FoodGPT: A Large Language Model in Food Testing Domain with Incremental Pre-training and Knowledge Graph Prompt

Currently, the construction of large language models in specific domains is done by fine-tuning on a base model. Some models also incorporate knowledge bases without the need for pre-training. This is because the base model already contains domain-specific knowledge during the pre-training process. We build a large language model for food testing. Unlike the above approach, a significant amount of data in this domain exists in Scanning format for domain standard documents. In addition, there is a large amount of untrained structured knowledge. Therefore, we introduce an incremental pre-training step to inject this knowledge into a large language model. In this paper, we propose a method for handling structured knowledge and scanned documents in incremental pre-training. To overcome the problem of machine hallucination, we constructe a knowledge graph to serve as an external knowledge base for supporting retrieval in the large language model. It is worth mentioning that this paper is a technical report of our pre-release version, and we will report our specific experimental data in future versions

Towards a cloud‑based automated surveillance system using wireless technologies

Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.Ministerio de Economía y Competitividad TEC2016-77785-PJunta de Andalucía P12-TIC-130

Pattern extraction from the world wide web

The World Wide Web is a source of huge amount of unlabeled information spread across different sources in varied formats. This presents us with both opportunities and challenges in leveraging such large amount of unstructured data to build knowledge bases and to extract relevant information. As part of this thesis, a semi-supervised logistic regression model called “Dual Iterative Pattern Relation Extraction” proposed by Sergey Brin is selected for further investigation. DIPRE presents a technique which exploits the duality between sets of patterns and relations to grow the target relation starting from a small sample. This project built in JAVA using Google AJAX Search API includes designing, implementing and testing DIPRE approach in extracting various relationships from the web

Neuro-Fuzzy Model in Supply Chain Management for Objects State Assessing

This article considers the task of objects state assessing in conditions of uncertainty by considering the supply chain strategy. To solve it, the need to use fuzzy-production knowledge bases and fuzzy inference algorithms as part of fuzzy decision support systems is being updated. As a tool for constructing a knowledge base, a neural-fuzzy model is proposed. The proposed type of fuzzy-production rules and the logic inference algorithm on rules for objects state assessing are described. A structure of a fuzzy neural network, consisting of six layers, each of which implements the corresponding stage of the logic inference algorithm, is proposed. As a result of training a fuzzy neural network, a system of fuzzy-production rules is formed, which make up the knowledge base of the decision support system for objects state assessing. On the basis of the proposed neuro-fuzzy model, a software package has been implemented for automating the processes of forming fuzzy-production rules. The main components of the software package are the knowledge base generation module and the fuzzy inference module. As an approbation of the neuro-fuzzy model, the formation of fuzzy rules for assessing the state of water lines at the cluster pumping stations in reservoir pressure maintenance systems has been carried out. The testing results confirmed the high efficiency of the neural-fuzzy model and the possibility of its practical use for the formation of fuzzy-production rules in various subject areas of human activity

Social insects as a model to study the molecular basis of ageing

One major gap in the current knowledge of the molecular bases of ageing is that most of the work has been done using short-lived model organisms such as fruitflies, nematodes, yeast and mice. Here, we argue that ants and social bee species provide an excellent complementary system to study ageing, and this for two reasons: first, in contrast to model organisms, ant and bee queens are extraordinarily long-lived, and second, there is a tremendous variation in lifespan among the genetically identical queens, workers (non-reproductive females) and males, with queens living up to 500 times longer than males and 10 times longer than workers. We review recent experimental work aimed at testing the role of antioxidant genes within the conceptual framework of the free radical theory of ageing, as well as studies investigating the role of juvenile hormone, vitellogenin and telomeres as mediators of ageing in social insects

Recent Advances on Pseudodynamic Hybrid Simulation of Masonry Structures

Hybrid Simulation has been introduced to simulate the seismic response of civil structures. The hybrid model of the emulated system combines numerical and physical subdomains and its dynamic response to a realistic excitation is simulated using a numerical time-stepping response history analysis. In the current practice, lumped parameters structural topologies such as shear type frames or inverted pendulum characterize the physical subdomain and the design of the testing setup is straightforward. Although hybrid simulation has been extensively exploited for testing concrete and steel structures, in the authors' knowledge, there is still a paucity of scientific publications devoted to masonry applications. This is in contrast to the inherent uncertainty carried by masonry failure mechanisms, which hinders any attempt of implementing predictive numerical models. From this perspective, this paper summarizes our recent research achievements aimed at extending hybrid simulation to distributed parameter specimens, such as masonry walls, using the minimum number of actuators. The great potential of reduction bases in driving the substructuring process has been shown in a previous work and here is enhanced to floating physical subdomains

Inductive Monitoring Systems: A CubeSat Ground-Based Prototype

Inductive Monitoring Systems (IMS) are the newest form of health monitoring available to the aerospace industry. IMS is a program that builds a knowledge base of nominal state vectors from a nominal data set using data mining techniques. The nominal knowledge base is then used to monitor new data vectors for off-nominal conditions within the system. IMS is designed to replace the current health monitoring process, referred to as model-based reasoning, by automating the process of classifying healthy states and anomaly detection. An IMS prototype was designed and implemented in MATLAB. A verification analysis then determined if the IMS program could connect to a CubeSat in a testing environment and could successfully monitor all sensors on board the CubeSat before in-flight use. This program consisted of two main algorithms, one for learning and one for monitoring. The learning algorithm creates the nominal knowledge bases and was developed using three data mining algorithms: the gap statistic method to find the optimal number of clusters, the K-means++ algorithm to initialize the centroids, and the K-means algorithm to partition the data vectors into the appropriate clusters. The monitoring algorithm employed the nearest neighbor searching algorithm to find the closest cluster and compared the new data vector with the closest cluster. The clusters found were used to establish the knowledge bases. Any data vector within the boundaries of the clusters was deemed nominal and any data vector outside the boundaries was deemed off-nominal. The learning and monitoring algorithms were then adapted to handle the data format used on a CubeSat and to monitor the data in real time. The developed algorithms were then integrated into a MATLAB GUI for ease of use. The learning and monitoring algorithms were verified with a 2-dimensional data set to ensure that they performed as expected. The final IMS CubeSat prototype was verified using 56-dimensional emulated data packages. Both verification methods confirmed that the IMS ground- based prototype was able to successfully identify all off-nominal conditions induced into the system