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The separation of data ownership and management rights in cloud storage architectures results in losing control over outsourced data, making it challenging to achieve deterministic deletion and verify-deletion results. This predicament ...

• Propose a three-layer framework for deterministic storage and deletion.
• Design access control method based on CP-ABE and random data blocks.
• Build strategies for data operation-proof generation and verification.
• Prove that the ...

Identifying code smells is a crucial task in software engineering that aims to uncover potential problems and bad practices in source code. Existing learning-based approaches have achieved good results in identifying code smells by learning ...

• A framework for identifying smells by fusing code semantics and metrics.
• Three AST-based code metrics for detecting code smells.
• Replacement of traditional metrics with proposed AST-based metrics.
• Effectiveness of the framework ...

Depression detection on social media aims to identify depressive tendencies within textual posts, providing timely intervention by the early detection of mental health issues. In predominant approaches, the Pre-trained Language Models(PLMs) are ...

• We enhance social media posts with trusted commonsense knowledge to improve the accuracy of depression detection.
• We employ clinical scales and three-way decision theory to screen out the parts from the vast number of user posts that ...

To meet the challenge of incompleteness within Knowledge Graphs, Knowledge Graph Embedding(KGE) has emerged as the fundamental methodology for predicting the missing link(Link Prediction), by mapping entities and relations as low-dimensional vectors in ...

With the rapid development of wireless communication technologies, Internet of Things (IoT) has emerged as one of the most important application scenarios. Due to the high density of IoT devices and the limited spectrum resources, along with the ...

• Epistemic uncertainty reveals the effects of dataset imbalance in machine learning.
• Uncertainty quantification is an effective tool for guiding a resampling strategy.
• Improving the quality of uncertainty quantification metrics ...

Several factors are known to determine the quality of machine learning models, one of which is the dataset quality. One problem related to the quality of a dataset is the imbalance issue. An imbalanced dataset contains significantly more data ...

The foundation of multi-task learning lies in the collaboration and interaction among tasks. However, in numerous real-world scenarios, certain tasks usually necessitate distinct, specialized knowledge. The mixing of these different task-specific ...

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• A proposed expert squad layer captures task-specific knowledge and shared features.
• A backbone network, built on expert squad layer, is proposed for multi-task learning.
• A SquadNet framework, based on innovations, improves multi-...

Nonconvex finite-sum optimization plays a crucial role in signal processing and machine learning, fueling the development of numerous centralized and distributed stochastic algorithms. However, existing stochastic optimization algorithms often ...

This paper proposes a new distributed leader–follower control architecture, termed linear formation control, to realize formation variations. The objective is to navigate a group of agents to reach a specific target formation, which is a linear ...

A subset S of the vertex set of a graph G is an F -isolating set of G if G − N [ S ] does not contain a copy of a member of F as a subgraph, where F is a family of connected graphs and N [ S ] is the closed neighborhood of S. The F -isolation ...

• An appealing and natural generalization of classical domination problems.
• The decision version of { P k }-ISOLATION remains NP-complete on chordal graphs and planar graphs.
• A linear time algorithm to compute a minimum { P k }-...

As an emerging primitive, Registered Functional Encryption (RFE) eliminates the key-escrow issue that threatens numerous works for functional encryption, by replacing the trusted authority with a transparent key curator and allowing each user to ...$\mathrm{}$$\mathcal{}$$\mathcal{}$

A delay, interruption, or failure in the wireless connection has a significant impact on the performance of wirelessly connected medical equipment. Researchers presented the fastest technological innovations and industrial changes to address these ...

As one type of frequently occurring code smells, feature envy negatively affects class cohesion, increases coupling between classes, and thus hampers software maintainability. While progress has been made in feature envy detection, two challenges ...${\mathit{}}_{\mathrm{}}$${\mathit{}}_{\mathrm{}}$${\mathit{}}_{\mathrm{}}{}_{\mathrm{}}$

This study addresses the challenges of extracting and analyzing biomedical information from vast amounts of unstructured text data using Large Language Models (LLMs). With the advent of big data and advancements in AI, traditional methods of ...

The proliferation of Internet-connected devices and the complexity of modern network environments have led to the collection of massive and high-dimensional datasets, resulting in substantial information redundancy and sample imbalance issues. These ...

With the rapid deployment and wide use of mobile services and applications, more and more sensitive user information is being transmitted wirelessly. Due to the broadcast nature of wireless transmissions, they are exposed to all surrounding entities and ...

Speech dereverberation aims to alleviate the negative impact of late reverberant components. The weighted prediction error (WPE) method is a well-established technique known for its superior performance in dereverberation. However, in scenarios ...

• We propose a novel dynamic confidence-based pseudo labeling strategy for SFUDA. By dynamically designating pseudo labels for those high-confidence target samples, the network can be progressively adapted to the target domain while ...

Recently, unsupervised domain adaptation (UDA) has attracted extensive interest in relieving the greedy requirement of vanilla deep learning for labeled data. It seeks for a solution to adapt the knowledge from a well-labeled training dataset (...

This paper explores the role of artificial intelligence (AI) and big data (BD) technologies in social impact enterprises in China, particularly focusing on their social activities. It demonstrates that these technologies are instrumental in building ...

Encoder stealing attacks pose a new challenge for ensuring the protection of security and safety of those models. Malicious users aim to replicate the target encoder’s functionality by training a stolen encoder with features returned by querying ...