Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multiple Unit Auction Algorithm: Some Theory and a Web Implementation

The paper discusses an electronic multiple unit discriminative auction and its implementation on the world wide web. The algorithm is designed to reduce the level of price discrimination and provide a useful and easy-to-use electronic auction mechanism. The decision of the bid price is suggested to the bidder, making the auction less cumbersome than traditional multiple unit auctions. We discuss the special cases of auction owner specified reservation prices for different quantity levels and bidder acceptance of partial quantities or not. Implications of the four resulting cases are explored and discussed. Images from the web-site, providing a prototype implementation of the auction algorithm, are displayed.
A new approach of gross errors detection for soft sensing data based on cluster analysis

Modeling data plays a very important role in the process of establishing an accurate soft sensing model. Gross errors detection for modeling data could ensure the good quality of modeling data, and then ensure the good performance of soft sensor model. In this paper, a new gross errors detection method based on cluster analysis is proposed. Unlike the traditional methods, the new method does not rely on the mechanism model. And the new method is suitable to the characters of soft sensor better. A new cluster algorithm is presented to detect the gross errors of modeling data based on the special characters of soft sensor. The new clustering algorithm detects the gross errors by analyzing the Euclidean distance between the data points and the center of data set. The experiments demonstrate that the new detection approach based on new clustering method could detect the gross error effectively.
Interagency collaboration in vocational rehabilitation for persons with mental health problems: the perspective of the service users and the professionals

There is an aspiration and policy within the European Union to fully involve persons with disabilities in the community; this implies an opportunity to gainful employment. A large percentage of disabled persons remain unemployed despite this policy, especially persons with mental health problems. This study aims at investigating how people with mental health problems and caseworkers in different agencies experience the vocational rehabilitation process from an interagency collaboration perspective. The study reveals a number of hindering factors portrayed and discussed in six themes – the time available for rehabilitation, management, new ways of working, absence of an actor, continuity and consensus – and also tables the implications for interagency collaboration in the field of vocational rehabilitation.
The 3W Model and Algebra for Unified Data Mining

Real data mining/analysis applications call for a framework which adequately supports knowledge discovery as a multi-step process, where the input of one mining operation can be the output of another. Previous studies, primarily focusing on fast computation of one speci c mining task at a time, ignore this vital issue. Motivated by this observation, we develop a unied model supporting all major mining and analysis tasks. Our model consists of three distinct worlds, corresponding to intensional and extensional dimensions, and to data sets. The notion of dimension is a centerpiece of the model. Equipped with hierarchies, dimensions integrate the output of seemingly dissimilar mining and analysis operations in a clean manner. We propose an algebra, called the dimension algebra, for manipulating (intensional) dimensions, as well as operators that serve as \bridges" between the worlds. We demonstrate by examples that several real data mining processes can be captured using our model and algebra. We demonstrate the naturality of the algebra by establishing several identities. Finally, we discuss e cient implementation of the proposed framework.
Leadership Metaphors: Developing Innovative Teaching Strategies

Research conducted over the last half century has revealed that optimal learning takes place in the brain when both the left-brain and the right brain work in balance. Educators can intentionally arouse the activation of one hemisphere of the brain over the other through the use of right brain strategies in language learning. These strategies, including analogy, metaphor, synthesis, and imagery connect the two separate thought processes of the brain, linking the sequential analytical knowledge of the left brain with the conceptual patterns and images of the right brain. In 1997 Clouse and colleagues developed a teaching strategy called “Whole-Part- Whole,” where the learning is tied directly with the framework of the learner. Through “just in time teaching,” the student learns the parts of a opened-ended case. The recursive design allows students to recreate a new “whole,” which becomes new applied knowledge. The whole-brain theory and the Whole-Part-Whole strategy of learning were used as the basis for an assignment at Vanderbilt University in which graduate students were asked to write a paper using metaphors to describe leadership skills of a leader that they knew. Students were required to tie the metaphor to leadership theory. Students were very creative in the development of metaphors to describe leaders that they enjoyed and admired.
Spinal reconstruction with pedicle screw-based instrumentation and rhBMP-2 in patients with neurofibromatosis and severe dural ectasia and spinal deformity: report of two cases and a review of the literature.

