Ethics of medical AI: the case of Watson for Oncology

Peer reviewe

A Multithreaded Java Framework for Information Extraction in the Context of Enterprise Application Integration

In this paper, we present a new multithreaded framework for information extraction with Java in heterogeneous enterprise application environments, which frees the developer from having to deal with the error-prone task of low-level thread programming. The power of this framework is demonstrated by an example of extracting product prices from web sites, but the framework is useful for numerous other purposes, too. Strong points of the framework are its performance, continuous feedback, and adherence to maximum response times. The description of the framework uses UML modeling techniques for visualizing multithreading. Moreover, we tackle Java problems of stopping running threads.

Enhancing and simplifying data security and privacy for multitiered applications

© 2020 Elsevier Inc. While databases provide capabilities to enforce security and privacy policies, two major issues still prevent applications from safely delegating such policies to the database. The first one is the loss of user identity in multitiered environments which renders the database security features of little to no value. The second issue is the unsafe coexistence between the security capabilities and fundamental database tenets which creates data leakage vulnerabilities. This paper proposes extensions to database systems to allow applications, such as those used in managing the operations of energy clouds, to safely delegate the security and privacy policies to the database. This delegation reduces complexity for applications and improves overall data security and privacy. Our performance evaluation shows that almost all the TPC-H queries perform the same or better when the security policy is enforced by the database. For the set of queries that performed better, the improvement observed ranges from 8 to 68%

Prof Saf

pertain to an organization's prioritization of safety relative to other concerns, such as productivity or quality control (Naveh, Katz-Navon & Stern, 2011; Zohar, 2000). Relating to what organizations may prioritize, safety climate also entails the kind of behaviors that are expected, supported and rewarded (Schneider, 1990). Characteristics of safety climate can impact workers' own safety values, which, in turn, influence their behaviors (Naveh, et al., 2011). Further, a positive safety climate has been linked to less burnout and fewer errors, near-hits and incidents that result in lost time from work (Christian, Bradley, Wallace, et al., 2009; Nahr-gang, Morgesun & Hofmann, 2011). In this sense, not only has safety climate been identified as a potential leading indicator of incident occurrence, but also evidence exists that a positive safety climate might strengthen the impact of job factors (e.g., job autonomy, supervisor support, coworker support) on workers' proactive behavior (Bronkhorst, 2015), although these factors are not well understood (Parker, Axtell & Turner, 2001). To that end, this article examines what role job autonomy, in particular, may have in forming workers' perceptions and subsequent OSH performance on the job. The authors begin by defining autonomy in the workplace to provide a consistent platform for studying the term.CC999999/ImCDC/Intramural CDC HHS/United States2019-04-17T00:00:00Z31007311PMC6469387636

New Paradigms for Active Learning

In traditional active learning, learning algorithms (or learners) mainly focus on the performance of the final model built and the total number of queries needed for learning a good model. However, in many real-world applications, active learners have to focus on the learning process for achieving finer goals, such as minimizing the number of mistakes in predicting unlabeled examples. These learning goals are common and important in real-world applications. For example, in direct marketing, a sales agent (learner) has to focus on the process of selecting customers to approach, and tries to make correct predictions (i.e., fewer mistakes) on the customers who will buy the product. However, traditional active learning algorithms cannot achieve the finer learning goals due to the different focuses. In this thesis, we study how to control the learning process in active learning such that those goals can be accomplished. According to various learning tasks and goals, we address four new active paradigms as follows. The first paradigm is learning actively and conservatively. Under this paradigm, the learner actively selects and predicts the most certain example (thus, conservatively) iteratively during the learning process. The goal of this paradigm is to minimize the number of mistakes in predicting unlabeled examples during active learning. Intuitively the conservative strategy is less likely to make mistakes, i.e., more likely to achieve the learning goal. We apply this new learning strategy in an educational software, as well as direct marketing. The second paradigm is learning actively and aggressively. Under this paradigm, unlabeled examples and multiple oracles are available. The learner actively selects the best multiple oracles to label the most uncertain example (thus, aggressively) iteratively during the learning process. The learning goal is to learn a good model with guaranteed label quality. The third paradigm is learning actively with conservative-aggressive tradeoff. Under this learning paradigm, firstly, unlabeled examples are available and learners are allowed to select examples actively to learn. Secondly, to obtain the labels, two actions can be considered: querying oracles and making predictions. Lastly, cost has to be paid for querying oracles or for making wrong predictions. The tradeoff between the two actions is necessary for achieving the learning goal: minimizing the total cost for obtaining the labels. The last paradigm is learning actively with minimal/maximal effort. Under this paradigm, the labels of the examples are all provided and learners are allowed to select examples actively to learn. The learning goal is to control the learning process by selecting examples actively such that the learning can be accomplished with minimal effort or a good model can be built fast with maximal effort. For each of the four learning paradigms, we propose effective learning algorithms accordingly and demonstrate empirically that related learning problems in real applications can be solved well and the learning goals can be accomplished. In summary, this thesis focuses on controlling the learning process to achieve fine goals in active learning. According to various real application tasks, we propose four novel learning paradigms, and for each paradigm we propose efficient learning algorithms to solve the learning problems. The experimental results show that our learning algorithms outperform other state-of-the-art learning algorithms

