Reviewerly/exHarmony huggingface dataset:
Constructions of symplectic LCD MDS codes from quasi-cyclic codes

<p style='text-indent:20px;'>Linear complementary dual (LCD) codes play an important role in data storage, communications systems and cryptography. In this paper, we construct some symplectic LCD codes from quasi-cyclic (QC) codes, and prove that these symplectic LCD codes with dimension two over <inline-formula><tex-math id="M1">\begin{document}$ \mathbb{F}_{2^a} $\end{document}</tex-math></inline-formula> are maximum-distance-separable (MDS) codes. By extending the generator matrices of symplectic LCD MDS codes with dimension two, we obtain a series of symplectic LCD codes over <inline-formula><tex-math id="M2">\begin{document}$ \mathbb{F}_{2^a} $\end{document}</tex-math></inline-formula>. As an application, some new entanglement-assisted quantum error-correcting codes (EAQECCs) over <inline-formula><tex-math id="M3">\begin{document}$ \mathbb{F}_{2^a} $\end{document}</tex-math></inline-formula> are constructed by symplectic LCD codes.
Knowledge graph embedding with entity attributes using hypergraph neural networks

Knowledge graph embedding is aimed at capturing the semantic information of entities by modeling the structural information between entities. For long-tail entities which lack sufficient structural information, general knowledge graph embedding models often show relatively low performance in link prediction. In order to solve such problems, this paper proposes a general knowledge graph embedding framework to learn the structural information as well as the attribute information of the entities simultaneously. Under this framework, a H-AKRL (Hypergraph Neural Networks based Attribute-embodied Knowledge Representation Learning) model is put forward, where the hypergraph neural network is used to model the correlation between entities and attributes at a higher level. The complementary relationship between attribute information and structural information is taken full advantage of, enabling H-AKRL to finally achieve the goal of improving link prediction performance. Experiments on multiple real-world data sets show that the H-AKRL model has significantly improved the link prediction performance, especially in the embeddings of long tail entities.
Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework

Simultaneous machine translation (SiMT) starts translating while receiving the streaming source inputs, and hence the source sentence is always incomplete during translating.Different from the full-sentence MT using the conventional seq-to-seq architecture, SiMT often applies prefix-to-prefix architecture, which forces each target word to only align with a partial source prefix to adapt to the incomplete source in streaming inputs.However, the source words in the front positions are always illusoryly considered more important since they appear in more prefixes, resulting in position bias, which makes the model pay more attention on the front source positions in testing.In this paper, we first analyze the phenomenon of position bias in SiMT, and develop a Length-Aware Framework to reduce the position bias by bridging the structural gap between SiMT and fullsentence MT.Specifically, given the streaming inputs, we first predict the full-sentence length and then fill the future source position with positional encoding, thereby turning the streaming inputs into a pseudo full-sentence.The proposed framework can be integrated into most existing SiMT methods to further improve performance.Experiments on two representative SiMT methods, including the state-of-theart adaptive policy, show that our method successfully reduces the position bias and thereby achieves better SiMT performance.
MobiTrust: Trust-Aware Pricing Scheme for Edge-Based Mobile Sensor-Cloud for Vehicular IoT

In this work, we propose MobiTrust, a game theoretic pricing scheme, to address the problem of ensuring trust in the edge-enabled mobile sensor-cloud (MSC) architecture for vehicular Internet-of-Things (IoT). In the edge-enabled MSC, a sensor-cloud service provider (SCSP) utilises multiple sensor-equipped vehicular IoT-based devices belonging to different owners and multiple edge devices deployed by different edge infrastructure providers (IPs) to provision Sensors-as-a-Service (Se-aaS). Since these entities are revenue-driven and they depend on each other for successful service provisioning, there is a need to establish trust among them to ensure the sustainability of MSC. Existing works on sensor-cloud address the issue of ensuring trust among selfish device-owners. However, these works do not consider the presence of IPs and assume that the IoT devices are stationary in nature, which renders them unsuitable for MSC. Therefore, in this work, we aim to address this drawback of the existing literature by proposing MobiTrust, which is comprised of two components. First, we propose a mobility-aware trust model designed based on the Transferable Belief Model, which utilizes different types of evidence collected by the devices to estimate their trust values. Second, we propose a trust-based pricing and resource allocation scheme based on the Stackelberg game that aims to maximise the profits and satisfaction of the various stakeholders. We analyze the proposed scheme theoretically and examine the existence of equilibrium in the proposed game. Results of extensive simulations show that MobiTrust increases profits for the stakeholders and QoS by 9% and 11%, respectively, compared to benchmarks.
SiNoptiC: swarm intelligence optimisation of convolutional neural network architectures for text classification

