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Electromagnetic Interference (EMI) failure is a common occurrence in electronic devices. Failing to comply with FCC/CE requirements set by government agencies delays the product time to market. Besides following proper design guidelines of layout, grounding, shielding, filtering, etc., using simulation to predict EMI failures during early design stage will greatly save time and cost. In this paper, EMI

In this paper, a near field scanning based method is utilized to characterize wireless coexistence issues in design of a practical electronic device. This device supports multiple wireless communication radios. Based on near field, the radiation at the intermodulation frequency when two different radios both operate are evaluated. Reduction of scanned near field is proved to be an effective method to predict

More-autonomous machine learning systems will make Alexa more self-aware, self-learning, and self-service.

Semantic segmentation is a challenging problem due to difficulties in modeling context in complex scenes and class confusions along boundaries. Most literature either focuses on context modeling or boundary refinement, which is less generalizable in open-world scenarios. In this work, we advocate a unified framework (UN-EPT) to segment objects by considering both context information and boundary artifacts

Adversarial attack on question answering systems over tabular data (TableQA) can help evaluate to what extent TableQA systems can understand natural language questions and reason with tables. However, generating natural language adversarial questions is difficult, because even a single character swap could lead to huge semantic difference in human perception. In this paper, we propose SAGE (Semantically

The authors enhance pretrained language models with Securities and Exchange Commission filings data to create better language representations for features used in a predictive model. Specifically, they train RoBERTa class models with additional financial regulatory text, which they denote as a class of RoBERTa-Fin models. Using different datasets, the authors assess whether there is material improvement

The Principle of Insufficient Reason (PIR) assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other. The Maximum Entropy Principle (MaxEnt) generalizes PIR to the case where statistical information like expectations are given. It is known that both principles result in paradoxical probability updates for joint distributions of cause

Pre-trained language models (PLMs) such as BERT and GPT learn general text representations and encode extensive world knowledge; thus, they can efficiently and accurately adapt to various downstream tasks. In this work, we propose to leverage these powerful PLMs as recommender systems and use prompts to reformulate the session-based recommendation task to a multi-token cloze task. We evaluate the proposed

In this article, we explain why an interventional view of recommendation provides a rigorous framework for thinking about recommender systems—enabling new insights both at a technical level for evaluation and learning, as well as at a conceptual level when we reason about the future of recommender systems. In some respects, the view of recommender systems as autonomous systems that act through their recommendations

Time series data are often corrupted by outliers or other kinds of anomalies. Identifying the anomalous points can be a goal on its own (anomaly detection), or a means to improving performance of other time series tasks (e.g. forecasting). Recent deep-learning-based approaches to anomaly detection and forecasting commonly assume that the proportion of anomalies in the training data is small enough to ignore

Open-book question answering is a subset of question answering (QA) tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic. This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). These questions have a yes–no–none

The open domain-dialogue system Alquist has a goal to conduct a coherent and engaging conversation that can be considered as one of the benchmarks of social intelligence. The fourth version of the system, developed within the Alexa Prize Socialbot Grand Challenge 4, brings two main innovations. The first addresses coherence, and the second addresses the engagingness of the conversation. For innovations

We present a chatbot implementing a novel dialogue management approach based on logical inference. Instead of framing conversation a sequence of response generation tasks, we model conversation as a collaborative inference process in which speakers share information to synthesize new knowledge in real time. Our chatbot pipeline accomplishes this modelling in three broad stages. The first stage translates

In this paper, we present the second iteration of Chirpy Cardinal, an open-domain dialogue agent developed for the Alexa Prize SGC4 competition. Building on the success of the SGC3 Chirpy, we focus on improving conversational flexibility, initiative, and coherence. We introduce a variety of methods for controllable neural generation, ranging from prefix-based neural decoding over a symbolic scaffolding,

In this paper, we present our Alexa Prize Grand Challenge 4 socialbot: Proto. Leveraging diverse sources of world knowledge, and powered by a suite of neural and rule-based natural language understanding modules, state-of-the-art neural generators, novel state-based deterministic generators, an ensemble of neural re- rankers, a robust post-processing algorithm, and an efficient overall conversation strategy

We present Viola, an open-domain dialogue system for spoken conversation that uses a topic-agnostic dialogue manager based on a simple generate-and-rank ap- proach. Leveraging recent advances of generative dialogue systems powered by large language models, Viola fetches a batch of response candidates from various neural dialogue models trained with different datasets and knowledge-grounding inputs. Additional

We report on the design and development of the CASPR system, a socialbot designed to compete in the Amazon Alexa Socialbot Challenge 4. CASPR’s distin- guishing characteristic is that it will use automated commonsense reasoning to truly “understand” dialogs, allowing it to converse like a human. Three main require- ments of a socialbot are that it should be able to “understand” users’ utterances, possess

We are completing the 4th year of the Alexa Prize Socialbot Grand Challenge that aims to advance human-computer interaction through conversations. 9 university teams participated in this year’s challenge to create socialbots that can converse coherently and engagingly with humans on a range of current events and popular topics such as entertainment, sports, politics, technology, and fashion. The supported