Miracle-dz/UniFAR-FTUs huggingface dataset:
0-Borel Fixed Ideals

An ideal is called Borel fixed if it is invariant under the action of the Borel group. The interest in studying 0-Borel fixed ideals comes from a theorem of Bayer, Galligo, and Stillman (cf. [Ei,(15.20)]), which shows that the generic initial ideal of any homogeneous ideal in S is Borel-fixed, and further if the characteristic of k is 0 then every Borel-fixed ideal is 0-Borel fixed. When char k 6= 0 there are examples of Borel-fixed ideals which are not 0-Borel fixed. 0-Borel fixed ideals were studied by Eliahou and Kervaire, who described in [EK] the minimal free resolution of S/I. The existence of a commutative associative DG (= differential graded) algebra structure on a minimal free resolution over a graded or local Noetherian ring is a powerful tool for investigating the properties of the resolution. The best example is the Koszul resolution of S/(y1, . . . , yi) for an S-regular sequence y1, . , yi. Results of Tate and Gulliksen provide another important example: the minimal resolution of k over S/Q, for any homogeneous ideal Q in S. In both cases the resolution is a free commutative divided power algebra. In general a commutative associative DG algebra structure might be much more complicated or might not exist at all. The latter happens even for finite resolutions as shown in [Av]. However, results of Avramov, Buchsbaum, Gover, Eisenbud, Kustin, Miller, Palmer, and Srinivasan build such a structure in many interesting cases. We extend [Sr,(3.6)], which shows that such a structure exists on the minimal free resolution of S/Q when Q is a power of the maximal ideal (x1, . . . , xr). The main difference between the resolutions of a 0-Borel fixed ideal and of a power of the maximal ideal is that the resolution of a power of the maximal ideal is linear, whereas the resolution of a 0-Borel fixed ideal is linear only if all the minimal generators of the ideal have the same degree. We prove the following:
A Belief Rule Based Expert System to Assess Meditation

Meditation is a practice, by regularly applying one can develop calm and quite mind. It is essential not only for developing pure mind but also for sound health. Therefore, the assessment of meditation is very important in every day of our life. It can be measured by using various human sensing factors such as sleep pattern, Galvanic Skin Response (GSR), Heart rate Variability (HRV), pulse difference etc. However, assessing meditation level is complex due to the presence of various types of uncertainties such as vagueness, ignorance, imprecision, incompleteness associated with these human sensing factors. This paper presents the design, development and application of an expert system to assess meditation under uncertainty. The belief rule-based inference methodology using evidential reasoning approach (RIMER), which can handle various types of uncertainties, employed to develop this expert system. The knowledge-base has been constructed by taking account of meditation practitioner's real data and expert's opinion. The practitioner's case studies have been used to validate the expert system. It has been observed that the system generated results are more reliable than from the expert assessment.
Minotor: Monitoring Timing and Behavioral Properties for Dependable Distributed Systems

Assessing the correct behavior of a given system at run-time can be achieved by monitoring its execution, and is complementary to off-line analysis such as static verification. In this work, we focus on run-time monitoring of system properties that include both causality and timing constraints, in distributed and time-constrained systems. Based on a description of a property that includes events and temporal constraints, expressed as a timed-arc Petri net, we show how to automatically transform it into a an executable and distributed monitoring engine. To that aim, we introduce a modification of the semantics of Petri nets to be able to execute it online on partial executions and distributed observation environments. We show how to use this formal framework to provide MINOTOR, a model-driven distributed monitoring system, describe its implementation and show its applicability on a transportation use-case.
Single trial variability in brain–computer interfaces based on motor imagery: Learning in the presence of labeling noise

This article addresses the issue of learning efficient linear spatial filters and a classification function to match noninvasive electroencephalographic (EEG) signals to motor imagery tasks voluntarily performed by the subjects. The new perspective used in this article consists in releasing the widely accepted hypothesis stating that motor tasks‐related brain activities should have similar time course across trials. This work proposes a learning model that takes into account two previously unconsidered sources of variability. First, the time course of the subject's brain activity, while performing a motor imagery task, will be considered as a trial‐dependent variable. This means that the optimal time, defined as an amount of time after the trial cue, chosen to determine the task performed by the subject might be different between distinct trials. The second released hypothesis deals with the spectral discriminative brain response. Although usual learning methods do not allow any dependency between the optimal discriminative time and frequency bands, our model takes into account this possible source of variability. Therefore, the brain response in distinct frequency bands, e.g., in the mu band or beta band, could be used by the decision function at distinct instants. Based on this underlying enhanced model, we propose a two‐step procedure. In the first step, the algorithm carefully analyzes, using cross‐validation techniques, the training data to identify previously mentioned sources of variability. In the second step, the enhanced frequency‐dependent linear spatial filters and the classification function are determined. As by‐products of this analysis, substantial piece of knowledge about motor imagery is provided. First, the method allows the identification and quantification of labeling noise in brain–computer interfaces (BCIs) based on motor imagery. Second, the algorithm gives a comparative estimation of the spectral time courses during motor imagery. This article makes an extensive use of the dataset I of BCI Competition IV, which took place in 2008. It consists of a training set and a test set of 59 EEG signals recording on four subjects while performing an asynchronous BCI experiment. The two‐step procedure presented in this article is shown to significantly outperform a comparative naive approach. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 148–157, 2011
The analysis of a simple k-means clustering algorithm