Although many rules have been suggested by several researchers for designing deep neural architectures, trial-and-error is often exploited in practice to find the optimal model for a given problem. Thus, the automation of deep neural architecture search methods is highly recommended. In this work, we address this problem by proposing a hybrid coupling of Convolutional Neural Networks (CNNs) architectures with the swarm intelligence, especially the Fish School Search (FSS) algorithm. This coupling is capable of discovering a promising architecture of a CNN on handling text classification tasks. The proposed method allows users to provide training data as input, and receive a CNN model as an output. It is completely automatic and capable of fast convergence. Computational results show the effectiveness of the proposed method in achieving the best classification loss among manually designed CNNs. This is the first work using FSS for automatically designing the architectures of CNNs.
Improving Stereo Matching Generalization via Fourier-Based Amplitude Transform

Stereo matching CNNs suffer from performance deteriorate when evaluated under different distributions from training data. Previous domain adaptation/generalization methods are hard to maintain a robust performance in different baselines and usually require difficult adversarial optimization or intricate network structure. To solve this problem, we propose Fourier-based amplitude transform (FAT), mapping the source image to the target style without altering semantic content, which requires no training to perform the domain alignment. Specifically, we leverage the Fourier transform and its inverse to swap the low-frequency amplitude component of the source data with the target data. To effectively map style and relieve the artifacts, we introduce two factors to control the replacing area: the distance of HSV distribution between source and target images; and the difference between the source left image and its warped left image. Experiments testify FAT can significantly bridge domain gaps, making source data distribution closer to target data. Furthermore, when only training on synthetic datasets, FAT can also help different baselines achieve competitive cross-domain generalization capabilities on real datasets.
SOA_NLP@LT-EDI-ACL2022: An Ensemble Model for Hope Speech Detection from YouTube Comments

Language should be accommodating of equality and diversity as a fundamental aspect of communication.The language of internet users has a big impact on peer users all over the world.On virtual platforms such as Facebook, Twitter, and YouTube, people express their opinions in different languages.People respect others' accomplishments, pray for their well-being, and cheer them on when they fail.Such motivational remarks are hope speech remarks.Simultaneously, a group of users encourages discrimination against women, people of color, people with disabilities, and other minorities based on gender, race, sexual orientation, and other factors.To recognize hope speech from YouTube comments, the current study offers an ensemble approach that combines a support vector machine, logistic regression, and random forest classifiers.Extensive testing was carried out to discover the best features for the aforementioned classifiers.In the support vector machine and logistic regression classifiers, charlevel TF-IDF features were used, whereas in the random forest classifier, word-level features were used.The proposed ensemble model performed significantly well among English, Spanish, Tamil, Malayalam, and Kannada YouTube comments.
GCPG: A General Framework for Controllable Paraphrase Generation

Controllable paraphrase generation (CPG) incorporates various external conditions to obtain desirable paraphrases.However, existing works only highlight a special condition under two indispensable aspects of CPG (i.e., lexically and syntactically CPG) individually, lacking a unified circumstance to explore and analyze their effectiveness.In this paper, we propose a general controllable paraphrase generation framework (GCPG), which represents both lexical and syntactical conditions as text sequences and uniformly processes them in an encoderdecoder paradigm.Under GCPG, we reconstruct commonly adopted lexical condition (i.e., Keywords) and syntactical conditions (i.e., Part-Of-Speech sequence, Constituent Tree, Masked Template and Sentential Exemplar) and study the combination of the two types.In particular, for Sentential Exemplar condition, we propose a novel exemplar construction method -Syntax-Similarity based Exemplar (SSE).SSE retrieves a syntactically similar but lexically different sentence as the exemplar for each target sentence, avoiding exemplar-side words copying problem.Extensive experiments demonstrate that GCPG with SSE achieves state-of-the-art performance on two popular benchmarks.In addition, the combination of lexical and syntactical conditions shows the significant controllable ability of paraphrase generation, and these empirical results could provide novel insight to user-oriented paraphrasing.
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference

Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, Donald Metzler. Findings of the Association for Computational Linguistics: ACL 2022. 2022.
C-SASO: A Clustering-Based Size-Adaptive Safer Oversampling Technique for Imbalanced SAR Ship Classification

