From jargon to clarity: bridging understanding through graded simplification of legal data

LEVEL С Type 1 ABSTRACT LEVEL В Type 2 (Principles) ABSTRACT LEVEL A CHARLES GUZZETTALEVEL A CHARLES GUZZETTA ferent levels of abstraction: either less than organizing laws (such as data) or more than organizing laws (such as systems). Without a framework, each contribution has approximately equal merit with each other suggestion and the cloture of a complex of principles that signify the profession is unlikely of achievement. Once a framework is established, the task might well proceed on more than one and in more than one direction at once. Part of this task would be to subject suggestions to tests which reveal their of in order to determine whether they are principles or something else. Another part would be to subject suggestions to tests revealing the of philosophical or scientific content. The following diagram simply illustrates this dualdirectional testing ladder: Dual-Directional Test If such tests could be developed and used to organize a set of social work principles which social workers could be expected to accept and follow, the profession might better defend its present and future programs and procedures without being trapped into defending them on grounds other than defensible social work grounds. Testing of principles against such a framework might help to eliminate much of the conflict and confusion in professional literature which prescribes certain policies or programs without a clear understanding of the nature of the principles underlying such recommendations. Sometimes such prescrip44 tions simply bear the wrong label. These tests could be even more helpful in making professional decisions concerning curriculum formulation, since currently, any attempt to develop a set of principles from the literature may be aborted by lack of agreement about the use of such terms as rules, ideas, and so on. These words are used in the literature interchangeably and conflictingly, and sets of principles developed from it without guiding rules are likely to consist of content, concepts, objectives, principles, and Chinese proverbs. While the level of abstraction decision (levels A, B, and С in Figure 2) and the degree of moral judgment decision (types 1 or 2; i.e., philosophical or scientific) have to be made almost simultaneously, it may be helpful to propose tests which could be used independently in making these decisions. For the abstraction-level decision, we may refer to A as description or such as casework is a helping process. Such statements may be challenged and may affect decisions about what services to provide. However, they could not be said to be organizing laws, or even accumulations of ideas widely applicable in practice. In the curriculum, they may represent content, but they are not principles; they are data. Level С might be used to indicate systems of laws so organized as to have far-reaching applications and to have implications for all aspects of practice. They may be seen as prescriptive and motivating and as such, they not only provide the outlines of currently acceptable practice, but indicate the lines along which future modification appropriately may take place. They can be designated as objectives, for objectives embody the requirements of the present and the hopes of the future. An example of an objective is: Social workers must develop the discipline and self-awareness essential to the professional. EDUCATION FOR SOCIAL WORK This content downloaded from 157.55.39.55 on Tue, 23 Aug 2016 05:37:25 UTC All use subject to http://about.jstor.org/terms Obviously, objectives are more than laws, just as laws are more than data. The laws themselves are represented by abstract B, between simple description or data (level A) and objectives (level C). These laws, or principles, are the systematic organizations of data which form the glue that holds together the entire profession, from education to practice. It is difficult to consider principles apart from the data which comprise them or the objectives which focus them. Yet, each of does have distinctive characteristics. A datum may report or describe or simply express feeling. A law organizes data, reports, descriptions, and such into a systematic set of rules or basic doctrine. Moreover, the constituent data may be deduced from the law. These laws (principles) may be put together into a system which prescribes present action and future goals (an objective). Conversely, the objectives control which principles are pertinent to the professional pursuit involved. Once the qualifications of principles are settled, the other part of the task must be faced determining the scientific-philosophical classification of the principle. For the purposes of examination, it might be expedient to assign laws or principles of mostly philosophical content to type 1 and those of mostly scientific content to type 2. In order to justify the inclusion of either type of principle as a professional requirement, it must be ascertained that the principle can be taught or transmitted, for only then is it possible to demand that aspirants to the profession accept them. When this classification system is applied, type 2 should give comparatively little trouble. Its scientific nature makes clear that it can be taught, for it is empirically provable, and as an empirically provable law, it is a universal. As any empirical concept, it has by definition the quality of being free of any moral charge in itself. One example of a concept may suffice to demonstrate this: Hunger is a factor in motivating human behavior/' There is no rightness or wrongFALL 1966 CONCEPTS AND PRECEPTS ness about this in a moral sense; it is simply a fact, universal and provable. It is more than a simple datum, for it unifies a whole series of observations of human behavior; it is not an objective, for it does not directly prescribe professional activity. Rather, it unifies, sets certain limits, and implies certain information. Type 1 is a more sticky case. The complications stem from the ascription of moral (philosophical) value. It might be charged that type 1 principles have no place in a social work curriculum. Questions which might be asked include: Can a moral be proved? (If not, it would be difficult to demand its acceptance.) Even if provable, can it be taught? Is it moral to demand acceptance of a particular moral? Clearly, each of these questions, and countless others, could produce volumes of discussion. For the purposes of this discussion (and in order to get on with it) it is possible to oversimplify and to answer each of the preceding questions with a bald yes. Indeed, philosophical positions can be proved, taught, and demanded to the extent that they are required for the establishment of professional status. The problem might be overcome by noting that the provability of philosophical positions is determined by means different from those used to test the provability of scientific positions. One need not despair at the lack of scientific methods to prove a philosophical thesis. This use of scientific method may well be as irrelevant in this case as would be the use of philosophical methods to prove that the Earth is a sphere. The major tool in philosophy is not experimentation, but logic. The moral maxims or precepts of social work may be proved with logic within the given culture to which they apply. One example of such a social work precept is, Professional resp nsibility takes precedence over personal interests. This is more than a simple datum, but it is not an objective. It is, rather, a non-scientific principle, and its moral and ethical component is inescapable.

