Reviewerly/exHarmony huggingface dataset:
Automatic Tuning of Privacy Budgets in Input-Discriminative Local Differential Privacy

LDP (Local Differential Privacy) and its variants have been recently studied to analyze personal data collected from IoT (Internet of Things) devices while strongly protecting user privacy. In particular, a recent study proposes a general privacy notion called ID-LDP (Input-Discriminative LDP), which introduces a privacy budget for each input value to deal with different levels of sensitivity. However, it is unclear how to set an appropriate privacy budget for each input value, especially in current situations where re-identification is considered a major risk, e.g., in GDPR. Moreover, the possible number of input values can be very large in IoT. Consequently, it is also extremely difficult to manually check whether a privacy budget for each input value is appropriate. In this paper, we propose algorithms to automatically tune privacy budgets in ID-LDP so that obfuscated data strongly prevent re-identification. We also propose a new instance of ID-LDP called OneID-LDP (One-Budget Input-Discriminative LDP) to prevent re-identification with high utility. Through comprehensive experiments using four real datasets, we show that existing instances of ID-LDP lack either utility or privacy – they overprotect personal data or are vulnerable to re-identification attacks. Then we show that our OneID-LDP mechanisms with our privacy budget tuning algorithm provide much higher utility than LDP mechanisms while strongly preventing re-identification.
Domain Adaptive Remote Sensing Scene Recognition via Semantic Relationship Knowledge Transfer

Scene recognition has attracted rising attentions of many researchers in the remote sensing fields, owing to the rapidly advancing of remote sensing devices in recent years. However, images obtained from various sensors dominate diverse sensor-specific characteristics, which will dramatically weaken the model transferability trained on a source data domain to a different target domain on account of the domain shift issues. To mitigate the domain discrepancy, most existing methods attend to align the cross-domain distributions. While the valuable knowledge of semantic relationships between different scenes is generally overlooked, and the underlying correlation across scenes cannot be fully discovered. For the sake of tackling this challenge, we propose an adaptive remote sensing scene recognition network, which can successfully transfer both the discriminative knowledge and cross-scene relationship from source to target. Specifically, in this paper, we acquire sensor-invariant representations in an adversarial manner and realize fine-grained conditional distribution alignment contrastively. In such a way, the tremendous domain gap can be mitigated to a large extent, and the discriminative and well-matched representations will be derived favorably. In addition, we explicitly construct class-wise relationship distributions belonging to two domains respectively and minimize their divergence to conduct semantic relationship knowledge transfer (SRKT), for the purpose of sufficiently unearthing the intrinsic semantic relative structures that can prompt generality of the model in the target domain. Finally, we conduct multiple experiments on representative multi-domain remote sensing benchmarks, and the extensive experimental results demonstrate the superiority of our proposed approach.
The Impact of Agile Methods on the Software Projects Implementation and Management

There are emerging patterns of failure and success as more software projects adopt Agile methods.Project managers now need to understand the factors that influence project performance, as well as the applicable practices to their projects.Although some companies claim that Agile methods can solve all their issues, few have consistently shown success over other types of software projects.Although Agile methods can accommodate change due to the volatile requirements, they can also expose project managers to risks when it comes to managing the multitude of pieces of work involved in a project.This paper aims to provide a comprehensive analysis of the various trade-offs that are involved in adopting the method.
A Framework for Software Defect Prediction Using Optimal Hyper-Parameters of Deep Neural Network

