Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Priorities in Concurrency Control for RTDBSs

In a real-time database system, it is difficult to meet all timing constrain ts due to the consistency requirements of the underlying database. Real-time database trans action cheduling requires the development of efficient concurrency control protocols t hat try to maximize the number of transactions satisfying their real-time constraints. Th erefore transaction priorities should be taken account when making decisions in concurrency contro l. Although optimistic approach has been shown to be better than locking protocols fo r RTDBS, it has the problems of unnecessary restarts and heavy restart overhead. In this paper , we ropose a new optimistic concurrency control protocol called OCC-PDATI which us es priority information in conflict resolution. In OCC-PDATI number of transaction restar t is minimized by dynamically adjusting the serialization order of the conflicting trans actions. Need for dynamic serialization order adjustment is checked and serialization order is up dated in the validation phase. This provides more freedom to adjust serialization ord er f conflicting transactions. OCC-PDATI has several advantages over the other optimistic concurrency control protocols. The protocol maintains all the nice properties wit h forward validation, high degree of concurrency, free from deadlock, early detection and resoluti on of conflicts, resulting in both less wasted resources and smaller number of restart s. All of these are important to the performance of RTDBS and contribute to greater chances o f meeting transaction deadlines. Performance studies of our protocol has been carried out in RODAIN RTDBMS and the results confirm the believe that OCC-PDATI clearly offers b tter changes for high priority transactions to complete according to their deadlines.
Samplesort: A Sampling Approach to Minimal Storage Tree Sorting

The methods currently in use and previously proposed for the choice of a root in minimal storage tree sorting are in reality methods for making inefficient statistical estimates of the median of the sequence to be sorted. By making efficient use of the information in a random sample chosen during input of the sequence to be sorted, significant improvements over ordinary minimal storage tree sorting can be made. A procedure is proposed which is a generalization of minimal storage tree sorting and which has the following three properties: (a) There is a significant improvement (over ordinary minimal storage tree sorting) in the expected number of comparisons required to sort the input sequence. (b) The procedure is statistically insensitive to bias in the input sequence. (c) The expected number of comparisons required by the procedure approaches (slowly) the information-theoretic lower bound on the number of comparisons required. The procedure is, therefore, “asymptotically optimal. ”
Causal deviance and the ascription of intent and blame

ABSTRACT Research indicates that actors who intentionally bring about harmful consequences are blamed more for their actions than those who do so unintentionally. However, in many instances of harmful behavior, intentions are ambiguous. The Culpable Control Model of Blame (CCM) predicts that the degree to which an actor is blamed for causing a harmful outcome is strongly influenced by information about the actor’s character, motives, or desires and that initial blame assessments impact important blame-related criteria such as judgements regarding the actor’s intent. Deviant causal sequences, those in which negative outcomes occur in ways that the actor did not foresee, are highly unlikely, or are coerced, include circumstances that could mitigate assignment of blame. Such sequences provide a test of predictions derived from the CCM. Findings of three studies supported a model in which participants ascribed greater blame to an actor with opprobrious character, motives, or desires, which, in turn, affected judgments of a main criterion related to intent, and, in the last step, accounted for ascriptions of intent. These findings support the CCM’s contention that blame-reactions to an actor and outcome influence judgments about criteria, such as intent, that are often considered to be determinants rather than consequences of blame.
Path analysis method to identify factors affecting consumer interest on online shopping

Current research indicates that a novelty, exciting and engaging shopping experience can positively affect consumer buying behavior. Identifying factors that influence the consumer behavior on use a virtual world for shopping will help retailers to create and maintain a presence in a virtual world, while positive impacting purchases and returns intentions. Therefore, the objectives of this study are to understand what factors affect consumer intention to shop in a virtual world and to analyze the relationships among these factors. Particularly, this study explores consumer innovativeness (consumer novelty seeking and consumer independent judgment making) as an external variable that influences consumer intention indirectly through its effects on consumer experiences with the virtual world application (ease of use, control, concentration, and enjoyment). Path analysis method is used to identify the variable given significant influence to consumer intention to shop. The finding indicates that independent variables Consumer Novelty Seeking and Consumer Independent Judgment Making have influence to Perceived concentration, Perceived enjoyment, and Ease of Use. These three variables give significant influence to consumer intention to shop.
Lossless encoding based on redistribution of statistics