Context: Cross-project defect prediction (CPDP) research has been popular and many CPDP methods were proposed. While these methods used cross-project data as is for their inputs, useless or noisy information in the cross-project data can cause the degradation of predictive and computation performance. Removing such information makes the cross-project data simple and it will affect the performance of CPDP methods. Objective: To identify and quantify the effects of the data simplification for CPDP methods. Method: We conducted experiments that compared the predictive performance between CPDP with and without the data simplification. We adopted a data simplification method based on an active learning method proposed for software effort estimation. The experiments adopted 44 versions of OSS projects, four prediction models, and two CPDP methods, namely, Burak-filter and cross-project selection. Results: The data simplification achieved significant improvement in predictive performance for the cross-project selection. It did not improve Burak-filter. Conclusion: The data simplification can be helpful for the cross-project selection in terms of predictive performance and size reduction of cross-project data.

SETL allows one to manipulate two main kinds of data items, namely simple data items and composite data items. The intuitive distinction between these is that composite data items have elements, or components, which are themselves other (simple or composite) data items.

Aiming at the issue that the current point cloud data simplification algorithm cannot accurately retain the subtle features of ellipsoidal complex surface, simplification algorithm of a point cloud data feature preservation based on principle component analysis (PCA) and adaptive mean shift (AMS) methods is proposed. First, K-means spatial clustering is used to make preliminary clustering and simplification of point cloud data. Second, the representative value of the subtle features of the point cloud data is calculated based on the PCA method. Then, according to the proposed improved adaptive mean shift method, the simplification of the point cloud data is developed. Finally, the point cloud data obtained by the denoising and smoothing of a large spherical crown workpiece collected at a certain equipment manufacturing site is used as the experimental object, and a comparative experiment is conducted between the proposed simplification algorithm and the k-means clustering algorithm, which proves the proposed algorithm can effectively maintain the subtle feature points of the ellipsoidal point cloud data.

Data simplification is an important factor of the spatial data generalization, which is an effective way to improve rendering speed. This paper firstly introduces the algorithms classification of the spatial line vector data in two-dimensional environment, and then it emphatically summarizes and analyzes the advantages and disadvantages of the algorithms which can be used in the spatial line vector data simplification in the three dimensional environment. The three-dimensional Douglas-Peucker algorithm with a certain overall characteristics has wide application prospect. The simplified algorithms in 3D environment represent the development direction of the future. But at present, the existing data simplification algorithms in 3D environment are not mature enough, they all have certain advantages and disadvantages, this makes their use is limited by a certain extent. The application of these simplified algorithms in 2D and 3D is mostly on multi-resolution expression. Developing from 2D algorithm to the direction of 3D algorithm, it also lists many works and problems that need us to do and study in the future.

The reconstruction of accurate freeform surface models of 3D scanned objects is a common task encountered in creating virtual reality environments for anatomical reconstruction, cartography, cultural artifact modeling, digital archaeology, infrastructure renewal, and computer-aided design. Difficulties occur in reconstructing smooth surfaces from these scanned data sets because the acquired data is very large and is often infiltrated with scanning errors. For surface reconstruction, visualization, and interactive virtual modeling, it is necessary to reduce the amount of raw scanned data. Many existing data simplification techniques are complex and not directly applicable to spline-based surface models. A novel two stage contour-based data simplification algorithm is introduced in this paper and applied to facial surface reconstruction, which may be used for human modeling for computer games or model creation for virtual museums. The first stage extracts a series of equally spaced sectioned contours directly from a dense 3D data points. In the second stage, each extracted contour is redefined as a cubic B-spline curve by reduced number of control points defined by a user defined reduction ratio. A lofted surface is finally created from these reconstructed contours. The effectiveness of the synthetic surface reconstruction algorithm is demonstrated using its deviation values from its original point cloud data set. The experimental results show that the proposed algorithm generates a fairly accurate spline based facial model with only 5-20% of the actual scanned data, based upon the surface complexity. Performance can be improved by increasing the number of extracted contours, followed by a greater reduction ratio in the second data simplification stage.

