Research on the Architecture Model of Volatile Data Forensics

AbstractThis paper proposed a new architecture model of volatile data forensic. The model applied to all the volatile data sources is a general model. It can rebuild the evidence data fragment to chains of evidence which contains the behavior characteristics, so as to assist investigators to do case analysis. With the accumulated experience, the model can help judicial officers to intelligently analyze the same type of computer crimes, and based on currently available information to predict the impending crimes

Comparative transcriptome analysis and simple sequence repeat marker development for two closely related Isodon species used as ‘Xihuangcao’ herbs

Purpose: To facilitate the molecular identification of original plants, resolve taxonomic problems and identify standards for ‘Xihuangcao’-based products on the market.Methods: A transcriptomic analysis of two closely related species, i.e., Isodon serra (Maxim.) (IS) and I. lophanthoides (Buch.-Ham. ex D. Don) Hara, was conducted by using the Illumina HiSeq 2500 platform, and expressed sequence tag-derived simple sequence repeat (EST-SSR) markers were developed based on these transcriptomes.Results: In total, 149,650 and 103,221 contigs were obtained, with N50 values of 1,400 and 1,516, from the IS and I. lophanthoides RNA-Seq datasets, respectively. These contigs were clustered into 107,777 and 68,220 unigenes, which were functionally annotated to identify the genes involved in therapeutic components. In total, 14,138 and 11,756 EST-SSR motifs were identified, and of these motifs, 7,453 and 6,428 were used to design primers for IS and I. lophanthoides, respectively. After PCR verification and fluorescence-based genotyping, 24 SSR markers with bright bands, high polymorphism, and single amplification were obtained and used to identify closely related Isodon species/varieties.Conclusion: These data could help herbal scientists identify high-quality herbal plants and provide a reference for genetic improvement and population genetic and phylogenetic studies investigating ‘Xihuangcao’ herbs.Keywords: Xihuangcao, Transcriptome, EST-SSR, Molecular marker

Which Channel to Ask My Question? Personalized Customer Service Request Stream Routing using Deep Reinforcement Learning

Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers' questions, for example, chatbot and hotline. On one hand, each channel has limited capacity to respond to customers' requests, on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules, which merely considers tradeoffs between resources and customers' satisfaction. To achieve the optimal tradeoff between resources and customers' satisfaction, we propose a new framework based on deep reinforcement learning, which directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning based routing method-double dueling deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning based framework is superior to the existing production system. Moreover, we also show our proposed PER-DoDDQN is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the trade-off where both channel resources and customers' satisfaction are optimal.Comment: 13 pages, 7 figure

Electrochemical Reducation of TiO2/Al2O3/C to Ti3AlC2 and Its Derived Two-Dimensional (2D) Carbides

Ti3AlC2 has been directly synthesized from TiO2/Al2O3/C mixture precursors (3TiO2/0.5Al2O3/1.5C and 2TiO2/0.5Al2O3/C) by a molten salt electrolysis process at 900?C and 3.2 V in molten CaCl2. The influence of initial carbon content on the electrosynthesized products has been investigated. The result shows that the main phase of the electrosynthesized products changes from Ti3AlC to Ti2AlC and then to Ti3AlC2 with the increasing carbon content, and the electrosynthesized Ti3AlC2 is carbon deficient. The morphology observation shows that the electrosynthesized Ti3AlC2 particles possess smooth surfaces and dense flake-like microstructure. The reaction mechanism of the electroreduction of TiO2/Al2O3/C mixture precursor has been discussed based on the time- and position-dependent phase constitution analysis. In addition, two-dimensional (2D) Ti3AlC2-derived carbides, i.e., Ti3C2Tx and TiCx have been successfully prepared from the electrosynthesized Ti3AlC2 by a chemical etching process and an electrochemical etching process, respectively. Both derived carbides exhibit the similar layered structure, in which single layer carbides are composed of plentiful nanometer carbides. It is suggested that the molten salt electrolysis process has a great potential to be used for the facile synthesis of Mn+1AXn phases (such as Ti3AlC2) from their oxides precursors, and the synthesized Mn+1AXn phases can be further converted into 2D carbidesauthorsversionPeer reviewe

One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems

The purpose of sequential recommendation is to utilize the interaction history of a user and predict the next item that the user is most likely to interact with. While data sparsity and cold start are two challenges that most recommender systems are still facing, many efforts are devoted to utilizing data from other domains, called cross-domain methods. However, general cross-domain methods explore the relationship between two domains by designing complex model architecture, making it difficult to scale to multiple domains and utilize more data. Moreover, existing recommendation systems use IDs to represent item, which carry less transferable signals in cross-domain scenarios, and user cross-domain behaviors are also sparse, making it challenging to learn item relationship from different domains. These problems hinder the application of multi-domain methods to sequential recommendation. Recently, large language models (LLMs) exhibit outstanding performance in world knowledge learning from text corpora and general-purpose question answering. Inspired by these successes, we propose a simple but effective framework for domain-agnostic recommendation by exploiting the pre-trained LLMs (namely LLM-Rec). We mix the user's behavior across different domains, and then concatenate the title information of these items into a sentence and model the user's behaviors with a pre-trained language model. We expect that by mixing the user's behaviors across different domains, we can exploit the common knowledge encoded in the pre-trained language model to alleviate the problems of data sparsity and cold start problems. Furthermore, we are curious about whether the latest technical advances in nature language processing (NLP) can transfer to the recommendation scenarios.Comment: 10 pages, 7 figures, 6 table