This study performs surface reduction treatments, including hydrogen plasma treatment and rapid thermal anneal (RTA) in hydrogen ambient, to reduce the nitrogen content in the surface layer of the <mml:math display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>TaN</mml:mtext></mml:mrow><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math>TaNx ultrathin film deposited by plasma-enhanced atomic layer deposition (PE-ALD). A four-point bend delamination test and a pull-off tensile test are used to study the interfacial strength of the PE-ALD thin film with copper. According to X-ray photoelectron spectroscopy and Auger electron spectroscopy, a new chemical phase with a very small nitrogen content, possibly <mml:math display="inline"><mml:mrow><mml:mi>β</mml:mi><mml:msub><mml:mrow><mml:mtext>-TaN</mml:mtext></mml:mrow><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math>β-TaNx , is formed on the PE-ALD <mml:math display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>TaN</mml:mtext></mml:mrow><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math>TaNx thin film after the RTA treatment. The increase in the Ta/N atomic ratio in the RTA-treated <mml:math display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>TaN</mml:mtext></mml:mrow><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math>TaNx thin film significantly improves the adhesion of the <mml:math display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>TaN</mml:mtext></mml:mrow><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math>TaNx film with the sputter-deposited Cu layer. However, the hydrogen-plasma-treated <mml:math display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>TaN</mml:mtext></mml:mrow><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math>TaNx thin film shows a slight decrease in nitrogen content but still demonstrates better adhesion with the Cu layer compared with the as-deposited one. While the Cu overlayer on the RTA-treated PE-ALD <mml:math display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>TaN</mml:mtext></mml:mrow><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math>TaNx thin film can sustain the thermal anneal at <mml:math display="inline"><mml:mrow><mml:mn>600</mml:mn><mml:mo>°</mml:mo><mml:mi mathvariant="normal">C</mml:mi></mml:mrow></mml:math>600°C , the one on the as-deposited <mml:math display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>TaN</mml:mtext></mml:mrow><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math>TaNx thin film exhibits voiding even at a temperature as low as <mml:math display="inline"><mml:mrow><mml:mn>400</mml:mn><mml:mo>°</mml:mo><mml:mi mathvariant="normal">C</mml:mi></mml:mrow></mml:math>400°C .

This article proposes a machine learning (ML)-based approach to solve a graph optimization problem, named network intervention with limited degradation (NILD), which aims at adding new edges to augment the graph to minimize the local clustering coefficient (LCC) of a target node. The main application of NILD is to perform network intervention , to improve the mental well-being of individuals. This article proposes a new framework, named network intervention with self-supervision (NISS), which employs reinforcement learning and self-supervised learning (SSL) to effectively solve the problem. We propose two new effective pretext tasks in SSL, Distance-to-target prediction task and LCC increment prediction task to improve the model performance. In addition, we also propose two new embedding approaches, neighborhood embedding (NE) and constraint property embedding (CPE), to capture the structural information of the graph. Extensive experiments on multiple real social networks and synthetic datasets show that our proposed approach significantly outperforms the other state-of-the-art baselines, including ML-based baselines and deterministic algorithms.

With the increasing amount of Internet of Things (IoT) data, there is an increasing need to manage those data efficiently and securely. In this paper, we propose a decentralized and privacy-preserving IoT data sharing and management system based on the Interplanetary File System (IPFS) and the IOTA Tangle. Our system enables data owners to securely upload and manage their data on IPFS. We maintain the privacy of the data through Fuzzy Identity-Based Encryption (FIBE). Our system also includes a tag-based search mechanism, which allows data users to easily find the data they need without knowing specific transaction IDs. Compared with existing solutions, our system offloads data owners from local storage and backup and provides a more efficient and effective data search mechanism.

In this article, we propose to study a novel research problem to boost group performance, that is, social-aware diversity-optimized group extraction (SDGE), which takes into consideration the two important factors: 1) group diversity and 2) social tightness. We prove the NP-hardness of SDGE and propose an effective algorithm, named group shrinking for diversity maximization (GSDM) with a performance guarantee, that is, GSDM is a three-approximation algorithm to the SDGE problem studied in this article. We further propose three effective pruning strategies that are able to boost the efficiency of GSDM but do not deteriorate its performance. We conduct extensive experiments on multiple large-scale real datasets to evaluate the performance of GSDM. The experimental results show that our proposed GSDM outperforms the other baseline approaches significantly, in terms of solution quality and efficiency. Moreover, the experimental results also confirm that our proposed pruning strategies indeed boost the efficiency of the algorithm.

The growing frequency of malicious attacks on Internet of Things (IoT) devices has rendered conventional approaches with static label-dependent risk assessment models obsolete, especially when coping with unknown and continuously evolving threats. To mitigate these challenges, a novel dynamic trust evaluation framework approach is proposed in this work. The proposed framework utilized unsupervised learning and zero-knowledge proofs to assess device risks in complex environments adaptively, with an accuracy rate of 98.96% for normal clustering and 95.39% for anomalies. K-means clustering algorithm is leveraged to distinguish risk patterns with an additional Decision Tree classification algorithm to analyze the distinguishing characteristics of the behaviors of normal and anomalous devices. The architecture is evaluated in a simulated environment based on real device interaction, with various malicious attacks proportions. In addition, Zero Trust Architecture is integrated into this novel framework to ensure no implicit trust exists between devices, which enforces trust assessment before any collaboration or data exchange.