Does the choice of response time threshold procedure substantially affect inferences concerning the identification and exclusion of rapid guessing responses? A meta-analysis

Background In testing contexts that are predominately concerned with power, rapid guessing (RG) has the potential to undermine the validity of inferences made from educational assessments, as such responses are unreflective of the knowledge, skills, and abilities assessed. Given this concern, practitioners/researchers have utilized a multitude of response time threshold procedures that classify RG responses in these contexts based on either the use of no empirical data (e.g., an arbitrary time limit), response time distributions, and the combination of response time and accuracy information. As there is little understanding of how these procedures compare to each other, this meta-analysis sought to investigate whether threshold typology is related to differences in descriptive, measurement property, and performance outcomes in these contexts. Methods Studies were sampled that: (a) employed two or more response time (RT) threshold procedures to identify and exclude RG responses on the same computer-administered low-stakes power test; and (b) evaluated differences between procedures on the proportion of RG responses and responders, measurement properties, and test performance. Results Based on as many as 86 effect sizes, our findings indicated non-negligible differences between RT threshold procedures in the proportion of RG responses and responders. The largest differences for these outcomes were observed between procedures using no empirical data and those relying on response time and accuracy information. However, these differences were not related to variability in aggregate-level measurement properties and test performance. Conclusions When filtering RG responses to improve inferences concerning item properties and group score outcomes, the actual threshold procedure chosen may be of less importance than the act of identifying such deleterious responses. However, given the conservative nature of RT thresholds that use no empirical data, practitioners may look to avoid the use of these procedures when making inferences at the individual-level, given their potential for underclassifying RG.

Dynamic Information Design: A Simple Problem on Optimal Sequential Information Disclosure

We study a dynamic information design problem in a finite-horizon setting consisting of two strategic and long-term optimizing agents, namely a principal (he) and a detector (she). The principal observes the evolution of a Markov chain that has two states, one “good” and one “bad” absorbing state, and has to decide how to sequentially disclose information to the detector. The detector’s only information consists of the messages she receives from the principal. The detector’s objective is to detect as accurately as possible the time of the jump from the good to the bad state. The principal’s objective is to delay the detector as much as possible from detecting the jump to the bad state. For this setting, we determine the optimal strategies of the principal and the detector. The detector’s optimal strategy is described by time-varying thresholds on her posterior belief of the good state. We prove that it is optimal for the principal to give no information to the detector before a time threshold, run a mixed strategy to confuse the detector at the threshold time, and reveal the true state afterward. We present an algorithm that determines both the optimal time threshold and the optimal mixed strategy that could be employed by the principal. We show, through numerical experiments, that this optimal sequential mechanism outperforms any other information disclosure strategy presented in the literature. We also show that our results can be extended to the infinite-horizon problem, to the problem where the matrix of transition probabilities of the Markov chain is time-varying, and to the case where the Markov chain has more than two states and one of the states is absorbing.

OD Matching of Metro IC Card Data Based on Analysis Function

In the case of passengers taking the subway many times in a short time, missing cards in and out of the station, and staying in the subway station for a long time, the previous table join method cannot accurately set the time threshold parameters and correctly match the OD pairs of passengers. In order to solve these problems, an OD matching method based on analysis function is proposed in this paper. LAG () is an analytic function in Oracle which allows you to access the row at a given offset prior to the current row without using a self-join. Metro IC card dataset stores the card swiping records of passengers entering and leaving the subway station every time. In this method, the dataset is sorted in ascending order according to the card number and card swiping time, and then, the lag function of Oracle is used to take the offset of the upper line of card ID, transaction date, transaction time, in and out sign, and station ID. Finally, the matching process is completed according to the OD conditions of card number, time, and inbound and outbound sign fields. This method does not need to set a time threshold and so as to deal with the situation where passengers stay too long in the subway station. The OD matching results on in and out IC swiping cards dataset in April and May 2019 of passengers of Xiamen Metro Line verify that analysis function method has better OD matching, missing swiping identification accuracy, and effect compared to the table join method.

The Order Selection Strategy of Polluting OEMs under Environmental Regulations

Under environmental regulations, the government restricts the economic activities of polluting OEMs (Original Equipment Manufacturers) in order to improve ecological and economic efficiency. The most direct measure is to limit the production capacity of the companies. Under the condition of limited capacity, the order selection strategy of OEMs will be the direct determinant of the company’s own profits. In the foundry market, there are many low-profit orders, while the number of high-profit orders is limited and uncertain. Companies who choose to wait for high-profit orders must bear the waiting costs and the risk of losing a certain profit. Therefore, it is of great significance for the long-term development of the company to select orders to obtain the best profit under the condition of limited production capacity. This paper takes polluting OEMs as the research object and studies the optimal order selection problems of companies under environmental regulations by establishing order selection decision models for different foundry cycles under the condition of limited production capacity. The study found that in the single foundry cycle, there will be an optimal waiting-time threshold for high-profit orders. Based on this optimal waiting-time threshold, the corresponding order selection strategy can be effectively formulated. However, in the multi-foundation cycle, since the optimal waiting-time threshold of high-profit orders is affected by the long-term average profit, the company’s optimal order selection strategy is based on the long-term average profit maximization.

Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing

Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.

The first GAEN-based COVID-19 contact tracing app in Norway identifies 80% of close contacts in "real life" scenarios.

The COVID-19 response in most countries depends on testing, isolation, contact tracing, and quarantine, which is labor- and time consuming. Therefore, several countries worldwide launched Bluetooth based apps as supplemental tools. We evaluated the new Norwegian GAEN (Google Apple Exposure Notification) based contact tracing app 'Smittestopp' under two relevant simulated scenarios, namely standing in a queue and riding public transport. We compared two configurations (C1: 58/63 dBm; C2: 58/68 dBm) with multiple weights (1.0-2.5) and time thresholds (10-15 min), by calculating notification rates among close contacts (≤2 meters, ≥15 min) and other non-close contacts. In addition, we estimated the effect of using different operating systems and locations of phone (hand/pocket) using Chi2. C2 resulted in significantly higher notification rates than C1 (p-value 0.05 - 0.005). The optimal setting resulted in notifications among 80% of close contacts and 34% of other contacts, using C2 with weights of 2.0 for the low and 1.5 for the middle bucket with a 13-minutes time threshold. Among other contacts, the notification rate was 67% among those ≤2 meters for <15 minutes compared to 19% among those >2 meters (p=0.004). Significantly (p-values 0.046 - 0.001) lower notification rates were observed when using the iOS operating systems or carrying the phone in the pocket instead of in the hand. This study highlights the importance of testing and optimizing the performance of contact tracing apps under 'real life' conditions to optimized configuration for identifying close contacts.