Software defect prediction (SDP) models are widely used to identify the defect-prone modules in the software system. SDP model can help to reduce the testing cost, resource allocation, and improve the quality of software. We propose a specific framework of optimized deep neural network (ODNN) to develop a SDP system. The best hyper-parameters of ODNN are selected using the stage-wise grid search-based optimization technique. ODNN involves feature scaling, oversampling, and configuring the base DNN model. The performance of the ODNN model on 16 datasets is compared with the standard machine learning algorithms viz. Naïve Bayes, Support Vector Machine, Random Forest, Ada Boost, and base DNN model in terms of Accuracy, F-measure, and Area Under Curve (AUC). Experimental results show that the ODNN framework outperforms base DNN (BDNN) with 11.90% (accuracy), 0.26 (f-measure), and 0.13 (AUC). The statistical analysis using Wilcoxon signed-rank test and Nemenyi test show that the proposed framework is more effective than state-of-the-art models.
Efficient Uncertainty Quantification for Under-Constraint Prediction Following Learning Using MCMC

We present an illustration of a method to ensure reliable uncertainty percolation, within supervised learning performed using Gaussian Processes (GP), and Markov Chain Monte Carlo based inference. We show the effect of variously propagating the uncertainty, on predictions undertaken on the output variable, at test inputs, subsequent to the learning of the functional relationship between the input and the output, where this functional relation is modelled as a realisation from a GP. The efficiency of imposing a physically motivated constraints on the output - via priors imposed on the GP covariance kernel hyperparameters - is compromised under certain strategies adopted to propagate uncertainty. Tools such as DNNs, that are relatively more blind to uncertainty learning/propagation, are found to be diversely inaccurate in their output prediction.
Cross-Layer Fusion for Feature Distillation

Knowledge distillation is a model compression technology, which can effectively improve the performance of a small student network by learning knowledge from a large pre-trained teacher network. In most previous works of feature distillation, the performance of the student is still lower than the teacher network due to it only being supervised by the teacher's features and labels. In this paper, we novelly propose Cross-layer Fusion for Knowledge Distillation named CFKD. Specifically, instead of only using the features of the teacher network, we aggregate the features of the teacher network and student network together by a dynamic feature fusion strategy (DFFS) and a fusion module. The fused features are informative, which not only contain expressive knowledge of teacher network but also have the useful knowledge learned by previous student network. Therefore, the student network learning from the fused features can achieve comparable performance with the teacher network. Our experiments demonstrate that the performance of the student network can be trained by our method, which can be closer to the teacher network or even better.
Distinguishing Sensitive and Insensitive Options for the Winograd Schema Challenge

The Winograd Schema Challenge (WSC) is a popular benchmark for commonsense reasoning. Each WSC instance has a component that corresponds to the mention of the correct answer option of the two options in the context. We observe that the answers of many instances are insensitive to the options. In this paper, based on this observation, we propose an approach based on fine-tuning the pre-trained language model for WSC by distinguishing sensitive and insensitive options. First, we split WSC instances into option-sensitive and insensitive categories, and use option expanding and option masking strategies to weaken the options so that the model does not pay attention to options when they are insensitive during fine-tuning. Second, we treat the two categories as intermediate-task of each other, and use transfer learning to improve the performance. We fine-tune BERT-Large and T5-XXL with our approach on WINOGRANDE, a new dataset of WSC, and the experiment shows our method outperforms baselines by a large margin, achieving state-of-the-art, which indicates the effectiveness of our instance-distinguishing strategy.
Meta-learning Siamese Network for Few-Shot Text Classification

Few-shot learning has been used to tackle the problem of label scarcity in text classification, of which meta-learning based methods have shown to be effective, such as the prototypical networks (PROTO). Despite the success of PROTO, there still exist three main problems: (1) ignore the randomness of the sampled support sets when computing prototype vectors; (2) disregard the importance of labeled samples; (3) construct meta-tasks in a purely random manner. In this paper, we propose a Meta-Learning Siamese Network, namely, Meta-SN, to address these issues. Specifically, instead of computing prototype vectors from the sampled support sets, Meta-SN utilizes external knowledge (e.g. class names and descriptive texts) for class labels, which is encoded as the low-dimensional embeddings of prototype vectors. In addition, Meta-SN presents a novel sampling strategy for constructing meta-tasks, which gives higher sampling probabilities to hard-to-classify samples. Extensive experiments are conducted on six benchmark datasets to show the clear superiority of Meta-SN over other state-of-the-art models. For reproducibility, all the datasets and codes are provided at https://github.com/hccngu/Meta-SN .
Deep Contrastive Multi-view Subspace Clustering