Abstract : K-means clustering is a very popular clustering technique which is used in numerous applications. Given a set of n data points in R(exp d) and an integer k, the problem is to determine a set of k points R(exp d), called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's algorithm. In this paper, we present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is very easy to implement. It differs from most other approaches in that it precomputes a kd-tree data structure for the data points rather than the center points. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time. Second, we have implemented the algorithm and performed a number of empirical studies, both on synthetically generated data and on real data from applications in color quantization, compression, and segmentation.
Automated decision technology for network design in cellular communication systems

The location and configuration of transmission infrastructure for cellular communication networks is a complex engineering task involving many competing objectives. While minimising commitment to infrastructure and maintenance, the network needs to meet quality of service requirements based on signal quality, area coverage and call traffic capacity. In response to this, operators must make decisions over antennae type, power control, tilt and azimuth of equipment in addition to site location. In this overview paper, we describe the problem facing cellular providers using FD-TDMA technology and present an automated approach for managing the entire design process, taking into account the full complexity of the problem. Meta-heuristic optimization procedures are used to both select sites and configure equipment. This results in a deterministic process for designing networks which is not contingent on human experience or judgement.
Mindful infrastructure as antecedent of innovation resilience behaviour of project teams

PurposeThe purpose of this paper is to investigate whether insights into high reliability organizations (HROs) are useful for innovation management teams. HRO teams can keep failure to a minimum level due to high alertness and resilience. Project teams working on innovation management could benefit from HRO principles and thus reduce their chances of failure.Design/methodology/approachA survey among in total 260 team members and team leaders of project teams in innovation management was conducted to study the relation between, on the one hand, organizational features of HROs (“mindful infrastructure”) and HRO principles (adjusted as “innovation resilience behaviour”, IRB), and on the other hand, between mindful infrastructure and IRB and project outcomes.FindingsFrom the results it could be concluded that mindful infrastructure associates with IRB, and that IRB has a mediating role in the relation between mindful infrastructure and project outcomes. Innovation management project teams can thus learn from the practice of HRO teams.Originality/valueTo the authors’ knowledge, HRO-thinking has not been applied to team behaviour in innovation management. A fruitful transfer of insights from the domain of safety and crisis management seems applicable to the domain of innovation.
Animal board invited review: advances in proteomics for animal and food sciences

Animal production and health (APH) is an important sector in the world economy, representing a large proportion of the budget of all member states in the European Union and in other continents. APH is a highly competitive sector with a strong emphasis on innovation and, albeit with country to country variations, on scientific research. Proteomics (the study of all proteins present in a given tissue or fluid – i.e. the proteome) has an enormous potential when applied to APH. Nevertheless, for a variety of reasons and in contrast to disciplines such as plant sciences or human biomedicine, such potential is only now being tapped. To counter such limited usage, 6 years ago we created a consortium dedicated to the applications of Proteomics to APH, specifically in the form of a Cooperation in Science and Technology (COST) Action, termed FA1002 – Proteomics in Farm Animals: www.cost-faproteomics.org. In 4 years, the consortium quickly enlarged to a total of 31 countries in Europe, as well as Israel, Argentina, Australia and New Zealand. This article has a triple purpose. First, we aim to provide clear examples on the applications and benefits of the use of proteomics in all aspects related to APH. Second, we provide insights and possibilities on the new trends and objectives for APH proteomics applications and technologies for the years to come. Finally, we provide an overview and balance of the major activities and accomplishments of the COST Action on Farm Animal Proteomics. These include activities such as the organization of seminars, workshops and major scientific conferences, organization of summer schools, financing Short-Term Scientific Missions (STSMs) and the generation of scientific literature. Overall, the Action has attained all of the proposed objectives and has made considerable difference by putting proteomics on the global map for animal and veterinary researchers in general and by contributing significantly to reduce the East–West and North–South gaps existing in the European farm animal research. Future activities of significance in the field of scientific research, involving members of the action, as well as others, will likely be established in the future.
Static Ranking of Scholarly Papers using Article-Level Eigenfactor (ALEF)

