In this paper, we aim to predict students’ learning perfor-mance 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 ap-propriate adaptive assistance for students to enhance their learning experience and optimize their performance. An ex-perimental study was conducted in order to collet gaze data and brainwave signals of fifteen students during an interac-tion with a virtual learning environment. Different classifica-tion 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.

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.

This paper presents a case-based reasoning (CBR) application for discovering similar patients with non-specific musculoskeletal disorders (MSDs) and recommending treatment plans using previous experiences. From a medical perspective, MSD is a complex disorder as its cause is often bounded to a combination of physiological and psychological factors. Likewise, the features describing the condition and outcome measures vary throughout studies. However, healthcare professionals in the field work in an experience-based way, therefore we chose CBR as the core methodology for developing a decision support system for physiotherapists which would assist them in the process of their co-decision making and treatment planning. In this paper, we focus on case representation and similarity modeling for the non-specific MSD patient data as well as we conducted initial experiments on comparing patient profiles.

The similarity assumption in Case-Based Reasoning (similar problems have similar solutions) has been questioned by several researchers. If knowledge about the adaptability of solutions is available, it can be exploited in order to guide retrieval. Several approaches have been proposed in this context, often assuming a similarity or cost measure defined over the solution space. In this paper, we propose a novel approach where the adaptability of the solutions is captured inside a metric Markov Random Field (MRF). Each case is represented as a node in the MRF, and edges connect cases whose solutions are close in the solution space. States of the nodes represent the adaptability effort with respect to the query. Potentals are defined to enforce connected nodes to share the same state; this models the fact that cases having similar solutions should have the same adaptability effort with respect to the query. The main goal is to enlarge the set of potentially adaptable cases that are retrieved (the recall) without significantly sacrificing the precision of retrieval. We will report on some experiments concerning a retrieval architecture where a simple kNN retrieval is followed by a further retrieval step based on MRF inference.

This paper presents the first results of the research into AI-based support of the room configuration process during the early design phases in architecture. Room configuration (also: room layout or space layout) is an essential stage of the initial design phase: its results are crucial for user-friendliness and success of the planned utilization of the architectural object. Our approach takes into account different possible actions of the configuration process, such as adding, removing, or (re)assigning of the room type. Its mode of operation is based on specific process chain clusters, where each cluster represents a contextual subset of previous configuration steps and provides a recurrent neural network trained on this cluster data only to suggest the next step, and a case base that is used to determine if the current process chain belongs to this cluster. The most similar cluster then tries to suggest the next step of the process. The approach is implemented in a distributed CBR framework for support of early conceptual design in architecture and was evaluated with a high number of process chain queries to prove its general suitability.

We demonstrate the value of learning dense representations (embeddings) of collections of codes representing various domains ofmo medical information. These embeddings are learned jointly using sparse representations of diagnosis, procedures and prescriptions extracted from medical claims, in order to infer semantic relationships both within, as well as between domains. We show that learning effective embeddings allows for a rich representation of a patient's clinical state at a point in time, a mechanism for assigning robust clinical similarity between patients, and a data representation which is generally useful in modeling various health care related events, such as the next most likely event (i.e. diagnosis, procedure or prescription), or the likelihood of a specific event in the future (e.g. an emergency room visit). Three methods are showcased in this paper including: general embedding, task-specific embedding, and a combination of the two which we have deemed "super" embedding for the purpose of this paper.

In this paper, we present the application of deep learning techniques to develop a modern model for the prediction of graft failure and survival analysis in liver transplant patients. We trained our model using the United Network for Organ Sharing (UNOS) dataset consisting of 59,115 patients from year 2002 to 2016 with around 150 features each. We also compare our model against an- other dataset – Scientific Registry of Transplant Recipients (SRTR) including 87,334 patients from year 2002 to 2018 – after selecting features by mapping them from UNOS data. Some of the most important features common to both datasets are Model for End-stage Liver Disease (MELD) score, patient body mass index (BMI), donor and patient age, cold ischemia time, and levels of various chemicals within the patient. To provide an additional tool to clinical practitioners in the allocation of a scarce resource, we developed a multi-task model to learn the survival function of a donor-recipient pair and hence predict the exact time of failure which outper- forms the traditional cox hazard models. The multi-task model produces very promising C-index results of 0.82 and 0.57 on the SRTR and UNOS datasets respectively.

In this work, we first attempt to replicate an earlier study on gene selection and clustering, and then we extend this work by applying a different type of hierarchical clustering to dis- cover interesting subsets of genes from breast cancer data. Replication of such studies is a known challenge and an ac- tive area of research in bioinformatics. The work presented in this paper is three-fold. First, we replicate a study conducted at the University of North Carolina to generate an initial set of genes. Second, we apply an approach called Distance Weighted Discrimination to fuse multiple, disparate breast cancer datasets into a single validation set. Third, we per- form hierarchical clustering and k-means clustering on this validation set to discover natural groupings and compare the clusters generated by both methods. While applying the hi- erarchical clustering is part of the reproduction step, we ex- tend the research by trying two different forms of hierarchi- cal clustering. We also apply k-means clustering for the same purpose and compare all three methods using Kaplan-Meier estimation and Cox proportional hazards regression. We dis- cover that among the three methods, k-means clustering gives us the best results.

Interaction with games can induce emotional reactions which could have an impact on players’ game experience and performance. Physiological sensors such as EEG and eye tracking represent an important mean to track these emotional reactions. In addition, virtual reality isolates the players from the external environment, strengthening the emotional measures. In this paper, we present an explorative study of the use of eye tracking for game adaptation according to the players’ excitement. Results showed that there exists a relationship between the modification of the game’s speed and the EEG excitement index and a correlation between eye movement and excitement as well. These results suggest that eye tracking could be a valid support or replacement of EEG data in game adaptation.