Recovery of Water and Salt from Hyper-Saline Mine Water using Freeze Crystallization

The Freezerbacks researched, designed, and economically evaluated a full-scale freeze crystallization process as well as two alternative full-scale processes: 5 stage multiple effect evaporation and reverse osmosis. All three processes were designed to treat hyper-saline mine water that was sent into evaporation pond systems. These systems were designed for Freeport-McMoRan’s mines that need to treat impacted water. The Freeport-McMoRan copper mine in Miami, Arizona was visited in order to gain insight about the problem. The mine is no longer actively mining copper and is in the process of reclaiming land used. An essential part of restoring the land is treating impacted water that is currently being recirculated throughout the process before discharging. Current methods, evaporation ponds, are neither time nor cost effective. Ultimately, the water needs to be purified to the EPA standard of the maximum concentration level of sulfates (250 mg/L). After the feed has been processed, a waste stream will be disposed of via existing evaporation ponds. The deciding factor between the processes is the economics and total recovery. Multiple effect evaporation can be modified to recover more than 50% of water therefore reducing the footprint for the evaporation ponds. Although the heat of vaporization for water is about six times greater (40.65 kJ/mol) than the heat of fusion for water (6.02 kJ/mol), the capital cost for freeze crystallization is greater, and the process is unused on an industrial scale. Reverse osmosis will purify 50% of the water with a simpler system and cheaper overall cost. All processes are being presented as viable, with preference for the reverse osmosis. A batch bench scale system was constructed to model freeze crystallization. It was designed to process one gallon of salt solution in a single vessel. The bench scale process overall recovered 72% of the water with a final salt composition that ranges from 1.44 wt.% to 5.10 wt.%. For full-scale design purposes, 2.5 wt.% recovery was assumed. Reverse osmosis further purified the melted ice to EPA standards. A thorough evaluation was conducted by generating a full-scale economic analysis for each process, taking into consideration the advantages and disadvantages of each. Important factors taken into consideration were capital and operating costs, complexity, total recovery of water, and concentration of sulfates in the water recovered. In the freeze crystallization process, impacted water is pumped through two units in a semi-batch process where ice is formed on concentric plate coils in vessels. A total of 75% water is first recovered by crystallization and then the recovered water is passed through a reverse osmosis membrane (RO) to recover 50% of the initial brine water at environmental specifications. The net present value (NPV) after 10 years of operation is $(21.4 million) with a 50$(9.44 million). The reverse osmosis process will require two stages and a total of 21 elements. Reverse osmosis proved to be the most economical with an NPV of $(2.96 million) and a 50% purified water recovery compared to the other two processes

Albatross: An optimistic consensus algorithm

The area of distributed ledgers is a vast and quickly developing landscape. At the heart of most distributed ledgers is their consensus protocol. The consensus protocol describes the way participants in a distributed network interact with each other to obtain and agree on a shared state. While classical consensus Byzantine fault tolerant (BFT) algorithms are designed to work in closed, size-limited networks only, modern distributed ledgers -- and blockchains in particular -- often focus on open, permissionless networks. In this paper, we present a novel blockchain consensus algorithm, called Albatross, inspired by speculative BFT algorithms. Transactions in Albatross benefit from strong probabilistic finality. We describe the technical specification of Albatross in detail and analyse its security and performance. We conclude that the protocol is secure under regular PBFT security assumptions and has a performance close to the theoretical maximum for single-chain Proof-of-Stake consensus algorithms