Multi-view subspace clustering has become a hot unsupervised learning task, since it could fuse complementary multi-view information from multiple data effectively. However, most existing methods either fail to incorporate the clustering process into the feature learning process, or cannot integrate multi-view relationships well into the data reconstruction process, which thus damages the final clustering performance. To overcome the above shortcomings, we propose the deep contrastive multi-view subspace clustering method (DCMSC), which is the first attempt to integrate the contrastive learning into deep multi-view subspace clustering. Specifically, DCMSC includes multiple autoencoders for self-expression learning to learn self-representation matrices for multiple views which would be fused into one unified self-representation matrix to effectively utilize the consistency and complementarity of multiple views. Meanwhile, to further exploit multi-view relations, DCMSC also introduces contrastive learning into multi-autoencoder network and Hilbert Schmidt Independence Criterion (HSIC) to better exploit complementarity. Extensive experiments on several real-world multi-view datasets demonstrate the effectiveness of our proposed method by comparing with state-of-the-art multi-view clustering methods.
Learning with Small Data: Subgraph Counting Queries

Deep Learning (DL) has been widely used in many applications, and its success is achieved with large training data. A key issue is how to provide a DL solution when there is no efficient training data to learn initially. In this paper, we explore a meta learning approach for a specific problem, subgraph isomorphism counting, which is a fundamental problem in graph analysis to count the number of a given pattern graph, p, in a data graph, g, that matches p. This problem is NP-hard, and needs large training data to learn by DL in nature. To solve this problem, we design a Gaussian Process (GP) model which combines graph neural network with Bayesian nonparametric, and we train the GP by a meta learning algorithm on a small set of training data. By meta learning, we obtain a generalized meta-model to better encode the information of data and pattern graphs and capture the prior of small tasks. We handle a collection of pairs (g, p), as a task, where some pairs may be associated with the ground-truth, and some pairs are the queries to answer. There are two cases. One is there are some with ground-truth (few-shot), and one is there is none with ground-truth (zero-shot). We provide our solutions for both. We conduct substantial experiments to confirm that our approach is robust to model degeneration on small training data, and our meta model can fast adapt to new queries by few/zero-shot learning.
Category-Highlighting Transformer Network for Question Retrieval

Question retrieval aims to find the semantically equivalent questions from question archives for a user question. Recently, Transformer-based models have significantly advanced the progress of question retrieval, which mainly focus on capturing the content-based semantic relations of two questions. However, they can not well capture the category-based semantic relations of two questions, even question categories are very important to identify the semantic equivalence of two questions. To capture both the content-based and category-based semantic relations, we study the issue of improving Transformer by highlighting and incorporating the category information. To this end, we innovatively propose the Category-Highlighting Transformer Network (CHT). Because questions are not equipped with explicit categories, CHT first uses a category identification unit to construct category-based semantic representations for the question and its embedded words. Second, to "deeply" capture the category-based and content-based semantic relations, we develop the category-highlighting Transformer by improving the self-attention unit with the category-based representations. The cascaded category highlighting Transformers are used for modelling "individual" semantics of a question and "joint" semantics of two questions. Extensive experiments on three public datasets show that the category-highlighting Transformer network outperforms the state-of-the-art solutions.
Speculation and Negation Scope Resolution via Machine Reading Comprehension Formulation with Data Augmentation