Time-stamped event sequences (TSEQs) are time-oriented data without value information, shifting the focus of users to the exploration of temporal event occurrences. TSEQs exist in application domains, such as sleeping behavior, earthquake aftershocks, and stock market crashes. Domain experts face four challenges, for which they could use interactive and visual data analysis methods. First, TSEQs can be large with respect to both the number of sequences and events, often leading to millions of events. Second, domain experts need validated metrics and features to identify interesting patterns. Third, after identifying interesting patterns, domain experts contextualize the patterns to foster sensemaking. Finally, domain experts seek to reduce data complexity by data simplification and machine learning support. We present IVESA, a visual analytics approach for TSEQs. It supports the analysis of TSEQs at the granularities of sequences and events, supported with metrics and feature analysis tools. IVESA has multiple linked views that support overview, sort+filter, comparison, details-on-demand, and metadata relation-seeking tasks, as well as data simplification through feature analysis, interactive clustering, filtering, and motif detection and simplification. We evaluated IVESA with three case studies and a user study with six domain experts working with six different datasets and applications. Results demonstrate the usability and generalizability of IVESA across applications and cases that had up to 1,000,000 events.

The article is devoted to the agricultural markets’ analysis (grains and sunflower) as an integral system having its peculiarities which make it different from other markets. These specific features are connected with heterogeneity of participants following various aims and working in different investment horizons, as well as seasonal functioning of this market. The calculations have been given proving the E. Peters’ hypothesis on the presence of fractal agricultural markets. The research adjusts the use of the fractal markets’ analysis results in creating agrarian markets models and the possibility of forecasting its participants’ and prices behavior taking into consideration models’ data simplification.

Semantically consistent understanding of data/content in information systems is a key issue in collaborative work settings. Even simple data -- such as timestamps -- can be a source of misunderstandings both in data entry and reporting tasks. Healthcare information systems generate timestamps during data entry situations. Timestamped data are used for various purposes, such as efficiency calculations, organizational development, and research. However, timestamps are often generated ambiguously in different ways: they are a source of semantic interpretation errors. In this paper we illustrate the phenomena of timestamp ambiguity in medical settings. We suggest that User Interaction Metadata (UIM) could reduce the problems caused by ambiguous and erroneous timestamps. UIM may reveal hidden heterogeneity in data interpretation. Additionally, it can be used to identify user behavior patterns that are currently unrecognizable from raw transaction data.

Introduction: Hemorrhagic Transformation (HT) in patients not treated with recanalization therapies remains a poorly understood phenomenon. Hypothesis: HT in patients not treated with reperfusion therapies can be predicted based on simple data. We also proposed an easy-to-use clinical scale for predicting HT in this population. Methods: A total of 492 patients admitted to a Stroke Unit were randomly selected between January 2015 and December 2017. All patients were followed for seven days. A multivariable model was built, and independent predictive factors were used to produce a predictive grading score for HT. Receiver operating characteristic (ROC) analysis was conducted to assess prognostic performance. The optimal cut point score to distinguish between HT-positive and negative groups was defined with the Youden index. Results: HT occurred in 19.3% (N = 95) of the patients. Factors independently associated with HT included: age ≤60 years (OR:2.2 95% CI:1.2-4, p=0.01 ), male gender (OR:3.4 95% CI:1.8-6.3 , p<0.001), lacunar syndrome (OR:0.12 95% CI:0.27-0.56, p=0.007), blood glucose at admission ≥180mg/dL (OR:2.7 95% CI:1.3-5.7, p=0.009), ASPECTS ≤7 (OR:5.3 95% CI:2.9-9.7, p<0.001), hyperdense middle cerebral artery (MCA) (OR:2.7 95% CI:1.3-5.9, p=0.007), leukoaraiosis (OR:1.9 95% CI:1.1-3.5, p=0.02), Diabetes Mellitus (OR:2.2 95% CI:1.1-4.5, p=0.02) and cardioembolism (OR:3.2 95% CI:1.8-5.9, p<0.001). The score derived from the logistic regression analysis was: log(p/1-p) = -4.34 +0.79(age) +1.24(male) -2.08(lacunar) +0.99(glucose) +1.67(ASPECTS) +0.99(MCA) +0.67(leukoaraiosis) +0.79(Diabetes) +1.18(cardioembolism). Scores ranged from -8 to 23. The scale had an area under the curve (AUC) of 0.84 (0.81-0.88) for general HT and 0.85 (0.80-0.91) for symptomatic HT. Each point on the scale had an odds ratio of 1.39 (1.26-1.53) for predicting HT. The cut point was 8 with a sensitivity of 82.1% for general HT and 88.6% for symptomatic HT. Conclusions: Our risk score suggests that the individual risk for HT in patients with acute ischemic stroke not treated with reperfusion therapies can be assessed using simple data, including epidemiological characteristics, laboratory data, neuroimaging findings and etiologic classification.