Neurofibromatosis is one of the most common genetic disorders, with type-I neurofibromatosis having a global prevalence of one in 3000 individuals1-4. Inherited in an autosomal dominant manner, type-1 neurofibromatosis may be known best for its cutaneous manifestations. Cafe au lait spots and peripheral neurofibromas arise as a result of unchecked proliferation of neural crest-derived melanocytes and Schwann cells, respectively5,6. These superficial lesions are generally benign and are often considered to be purely a cosmetic issue7. In contrast, the osteopathological manifestations of type-1 neurofibromatosis are of far greater clinical concern. Spinal deformity, particularly kyphoscoliosis of the thoracic spine, is the most common abnormality (present in 10% to 60% of cases)4,8-10. Although the precise etiology of these spinal abnormalities is not well understood and most are probably multifactorial, a variety of pathologic processes have been implicated4,11-13. Dural ectasia may result from cerebral spinal fluid pulsations, which lead to dilatation of the weakened dural sac, with erosion of the surrounding vertebral elements as the dural sac enlarges14. Peripheral neurofibromas can expand into adjacent ribs, facet joints, pedicles, and paravertebral musculature4,12. Intrinsic pathologic conditions, such as osteomalacia and general mesodermal dysplasia, can also contribute to spinal instability in a more occult fashion13. Evidence of these processes on imaging studies includes rib penciling, meningoceles, expanded neural foramina, vertebral scalloping and wedging, and soft-tissue masses15-17. Clinical sequelae, ranging from simple back pain to decreased pulmonary function and quadriparesis, have been associated with the spinal manifestations of type-1 neurofibromatosis18,19. Despite the potential for major neurologic complications, a substantial proportion of patients with type-1 neurofibromatosis exhibit normal neurologic function4 …
Self-exclusion: A Proposed Gateway to Treatment Model

Self-exclusion is a programme utilized by the gaming industry to limit access to gaming opportunities for problem gamblers. It is based on the following principles: (1) The gaming industry recognizes that a proportion of community members gamble excessively and have difficulty controlling gambling behaviours; (2) The gaming industry has a responsibility to provide a safe gaming environment and to assist in minimizing the negative impact on individuals displaying problem gambling behaviours; (3) Individuals must accept personal responsibility for limiting gambling behaviours to affordable levels; and (4) Self-exclusion is not a treatment designed to address psychological processes. The objective of the present paper is to propose a unifying structure for self-exclusion programmes as a gateway to treatment based on a system operated by independent ‘educators’ whose roles and responsibilities include informing individuals of the purpose of self-exclusion, establishing links and a gateway for access to supplementary services and monitoring and reporting the effectiveness of the overall programme.
A Theory of the Jazz Community

ONE OF the ubiquitous trends in social behavior is the formation of comprehensive systems of interaction known as communities. Everywhere in the world and throughout man's existence on earth, the formation of communities has been evident, from the primitive tribe to the contemporary nation-state. Among the exciting developments within the more complex communities of mankind, such as cities and nations, is the emergence of subcommunity forms which both complement and contest the arrangements of the larger society. Status communities and ethnic communities of many sorts belong among the contemporary sub-communities. It is instructive to apply this concept of sub-community formation to areas of behavior such as the worlds of art and jazz, when that behavior can be shown to approach closure and completeness with respect to a distinctive way of life. This paper deals with two major tasks: (1) the development of a special theory of the jazz community as an unique form of status community, and (2) a judgment as to the validity of referring to the social world of the jazz musician as a community. It should be mentioned that while this formulation is conceived in terms of the jazz subculture, it also has widespread application elsewhere in the area of social deviance. Since the concept of status group has its roots in the general theory of community, we shall briefly outline that concept.
Visually localizing design problems with disharmony maps

Assessing the quality of software design is difficult, as "design" is expressed through guidelines and heuristics, not rigorous rules. One successful approach to assess design quality is based on detection strategies, which are metrics-based composed logical conditions, by which design fragments with specific properties are detected in the source code. Such detection strategies, when executed on large software systems usually return large sets of artifacts, which potentially exhibit one or more "design disharmonies", which are then inspected manually, a cumbersome activity. In this article we present disharmony maps, a visualization-based approach to locate such flawed software artifacts in large systems. We display the whole system using a 3D visualization technique based on a city metaphor. We enrich such visualizations with the results returned by a number of detection strategies, and thus render both the static structure and the design problems that affect a subject system. We evaluate our approach on a number of open-source Java systems and report on our findings.
Planning-driven behavior selection network for controlling a humanoid robot