A new technique for optimally encoding a given source statistical properties of which are described by the first-order model is introduced. The calculation of codeword lengths is based on construction of a new source with statistics that is determined by the consecutive redistribution of the probabilities of symbols in accordance with their original probabilities at each stage of the encoding. The proposed method performs equally well for different orders of symbol probabilities. While codewords are generated by a separate combinatorial procedure, the overall computational cost of the proposed method is lower than that for the Huffman code.
A combined protection and control strategy to enhance the LVRT capability of a wind turbine driven by DFIG

In order to enhance the low voltage ride-through (LVRT) capability of a wind turbine driven doubly fed induction generator (DFIG) this paper proposes a combined protection and control strategy including the Active Crowbar and Battery Energy Storage System. The conventional crowbar is activated to protect the rotor side converter during the fault interval, premature removal the crowbar will not achieve good purpose of protection for the converter, too late removal will make the motor absorption the reactive power too much from the grid. This problem can be improved by using a control scheme which is auto-switching the crowbar according to the size of rotor current. Furthermore, under fault condition the voltage will ripple in the DC link side, a battery energy storage system (BESS) connected to the DC bus is proposed which is controlled to attenuate the DC voltage ripple via absorbing the redundant power stored in the DC link capacitor. Simulation results show the effectiveness of this combined protection and control strategy.
Moderating and Mediating Effects of Team Identification in Regard to Causal Attributions and Summary Judgments Following a Game Outcome

Fans’ causal attributions for a game outcome refer to their assessments of the underlying reasons for why things turned out as they did. We investigate the extent to which team identification moderates fans’ attributional responses to a game outcome so as to produce a self-serving bias that favors the preferred team. Also explored is the ability of team identification to mediate the effect of attributions on the summary judgments of basking in reflected glory (BIRG) and satisfaction with the team’s perfor mance. Consistent with a self-serving bias, we found that highly identified fans were more likely to attribute a winning effort to stable and internal causes than were lowly identified fans. Moreover, the extremity of response between winners and losers was greater among highly identified fans than lowly identified fans. Team identification was also found to mediate the influence of (a) stability on BIRGing and (b) internal control on BIRGing. No such mediation effects were observed in the case of satisfaction. Managerial implications are discussed. Attribution theory focuses on a fundamental need that people have to explain the underlying causes of important events or outcomes (Weiner, 1986). Causal attributions are a type of general fact-based knowledge (i.e., concerns matters such as who, what, when, where, why, and how) that allows people to comprehend the meaning of an event. Given its centrality to human nature, it is therefore not surprising that fans put so much effort into identifying the reasons behind a preferred team’s victory or defeat. One need only listen to sports talk radio or read a sports blog on the day after an important game to appreciate the prevalence of such behavior among fans. Understanding how fans interpret sporting event outcomes is important to managers because it allows them to better understand the ways in which their product is being consumed. Research has consistently shown that causal attributions influence consumers’ reactions to a product or service (Folkes, 1988; Folkes, Koletsky, & Graham, 1987; Weiner, 2000). We consider two such reactions in the context of the consumption of a sporting event: satisfaction with the team’s
Erasure Coding for Fault-Oblivious Linear System Solvers

Dealing with hardware and software faults is an important problem as parallel and distributed systems scale to millions of processing cores and wide area networks. Traditional methods for dealing with faults include checkpoint-restart, active replicas, and deterministic replay. Each of these techniques has associated resource overheads and constraints. In this paper, we propose an alternate approach to dealing with faults, based on input augmentation. This approach, which is an algorithmic analog of erasure coded storage, applies a minimally modified algorithm on the augmented input to produce an augmented output. The execution of such an algorithm proceeds completely oblivious to faults in the system. In the event of one or more faults, the real solution is recovered using a rapid reconstruction method from the augmented output. We demonstrate this approach on the problem of solving sparse linear systems using a conjugate gradient solver. We present input augmentation and output recovery techniques. Through detailed experiments, we show that our approach can be made oblivious to a large number of faults with low computational overhead. Specifically, we demonstrate cases where a single fault can be corrected with less than 10% overhead in time, and even in extreme cases (fault rates of 20%), our approach is able to compute a solution with reasonable overhead. These results represent a significant improvement over the state of the art.
A Framework for Learning Knowledge-Powered Word Embedding

Neural network techniques are widely applied to obtain high-quality distributed representations of words, i.e., word embeddings, to address text mining and natural language processing tasks. Recently, efficient methods have been proposed to learn word embeddings from context that captures both semantic and syntactic relationships between words. However, it is challenging to handle unseen words or rare words with insufficient context. In this paper, we propose a framework that can leverage general pairwise word similarity to address these challenges. As an example, we propose to take advantage of seemingly less obvious but essentially important morphological word similarity to show the power of our framework. In particular, we introduce a novel neural network architecture that leverages both contextual information and morphological word similarity to learn word embeddings. Experiments on an analogical reasoning task demonstrates that the proposed method can greatly enhance the effectiveness of word embeddings.
Risk Assessment of Crisis Management in Response to Natural Disasters with an Emphasis on Earthquakes

