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Aiming to handle the complexity inherent to the human textual communication, Automatic Document Classification (ADC) methods often adopt several simplifications. One such simplification is to consider independent the terms that compose documents, which may hide important relationships between them. These relationships can encapsulate non-trivial and effective patterns to improve classification effectiveness. In this work, we propose NetClass, a new network-based model for documents that explicitly considers term relationships and introduce a family of relational algorithms for ADC, such as the LRN-WRN classifier—a lazy relational ADC algorithm that not only exploits relationships between terms but also neighborhood information. As our extensive experimental evaluation shows, the proposed LRN-WRM achieves competitive performance when compared to the state-of-the-art in ADC, including SVM, considering seven distinct domains. More specifically, LRN-WRN outperforms state-of-the-art classifiers in 5 out of 7 domains, being within the top-2 best-performing classifier in all assessed domains. Our evaluation highlights the high effectiveness of our proposal, as well as its efficiency in terms of runtime. Indeed, besides effectiveness and efficiency, the simplicity and the absence of a complex parameter tuning of our proposal are key characteristics that make our algorithms interesting alternatives for ADC. Particularly, as highlighted by our experimental evaluation, LRN-WRM was shown to be a promising alternative to dynamic domains with a huge volume of short texts (e.g., social media content) or with several classes.

Advanced analytics and artificial intelligence are powerful technologies that, along with their benefits, create new threats to privacy, equality, fairness and transparency. Existing law does not yet protect sufficiently against these threats. This has led some organizations to pursue what they call “data ethics” or “AI ethics” in an attempt to bring advanced analytics and AI more into line with societal values and so legitimate their growing use of these technologies. To date, much of the scholarship on data ethics has sought either to define the ethical principles to which organization should aspire, or to map out the laws and regulations needed to push organizations towards these ethical goals. While these two lines of inquiry are important, the literature is missing a critical third dimension: empirical work on how organizations are actually governing the threats that their use of advanced analytics and AI can generate. Good regulatory design requires such knowledge. Yet, while there have been important studies of how organizations manage privacy “on the ground” (Bamberger and Mulligan 2015), there has been little such work on the governance of advanced analytics and AI. This report begins to fill this gap. Focusing on private sector organizations, the authors interviewed corporate privacy managers deemed by their peers to be leaders in the governance of advanced analytics and AI, as well as the lawyers, consultants and thought leaders who advise them on this topic. They also surveyed a wider range of privacy mangers. The study sought to answer three, fundamental questions about business data ethics management: (1) How do leading companies conceptualize the threats that their use of advanced analytics and AI pose for individuals, groups and the broader society? (2) If it is true that the law does not yet require companies to reduce these risks, then why are they pursuing data ethics? and (3) How are companies pursuing data ethics? What substantive benchmarks, management processes and technological solutions do they use towards this end? The authors previously shared on SSRN their preliminary findings. This final report provides a much fuller picture. The report should provide legislators and policymakers with an empirical foundation for their efforts to regulate advanced analytics and AI, at the same time as it gives interested organizations ideas on how to improve their data ethics management.

Companies today increasingly say that they seek to engage in the "ethical" use of AI and advanced analytics. On one level, this makes sense. Along with their benefits, these technologies pose threats that traditional privacy law is ill-suited to address. To protect consumers and their own reputations, companies need to do more than simply comply with the law. They need to handle data ethically and responsibly. But what, specifically, does that mean? How are companies drawing the difficult lines between what is “ethical” and fair, and what is not? How are they managing their operations to achieve this end? More fundamentally, if it is true that the law does not require these measures, then how do these companies rationalize investing in them? Much of the existing literature on these questions is normative and abstract. There is little descriptive research on how corporate data ethics manifests itself in practice. An inter-disciplinary Ohio State research team is seeking to fill this gap. For the past two years, the team has been interviewing Chief Privacy Officers and others to better understand why leading companies are pursuing data ethics, and what this looks like in practice. The team is currently undertaking a second, survey phase of the research. Given the rapidity with which this field is developing, the researchers decided not to wait for the completion of the survey phase to share this initial report of findings from the interview phase. This Report provides first-hand accounts of the ethical dilemmas that companies encounter, the emerging substantive frameworks that they use to assess them, and the management processes that they employ to pursue data ethics. The researchers then hypothesize why the field of corporate data ethics is developing and why distinct types of companies may approach the task differently. This initial report should provide useful ideas for companies seeking to pursue data ethics. It should also offer insights, and raise questions, for those seeking to improve the law, policy and practice of corporate data ethics.

