With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

Word and graph embeddings are widely used in deep learning applications. We present a data structure that captures inherent hierarchical properties from an unordered flat embedding space, particularly a sense of direction between pairs of entities. Inspired by the notion of \textit{distributional generality}, our algorithm constructs an arborescence (a directed rooted tree) by inserting nodes in descending order of entity power (e.g., word frequency), pointing each entity to the closest more powerful node as its parent. We evaluate the performance of the resulting tree structures on three tasks: hypernym relation discovery, least-common-ancestor (LCA) discovery among words, and Wikipedia page link recovery. We achieve average 8.98\% and 2.70\% for hypernym and LCA discovery across five languages and 62.76\% accuracy on directed Wiki-page link recovery, with both substantially above baselines. Finally, we investigate the effect of insertion order, the power/similarity trade-off and various power sources to optimize parent selection.

To provide effective and enjoyable human-robot interaction, it is important for social robots to exhibit nonverbal behaviors, such as a handshake or a hug. However, the traditional approach of reproducing pre-coded motions allows users to easily predict the reaction of the robot, giving the impression that the robot is a machine rather than a real agent. Therefore, we propose a neural network architecture based on the Seq2Seq model that learns social behaviors from human-human interactions in an end-to-end manner. We adopted a generative adversarial network to prevent invalid pose sequences from occurring when generating long-term behavior. To verify the proposed method, experiments were performed using the humanoid robot Pepper in a simulated environment. Because it is difficult to determine success or failure in social behavior generation, we propose new metrics to calculate the difference between the generated behavior and the ground-truth behavior. We used these metrics to show how different network architectural choices affect the performance of behavior generation, and we compared the performance of learning multiple behaviors and that of learning a single behavior. We expect that our proposed method can be used not only with home service robots, but also for guide robots, delivery robots, educational robots, and virtual robots, enabling the users to enjoy and effectively interact with the robots.

The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language data. This work proposes an approach to global language data governance that attempts to organize data management amongst stakeholders, values, and rights. Our proposal is informed by prior work on distributed governance that accounts for human values and grounded by an international research collaboration that brings together researchers and practitioners from 60 countries. The framework we present is a multi-party international governance structure focused on language data, and incorporating technical and organizational tools needed to support its work.

This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resulted from active collaboration among researchers across eight research teams - I$^2$R (Singapore), UEF (Finland), VALPT (Italy, Spain), NEC (Japan), THUEE (China), LIA (France), NUS (Singapore), INRIA (France) and TJU (China). The submission was based on the fusion of top performing sub-systems and sub-fusion systems contributed by individual teams. Efforts have been spent on the use of common development and validation sets, submission schedule and milestone, minimizing inconsistency in trial list and score file format across sites.

We consider the task of weighted first-order model counting (WFOMC) used for probabilistic inference in the area of statistical relational learning. Given a formula $\phi$, domain size $n$ and a pair of weight functions, what is the weighted sum of all models of $\phi$ over a domain of size $n$? It was shown that computing WFOMC of any logical sentence with at most two logical variables can be done in time polynomial in $n$. However, it was also shown that the task is $\texttt{#}P_1$-complete once we add the third variable, which inspired the search for extensions of the two-variable fragment that would still permit a running time polynomial in $n$. One of such extension is the two-variable fragment with counting quantifiers. In this paper, we prove that adding a linear order axiom (which forces one of the predicates in $\phi$ to introduce a linear ordering of the domain elements in each model of $\phi$) on top of the counting quantifiers still permits a computation time polynomial in the domain size. We present a new dynamic programming-based algorithm which can compute WFOMC with linear order in time polynomial in $n$, thus proving our primary claim.

The graph represents a network of 2,085 Twitter users whose tweets in the requested range contained "#cloudcomputing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 17 October 2022 at 15:12 UTC. The requested start date was Monday, 17 October 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 10-hour, 26-minute period from Friday, 14 October 2022 at 13:27 UTC to Sunday, 16 October 2022 at 23:53 UTC.