Microsoft Research hosted the 2016 WSDM Cup Challenge based on the Microsoft Academic Graph. The goal was to provide static rankings for the articles that make up the graph, with the rankings to be evaluated against those of human judges. While the Microsoft Academic Graph provided metadata about many aspects of each scholarly document, we focused more narrowly on citation data and used this contest as an opportunity to test the Article Level Eigenfactor (ALEF), a novel citation-based ranking algorithm, and evaluate its performance against competing algorithms that drew upon multiple facets of the data from a large, real world dataset (122M papers and 757M citations). Our final submission to this contest was scored at 0.676, earning second place.
CRL/NMSU and Brandeis: Description of the MucBruce System as Used for MUC-4

Through their involvement in the Tipster project the Computing Research Laboratory at New Mexico State University and the Computer Science Department at Brandeis University are developing a method for identifying articles of interest and extracting and storing specific kinds of information from large volumes of Japanese and English texts. We intend that the method be general and extensible. The techniques involved are not explicitly tied to these two languages nor to a particular subject area. Development for Tipster has been going on since September, 1992.
An integrative model of consumer trust in internet shopping

Despite the phenomenal growth of Internet users, the penetration rate of Internet shopping remains low. Previous works suggesting that the lack of trust is the major impediment stops consumers from building exchange relationships with Internet vendors. In this study, we synthesize the three theoretical perspectives of trust and perceived risk theory to develop an integrative model of consumer trust in Internet shopping. We empirically test the research model with a sample of 405 students in a local university. The results show that both perceived integrity and perceived risk have the greatest influence on consumer trust in Internet shopping. Propensity to trust exhibits a significant moderating effect on consumer trust and its antecedents. The implications of this study are noteworthy for both researchers and practitioners.
Development of shear-mode piezoelectric microactuator for precise head positioning

We have developed a novel piezoelectric micro-actuator for dual-stage actuator systems in magnetic disk drives. This microactuator is based on the shear deformation of piezoelectric elements and drives the head suspensionassembly. The actuator is suitable for thin devices and is easily manufactured because of its simple stack configuration. We installed the microactuator in one of Fujitsu's 3.5-inch commercial drives to evaluate the servo system of a dual-stage actuator. The dual-stage actuator system achieved a non-repeatable position error (NRPE) 3a of 0.036 μm. The dual-stage servo system reduced the NRPE by 35% compared with the conventional single actuator system, even in HDDs with a high rotational speed of 10000 rpm.
DEVELOPING TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE IN USING COMPUTERIZED SCIENCE LABORATORY ENVIRONMENT: AN ARRANGEMENT FOR SCIENCE TEACHER EDUCATION PROGRAM

Over the last decade, traditional educational practices in teacher education have not provided prospective teachers with all the necessary skills for teaching students to acquire the skills to cope with the challenges of society in the 21 century. For this reason, there is a worldwide trend toward producing teachers with high teaching competency. To promote competency in using technologies to the teaching of specific content in the classroom context, the epistemology of Technological Pedagogical Content Knowledge (now known as TPCK or TPACK) is used as a basis for designing a particular arrangement of courses for science teacher education programs, thereby to help meet the needs of the 21 century teacher education development. In order to situate the transfer of how to cultivate TPACK in science teacher education program for others, this article demonstrates details of the alignment of courses for preservice science teacher preparation based on a proposed framework of a TPACK-based computerized laboratory environment for science teaching. Also, the implications of adaptation and the support of the transfer of the program would enable an effective transfer of design and practice in order to prepare preservice teachers in science teaching. The possibility of transferring such course arrangement to the other contexts of science teacher education programs could not be discounted.
Persistent Misperceptions: Americans’ Misplaced Confidence in Privacy Policies, 2003–2015

This paper examines the persistence of Americans’ misunderstanding of the function of privacy policies. We also identify groups that have misplaced confidence in the privacy policy label and address whether the groups’ patterns of misperception have changed over time. The findings add a new dimension to the argument that the usefulness of privacy policies needs to be reassessed. As a remedy, we call for media literacy programs to address structural features of media systems that lead to broadly held misperceptions such as the one examined here.
Exact Penalization, Level Function Method, and Modified Cutting-Plane Method for Stochastic Programs with Second Order Stochastic Dominance Constraints