Ship classification using Synthetic Aperture Radar induces effective marine applications but suffers from imbalanced datasets. Common ship types (majority classes) contain many more instances than rare ship types (minority classes), resulting in performance loss since minority instances tend to be ignored. Meanwhile, it is difficult and time-consuming to produce reliable instances for rare ships. The synthetic minority oversampling techniques have shown great potential to balance the distribution of classes by synthesizing minority instances. However, the newly synthesized instances may overlap with the majority instances, reducing the separability among classes, or being far away from the classification boundary, which is meaningless. This paper proposes a clustering-based size-adaptive safer oversampling technique to address the imbalanced classification problem. Proven schemes, including cluster minority instances, adaptively allocate oversampling sizes, and assign weights are adopted for selecting minority instances. Then, new instances are synthesized according to the safe metric of selected instances, instead of randomly inserted. Furthermore, multiple handcrafted features are tested to provide a clearer classification boundary. The proposed comprehensively considers the usefulness and safety of synthesizing instances. Experiments on the OpenSARShip and the FUSAR-Ship datasets demonstrate that the proposed technique achieves significantly better results.
Deep Batch Active Learning and Knowledge Distillation for Person Re-Identification

For deep learning model training, most existing supervised learning-based person re-identification (Re-ID) models require considerable data with annotations as samples. However, it is labor-intensive to generate labeled data in many real-world situations. Meanwhile, the large scale of the existing models increases the load of model learning. To this end, this paper proposes a person Re-ID method by deep batch active learning and knowledge distillation. With the goal of minimizing the human labeling cost and maximizing the performance of the person Re-ID model, a batch active learning algorithm is applied to person Re-ID, which selects samples with both uncertainty and diversity, and the model needs only a small amount of labeled data to achieve a high performance. In addition, a knowledge distillation approach is used to compress the original backbone model to reduce the model scale while maintaining the model performance. Furthermore, experiments on two public datasets demonstrate the effectiveness and superiority of the proposed method.
Single-Trace Attack Using One-Shot Learning With Siamese Network in Non-Profiled Setting

Recently, many studies have shown that using deep learning for side-channel attacks offers several advantages, including simplification of the attack phase and target breaking, even in protected implementations, while presenting outstanding attack performance. Power and electromagnetic analysis, which is known as the most robust attack, can be classified into profiling and non-profiling attacks. In the real world, a non-profiling attack is more ideal than a profiling attack. In particular, studies on non-profiling attacks using deep learning for asymmetric cryptosystems are rare and have shortcomings, such as a long analysis time. In this paper, we propose a novel non-profiling attack method for asymmetric cryptosystems that requires only a single trace and a reasonably short attack time to recover a full private key, overcoming the limitations of previous studies. The proposed method applies one-shot learning with a convolutional Siamese network, which is used for the first time in side-channel attacks. Thus, our proposed method can leak private keys used in a protected public-key cryptosystem with up to 100% accuracy with only one single trace in a non-profiled setting.
Immersive learning from basic design for communication design: A theoretical framework

In this paper we discuss the two changes that basic design education faced: one is the teaching need of transformation from visual to multisensory and synaesthetic communication; the other is the use of virtual environments to teach design. As an answer to the trend of constructivist learning, also in order to fulfill the need for multisensory training, the discussion of an innovative learning environment for basic design education has become essential. The problems remain, as virtual technology has limitations regarding visualizing abstract concepts. This research aims to build an immersive virtual environment to teach basic design, along with the value of subjective immersive experience for design learning in general. The study presented in this paper proposes a theoretical framework, starting with the redefinition of the concepts of “immersion” and “presence” from a cognitive perspective (Scuri, 2017). The main research method is based on two groups of case studies; through literature review and secondary research, this work categorizes the factors of presence into a three-dimensional framework, also defining the four typologies of immersion and two in-class educational models. The paper presents the results of the research at the first phase, aimed at bridging the gap between design learning and virtual spaces. Through the framework addressed, we are able to frame an actual design tool with the help of online platforms and tools.
An architectural proposal to protect the privacy of health data stored in the Blockchain

Blockchain is a public ledger, decentralized and distributed on the peer-to-peer network that uses a block structure to verify and store data, employing a reliable consensus mechanism. With the rapid development of this technology in recent years, several concerns and obstacles to its application in some scenarios began to emerge, among them privacy being one of the topics mentioned in several works. This work proposes an architecture to guarantee the privacy of health data, which are stored within a Blockchain network in a decentralized way, through the use of encryption techniques that will be compared such as RSA (Rivest-Shamir-Adleman) and the ECC (Elliptic Curve Cryptography).
Self-Supervised Cross Domain Social Recommendation