In a paper on'Artificial Intelligence in Nursing' presented jointly by Dr Ramesh M.Sc Phd, HoD Medical Surgical Nursing, St Paul's Hospital Millennium Medical College, Ethiopia, and Dr S. Indira, Dean of Narayana Nursing College, said AI offers three advantages over traditional methods -- the ability to quickly consider large volumes of data in risk prediction, increased intervention specificity (accurately flagging patients most at-risk) and automated adjustments in variable selection and calculation. "AI can help detect which patient features are most important in public health applications, allowing for more focused preventive interventions. "AI may more accurately predict fall risk without manual calculation and provide automatic warning systems," said Prof. Ramesh and Dr Indira. They have also highlighted the role of mobile health technologies (smartphones, smartphone apps, and wearable technologies) to help manage chronic illnesses by receiving and sending data directly between patients and care-providers, creating a comprehensive picture of the dynamic state of a patient's health in their everyday environments. According to the duo, sensor-based technologies, when placed in the home or hospital environment and used in combination, help nurses compose text and multimedia messages (for sharing photos and videos), measure body movement and collect weight, movement, and environmental (temperature, light, sound, air quality) data. In a paper on'Artificial Intelligence in Nursing' presented jointly by Dr Ramesh M.Sc Phd, HoD Medical Surgical Nursing, St Paul's Hospital Millennium Medical College, Ethiopia, and Dr S. Indira, Dean of Narayana Nursing College, said AI offers three advantages over traditional methods -- the ability to quickly consider large volumes of data in risk prediction, increased intervention specificity (accurately flagging patients most at-risk) and automated adjustments in variable selection and calculation. "AI can help detect which patient features are most important in public health applications, allowing for more focused preventive interventions.

We introduce the Block-Recurrent Transformer, which applies a transformer layer in a recurrent fashion along a sequence, and has linear complexity with respect to sequence length. Our recurrent cell operates on blocks of tokens rather than single tokens during training, and leverages parallel computation within a block in order to make efficient use of accelerator hardware. The cell itself is strikingly simple. It is merely a transformer layer: it uses self-attention and cross-attention to efficiently compute a recurrent function over a large set of state vectors and tokens. Our design was inspired in part by LSTM cells, and it uses LSTM-style gates, but it scales the typical LSTM cell up by several orders of magnitude. Our implementation of recurrence has the same cost in both computation time and parameter count as a conventional transformer layer, but offers dramatically improved perplexity in language modeling tasks over very long sequences. Our model out-performs a long-range Transformer XL baseline by a wide margin, while running twice as fast. We demonstrate its effectiveness on PG19 (books), arXiv papers, and GitHub source code. Our code has been released as open source.

Let two sequences of eventualities x (signifying the sequence, x0,x1, x2,...,xn-1) and y (signifying the sequence, y0, y1, y2,..,yn-1) both recur over the same time interval and it is required to determine whether or not a subinterval exists within the said interval which is a common subinterval of the intervals of occurrence of xp and yq. This paper presents two algorithms for solving the problem. the first explores an arbitrary cycle of the double recurrence for the existence of such an interval. its worst case running time is quadratic. The other algorithm is based on the novel notion of gcd-partitions and has a linear worst case running time. If the eventuality sequence pair (W,z) is a gcd-partition for the double recurrence (x, y),then, from a certain property of gcd-partitions, within any cycle of the double recurrence, there exists r and s such that intervals of occurrence of xp and yq are non-disjoint with the interval of co-occurrence of wr and zs. As such, a coincidence between xp and yq occurs within a cycle of the double recurrence if and only if such r and s exist so that the interval of co-occurrence of wr and zs shares a common interval with the common interval of occurrences of xp and yq. The algorithm systematically reduces the number of wr and zs pairs to be explored in the process of finding the existence of the coincidence.