Level function methods and cutting-plane methods have been recently proposed to solve stochastic programs with stochastic second order dominance (SSD) constraints. A level function method requires an exact penalization setup because it can only be applied to the objective function, not the constraints. Slater constraint qualification (SCQ) is often needed for deriving exact penalization. It is well known that SSD usually does not satisfy SCQ and various relaxation schemes have been proposed so that the relaxed problem satisfies the SCQ. In this paper, we show that under some moderate conditions the desired constraint qualification can be guaranteed through some appropriate reformulation of the constraints rather than relaxation. Exact penalization schemes based on $L_1$-norm and $L_\infty$-norm are subsequently derived through Robinson's error bound on convex systems and Clarke's exact penalty function theorem. Moreover, we propose a modified cutting-plane method which constructs a cutting-plane through t.
Marginal inferential models

Inferential models (IMs) provide a general framework for prior-free, frequencycalibrated, posterior probabilistic inference. The fundamental idea is the use of unobservable auxiliary variables to describe the underlying uncertainty about the parameter of interest. When nuisance parameters are present, a marginalization step can reduce the dimension of the auxiliary variable, which in turn leads to more efficient inference. Unlike classical marginalization procedures that use optimization or integration, the proposed framework works with a set union operation. Sufficient conditions are given for when this marginalization operation can be performed without loss of information, and in such cases we prove that an appropriately constructed IM is calibrated, in a frequentist sense, for marginal inference. In problems where these sufficient conditions are not met, we propose a generalized marginalization technique based on parameter expansion that leads to possibly conservative marginal inference. Details are given for two benchmark examples: Stein’s many-normal-means problem and the Behrens–Fisher problem.
Optimal Effort in Consumer Choice

This paper develops a theoretical model of optimal effort in consumer choice. The model extends previous consumer choice models in that the consumer not only chooses a product, but also decides how much effort to apply to a given choice problem. The model yields a unique optimal level of effort, which depends on the consumer’s cost of effort, the expected utility gain of a correct choice, and the complexity of the choice set. We show that the relationship between effort and cost of effort is negative, whereas the relationships between effort and product utility difference and choice task complexity are undetermined. To resolve this theoretical ambiguity and to explore our model empirically, we investigate the relationships between effort and cost of effort, product utility difference and choice task complexity using data from a conjoint choice study of two-alternative consumer restaurant choices. Response time is used as a proxy for effort and consumer involvement measures capture individual differences in (relative) cost of effort and perceived complexity. Effort is explained using the (estimated) utility difference between alternatives, the number of elementary information processes (EIP’s) required to solve the choice problem optimally and respondent specific cost of effort and complexity perceptions. The predictions of the theoretical model are supported by our empirical findings. Response time increases with lower cost of effort and greater perceived complexity (i.e. higher involvement). We find that across the range of choice tasks in our survey, effort increases linearly with smaller product utility differences and greater choice task complexity.
Early Experience and Emotional Development: The Emergence of Wariness of Heights

Because of its biological adaptive value, wariness of heights is widely believed to be innate or under maturational control. In this report, we present evidence contrary to this hypothesis, and show the importance of locomotor experience for emotional development. Four studies bearing on this conclusion have shown that (1) when age is held constant, locomotor experience accounts for wariness of heights; (2) “artificial” experience locomoting in a walker generates evidence of wariness of heights; (3) an orthopedically handicapped infant tested longitudinally did not show wariness of heights so long as he had no locomotor experience; and (4) regardless of the age when infants begin to crawl, it is the duration of locomotor experience and not age that predicts avoidance of heights. These findings suggest that when infants begin to crawl, experiences generated by locomotion make possible the development of wariness of heights.
Software Requirement Prioritization using Machine Learning

Requirement engineering plays a very important role in software development life cycle (SDLC). Generally Software projects suffer with the problem of various types and categories of requirements and are also delimited by constraints like time and budget. To deal with this type of requirement complexity, project managers need to prioritize the requirements of the proposed software effectively. To decide about prioritization and consideration of a set of requirements is a strategic concern. This process is known as requirements prioritization. This paper proposes a novel requirements prioritization approach called Gradient Descent Ranking (GDRank), which combines project’s stakeholders preferences with Functional and Non Functional requirements, their ordering and approximations are estimated through machine learning techniques. For validation purpose the proposal is compared with various other prominent requirement prioritization methods. Keywords-Quality Function Deployment (QFD), Requirements Prioritization, Machine Learning