This research evaluated team members’ perceptions of alternative decision analysis approaches when they were asked to analyze a simple or complex data set under a time constraint. There has been significant work done to understand what criteria should be included in this class of problems. However, less work has been done examining the selection of a specific decision analysis approach. This is an area of concern since research has shown that selecting a different decision analysis approach can change the results even when the same data is analyzed. This research effort is a preliminary look at perceptions of alternative decision analysis approaches. A mixed method study analyzing quantitative and qualitative data was completed. Teams were assigned to use one of five different decision analysis approaches, including Weighted Sum, SMART, TOPSIS, AHP, and ELECTRE I. After applying the approach to either a simple or complex data set, team members were surveyed to determine perceptions including comfort with the approach, its results, and ease/difficulty of explaining the approach to coworkers. Discussions were also held with team members to gain additional insights. The study indicated that team members preferred less technically challenging decision analysis approaches, such as Weighted Sum and to a lesser degree SMART, when they were using a simple data set because they found the approach easier to use and to explain to others. The more technically challenging decision analysis approaches, such as AHP and ELECTRE I, were preferred with a complex data set because they provided better insight into the problem. Teams in this study did selected different alternatives when they used different decision analysis approaches. Demographic data was also collected but was not found to influence results in this study.

Background: Data on migration trends are crucially lacking in developing countries. The lack of basic information on migration contrasts sharply with the increasing importance of migration in the policy agenda of both sending and receiving countries. Objective: The general objectives of this paper are: to show how trends in international migration can be reconstructed with three questions in a household survey; to evaluate the precision of the estimates; and to test how sensitive the estimates are to several methodological choices and assumptions. Methods: Migration trends are reconstructed with event history models. The reconstruction uses data collected through migration surveys conducted in cities in three countries (Senegal, Democratic Republic of Congo, and Ghana) as part of the MAFE (Migration between Africa and Europe) project. Specifically, two types of data are used: simple data on the first migration of children of household heads, collected through household surveys, and full migration histories of children collected in biographic surveys. First, we evaluate the precision of our estimates using data collected in the household questionnaire. Next, the sensitivity of our results to different methodological choices and assumptions is evaluated. Results: Migration trends measured with simple data from household surveys are broadly consistent with results obtained from full migration histories. However, increases in migration trends tend to be underestimated with household data. Migration probabilities are also affected by large confidence intervals. Conclusions: Estimates using household data may be affected by large confidence intervals, and migrations trends are influenced by the simplifying assumptions that are made when using these data. Despite these limitations, estimates based on three simple questions provide useful information on migration levels and trends.

PurposeReal-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.Design/methodology/approachThe presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.FindingsThis work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.Research limitations/implicationsThe KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.Practical implicationsThe presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.Originality/valueWhile automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.

In single-pixel imaging, a series of illumination patterns are projected onto an object and the reflected or transmitted light from the object is integrated by a photodetector (the single-pixel detector). Then, from the set of received photodetector signals, the image of the object can ultimately be reconstructed. However, this reconstruction is not only computationally expensive, but also unnecessary for purposes such as image classification tasks. This work proposes a reconstruction-free multi-class image classification framework that, unlike most of the existing approaches, exploits the sequential nature of the problem. Indeed, by accumulating evidence of the sequence of scalar values, a decision is made after each measurement on whether already classifying the object being imaged, or waiting for more measurements. This online decision relies on a mechanism to achieve a recognition-delay trade-off that induces behaviours within the conservative-to-aggressive spectrum, which suit distinct requirements in different applications. Additionally, the presentation order of the illumination patterns makes a difference in terms of the reconstruction quality (if required) and classification performance when a limited number of patterns is used. Nevertheless, in many cases, simple data- and task-agnostic orders, such as random or frequency-based orders, are commonly used. To address this, a novel sparse-representation-based strategy is presented that sorts the patterns according to their general and discriminability utilities. Both, the online classification framework including the recognition-delay trade-off mechanism, and the data- and task-aware pattern ordering proposed, are experimentally assessed, with encouraging results, on the MNIST digits and CalTech 101 Silhouettes datasets.

A calibration and range-data extraction algorithm for an omnidirectional laser range finder of a free-ranging mobile robot