The aim of this study was to explain the risks of the crisis management process in responding to natural disasters with the focus on earthquakes. This quantitative, descriptive, analytic study was conducted in Doroud City, Lorestan province, Iran in 2017. The society of the research comprises all members of the Doroud Crisis Management team, out of which 140 people were selected by stratified random sampling. In order to collect data, self-made questionnaires comprising 40 questions and 6 domains (structural, therapeutic, help and rescue, safety, hygiene, logistics)and based on the Likert Five Option Spectrum (extremely low=1, extremely high=5)were used. To ensure the validity of the questionnaires, they were assessed and verified by experts in the field. To assess the reliability, however, the Cronbach's Alpha Coefficient of 0.89 was applied. The data was analyzed by mean, standard deviation, and Independent T-test and variance analysis using SPSS21. The findings showed that safety with a mean and standard deviation of 4.91±0.68 was the highest and structure with a mean and standard deviation of 2.03±0.64 was the lowest domains under the assessment of crisis management. Also, training courses in therapeutic and hygiene domains, and work experience in therapeutic, help and rescue, security, logistics domains made a considerable difference (p=0.05). From the perspective of the sample under study, the domains of therapeutic and hygiene have the highest importance factor, logistics was allocated the lowest priority. Applying the proper assessment of different risk frameworks, the researchers reviewed each domain proportionate to the probability of an earthquake in the study area. This way by identifying the potential hazards and risks in view of important and effective domains, the dangers could be prioritized and management programs could be implemented.
Digital FIR Hilbert Transformers: Fundamentals and Efficient Design Methods

© 2012 Troncoso Romero and Jovanovic Dolecek, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Digital FIR Hilbert Transformers: Fundamentals and Efficient Design Methods
Analysing the stability of series-shunt circuits based on voltage- and current- feedback OpAmps through SPICE AC simulations

Two methods for determining the loop gain of OpAmp-based circuits with series-shunt feedback through simulations are analyzed and compared: the standard method — that involves breaking the feedback loop by inserting an independent voltage source with DC=0 and AC=1 — and a more precise method, based on the Rosenstark theorem. Only the cases for the traditional (voltage-feedback) and the current-feedback OpAmps are discussed in the paper but the conclusions can be extended to other types of amplifiers.
The obligations and common ground structure of task oriented conversations

In this paper a theory of dialogue structure for task oriented conversations, and its associated tagging scheme is presented. The theory introduces two linguistic structures supporting the dialogue which are called the obligations and common ground. The analysis of a transaction of a corpus in task oriented conversations is also presented. The empirical work from which this theory evolved is also briefly discussed.
Integration of a Community-Based WWW Filter as a Capstone Research Experience for Undergraduate Computer Science Students

The College’s of New Jersey’s Computer Science department offers capstone mentored research courses to qualifying juniors and seniors. Students can typically enroll in up to two such courses, offering the opportunity to work in small groups or a one-on-one basis with a full-time faculty member, typically through the incorporation a faculty member’s research interest area or an ongoing project. These mentored research courses seek to expose the students to a more rigorous exploration of problem solving through individual research. The typical student in a mentored research course is generally one that is intending to or considering pursuing graduate studies. This paper examines the communiFilter project that has been offered as a mentored research course since the fall 2004 semester. The authors describe the project’s goals, design, and implementation a community-based World Wide Web content filter. Additionally, the paper details the implementation of such a project through the mentored research offerings.
Localization in Wireless Sensor Network Using a Mobile Anchor in Obstacle Environment

This paper presents localization in wireless sensor networks (WSNs) in presence of obstacles using a mobile anchor. The proposed scheme uses a mobile anchor, which finds its location via global positioning system (GPS). The mobile anchor broadcasts its coordinate while it navigates through sensing region. The static sensor nodes receive beacon messages when the mobile anchor and sensor nodes have overlapping signals. The sensor nodes process the beacon messages to find their own coordinates using location of the anchor. The anchor moves in a snake-like method, which consumes less energy compare to the other approaches such as random methods. In this method the need for specific ranging hardware is eliminated on the sensor nodes. This elimination voids the communication requirement between sensor nodes. Further, the presence of obstacles in the environment has almost no effect on the output of the system causes the obstacle can be detected by the mobile anchor. In their project, Castalia simulator is used for the simulation and validation purpose. The output of the simulation results of localization problem confirm less localization error and less energy consumption over other range free solution using mobile anchor considering an extensive variety of network conditions.
LARGE-EDDY SIMULATION OF ISOTROPIC HOMOGENEOUS DECAYING TURBULENCE