Due the recent increase of the volume of data that has been generated, organizing this data has become one of the biggest problems in Computer Science. Among the different strategies propose to deal efficiently and effectively for this purpose, we highlight those related to clustering, more specifically, density-based clustering strategies, which stands out for its ability to define clusters of arbitrary shape and the robustness to deal with the presence of data noise, such as DBSCAN and OPTICS. However, these algorithms are still a computational challenge since they are distance-based proposals. In this work we present a new approach to make OPTICS feasible based on data indexing strategy. Although the simplicity with which the data are indexed, using graphs, it allows explore various parallelization opportunities, which were explored using graphic processing unit (GPU). Based on this structure, the complexity of OPTICS is reduced to O(E *logV ) in the worst case, becoming itself very fast. In our evaluation we show that our proposal can be over 200x faster than its sequential version using CPU.

Background context Chronic atlantoaxial rotatory fixation (AARF) is uncommon as acute AARF is easily reduced either spontaneously or by conservative methods. Various anterior and posterior surgical approaches for a chronic AARF have been reported because of the difficulty encountered in obtaining reduction. Purpose To describe a novel technique of reduction of a chronic AARF using a temporary transverse transatlantal rod. Study design Technical report. Methods A 13-year-old girl presented with an 8-month-old chronic AARF with typical torticollis and “cock-robin” posture of the head with a normal neurology. As closed reduction with skull traction for 2 weeks failed to reduce the deformity, the patient underwent C1–C2 fusion. C1 lateral mass and C2 pedicle screws were inserted under computer navigation. A temporary transverse rod across the atlas and axis was placed to secure a three-column fixation to derotate the subluxed atlas into anatomical alignment. Rods were then connected between the C1 lateral masses and the C2 pedicle screws and fusion obtained with autologous iliac crest grafts. Result Anatomic reduction of the atlantoaxial region was obtained without neural compromise, and satisfactory fusion was observed at 6-months follow-up. Conclusion A temporary transatlantal rod provides a secure anchor point for easy maneuverability for reduction of a chronic AARF and has the advantage of being used even in the absence of the posterior arch of the atlas.

We present fast distributed local control connected dominating set (CDS) algorithms for wireless ad hoc networks. We present two randomized distributed algorithms, CDSColor and CDSTop which take into account the effect of wireless interference and the consequent loss of messages during the execution of the algorithm. These algorithms produce a CDS of constant size and constant stretch ratio with high probability, and converge in polylogarithmic running time. Specifically, algorithm CDSColor requires the nodes to know (estimates of) the maximum degree @D and the size of the network n and converges in O(@Dlog^2n) time. Algorithm CDSTop requires the nodes to know their three-hop topology and (an estimate of) the network size n and converges in O(log^2n) time. To the best of our knowledge, these are the first distributed interference-aware CDS algorithms for wireless ad hoc networks which break the linear running-time barrier.

