There are several approaches which are used by Java Persistence API [12], [13], [14], Serialization mechanism [13] Multi-agent systems are often limited in terms of persistence, DBMS [14] and JADE Persistence Services [15]. This issue is more prevalent for applications in persistency framework did improve the flexibility and stability which agent states changes frequently. This makes the existing of an agent-based system however it increases the complexity methods less usable as they increase the agent's complexity of agents whose state changes in real-time. In the case when and are less scalable. This research study has presented a a certain object of agent is persisted in the database using novel in-memory agent persistence framework. Two prototypes composite keys, there is an increase in complexity to persist have been implemented, one using the proposed solution and and find the object. Increase in artifact size, framework complexity, the other using an established agent persistency environment. There is also a chance of Virtual Machine instance similar level of persistency. These findings will help future failure which will further increase the recovery time of the real-time multiagent systems to become scalable and persistent agent placed on a single instance due to VM churn time. in a dynamic cloud environment. However, when the data persistence is for a longer period or the state of the object frequently updates, the II. 2. I Moreover, when there is a change in many areas including aircraft maintenance, electronic book any data structure of the agent, the serialized object cannot be buying, network security, military logistic planning and maintaining deserialized even when you know the session id.

We present CoDEx, a set of knowledge graph Completion Datasets Extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. To characterize CoDEx, we contribute thorough empirical analyses and benchmarking experiments. First, we analyze each CoDEx dataset in terms of logical relation patterns. Next, we report baseline link prediction and triple classification results on CoDEx for five extensively tuned embedding models. Finally, we differentiate CoDEx from a popular link prediction benchmark by showing that CoDEx covers more diverse and interpretable content, and contains fewer relation patterns that can be covered by trivial frequency-based rules. Data, code, and pretrained models are available at https://github.com/tsafavi/codex.

Reasoning with declarative knowledge (RDK) and sequential decision-making (SDM) are two key research areas in artificial intelligence. RDK methods reason with declarative domain knowledge, including commonsense knowledge, that is either provided a priori or acquired over time, while SDM methods (probabilistic planning and reinforcement learning) seek to compute action policies that maximize the expected cumulative utility over a time horizon; both classes of methods reason in the presence of uncertainty. Despite the rich literature in these two areas, researchers have not fully explored their complementary strengths. In this paper, we survey algorithms that leverage RDK methods while making sequential decisions under uncertainty. We discuss significant developments, open problems, and directions for future work.

Competitions play an invaluable role in the field of forecasting, as exemplified through the recent M4 competition. The competition received attention from both academics and practitioners and sparked discussions around the representativeness of the data for business forecasting. Several competitions featuring real-life business forecasting tasks on the Kaggle platform has, however, been largely ignored by the academic community. We believe the learnings from these competitions have much to offer to the forecasting community and provide a review of the results from six Kaggle competitions. We find that most of the Kaggle datasets are characterized by higher intermittence and entropy than the M-competitions and that global ensemble models tend to outperform local single models. Furthermore, we find the strong performance of gradient boosted decision trees, increasing success of neural networks for forecasting, and a variety of techniques for adapting machine learning models to the forecasting task.

Estimating time-varying graphical models are of paramount importance in various social, financial, biological, and engineering systems, since the evolution of such networks can be utilized for example to spot trends, detect anomalies, predict vulnerability, and evaluate the impact of interventions. Existing methods require extensive tuning of parameters that control the graph sparsity and temporal smoothness. Furthermore, these methods are computationally burdensome with time complexity O(NP^3) for P variables and N time points. As a remedy, we propose a low-complexity tuning-free Bayesian approach, named BADGE. Specifically, we impose temporally-dependent spike-and-slab priors on the graphs such that they are sparse and varying smoothly across time. A variational inference algorithm is then derived to learn the graph structures from the data automatically. Owning to the pseudo-likelihood and the mean-field approximation, the time complexity of BADGE is only O(NP^2). Additionally, by identifying the frequency-domain resemblance to the time-varying graphical models, we show that BADGE can be extended to learning frequency-varying inverse spectral density matrices, and yields graphical models for multivariate stationary time series. Numerical results on both synthetic and real data show that that BADGE can better recover the underlying true graphs, while being more efficient than the existing methods, especially for high-dimensional cases.