A humanoid robot has uncertain sensor data, and produces larger errors arising from the control process than other types of robot having wheels. The humanoid robot for providing service should not only generate proper behaviors to accomplish goals in unstable environments, but also consider flexible scalability to reflect demanding user's requests. We propose a control system based on planning-driven behavior selection network so as to generate autonomous behaviors of the robot rapidly and suitably. In this paper, the behavior selection network, one of behavior-based methods, is modularized considering sub-goals. The STRIPS planning makes a sequence of robot behaviors automatically by controlling the modules. The proposed system can control the robot to cope with the various environments, as well as to achieve goals according to the user's demand. Moreover, since the BSN and STRIPS planning structures are internally independent, the proposed system is scalable flexibly to increasing user requests. We confirm the usability of the proposed system by performing several test scenarios with NAO robot. Experiments show that the proposed system is able to make a behavioral sequence to fulfill goals appropriately, and can create behaviors of the robot in the various situations. We can also confirm an accuracy of 85.7% through applying the proposed system in the real world.
The Creative Structuring of Counterintuitive Worlds

Recent research has shown a memory advantage for minimally counterintuitive (MCI) concepts, over concepts that are either intuitive (INT) or maximally counterintuitive (MXCI), although the general result is heavily affected by context. Items from one such study were given to subjects who were asked to create novel stories using at least three concepts from a list containing all three types. Results indicated a preference for using MCI items (as in the recall studies), and further disclosed two styles of usage, an accommodative style and an assimilative style. The results extend recent memory research and suggest extensions to recent theories intended to explain the prevalence of counterintuitive religious concepts.
Force feedback requirements for efficient laparoscopic grasp control

During laparoscopic grasping, tissue damage may occur due to use of excessive grasp forces and tissue slippage, whereas in barehanded grasping, humans control their grasp to prevent slippage and use of excessive force (safe grasp). This study investigates the differences in grasp control during barehanded and laparoscopic lifts. Ten novices performed lifts in order to compare pinch forces under four conditions: barehanded; using tweezers; a low-efficient grasper; and a high-efficient grasper. Results showed that participants increased their pinch force significantly later during a barehanded lift (at a pull-force level of 2.63 N) than when lifting laparoscopically (from pull-force levels of 0.77 to 1.08 N). In barehanded lifts all participants could accomplish a safe grasp, whereas in laparoscopic lifts excessive force (up to 7.9 N) and slippage (up to 38% of the trials) occurred frequently. For novices, it can be concluded that force feedback (additional to the hand-tool interface), as in skin-tissue contact, is a prerequisite to maintain a safe grasp. Much is known about grasp control during barehanded object manipulation, especially the control of pinch forces to changing loading, whereas little is known about force perception and grasp control during tool usage. This knowledge is a prerequisite for the ergonomic design of tools that are used to manipulate objects.
Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study

Background Public health officials and policy makers in the United States expend significant resources at the national, state, county, and city levels to measure the rate of influenza infection. These individuals rely on influenza infection rate information to make important decisions during the course of an influenza season driving vaccination campaigns, clinical guidelines, and medical staffing. Web and social media data sources have emerged as attractive alternatives to supplement existing practices. While traditional surveillance methods take 1-2 weeks, and significant labor, to produce an infection estimate in each locale, web and social media data are available in near real-time for a broad range of locations. Objective The objective of this study was to analyze the efficacy of flu surveillance from combining data from the websites Google Flu Trends and HealthTweets at the local level. We considered both emergency department influenza-like illness cases and laboratory-confirmed influenza cases for a single hospital in the City of Baltimore. Methods This was a retrospective observational study comparing estimates of influenza activity of Google Flu Trends and Twitter to actual counts of individuals with laboratory-confirmed influenza, and counts of individuals presenting to the emergency department with influenza-like illness cases. Data were collected from November 20, 2011 through March 16, 2014. Each parameter was evaluated on the municipal, regional, and national scale. We examined the utility of social media data for tracking actual influenza infection at the municipal, state, and national levels. Specifically, we compared the efficacy of Twitter and Google Flu Trends data. Results We found that municipal-level Twitter data was more effective than regional and national data when tracking actual influenza infection rates in a Baltimore inner-city hospital. When combined, national-level Twitter and Google Flu Trends data outperformed each data source individually. In addition, influenza-like illness data at all levels of geographic granularity were best predicted by national Google Flu Trends data. Conclusions In order to overcome sensitivity to transient events, such as the news cycle, the best-fitting Google Flu Trends model relies on a 4-week moving average, suggesting that it may also be sacrificing sensitivity to transient fluctuations in influenza infection to achieve predictive power. Implications for influenza forecasting are discussed in this report.
The Impact of Two Standards-Based Mathematics Curricula on Student Achievement in Massachusetts