As a key sub-task in the field of speculation and negation extraction, Speculation and Negation Scope Resolution (SpNeSR) focuses on extracting speculative and negative texts within sentences, i.e., distinguishing between factual and non-factual information, which means it is an important and fundamental task in Natural Language Processing (NLP) community. Previous work utilized various methods for SpNeSR that are quite domain-specific, and failed to build a unified framework with good generalization. In addition, they were limited by the sizes of datasets and ignored producing more samples for training, since SpNeSR is a data-hungry task. With consideration of the above problems, we not only propose a unified Machine Reading Comprehension (MRC) formulation for SpNeSR, but also design a Data Augmentation (DA) method fit for scopes. Experimental results on several English and Chinese corpora manifest that both MRC and DA mechanism are effective, and our MRC model with DA is superior to several state-of-the-arts.
Improving Event Representation with Supervision from Available Semantic Resources

Learning distributed representations of events is an indispensable but challenging task for event understanding. Existing studies address this problem by either composing the embeddings of event arguments as well as their attributes, or exploiting various relations between events like co-occurrence and discourse relations. In this paper we argue that the knowledge learned from sentence embeddings and word semantic meanings could be leveraged to produce superior event embeddings. Specifically, we utilize both natural language inference datasets for learning sentence embeddings and the knowledge base WordNet for word semantics. We propose a Multi-Level Supervised Contrastive Learning model (MLSCL) for learning event representations. Our model fuses diverse semantic resources at the levels of sentences, events, and words in an end-to-end way. We conduct comprehensive experiments on three similarity tasks and one script prediction task. Experimental results show that MLSCL achieves new state-of-the-art performances on all tasks consistently and higher training efficiency than prior competitive model SWCC.
Broadcast, Trace and Revoke with Optimal Parameters from Polynomial Hardness

A broadcast, trace and revoke system generalizes broadcast encryption as well as traitor tracing. In such a scheme, an encryptor can specify a list $$L \subseteq N$$ of revoked users so that (i) users in L can no longer decrypt ciphertexts, (ii) ciphertext size is independent of L, (iii) a pirate decryption box supports tracing of compromised users. The "holy grail" of this line of work is a construction which resists unbounded collusions, achieves all parameters (including public and secret key) sizes independent of |L| and |N|, and is based on polynomial hardness assumptions. In this work we make the following contributions:
Recognition of Handwritten Assamese Characters

Handwriting recognition has earned a fair amount of attention in the field of pattern recognition. A lot of work has been done so far for the efficient recognition of handwritten characters. So far, various techniques have been put forward for handwritten character recognition. However, not much work has been done yet for the recognition of Assamese handwritten characters because of their complex structure and limited usage. The Assamese language spoken in some of the northeastern parts of India is lagging in this field. This paper aims to make human Assamese handwriting machine-readable. Successful recognition will enable machines to read forms, postal addresses, and signboards written in Assamese. It will also lead to advancements in other areas such as translations of handwritten Assamese scripts to other languages as well as enable a text-to-speech system, thus making the Assamese language more acknowledgeable. This paper attempts to recognize handwritten Assamese characters using Convolution Neural Network (CNN) by collecting our own dataset. We have judged the performance of our system by the performance metrics: Precision, Recall, and F1 score.
Privately Puncturing PRFs from Lattices: Adaptive Security and Collusion Resistant Pseudorandomness

A private puncturable pseudorandom function (PRF) enables one to create a constrained version of a PRF key, which can be used to evaluate the PRF at all but some punctured points. In addition, the constrained key reveals no information about the punctured points and the PRF values on them. Existing constructions of private puncturable PRFs are only proven to be secure against a restricted adversary that must commit to the punctured points before viewing any information. It is an open problem to achieve the more natural adaptive security, where the adversary can make all its choices on-the-fly. In this work, we solve the problem by constructing an adaptively secure private puncturable PRF from standard lattice assumptions. To achieve this goal, we present a new primitive called explainable hash, which allows one to reprogram the hash function on a given input. The new primitive may find further applications in constructing more cryptographic schemes with adaptive security. Besides, our construction has collusion resistant pseudorandomness, which requires that even given multiple constrained keys, no one could learn the values of the PRF at the punctured points. Private puncturable PRFs with collusion resistant pseudorandomness were only known from multilinear maps or indistinguishability obfuscations in previous works, and we provide the first solution from standard lattice assumptions.
SuperPack: Dishonest Majority MPC with Constant Online Communication

