Labor shortages in the United States are impacting a number of industries including the meat processing sector. Collaborative technologies that work alongside humans while increasing production abilities may support the industry by enhancing automation and improving job quality. However, existing automation technologies used in the meat industry have limited collaboration potential, low flexibility, and high cost. The objective of this work was to explore the use of a robot arm to collaboratively work alongside a human and complete tasks performed in a meat processing facility. Toward this objective, we demonstrated proof-of-concept approaches to ensure human safety while exploring the capacity of the robot arm to perform example meat processing tasks. In support of human safety, we developed a knife instrumentation system to detect when the cutting implement comes into contact with meat within the collaborative space. To demonstrate the capability of the system to flexibly conduct a variety of basic meat processing tasks, we developed vision and control protocols to execute slicing, trimming, and cubing of pork loins. We also collected a subjective evaluation of the actions from experts within the U.S. meat processing industry. On average the experts rated the robot's performance as adequate. Moreover, the experts generally preferred the cuts performed in collaboration with a human worker to cuts completed autonomously, highlighting the benefits of robotic technologies that assist human workers rather than replace them. Video demonstrations of our proposed framework can be found here: https://youtu.be/56mdHjjYMVc

The recent proliferation of publicly available graph-structured data has sparked an interest in machine learning algorithms for graph data. Since most traditional machine learning algorithms assume data to be tabular, embedding algorithms for mapping graph data to real-valued vector spaces has become an active area of research. Existing graph embedding approaches are based purely on structural information and ignore any semantic information from the underlying domain. In this paper, we demonstrate that semantic information can play a useful role in computing graph embeddings. Specifically, we present a framework for devising embedding strategies aware of domain-specific interpretations of graph nodes and edges, and use knowledge of downstream machine learning tasks to identify relevant graph substructures. Using two real-life domains, we show that our framework yields embeddings that are simple to implement and yet achieve equal or greater accuracy in machine learning tasks compared to domain independent approaches.

In this paper, we consider the problem of detecting unknown cyberattacks from audit data of system-level events. A key challenge is that different cyberattacks will have different suspicion indicators, which are not known beforehand. To address this we consider a multi-view anomaly detection framework, where multiple expert-designed ``views" of the data are created for capturing features that may serve as potential indicators. Anomaly detectors are then applied to each view and the results are combined to yield an overall suspiciousness ranking of system entities. Unfortunately, there is often a mismatch between what anomaly detection algorithms find and what is actually malicious, which can result in many false positives. This problem is made even worse in the multi-view setting, where only a small subset of the views may be relevant to detecting a particular cyberattack. To help reduce the false positive rate, a key contribution of this paper is to incorporate feedback from security analysts about whether proposed suspicious entities are of interest or likely benign. This feedback is incorporated into subsequent anomaly detection in order to improve the suspiciousness ranking toward entities that are truly of interest to the analyst. For this purpose, we propose an easy to implement variant of the perceptron learning algorithm, which is shown to be quite effective on benchmark datasets. We evaluate our overall approach on real attack data from a DARPA red team exercise, which include multiple attacks on multiple operating systems. The results show that the incorporation of feedback can significantly reduce the time required to identify malicious system entities.