In this paper we propose a new algorithm for solving general two-player turn-taking games that performs symbolic search utilizing binary decision diagrams (BDDs). It consists of two stages: First, it determines all breadth-first search (BFS) layers using forward search and omitting duplicate detection, next, the solving process operates in backward direction only within these BFS layers thereby partitioning all BDDs according to the layers the states reside in. We provide experimental results for selected games and compare to a previous approach. This comparison shows that in most cases the new algorithm outperforms the existing one in terms of runtime and used memory so that it can solve games that could not be solved before with a general approach.
Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the ``long tail'' is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized diversification re-ranking approach to increase the representation of less popular items in recommendations while maintaining acceptable recommendation accuracy. Our approach is a post-processing step that can be applied to the output of any recommender system. We show that our approach is capable of managing popularity bias more effectively, compared with an existing method based on regularization. We also examine both new and existing metrics to measure the coverage of long-tail items in the recommendation.
Hampton, Andrew (University of Memphis) | Rus, Vasile (University of Memphis) | Andrasik, Frank (University of Memphis) | Nye, Benjamin (University of Southern California) | Graesser, Art (University of Memphis)
Navigating a career constitutes one of life’s most enduring challenges, particularly within a unique organization like the US Navy. While the Navy has numerous resources for guidance, accessing and identifying key information sources across the many existing platforms can be challenging for sailors (e.g., determining the appropriate program or point of contact, developing an accurate understanding of the process, and even recognizing the need for planning itself). Focusing on intermediate goals, evaluations, education, certifications, and training is quite demanding, even before considering their cumulative long-term implications. These are on top of generic personal issues, such as financial difficulties and homesickness when at sea for prolonged periods. We present the preliminary construction of a conversational intelligent agent designed to provide a user-friendly, adaptive environment that recognizes user input pertinent to these issues and provides guidance to appropriate resources within the Navy. User input from “counseling sessions” is linked, using advanced natural language processing techniques, to our framework of Navy training and education standards, promotion protocols, and organizational structure, producing feedback on resources and recommendations sensitive to user history and stated career goals. The proposed innovative technology monitors sailors’ career progress, proactively triggering sessions before major career milestones or when performance drops below Navy expectations, by using a mixed-initiative design. System-triggered sessions involve positive feedback and informative dialogues (using existing Navy career guidance protocols). The intelligent agent also offers counseling for personal problems, triggering targeted dialogues designed to gather more information, offer tailored suggestions, and provide referrals to appropriate resources or to a human counselor when in-depth counseling is warranted. This software, currently in alpha testing, has the potential to serve as a centralized information hub, engaging and encouraging sailors to take ownership of their career paths in the most efficient way possible, benefiting both individuals and the Navy as a whole.
In this paper, we aim to predict students' learning performance by combining two-modality sensing variables, namely eye tracking that monitors learners' eye movements and elec-troencephalography (EEG) that measures learners' cerebral activity. Our long-term goal is to use both data to provide appropriate adaptive assistance for students to enhance their learning experience and optimize their performance. An experimental study was conducted in order to collet gaze data and brainwave signals of fifteen students during an interaction with a virtual learning environment. Different classification algorithms were used to discriminate between two groups of learners: students who successfully resolve the problem-solving tasks and students who do not. Experimental results demonstrated that the K-Nearest Neighbor classifier achieved good accuracy when combining both eye movement and EEG features compared to using solely eye movement or EEG.
This paper presents a method for generating single-variable limit problems for an introductory Calculus course. Our method generates problems in two steps. The first step uses an evolutionary approach to construct unique functions $f$. The second step involves an analysis of $f$ to compute distinct ``approach'' values. Our experimental procedures demonstrate the limitations and utility of our approach.
Malepathirana, Tamasha (University of Moratuwa) | Perera, Rashindrie (University of Moratuwa) | Abeysinghe, Yasasi (University of Moratuwa) | Albar, Yumna (University of Moratuwa) | Thayasivam, Uthayasanker (University of Moratuwa)
Hierarchical review aspect aggregation is an important challenge in review summarization. Currently, agglomerative clustering is widely used for hierarchical aspect aggregation. We identify an important but less studied issue in using agglomerative clustering for the aforementioned task. This paper proposes a novel approach to generate a multi-way hierarchy by adaptation of the multivariate concept. Furthermore, we propose a novel experimentation approach to evaluate the acceptability of the aspect relations obtained from the hierarchy generated.
Network security is a constant challenge, with new attacks and vulnerabilities being frequently introduced. Application layer Denial of Service (DoS) attacks are a rising attack variant, which inflicts network stress and service interruptions. The implementation of detection and mitigation techniques for such attacks have been a priority for some time, but more sophisticated attack permutations are constantly being introduced, often making prior prevention techniques ineffective. In this work, we focus specifically on the detection of Slow HTTP POST DoS attacks. We execute several Slow HTTP POST attack configurations within a live network environment to represent a real-world attack scenario, with varying levels of severity. For our methodology, we utilize features of network flow (Netflow) traffic to detect these attack configurations. Netflow has proven to be a more scalable solution compared to full packet capture when performing data collection, allowing for near real-time network monitoring. Eight machine learners were implemented to determine which learner would achieve optimal performance metrics when detecting Slow HTTP POST attacks. As our data is very large, we also evaluate the use of data sampling techniques to increase attack detection performance. Overall, our results show a high detection rate when detecting Slow HTTP POST attacks, achieving relatively low false alarm rates.
Velampalli, Sirisha (C.R.Rao Advanced Institute of Mathematics, Statistics and Computer Science) | Mookiah, Lenin (Tennessee Technological University) | Eberle, William (Tennessee Technological University)
Recently, there has been much attention on tools and techniques for visualizing and acquiring new knowledge and insights. In the VAST 2018 competition, one of the challenges is to discover the fraudulent group of employees at Kasios, a furniture manufacturing company. In this work, we use a graph-based approach that analyzes the data for suspicious employee activities at Kasios. Graph based approaches enable one to handle rich contextual data and provide a deeper understanding of data due to the ability to discover patterns in databases that are not easily found using traditional query or statistical tools. We focus on graph based knowledge discovery in structural data to mine for interesting patterns and anomalies. Our approach first reports the normative patterns in the data, and then discovers any anomalous patterns associated with the previously discovered patterns. For visualizing the suspicious patterns, we also use the enterprise graph database Neo4j. Neo4j Browser provides a way to visualize graph structures.