We present provably efficient parallel algorithms for sweep scheduling, which is a commonly used technique in radiation transport problems, and involves inverting an operator by iteratively sweeping across a mesh from multiple directions. Each sweep involves solving the operator locally at each cell. However, each direction induces a partial order in which this computation can proceed. On a distributed computing system, the goal is to schedule the computation, so that the length of the schedule is minimized. Due to efficiency and coupling considerations, we have an additional constraint, namely, a mesh cell must be processed on the same processor along each direction. Problems similar in nature to sweep scheduling arise in several other applications, and here we formulate a combinatorial generalization of this problem that captures the sweep scheduling constraints,and call it the generalized sweep scheduling problem. Several heuristics have been proposed for this problem; see [S. Pautz, An algorithm for parallel S n sweeps on unstructured meshes, Nucl. Sci. Eng. 140 (2002) 111–136; S. Plimpton, B. Hendrickson, S. Burns, W. McLendon, Parallel algorithms for radiation transport on unstructured grids, Super Comput. (2001)] and the references therein; but none of these have provable worst case performance guarantees. Here we present a simple, almost linear time randomized algorithm for the generalized sweep scheduling problem that (provably) gives a schedule of length at most O ( log 2 n ) times the optimal schedule for instances with n cells, when the communication cost is not considered, and a slight variant, which coupled with a much more careful analysis, gives a schedule of (expected) length O ( log m log log log m ) times the optimal schedule for m processors. These are the first such provable guarantees for this problem. The algorithm can be extended with an additional multiplicative factor in the case when we have inter-processor communication latency, in the models of Rayward-Smith [UET scheduling with inter-processor communication delays, Discrete Appl. Math. 18 (1) (1987) 55–71] and Hwang et al. [Scheduling precedence graphs in systems with inter-processor communication times, SIAM J. Comput. 18(2) (1989) 244–257]. Our algorithms are extremely simple, and use no geometric information about the mesh; therefore, these techniques are likely to be applicable in more general settings. We also design a priority based list schedule using these ideas, with the same theoretical guarantee, but much better performance in practice; combining this algorithm with a simple block decomposition also lowers the overall communication cost significantly. Finally, we perform a detailed experimental analysis of our algorithm. Our results indicate that the algorithm compares favorably with the length of the schedule produced by other natural and efficient parallel algorithms proposed in the literature [S. Pautz, An Algorithm for parallel S n sweeps on unstructured meshes, Nucl. Sci. Eng. 140 (2002) 111–136; S. Plimpton, B. Hendrickson, S. Burns, W. McLendon, Parallel algorithms for radiation transport on unstructured grids, Super Comput. (2001)].

The aim of this paper is to make an introspective study of some aspects of the use of formal methods in development and assessment of automation systems. The authors do not wish to enter into yet another polemical debate on the pros and cons of formal methods. Instead, the paper proposes a holistic approach to the subject. The authors describe the outlines of a concept called the “BMW approach” (the resemblance of this name to a famous make of German cars is only a coincidence !). The BMW approach presents a symbiosis of the three orthogonal components of any methodology: notation (Beschreibungsmittel), method (Methode) and tool (Werkzeug). The BMW approach has been successfully applied in several railways-related projects done at IfRA.

Load balancing involves assigning to each processor work proportional to its performance, minimizing the execution time of the program. Although static load balancing can solve many problems (e.g., those caused by processor heterogeneity and non uniform loops) for most regular applications, the transient external load due to multiple users on a network of workstations necessitates a dynamic approach to load balancing. We examine the behavior of global vs. local, and centralized vs. distributed, load balancing strategies. We show that different schemes are best for different applications under varying program and system parameters. Therefore, customized load balancing schemes become essential for good performance. We present a hybrid compile time and run time modeling and decision process which selects (customizes) the best scheme, along with automatic generation of parallel code with calls to a run time library for load balancing.

We present a new algorithm for concurrent access to array-based priority queue heaps. Deletions proceed top-down as they do in a previous algorithm due to Rao and Kumar [1988 (IEEE Trans. Computers 37(12))], but insertions proceed bottom-up, and consecutive insertions use a bit-reversal technique to scatter accesses across the fringe of the tree, to reduce contention. Because insertions do not have to traverse the entire height of the tree (as they do in previous work), as many as O(M) operations can proceed in parallel, rather than O(log M) on a heap of size M. Experimental results on a Silicon Graphics Challenge multiprocessor demonstrate good overall performance for the new algorithm on small heaps, and significant performance improvements over known alternatives on large heaps with mixed insertion/deletion workloads.