify relations between entities spread over multiple sentences. When one entity is paired with separate entities, the importance of its mentions varies, which means the entity representation should be different. However, most of the previous RE models failed to make the relation classification entity-pair aware effectively. To that end, we propose a novel adaptation to simultaneously utilize the encoder and decoder of the sequence-to-sequence (Seq2Seq) pre-trained model BART in a non-generative manner to tackle the Doc-RE task. The encoder encodes the document to get the entity-aware contextualized mention representation. The decoder uses a non-causal self-attention mechanism and masked cross-attention to model the interactions between the entity pair under consideration explicitly. By doing so, we can fully take advantage of the pre-trained model in the encoder and decoder sides. And experiments in three Doc-RE datasets show that our model can not only take more advantage of BART, but surpass various BERT and RoBERTa based models.