Nonnegative Matrix Factorization (NMF) algorithms are fundamental tools in learning low-dimensional features from vector-valued data, Nonnegative Tensor Factorization (NTF) algorithms serve a similar role for dictionary learning problems for multi-modal data. Also, there is often a critical interest in \textit{online} versions of such factorization algorithms to learn progressively from minibatches, without requiring the full data as in conventional algorithms. However, the current theory of Online NTF algorithms is quite nascent, especially compared to the comprehensive literature on online NMF algorithms. In this work, we introduce a novel online NTF algorithm that learns a CP basis from a given stream of tensor-valued data under general constraints. In particular, using nonnegativity constraints, the learned CP modes also give localized dictionary atoms that respect the tensor structure in multi-model data. On the application side, we demonstrate the utility of our algorithm on a diverse set of examples from image, video, and time series data, illustrating how one may learn qualitatively different CP-dictionaries by not needing to reshape tensor data before the learning process. On the theoretical side, we prove that our algorithm converges to the set of stationary points of the objective function under the hypothesis that the sequence of data tensors have functional Markovian dependence. This assumption covers a wide range of application contexts including data streams generated by independent or MCMC sampling.

Most of the two-dimensional (2D) hydraulic/hydrodynamic models are still computationally too demanding for real-time applications. In this paper, an innovative modelling approach based on a deep convolutional neural network (CNN) method is presented for rapid prediction of fluvial flood inundation. The CNN model is trained using outputs from a 2D hydraulic model (i.e. LISFLOOD-FP) to predict water depths. The pre-trained model is then applied to simulate the January 2005 and December 2015 floods in Carlisle, UK. The CNN predictions are compared favourably with the outputs produced by LISFLOOD-FP. The performance of the CNN model is further confirmed by benchmarking against a support vector regression (SVR) method. The results show that the CNN model outperforms SVR by a large margin. The CNN model is highly accurate in capturing flooded cells as indicated by several quantitative assessment matrices. The estimated error for reproducing maximum flood depth is 0 ~ 0.2 meters for the 2005 event and 0 ~ 0.5 meters for the 2015 event at over 99% of the cells covering the computational domain. The proposed CNN method offers great potential for real-time flood modelling/forecasting considering its simplicity, superior performance and computational efficiency.

South African organisations yesterday committed to being responsible in their use of artificial intelligence (AI) technologies. Stakeholders yesterday participated in a virtual event – AI Dialogue South Africa – which culminated in the signing of the expression of interest (EOI) that advocates for responsible AI. The AI Dialogue South Africa is spearheaded by Convergence Partners, Accenture, University of Johannesburg, Digital Council Africa and Sun & Shield Technologies. In a statement, the organisations say much like a microcosm of our socio-economic context, the AI landscape in SA is uneven and burdened with regulatory challenges. They add that if not addressed, these challenges could give more power to those who already control AI systems, evoking concerns about power dynamics and how the role of humans will be redefined.

One of these textual information is the customer comments or reviews. People usually prefer to read the reviews before buying or using a service to make the right decision. This behavior is also common before the existence of the Internet. From this amount of available data, researches attempt to handle and use these data to have a specific and useful knowledge. Sentiment analysis (SA) is the process of determining the opinion or feeling of a piece of text. Sentiment means feelings, attitudes, emotions and opinions. The applications of sentiment analysis are numerous such as politics or political science, law, e-commerce, sociology and psychology. In e-commerce, the sentiment analysis is super useful for gaining insight into customer opinions; once they understand how the customer feels after analyzing their comments or reviews, they can identify what they like and dislike and build things like recommendation systems, or enhance the product or the service.

Although NumNet achieves superior performance than Numerical reasoning over texts, such as addition, other numerically-aware models (Hu et al., 2019a; Andor subtraction, sorting and counting, is a et al., 2019; Geva et al., 2020; Chen et al., 2020), we challenging machine reading comprehension argue that NumNet is insufficient for sophisticated numerical task, since it requires both natural language understanding reasoning, since it lacks two critical ingredients and arithmetic computation. To for numerical reasoning: address this challenge, we propose a heterogeneous 1. Number Type and Entity Mention. The number graph representation for the context of comparison graph in NumNet is not able to identify the passage and question needed for such reasoning, different number types, and lacks the information of and design a question directed graph entities mentioned in the document that connect the attention network to drive multi-step numerical number nodes.