We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks. TacoBot is designed with a user-centered principle and aspires to deliver a collaborative and accessible dialogue experience. Towards that end, it is equipped with accurate language understanding, flexible dialogue management, and engaging response generation. Furthermore, TacoBot is backed by a strong search engine and an automated end-to-end test suite. In bootstrapping the development of TacoBot, we explore a series of data augmentation strategies to train advanced neural language processing models and continuously improve the dialogue experience with collected real conversations. At the end of the semifinals, TacoBot achieved an average rating of 3.55/5.0.

Speaking at the Aspen Security Forum, Gen. John Raymond the head of U.S. Space Command, discussed the main issues in space and said there was a need for a rules-based order and described the present situation as being like the'wild, Wild West.' (Video courtesy: Aspen Security Forum.) Chief of Space Operations for the U.S. Space Force Gen. John'Jay' Raymond stressed the need for international norms when it comes to space operations, while pointing to problems posed by Russia and China. Addressing the Aspen Security Forum on Tuesday, Raymond said China was growing its program at a fast pace, explaining "China has gone from zero to 60 very quickly, and they are clearly our pacing challenge because…they're moving at speed they have the economy to support the development. "They're really doing two things: the first thing they're doing is they're building space capabilities for their own use, so just like we've enjoyed space capabilities that we've been able to integrate, China has built a space program to do the same thing," Raymond said while noting this "provides them advantage and that provides risk to our forces. The other thing that they're doing, they have seen the advantages that space has provided us as: we've integrated space and cyber and multi domain operations, and to be honest they don't like what they see." Raymond further explained that while space operations are hardly something new, the area has exploded in recent years to the point of being far more difficult to manage. "One of the challenges is there are no rules or very few rules," Raymond said. Raymond said that the U.S. is trying to lead the way, and that there have been discussions among other countries and the United Nations. "This is something that we're trying to establish the the the norms, if you will, the rules of the road," he said. One example Raymond discussed was the issue of space debris. He mentioned how Vice President Kamala Harris announced that the U.S. will not conduct destructive, direct-ascent anti-satellite (ASAT) missile testing while calling on other nations to make similar commitments. These tests create long-lasting debris in space that can threaten existing satellites and pose dangers to astronauts. Russia conducted such a test in 2021, and China did the same in 2007. Roger Towberman displays his insignia during a presentation of the United States Space Force flag in the Oval Office of the White House in Washington, D.C., May 15, 2020. Raymond said Russia's test resulted in blowing up a satellite into more than 1,500 pieces, while China's test created 3,000 pieces of debris. Raymond added that the U.S. has been trying to manage these sorts of situations. "We act as the space traffic control for the world.

As the importance of intrusion detection and prevention systems (IDPSs) increases, great costs are incurred to manage the signatures that are generated by malicious communication pattern files. Experts in network security need to classify signatures by importance for an IDPS to work. We propose and evaluate a machine learning signature classification model with a reject option (RO) to reduce the cost of setting up an IDPS. To train the proposed model, it is essential to design features that are effective for signature classification. Experts classify signatures with predefined if-then rules. An if-then rule returns a label of low, medium, high, or unknown importance based on keyword matching of the elements in the signature. Therefore, we first design two types of features, symbolic features (SFs) and keyword features (KFs), which are used in keyword matching for the if-then rules. Next, we design web information and message features (WMFs) to capture the properties of signatures that do not match the if-then rules. The WMFs are extracted as term frequency-inverse document frequency (TF-IDF) features of the message text in the signatures. The features are obtained by web scraping from the referenced external attack identification systems described in the signature. Because failure needs to be minimized in the classification of IDPS signatures, as in the medical field, we consider introducing a RO in our proposed model. The effectiveness of the proposed classification model is evaluated in experiments with two real datasets composed of signatures labeled by experts: a dataset that can be classified with if-then rules and a dataset with elements that do not match an if-then rule. In the experiment, the proposed model is evaluated. In both cases, the combined SFs and WMFs performed better than the combined SFs and KFs. In addition, we also performed feature analysis.

