Efektivitas Aplikasi SIAP terhadap Kedisiplinan Pegawai Sekretariat Daerah Kota Sukabumi

ABSTRACTResearch on Employee Attendance Information System Applications (SIAP) on this discipline is motivated by1) low level of employee discipline relating to attendance and punctuality at work, 2) easy application to be manipulated by employees, 3) frequent system disturbances (errors) in the application when used by employees, which have an impact on the ineffectiveness of the SIAP system. Research analysis using Information Systems theory from Davis and Discipline Theory from Singodimedjo.The method used is quantitative with associative models. Respondents were civil servants in the Regional Secretariat of Sukabumi, totaling 160 people. 61 samples were taken using proportionate stratified random sampling technique. The results of data analysis showed a correlation coefficient of 0.918. The coefficient of determination is 84.3%. For this reason, researchers suggest that the local government of Sukabumi City improve the accuracy of the SIAP attendance system by adding a face camera system.Keywords: SIAP Application, Face Camera, Discipline, Information System

Behavior models of employees and their increasing role in the theory and practice of management

The authors study two basic approaches to modeling human behavior that have a long history. The first one is connected with shaping desired behavior models. Such models can be found in sacred books of many religions. The second approach is connected with characterizing behavior by means of a certain associative image. The authors present a comparative characteristic of both approaches and reveal their methodological difference. The article describes the problems that arise while using associative models of employees’ behavior in a modern organization. At the same time the authors point out the increasing for many countries significance of the approach connected with shaping desired employees’ behavior models. So they use system basis to structure the basic tasks of shaping desired employees’ behavior models. It helps an organization to find a necessary number of employees’ behavior models. They will facilitate achieving the organization’s objectives, solving its tasks, effective exploitation of the resources and achieving the results. The attention is mainly paid to minimization of the increasing threats of the external environment, particularly to neutralization of the influence of those organizations and individuals whose values do not meet the interests of a certain society and business entities. The authors reveal strategic and current relevance of shaping desired employees’ behavior models for organizations and state, and suggest the way to solve this task. The central place is given to government agencies and the leader’s personal example. The article contains a list of major works to be done within a national project on neutralization of the increasing negative influence of the external environment. There is also a list of conditions for harmonic combination of both approaches to modeling employees’ behavior in practice to obtain additional managerial effect. The authors insist that government regulation of shaping desired employees’ behavior models in organizations is inevitable historically. Delays will only increase the lost profit and the costs for compensating the growing damage.

DI2: prior-free and multi-item discretization of biological data and its applications

Background A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumptions which are arguably insufficient to handle the diversity and heterogeneity of clinical and molecular variables within a given dataset. In addition, although an increasing number of symbolic approaches in bioinformatics are able to assign multiple items to values occurring near discretization boundaries for superior robustness, there are no reference principles on how to perform multi-item discretizations. Results In this study, an unsupervised discretization method, DI2, for variables with arbitrarily skewed distributions is proposed. Statistical tests applied to assess differences in performance confirm that DI2 generally outperforms well-established discretizations methods with statistical significance. Within classification tasks, DI2 displays either competitive or superior levels of predictive accuracy, particularly delineate for classifiers able to accommodate border values. Conclusions This work proposes a new unsupervised method for data discretization, DI2, that takes into account the underlying data regularities, the presence of outlier values disrupting expected regularities, as well as the relevance of border values. DI2 is available at https://github.com/JupitersMight/DI2

Analysis of the Tilapia (Oreochromis spp.) Value Chain in the State of Veracruz Rural Aquaculture for the Small Producer

Objective: To analyze the importance of the aquaculture value chain links in the state of Veracruz, Mexico, especially those of production and marketing. Methodology: The information was obtained in the six main tilapia (Oreochromis spp.) production regions in the state of Veracruz through poles based in a questionnaire that addresses key informants; variables related to each link and chain agent were considered; five juvenile producers, 41 tilapia producers and 12 marketers. Results: A fish farming value chain map was generated with the description of distribution channels, production cost estimation and sales income, as well as the participation of producers in demand. Implications: The implementation of integrative models is required in order to have a constant supply of inputs from suppliers in farms. Also, associative models that allow accessing markets in units where the high payment availability for the product should be developed. Conclusions: Chain economic agents are related. Upon meeting the quality and performance required by marketers, there is potential to develop value aggregation strategies through associativity models, linked to service businesses such as restaurants

Second-Order Conditioning and Conditioned Inhibition in Different Moments of the Same Training: The Effect of A+ and AX− Trial Number

The feature negative discrimination (A+/AX−) can result in X gaining excitatory properties (second-order conditioning, SOC) or in X gaining inhibitory properties (conditioned inhibition, CI), a challenging finding for most current associative learning theories. Research on the variables that modulate which of these phenomena would occur is scarce but has clearly identified the trial number as an important variable. In the set of experiments presented here, the effect of trial number was assessed in a magazine training task with rats as a function of both the conditioning sessions and the number of A+ and AX− trials per session, holding constant the total number of trials per session. The results indicated that SOC is most likely to be found at the beginning of training when there are many A+ and few AX− trials, and CI (as assessed by a retardation test) is most likely to be found at the end of training when there are few A+ and many AX− trials. Both phenomena were also found at different moments of training when the number of A+ trials was equal to the number of AX− trials. These results cannot be predicted by acquisition-focused associative models but can be predicted by theories that distinguish between learning and performance.

