Efficient Authenticated Encryption Schemes with Public Verifiability

An authenticated encryption scheme allows messages to be encrypted and authenticated simultaneously. In 2003, Ma and Chen proposed such a scheme with public verifiability. That is, in their scheme the receiver can efficiently prove to a third party that a message is indeed originated from a specific sender. In this paper, we first identify two security weaknesses in the Ma-Chen authenticated encryption scheme. Then, based on the Schnorr signature, we proposed an efficient and secure improved scheme such that all the desired security requirements are satisfied.Comment: Early version appears in the Proc. of The 60th IEEE Vehicular Technology Conference (VTC 2004-Fall) - Wireless Technologies for Global Security. IEEE, 200

Preparation of Cu2ZnSnS/Se4 Thin Films from Oxide Precursors and its Prospect for Other Cu2MSnS4 Thin Films

In this chapter, the preparation of Cu2ZnSnSe4 (CZTSe) and Cu2ZnSnS4 (CZTS) thin films from oxide precursors was described. Such an oxides‐based route is a low cost, facile way for the kesteries thin films. The rationality of applying oxides method into CZTSe and CZTS thin films was also clarified, including the reactive thermodynamics and annealing process. Finally, this oxide‐based approach is also expected for the preparation of the other Cu2MSnS4 (M= Co2+, Fe2+, Ni2+, Mn2+) thin films

Automatic Modeling for Modular Reconfigurable Robotic Systems: Theory and Practice

A modular reconfigurable robot consists of a collection of individual link and joint components that can be assembled into a number of different robot ge-ometries. Compared to a conventional industrial robot with fixed geometry, such a system can provide flexibility to the user to cope with a wide spectru

TeGit: Generating High-Quality Instruction-Tuning Data with Text-Grounded Task Design

High-quality instruction-tuning data is critical to improving LLM capabilities. Existing data collection methods are limited by unrealistic manual labeling costs or by the hallucination of relying solely on LLM generation. To address the problems, this paper presents a scalable method to automatically collect high-quality instructional adaptation data by training language models to automatically design tasks based on human-written texts. Intuitively, human-written text helps to help the model attenuate illusions during the generation of tasks. Unlike instruction back-translation-based methods that directly take the given text as a response, we require the model to generate the \textit{instruction}, \textit{input}, and \textit{output} simultaneously to filter the noise. The results of the automated and manual evaluation experiments demonstrate the quality of our dataset.Comment: Work in progres

The contribution of ultracompact dark matter minihalos to the isotropic radio background

The ultracompact minihalos could be formed during the earlier epoch of the universe. The dark matter annihilation within them is very strong due to the steep density profile, $\rho \sim r^{-2.25}$. The high energy electrons and positrons from the dark matter annihilation can inverse Compton scatter (ICS) with the background photons, such as CMB photons, to acquire higher energy. On the other hand, the synchrotron radiation can also be produced when they meet the magnetic field. In this paper, we study the signals from the UCMHs due to the dark matter annihilation for the radio, X-ray and $\gamma$-ray band. We found that for the radio emission the UCMHs can provide one kind of source for the radio excess observed by ARCADE 2. But the X-ray signals due to the ICS effect or the $\gamma$-ray signals mainly due to the prompt emission from dark matter would exceed the present observations, such as Fermi, COMPTEL and CHANDRA. We found that the strongest limits on the fraction of UCMHs come from the X-ray observations and the constraints from the radio data are the weakest.Comment: 6 pages, 8 figures, Comments Welcome! Some Refs. are added, some presentation have been corrected. The conclusions remain unchanged. One important reference has been corrected. Some presentations are changed and added according to the referee's comments. Accepted for publication in PR

Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams

Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, while the latter makes a full review of instances from past tasks impractical for the model, resulting in forgetting of learned SQL structures and database schemas. To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks. The improved solution SFNet takes advantage of the intrinsic connection between CL and SSL. It uses in-memory past information to help current SSL, while adding high-quality pseudo instances in memory to improve future replay. The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.Comment: Accepted by AAAI-202

Information filtering based on corrected redundancy-eliminating mass diffusion

Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects’ attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets— Movilens, Netflix and Amazon—show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices

Quantifying strange property of attractors in quasiperiodically forced systems

This work is supported by the National Natural Science Foundation of China (Nos. 11832009, 12002300, 12072291 and 12362002), and the Natural Science Foundation of Hebei Province, China (Grant No. A2021203013).Peer reviewedPostprin