Designing smart home services is a complex task when multiple services with a large number of sensors and actuators are deployed simultaneously. It may rely on knowledge-based or data-driven approaches. The former can use rule-based methods to design services statically, and the latter can use learning methods to discover inhabitants' preferences dynamically. However, neither of these approaches is entirely satisfactory because rules cannot cover all possible situations that may change, and learning methods may make decisions that are sometimes incomprehensible to the inhabitant. In this paper, PBRE (Pedagogic Based Rule Extractor) is proposed to extract rules from learning methods to realize dynamic rule generation for smart home systems. The expected advantage is that both the explainability of rule-based methods and the dynamicity of learning methods are adopted. We compare PBRE with an existing rule extraction method, and the results show better performance of PBRE. We also apply PBRE to extract rules from a smart home service represented by an NRL (Neural Network-based Reinforcement Learning). The results show that PBRE can help the NRL-simulated service to make understandable suggestions to the inhabitant.

Rule set learning has long been studied and has recently been frequently revisited due to the need for interpretable models. Still, existing methods have several shortcomings: 1) most recent methods require a binary feature matrix as input, while learning rules directly from numeric variables is understudied; 2) existing methods impose orders among rules, either explicitly or implicitly, which harms interpretability; and 3) currently no method exists for learning probabilistic rule sets for multi-class target variables (there is only one for probabilistic rule lists). We propose TURS, for Truly Unordered Rule Sets, which addresses these shortcomings. We first formalize the problem of learning truly unordered rule sets. To resolve conflicts caused by overlapping rules, i.e., instances covered by multiple rules, we propose a novel approach that exploits the probabilistic properties of our rule sets. We next develop a two-phase heuristic algorithm that learns rule sets by carefully growing rules. An important innovation is that we use a surrogate score to take the global potential of the rule set into account when learning a local rule. Finally, we empirically demonstrate that, compared to non-probabilistic and (explicitly or implicitly) ordered state-of-the-art methods, our method learns rule sets that not only have better interpretability but also better predictive performance.

Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to predict and fail to generalize on out-of-sample data. While many other approaches have been proposed to address this issue from the computation perspective such as new architectures or training procedures, we believe a method that allows researchers to discover biases, and adjust the data or the models in an earlier stage will be beneficial. Thus, we introduce MRCLens, a toolkit that detects whether biases exist before users train the full model. For the convenience of introducing the toolkit, we also provide a categorization of common biases in MRC.

Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context. Understanding the decisions and intentions of an RL policy offer avenues to incorporate safety into the policy by limiting undesirable actions. We propose the use of a Boolean Decision Rules model to create a post-hoc rule-based summary of an agent's policy. We evaluate our proposed approach using a DQN agent trained on an implementation of a lava gridworld and show that, given a hand-crafted feature representation of this gridworld, simple generalised rules can be created, giving a post-hoc explainable summary of the agent's policy. We discuss possible avenues to introduce safety into a RL agent's policy by using rules generated by this rule-based model as constraints imposed on the agent's policy, as well as discuss how creating simple rule summaries of an agent's policy may help in the debugging process of RL agents.

Most business process automation is still developed using traditional automation technologies such as workflow engines. These systems provide domain specific languages that require both business knowledge and programming skills to effectively use. As such, business users often lack adequate programming skills to fully leverage these code oriented environments. We propose a paradigm for the construction of business automations using natural language. The approach applies a large language model to translate business rules and automations described in natural language, into a domain specific language interpretable by a business rule engine. We compare the performance of various language model configurations, across various target domains, and explore the use of constrained decoding to ensure syntactically correct generation of output.

Protecting private patient data is critical for any healthcare organization. From securing systems from outside hackers to monitoring and controlling internal access, there are a multitude of steps any organization can take to better protect PHI and EMR data. See Also: OnDemand Fireside Chat Zero Tolerance: Controlling The Landscape Where You'll Meet Your Adversaries One of the recent developments in this arena is machine learning driven access monitoring. Unlike traditional, rules-based access monitoring, this new, more adaptive technology is changing how organizations monitor, assess, and control access. Access monitoring is exactly what it sounds like -- monitoring user access to network resources, critical data, and high-risk access points.

Asian security experts have welcomed the victory by Prime Minister Fumio Kishida's ruling coalition in Sunday's House of Councilors election, saying Japan's political stability and leadership are vital for shoring up a rules-based international order being challenged by countries such as China and Russia. Citing Russia's aggression in Ukraine, China's increased assertiveness in the Indo-Pacific and North Korea's nuclear and missile development, the experts either expressed support for or did not oppose Kishida's calls for boosting Japan's defense capabilities and amending the Constitution, including adding a reference to the Self-Defense Forces in the war-renouncing Article 9. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page.