Assignment Rules Research Articles

Efficient hierarchical identity based encryption scheme in the standard model over lattices

Using lattice basis delegation in a fixed dimension, we propose an efficient lattice-based hierarchical identity based encryption (HIBE) scheme in the standard model whose public key size is only (dm 2 + mn) log q bits and whose message-ciphertext expansion factor is only log q, where d is the maximum hierarchical depth and (n, m, q) are public parameters. In our construction, a novel public key assignment rule is used to averagely assign one random and public matrix to two identity bits, which implies that d random public matrices are enough to build the proposed HIBE scheme in the standard model, compared with the case in which 2d such public matrices are needed in the scheme proposed at Crypto 2010 whose public key size is (2dm 2 + mn +m) log q. To reduce the message-ciphertext expansion factor of the proposed scheme to log q, the encryption algorithm of this scheme is built based on Gentry’s encryption scheme, by which m 2 bits of plaintext are encrypted into m 2 log q bits of ciphertext by a one time encryption operation. Hence, the presented scheme has some advantages with respect to not only the public key size but also the message-ciphertext expansion factor. Based on the hardness of the learning with errors problem, we demonstrate that the scheme is secure under selective identity and chosen plaintext attacks.

Commentary: Can a Quasi-experimental Design Be a Better Idea than an Experimental One?

Commentary: Can a Quasi-experimental Design Be a Better Idea than an Experimental One?

Poor Choices: The Sociopolitical Context of "Grand Theft Education".

In recent years, districts have paid special attention to the common practice of "district hopping," families bending geographic school assignment rules by sending a child to a school in a district where the child does not formally reside-usually to a district that is more desirable because of higher performing schools or greater educational resources. In several high-profile cases, mothers who engaged in district hopping were charged with "grand theft" of educational services. By situating these cases in the broader context of market-based reforms, we refocus attention on the responses of districts rather than the actions of parents. We argue that increased privatization of education and growing dominance of a "private-goods" model of schooling create the conditions necessary for framing these actions as "theft."

Evaluation of Automatically Assigned Job-Specific Interview Modules

In community-based epidemiological studies, job- and industry-specific 'modules' are often used to systematically obtain details about the subject's work tasks. The module assignment is often made by the interviewer, who may have insufficient occupational hygiene knowledge to assign the correct module. We evaluated, in the context of a case-control study of lymphoid neoplasms in Asia ('AsiaLymph'), the performance of an algorithm that provided automatic, real-time module assignment during a computer-assisted personal interview. AsiaLymph's occupational component began with a lifetime occupational history questionnaire with free-text responses and three solvent exposure screening questions. To assign each job to one of 23 study-specific modules, an algorithm automatically searched the free-text responses to the questions 'job title' and 'product made or services provided by employer' using a list of module-specific keywords, comprising over 5800 keywords in English, Traditional and Simplified Chinese. Hierarchical decision rules were used when the keyword match triggered multiple modules. If no keyword match was identified, a generic solvent module was assigned if the subject responded 'yes' to any of the three solvent screening questions. If these question responses were all 'no', a work location module was assigned, which redirected the subject to the farming, teaching, health professional, solvent, or industry solvent modules or ended the questions for that job, depending on the location response. We conducted a reliability assessment that compared the algorithm-assigned modules to consensus module assignments made by two industrial hygienists for a subset of 1251 (of 11409) jobs selected using a stratified random selection procedure using module-specific strata. Discordant assignments between the algorithm and consensus assignments (483 jobs) were qualitatively reviewed by the hygienists to evaluate the potential information lost from missed questions with using the algorithm-assigned module (none, low, medium, high). The most frequently assigned modules were the work location (33%), solvent (20%), farming and food industry (19%), and dry cleaning and textile industry (6.4%) modules. In the reliability subset, the algorithm assignment had an exact match to the expert consensus-assigned module for 722 (57.7%) of the 1251 jobs. Overall, adjusted for the proportion of jobs in each stratum, we estimated that 86% of the algorithm-assigned modules would result in no information loss, 2% would have low information loss, and 12% would have medium to high information loss. Medium to high information loss occurred for <10% of the jobs assigned the generic solvent module and for 21, 32, and 31% of the jobs assigned the work location module with location responses of 'someplace else', 'factory', and 'don't know', respectively. Other work location responses had ≤8% with medium to high information loss because of redirections to other modules. Medium to high information loss occurred more frequently when a job description matched with multiple keywords pointing to different modules (29-69%, depending on the triggered assignment rule). These evaluations demonstrated that automatically assigned modules can reliably reproduce an expert's module assignment without the direct involvement of an industrial hygienist or interviewer. The feasibility of adapting this framework to other studies will be language- and exposure-specific.