Social recommendation focuses on leveraging social information to enhance recommendation quality for social users. It requires that rating histories of these social users are available, which presents a great challenge for recommendation on cold-start social users who have no rating records. In fact, cold-start social recommendation can also be regarded as a cross domain recommendation(CDR) task, where user-item interactions form information domain and user-user links form social domain. Mainstream CDR approaches map or share bridge users representations between domains to enhance performance on non-bridge users in target domain. However, the performance of CDR methods heavily relies on the bridge users scale, which leads the system very vulnerable. In order to eliminate above mentioned problem, in this paper, we propose a novel Self-supervised Cross Domain Social Recommendation(SCDSR), aiming at cold-start social users. By innovatively integrating information domain and social domain into a heterogeneous graph, SCDSR builds higher-order connections between cold-start social users and items via very limited bridge users. Meanwhile, SCDSR employs mutual information maximization on heterogeneous graph with self-supervised signals to optimize node representation learning. Finally, extensive experiments on two real-world datasets (i.e., Epinions and Dianping) clearly demonstrate the effectiveness of our proposed method.
Security of IoT Device: Perspective Forensic/Anti-Forensic Issues on Invalid Area of NAND Flash Memory

NAND flash memory-based IoT device can potentially still leave behind original personal data in an invalid area even if the data has been deleted. In this paper, we raise the forensic issue of original data remaining in unmanaged blocks caused by NAND flash memory and introduce methods for secure deletion of such data in the invalid area. We also propose a verification technique for secure deletion that is performed based on cell count information, which refers to the difference in bits between personal data and data stored in the block. The pass/fail of the verification technique according to the cell count information is determined in consideration of error correction capabilities. With the forensic issue of de-identification being a vital theme in the big data industry, the threat of serious privacy breaches coupled with our proposal to prevent these attacks will prove to be critical technological necessities in the future.
VisVisual: A Toolkit for Teaching and Learning Data Visualization

This article describes the motivation, design, and evaluation of the VisVisual toolkit to engage students in learning essential visualization concepts, algorithms, and techniques. The toolkit includes four independent components: 1) VolumeVisual, 2) FlowVisual, 3) GraphVisual, and 4) TreeVisual, covering scalar and vector data visualization in scientific visualization and graph and tree layouts in information visualization. Complementary to the toolkit design is resource development, aiming to help instructors integrate VisVisual into their curriculum.
Low-Resource NER by Data Augmentation With Prompting

Named entity recognition (NER) is a fundamental information extraction task that seeks to identify entity mentions of certain types in text. Despite numerous advances, the existing NER methods rely on extensive supervision for model training, which struggle in a low-resource scenario with limited training data. In this paper, we propose a new data augmentation method for low-resource NER, by eliciting knowledge from BERT with prompting strategies. Particularly, we devise a label-conditioned word replacement strategy that can produce more label-consistent examples by capturing the underlying word-label dependencies, and a prompting with question answering method to generate new training data from unlabeled texts. The experimental results have widely confirmed the effectiveness of our approach. Particularly, in a low-resource scenario with only 150 training sentences, our approach outperforms previous methods without data augmentation by over 40% in F1 and prior best data augmentation methods by over 2.0% in F1. Furthermore, our approach also fits with a zero-shot scenario, yielding promising results without using any human-labeled data for the task.
Neural Subgraph Explorer: Reducing Noisy Information via Target-oriented Syntax Graph Pruning

Recent years have witnessed the emerging success of leveraging syntax graphs for the target sentiment classification task. However, we discover that existing syntax-based models suffer from two issues: noisy information aggregation and loss of distant correlations. In this paper, we propose a novel model termed Neural Subgraph Explorer, which (1) reduces the noisy information via pruning target-irrelevant nodes on the syntax graph; (2) introduces beneficial first-order connections between the target and its related words into the obtained graph. Specifically, we design a multi-hop actions score estimator to evaluate the value of each word regarding the specific target. The discrete action sequence is sampled through Gumble-Softmax and then used for both of the syntax graph and the self-attention graph. To introduce the first-order connections between the target and its relevant words, the two pruned graphs are merged. Finally, graph convolution is conducted on the obtained unified graph to update the hidden states. And this process is stacked with multiple layers. To our knowledge, this is the first attempt of target-oriented syntax graph pruning in this task. Experimental results demonstrate the superiority of our model, which achieves new state-of-the-art performance.
CHALLENGES AND OPPORTUNITIES IN INTEGRATING INTERACTIVE CHATBOTS INTO CODE REVIEW EXERCISES: A PILOT CASE STUDY

CHALLENGES AND OPPORTUNITIES IN INTEGRATING INTERACTIVE CHATBOTS INTO CODE REVIEW EXERCISES: A PILOT CASE STUDY