Since the passage of the Education Reform Act in 1993, Massachusetts has developed curriculum frameworks and a new statewide testing system. As school districts align curriculum and teaching practices with the frameworks, standards-based mathematics programs are beginning to replace more traditional curricula. This paper presents a quasi-experimental study using matched comparison groups to investigate the impact of one elementary and one middle school standards-based mathematics program in Massachusetts on student achievement. The study compares statewide standardized test scores of fourth-grade students using Everyday Mathematicsand eighth-grade students using Connected Mathematicsto test scores of demographically similar students using a mix of traditional curricula. Results indicate that students in schools using either of these standards-based programs as their primary mathematics curriculum performed significantly better on the 1999 statewide mathematics test than did students in traditional programs attending matched comparison schools. With minor exceptions, differences in favor of the standards-based programs remained consistent across mathematical strands, question types, and student sub-populations.
The Growth of Voice: Expanding Possibilities for Representing Self in Research Writing.

Novice researchers are expected to participate through writing in the particular discourse community in which their research is located, yet if they come from other languages and other academic cultures and traditions than those of the education provider, they may experience the process of writing as the process of silencing their voices. Many supervisors of such researchers face the dilemma of encouraging compliance with the dominant conventions - opting thus for the safety of homogeneity - or of fostering greater diversity in voice and discourse organisation, opting thus for the risk of negative reader response. Through retrospective and introspective reflections by the authors, using sociocultural and postcolonial perspectives on academic writing, we explore the processes we used in negotiating Ha's representation of self in her novice research writing. We look in particular at the roles a supervisor and student can play in helping a writer to make informed choices about ways of making meaning that satisfy her intentions, so that the writer's own voice can grow. We also trace how this initiative contributed to a gradual process of recognition of greater diversity in writing by the academic community in the institution where we work, and discuss how this represents an ethical approach to academic writing.
Neural Decoding: A Predictive Viewpoint

Decoding in the context of brain-machine interface is a prediction problem, with the aim of retrieving the most accurate kinematic predictions attainable from the available neural signals. While selecting models that reduce the prediction error is done to various degrees, decoding has not received the attention that the fields of statistics and machine learning have lavished on the prediction problem in the past two decades. Here, we take a more systematic approach to the decoding prediction problem and search for risk-optimized reverse regression, optimal linear estimation (OLE), and Kalman filter models within a large model space composed of several nonlinear transformations of neural spike counts at multiple temporal lags. The reverse regression decoding framework is a standard prediction problem, where penalized methods such as ridge regression or Lasso are routinely used to find minimum risk models. We argue that minimum risk reverse regression is always more efficient than OLE and also happens to be 44% more efficient than a standard Kalman filter in a particular application of offline reconstruction of arm reaches of a rhesus macaque monkey. Yet model selection for tuning curves–based decoding models such as OLE and Kalman filtering is not a standard statistical prediction problem, and no efficient method exists to identify minimum risk models. We apply several methods to build low-risk models and show that in our application, a Kalman filter that includes multiple carefully chosen observation equations per neural unit is 67% more efficient than a standard Kalman filter, but with the drawback that finding such a model is computationally very costly.
Verbal Short-Term Memory Encoding of Learning Disabled, Deaf, and Normal Readers

The present study investigated the hypothesis that nonstrategic verbal encoding abilities are deficient in learning disabled readers. Normal, deaf, and learning disabled children matched on chronological age, IQ, and sex were randomly assigned to named and unnamed stimulus pretraining conditions and compared on subsequent performance on a probe-type serial memory task. Although overt rehearsal was inhibited, strong primacy effects were found for both the named and the unnamed condition. The named condition was superior to the unnamed condition for both normal and deaf but not for learning disabled children. Results were interpreted to indicate a deficient verbal-visual integrative process in disabled children occurring prior to the application of mnemonic strategies.
Coded slotted ALOHA with varying packet loss rate across users

The recent research has established an analogy between successive interference cancellation in slotted ALOHA framework and iterative belief-propagation erasure-decoding, which has opened the possibility to enhance random access protocols by utilizing theory and tools of erasure-correcting codes. In this paper we present a generalization of the and-or tree evaluation, adapted for the asymptotic analysis of the slotted ALOHA-based random-access protocols, for the case when the contending users experience different channel conditions, resulting in packet loss probability that varies across users. We apply the analysis to the example of frameless ALOHA, where users contend on a slot basis. We present results regarding the optimal access probabilities and contention period lengths, such that the throughput and probability of user resolution are maximized.