Predicting Student Academic Performance by Means of Associative Classification

The Learning Analytics community has recently paid particular attention to early predict learners’ performance. An established approach entails training classification models from past learner-related data in order to predict the exam success rate of a student well before the end of the course. Early predictions allow teachers to put in place targeted actions, e.g., supporting at-risk students to avoid exam failures or course dropouts. Although several machine learning and data mining solutions have been proposed to learn accurate predictors from past data, the interpretability and explainability of the best performing models is often limited. Therefore, in most cases, the reasons behind classifiers’ decisions remain unclear. This paper proposes an Explainable Learning Analytics solution to analyze learner-generated data acquired by our technical university, which relies on a blended learning model. It adopts classification techniques to early predict the success rate of about 5000 students who were enrolled in the first year courses of our university. It proposes to apply associative classifiers at different time points and to explore the characteristics of the models that led to assign pass or fail success rates. Thanks to their inherent interpretability, associative models can be manually explored by domain experts with the twofold aim at validating classifier outcomes through local rule-based explanations and identifying at-risk/successful student profiles by interpreting the global rule-based model. The results of an in-depth empirical evaluation demonstrate that associative models (i) perform as good as the best performing classification models, and (ii) give relevant insights into the per-student success rate assignments.

CHANGES IN THE VANADIUM MIGRATION FORMS ON GEOCHEMICAL BARRIERS IN THE RIVER-SEA MIXING ZONES

The article is devoted to the study of vanadium, a metal capable of stimulating the growth of phytoplankton in situ and has the greatest biological activity in dissolved form. The pattern of an increase in the concentration of vanadium dissolved forms in the mixing zones during the transition from river waters to seawaters is known. In this article, we examine the behavior, ratio and change in the concentrations of vanadium dissolved and suspended forms during the passage of geochemical barriers. The estuarine zone of the Razdolnaya River–Amur Bay (Sea of Japan) is considered as "river-sea" mixing zone. Modelling of physicochemical processes was carried out using the Selector-S and MINTEQA2/PRODEFA2 software systems. Ion-associative models of sea and river water were built and the modelling of the process of their mixing was carried out using the Selector-S software package. The sorption process was simulated using the MINTEQA2/PRODEFA2 software package. The results of modelling physicochemical processes occurring at geochemical barriers help to understand the reasons for changes in concentrations, both total vanadium and biologically active dissolved vanadium forms, during the passage of geochemical barriers in the "river-sea" mixing zones. The results showed that there is a change in the dissolved forms of vanadium migration, their transformation and an increase in the concentration of dissolved forms of vanadium at the geochemical barrier

Pavlovian conditioning under partial reinforcement: The effects of non-reinforced trials versus cumulative CS duration

A core feature of associative models, such as those proposed by Allan Wagner (Rescorla & Wagner, 1972; Wagner, 1981), is that conditioning proceeds in a trial-by-trial fashion, with increments and decrements in associative strength occurring on each occasion that the conditioned stimulus (CS) is present either with or without the unconditioned stimulus (US). A very different approach has been taken by theories that assume animals continuously accumulate information about the total length of time spent waiting for the US both during the CS and in the absence of the CS (e.g., Gallistel & Gibbon, 2000). Here we describe three experiments using within-subject designs that tested between trial-based and time-accumulation accounts of the acquisition of conditioned responding using magazine approach conditioning in rats. We found that responding was affected by the total (cumulative) duration of exposure to the CS without the US rather than the number of trials on which the CS occurred without the US. We also found that exposure to the CS without the US had the same effect on conditioning whether that exposure occurred shortly (60 s) before each CS-US pairing or whether it occurred long (240 s) before each pairing. These findings are more consistent with time-accumulation models of conditioning than trial-based models like the Rescorla-Wagner model and Wagner’s (1981) Sometimes Opponent Process model. We discuss these findings in relation to other evidence that favours trial-based models rather than time-accumulation models.

Computational Modeling of Backwards-Blocking Reasoning in Human Adults

Causal reasoning is a fundamental cognitive ability that enables humans to learn about the complex interactions in the world around them. However, it remains unknown whether causal reasoning is underpinned by a Bayesian mechanism or an associative one. For example, some maintain that a Bayesian mechanism underpins human causal reasoning because it can better account for backward-blocking (BB) and indirect screening-off (IS) findings than certain associative models. However, the evidence is mixed about the extent to which learners engage in both kinds of reasoning. Here, we report an experiment and several computational models that examine to what extent adults engage in BB and IS reasoning using the blicket-detector design. The results revealed that adults’ causal ratings in a backwards-blocking and indirect screening-off condition were consistent with associative rather than a Bayesian computational model. These results are interpreted to mean that adults use associative processes to reason about causal events.