Simulations of three dimensional freely decaying homogeneous turbulence in a periodic cube have been used to examine in a detailed and quantitative manner the behaviour of a Large Eddy Simulation (LES) using implicit subgrid modelling. This paper details the form and behaviour of the implicit subgrid models for the Minmod and third order limiting methods at several mesh resolutions. It is shown that for simulations above 32^3 the decay of kinetic energy follows a power law with a decay exponent between 1:2 and 1:4, except in the case of turbulence with a constrained length scale for which the decay exponent is 2:1. This is in very good agreement with experimental data and theoretical analysis where the exponent ? 1:2 ? 1:4 unconstrained, and 2:0 when constrained. At a resolution of 32^3 the number of degrees of freedom are not sufficient to allow a turbulent flow, and velocity derivative statistics are Gaussian. The skewness of the velocity derivative is lower than existing explicit LES and Direct Numerical Simulation (DNS) simulations, but in good agreement with the most recent experimental results. There is a limited sub-inertial range with the three-dimensional Kolmogorov constant ? 1:9, also in agreement with DNS. The less dissipative nature of the third order limiter gives better skewness at a lower grid resolutions, however both give good results in terms of energy dissipation and growth of the length scales.
GRECO: A Distributed Genetic Algorithm for Reliable Application Placement in Hybrid Clouds

Hybrid clouds, consisting of multiple individual smaller clouds with heterogeneous capabilities, are becoming more and more popular through concepts such as inter-clouds, fog-and edge-computing. They provide fast computations without introducing large network latency. However, such cloud environments often contain unreliable nodes and links that are failure prone. Therefore, the deployment of applications requiring availability guarantees is a current research challenge. If such a placement algorithm is CPU-, memory-, network-and availability-aware, the applications can use the resources as optimal as possible with a small failure probability. The optimal deployment of applications in the network infrastructure is a NP-hard problem and, as a consequence, exact algorithms to solve it are not scalable. In this paper we propose GRECO, a distributed genetic algorithm to place service-oriented applications on a hybrid cloud, by defining a representation of an application placement in a biased-random-key chromosome and using a fault-tolerance distributed pool model. When compared with an existing Integer Linear Programming approach, simulations show that GRECO is scalable (speedup of a factor of 1000) and obtain near optimal performance results.
Equal Opportunity in Higher Education: The Past and Future of California's Proposition 209.

"Equal Opportunity in Higher Education "examines issues pertaining to equal opportunity affirmative action, challenges to it, and alternatives for improving opportunities for underrepresented groups in higher education today. Its starting point is California s Proposition 209, which ended race-based affirmative action in public education and the workplace in 1996. The book carefully considers how Proposition 209 reflects national trends that have changed higher education policy and practice, from admissions to student diversity to standards. With a roster of leading scholars and administrators including Chancellor Robert Birgeneau of the University of California, Berkeley, and President Mary Sue Coleman of the University of Michigan "Equal Opportunity in Higher Education "is a crucial assessment of one of the most important issues facing higher education. ""
Knowledge base for finite-element mesh design learned by inductive logic programming

This paper addresses an important application of machine learning (ML) in design. One of the major bottlenecks in the process of engineering analysis by using the finite-element method—a design of the finite-element mesh—was a subject of improvement. Defining an appropriate geometric mesh model that ensures low approximation errors and avoids unnecessary computational overhead is a very difficult and time-consuming task based mainly on the user's experience. A knowledge base for finite-element mesh design has been constructed using the ML techniques. Ten mesh models have been used as a source of training examples. The mesh dataset was probably the first real-world relational dataset and became one of the most widely used training set for experimenting with inductive logic programming (ILP) systems. After several experiments with different ML systems in the last few years, the ILP system CLAUDIEN was chosen to construct the rules for determining the appropriate mesh resolution values. The ILP has been found to be an effective approach to the problem of mesh design. An evaluation of the resulting knowledge base shows that the mesh design patterns are captured well by the induced rules and represent a solid basis for practical application. The aim of this paper is not only to present the real-life ML application to design, but also to describe and discuss a relation of the work being done to the topic of this special issue: the proposed “dimensions” of ML in design.