In this work we present a novel actively secure dishonest majority MPC protocol, SuperPack, whose efficiency improves as the number of honest parties increases. Concretely, let $$0<\epsilon <1/2$$ and consider an adversary that corrupts $$t<n(1-\epsilon )$$ out of n parties. SuperPack requires $$6/\epsilon $$ field elements of online communication per multiplication gate across all parties, assuming circuit-dependent preprocessing, and $$10/\epsilon $$ assuming circuit-independent preprocessing. In contrast, most of previous works such as SPDZ (Damgård et al., ESORICS 2013) and its derivatives perform the same regardless of whether there is only one honest party, or a constant (non-majority) fraction of honest parties. The only exception is due to Goyal et al. (CRYPTO 2022), which achieves $$58/\epsilon + 96/\epsilon ^2$$ field elements assuming circuit-independent preprocessing. Our work improves this result substantially by a factor of at least 25 in the circuit-independent preprocessing model. Practically, we also compare our work with the best concretely efficient online protocol Turbospeedz (Ben-Efraim et al., ACNS 2019), which achieves $$2(1-\epsilon )n$$ field elements per multiplication gate among all parties. Our online protocol improves over Turbospeedz as n grows, and as $$\epsilon $$ approaches 1/2. For example, if there are $$90\%$$ corruptions ( $$\epsilon =0.1$$ ), with $$n=50$$ our online protocol is $$1.5\times $$ better than Turbospeedz and with $$n=100$$ this factor is $$3\times $$ , but for $$70\%$$ corruptions ( $$\epsilon =0.3$$ ) with $$n=50$$ our online protocol is $$3.5\times $$ better, and for $$n=100$$ this factor is $$7\times $$ . Our circuit-dependent preprocessing can be instantiated from OLE/VOLE. The amount of OLE/VOLE correlations required in our work is a factor of $$\approx \epsilon n/2$$ smaller than these required by Le Mans (Rachuri and Scholl, CRYPTO 2022) leveraged to instantiate the preprocessing of Turbospeedz. Our dishonest majority protocol relies on packed secret-sharing and leverages ideas from the honest majority TurboPack (Escudero et al., CCS 2022) protocol to achieve concrete efficiency for any circuit topology, not only SIMD. We implement both SuperPack and Turbospeedz and verify with experimental results that our approach indeed leads to more competitive runtimes in distributed environments with a moderately large number of parties.
Exploiting Non-full Key Additions: Full-Fledged Automatic Demirci-Selçuk Meet-in-the-Middle Cryptanalysis of SKINNY

The Demirci-Selçuk meet-in-the-middle (DS-MITM) attack is a sophisticated variant of differential attacks. Due to its sophistication, it is hard to efficiently find the best DS-MITM attacks on most ciphers except for AES. Moreover, the current automatic tools only capture the most basic version of DS-MITM attacks, and the critical techniques developed for enhancing the attacks (e.g., differential enumeration and key-dependent-sieve) still rely on manual work. In this paper, we develop a full-fledged automatic framework integrating all known techniques (differential enumeration, key-dependent-sieve, and key bridging, etc.) for the DS-MITM attack that can produce key-recovery attacks directly rather than only search for distinguishers. Moreover, we develop a new technique that is able to exploit partial key additions to generate more linear relations beneficial to the attacks. We apply the framework to the SKINNY family of block ciphers and significantly improved results are obtained. In particular, all known DS-MITM attacks on the respective versions of SKINNY are improved by at least 2 rounds, and the data, memory, or time complexities of some attacks are reduced even compared to previous best attacks penetrating less rounds.