Despite years of in-depth study there is much still to learn about a spider's web, from the intricate patterns to the tensile strength and optoelectronic architectures. Webs are highly-complex structures, as with spider webs actively springing towards prey as the result of electrically-conductive glue spread across their surface. Webs also contain multiple silk types, with viscid silk (stretchy, wet and sticky) and dragline silk (stiff and dry) being responsible for the strength of the web. In a newly reported research topic, scientists from Johns Hopkins University have discovered how spiders build webs. This has been revealed through a combination of night vision and artificial intelligence.

Artificial Intelligence is a recent notion that we've all heard about and may even be familiar with. Because you choose to read this post, you will undoubtedly gain from the trading features of AI that we have discussed ahead. It is essential to understand how they have aided profitable trading in today's world. The study and engineering of developing intelligent robots in their most basic form are known as artificial intelligence (AI). It takes into consideration intelligent computer programs that can calculate, reason, learn from experience, adapt to new conditions, and handle complicated issues, to name a few examples.

Utility-driven mining is an important task in data science and has many applications in real life. High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining. HUSPM aims to discover all sequential patterns with high utility. However, the existing algorithms of HUSPM can not provide an accurate probability to deal with some scenarios for prediction or recommendation. High-utility sequential rule mining (HUSRM) was proposed to discover all sequential rules with high utility and high confidence. There is only one algorithm proposed for HUSRM, which is not enough efficient. In this paper, we propose a faster algorithm, called US-Rule, to efficiently mine high-utility sequential rules. It utilizes rule estimated utility co-occurrence pruning strategy (REUCP) to avoid meaningless computation. To improve the efficiency on dense and long sequence datasets, four tighter upper bounds (LEEU, REEU, LERSU, RERSU) and their corresponding pruning strategies (LEEUP, REEUP, LERSUP, RERSUP) are proposed. Besides, US-Rule proposes rule estimated utility recomputing pruning strategy (REURP) to deal with sparse datasets. At last, a large number of experiments on different datasets compared to the state-of-the-art algorithm demonstrate that US-Rule can achieve better performance in terms of execution time, memory consumption and scalability.

Artificial Intelligence is the behaviour or rules followed by or created by machines to imitate human or animal intelligence. There are several scenarios where one might use artificial intelligence for problem solving or various tasks. Artificial intelligence can be achieved through machine learning. Machine learning is the process of achieving artificial intelligence in a computer system either by supervision or learning itself. The 2 types of artificial intelligence are a rule-based model that simply follows instructions given to it and a machine learning model that trains on useful data first before predicting future results or solving problems.

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

In recent years, there has been significant work on increasing both interpretability and debuggability of a Deep Neural Network (DNN) by extracting a rule-based model that approximates its decision boundary. Nevertheless, current DNN rule extraction methods that consider a DNN's latent space when extracting rules, known as decompositional algorithms, are either restricted to single-layer DNNs or intractable as the size of the DNN or data grows. In this paper, we address these limitations by introducing ECLAIRE, a novel polynomial-time rule extraction algorithm capable of scaling to both large DNN architectures and large training datasets. We evaluate ECLAIRE on a wide variety of tasks, ranging from breast cancer prognosis to particle detection, and show that it consistently extracts more accurate and comprehensible rule sets than the current state-of-the-art methods while using orders of magnitude less computational resources.

Community clinics say the easing of restrictions on telehealth during the pandemic has made it possible for health workers to connect with hard-to-reach patients via a phone call -- people who are poor, elderly or live in remote areas, and don't have access to a computer or cell phone with video capability. Community clinics say the easing of restrictions on telehealth during the pandemic has made it possible for health workers to connect with hard-to-reach patients via a phone call -- people who are poor, elderly or live in remote areas, and don't have access to a computer or cell phone with video capability. Caswell County, where William Crumpton works, runs along the northern edge of North Carolina and is a rural landscape of mostly former tobacco farms and the occasional fast-food restaurant. "There are wide areas where cell phone signals are just nonexistent," Crumpton says. "Things like satellite radio are even a challenge."

Symbolic reasoning, rule-based symbol manipulation, is a hallmark of human intelligence. However, rule-based systems have had limited success competing with learning-based systems outside formalized domains such as automated theorem proving. We hypothesize that this is due to the manual construction of rules in past attempts. In this work, we ask how we can build a rule-based system that can reason with natural language input but without the manual construction of rules. We propose MetaQNL, a "Quasi-Natural" language that can express both formal logic and natural language sentences, and MetaInduce, a learning algorithm that induces MetaQNL rules from training data consisting of questions and answers, with or without intermediate reasoning steps. Our approach achieves state-of-the-art accuracy on multiple reasoning benchmarks; it learns compact models with much less data and produces not only answers but also checkable proofs. Further, experiments on a real-world morphological analysis benchmark show that it is possible for our method to handle noise and ambiguity. Code will be released at https://github.com/princeton-vl/MetaQNL.

The North Carolina Agriculture and Technical State University (NC A&T) in collaboration with Georgia Tech Research Institute (GTRI) has developed methodologies for creating simulation-based technology tools that are capable of inferring the perceptions and behavioral states of autonomous systems. These methodologies have the potential to provide the Test and Evaluation (T&E) community at the Department of Defense (DoD) with a greater insight into the internal processes of these systems. The methodologies use only external observations and do not require complete knowledge of the internal processing of and/or any modifications to the system under test. This paper presents an example of one such simulation-based technology tool, named as the Data-Driven Intelligent Prediction Tool (DIPT). DIPT was developed for testing a multi-platform Unmanned Aerial Vehicle (UAV) system capable of conducting collaborative search missions. DIPT's Graphical User Interface (GUI) enables the testers to view the aircraft's current operating state, predicts its current target-detection status, and provides reasoning for exhibiting a particular behavior along with an explanation of assigning a particular task to it.

Cybersecurity has been a concern for quite a while now. In the latest years, cyberattacks have been increasing in size and complexity, fueled by significant advances in technology. Nowadays, there is an unavoidable necessity of protecting systems and data crucial for business continuity. Hence, many intrusion detection systems have been created in an attempt to mitigate these threats and contribute to a timelier detection. This work proposes an interpretable and explainable hybrid intrusion detection system, which makes use of artificial intelligence methods to achieve better and more long-lasting security. The system combines experts' written rules and dynamic knowledge continuously generated by a decision tree algorithm as new shreds of evidence emerge from network activity.