Digital phenotyping is the "moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices," according to Onnela and Rauch. Digital phenotyping research has historically contained many entry barriers due to high costs and complexity. However, the growing popularity of personal devices such as mobile phones has enabled researchers to collect participant data with more convenience and lower costs than ever before. This paper presents the Intelligent Phenotype Analysis Suite (IPAS), a new AI-powered tool for streamlined phenotype investigation. This tool provides researchers the ability to generate descriptions, predictions, and visualizations of their digital phenotyping data through a simple, novel chat-bot interface. IPAS combines an array of data science techniques with natural language processing capabilities of large language models to accelerate the data analysis process for researchers. IPAS extracts raw Beiwe data, an intuitive data collection platform which only requires participants to install an application on their mobile phone and permit data collection. Furthermore, we evaluate the accuracy of IPAS by using key LLM performance metrics: Precision, Recall, and F1 Score. While testing, IPAS sometimes struggled to generate code. However, in all test prompts, IPAS correctly identified the pre-written function needed to perform the requested task. Altogether, IPAS improves prior methods by enabling researchers of all levels of experience to analyze digital phenotyping data using natural language queries.

Domain-specific knowledge is necessary for critical analysis and decision-making in any scientific field. As a result, it is important that we have mechanisms for collecting and applying knowledge contributed by the larger scientific community. The current paradigm involves collecting knowledge in human-readable scientific papers across various scientific journals. Extracting useful information from these papers is a labor-intensive task and the growing population of papers makes it difficult to consider older works. The implementation of a knowledge network would allow for the automation of this process, but there is no existing pipeline for the reorganization of data collected through Ecological Momentary Assessment (EMA) into knowledge graphs. In this paper, we present EMA-KN, an automatically generated knowledge graph built using the AI-KG architecture. This architecture features state-of-the-art extraction by employing the DyGIE++ and StanfordCoreNLP tools. We test our pipeline using a dataset of 74 EMA-related papers and compare the output to that of AI-KG using a dataset of 74 CS-related papers to capture the success of knowledge graph construction. Further, we evaluate knowledge graph embedding using different metrics. Results show that our pipeline has a slightly lower performance rate than AI-KG, sacrificing triple quality for domain plug-ability. In the future, we seek to improve the system to match the performance of dedicated domain-specific solutions.

The multiple longest common subsequence (MLCS) problem, related to the identification of sequence similarity, is an important problem in many fields. As an NP-hard problem, its exact algorithms have difficulty in handling large-scale data and time- and space-efficient algorithms are required in real-world applications. To deal with time constraints, anytime algorithms have been proposed to generate good solutions with a reasonable time. However, there exists little work on space-efficient MLCS algorithms. In this paper, we formulate the MLCS problem into a graph search problem and present two space-efficient anytime MLCS algorithms, SA-MLCS and SLA-MLCS. SA-MLCS uses an iterative beam widening search strategy to reduce space usage during the iterative process of finding better solutions. Based on SA-MLCS, SLA-MLCS, a space-bounded algorithm, is developed to avoid space usage from exceeding available memory. SLA-MLCS uses a replacing strategy when SA-MLCS reaches a given space bound. Experimental results show SA-MLCS and SLA-MLCS use an order of magnitude less space and time than the state-of-the-art approximate algorithm MLCS-APP while finding better solutions. Compared to the state-of-the-art anytime algorithm Pro-MLCS, SA-MLCS and SLA-MLCS can solve an order of magnitude larger size instances. Furthermore, SLA-MLCS can find much better solutions than SA-MLCS on large size instances.

Finding the longest common subsequence (LCS) of multiple strings is an NP-hard problem, with many applications in the areas of bioinformatics and computational genomics. Although significant efforts have been made to address the problem and its special cases, the increasing complexity and size of biological data require more efficient methods applicable to an arbitrary number of strings. In this paper, we present a new algorithm for the general case of multiple LCS (or MLCS) problem, i.e., finding an LCS of any number of strings, and its parallel realization. The algorithm is based on the dominant point approach and employs a fast divide-and-conquer technique to compute the dominant points. When applied to a case of three strings, our algorithm demonstrates the same performance as the fastest existing MLCS algorithm designed for that specific case. When applied to more than three strings, our algorithm is significantly faster than the best existing sequential methods, reaching up to 2-3 orders of magnitude faster speed on large-size problems. Finally, we present an efficient parallel implementation of the algorithm. Evaluating the parallel algorithm on a benchmark set of both random and biological sequences reveals a near-linear speedup with respect to the sequential algorithm.

In this paper, we present two ways to improve the precision of HITS-based algorithms on Web documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance-scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.

Active learning is an effective learning approach. In this article we present an intelligent agent-assisted environment for active learning to better support the student-centered, self-paced, and highly interactive learning approach. The environment uses the student’s learning-related profile such as learning style and background knowledge in selecting, organizing, and presenting learning material, and it adopts a new approach to course content organization and delivery based on smart instructional components that can be integrated into a wide range of courses. The environment is being implemented using the prevalent Internet, Web, digital library, and multiagent technologies.