An effective discrete artificial bee colony algorithm with idle time reduction techniques for two-sided assembly line balancing problem of type-II

Two-sided assembly line is broadly utilized to manufacture high-volume large-size products. Reconfiguration of this line is a major practice in industry, which is known as two-sided assembly line balancing problem of type-II. To solve this NP-hard problem, this paper proposes an improved discrete artificial bee colony (DABC) algorithm via blending idle time reduction techniques. The cycle time compression mechanism speeds up the search process by reducing total idle times to a larger extent on earlier iterations. Task assignment rule is specifically designed to eliminate sequence-dependent idle times. The overload and underload are taken into account as a secondary objective to reduce the remaining idle times. To achieve a fine balance between the diversification and intensification, the employed bees in the DABC algorithm generate new food sources by combining the features of two solutions, and the onlookers expand the search space with the variable neighborhood search. Nine recent meta-heuristics including a simulated annealing algorithm and an ant colony optimization algorithm are also extended for the TALBP-II to test the performance of the proposed DABC algorithm. Experimental results demonstrate that the proposed method outperforms the nine compared algorithms and find 22 brand-new results for large-size problems.

Dynamical Spectrum Sharing and Medium Access Control for Heterogeneous Cognitive Radio Networks

This paper tackles the issue of spectrum sharing and medium access control among heterogeneous secondary users. Two solutions are proposed in this paper. The first solution can be used in centralized fashion where a central entity exists which decides transmission power for all secondary users. This solution tries to minimize the time required by secondary users to clear their queues. The second solution assumes the autonomy of secondary users where the decision to update transmission power is distributed among users. Dynamical system approach is used to model system behavior. The trajectory of interference noise level suffered by secondary users is used to update transmission power at the beginning of each time frame based on the proposed dynamic power assignment rule. This rule couples the responses of all secondary users in a way which simplifies future interference noise forecasting. A forecasting engine based on deep neural network is proposed. This engine gives secondary users the ability to acquire useful knowledge from surrounding wireless environment. As a result, better transmission power allocation is achieved. Evaluation experiments have confirmed that adopting deep neural network can improve the performance by 46% on average. All of the proposed solutions have achieved an outstanding performance.

A Multi-Element Approach to Location Inference of Twitter: A Case for Emergency Response

Since its inception, Twitter has played a major role in real-world events—especially in the aftermath of disasters and catastrophic incidents, and has been increasingly becoming the first point of contact for users wishing to provide or seek information about such situations. The use of Twitter in emergency response and disaster management opens up avenues of research concerning different aspects of Twitter data quality, usefulness and credibility. A real challenge that has attracted substantial attention in the Twitter research community exists in the location inference of twitter data. Considering that less than 2% of tweets are geotagged, finding location inference methods that can go beyond the geotagging capability is undoubtedly the priority research area. This is especially true in terms of emergency response, where spatial aspects of information play an important role. This paper introduces a multi-elemental location inference method that puts the geotagging aside and tries to predict the location of tweets by exploiting the other inherently attached data elements. In this regard, textual content, users’ profile location and place labelling, as the main location-related elements, are taken into account. Location-name classes in three granularity levels are defined and employed to look up the location references from the location-associated elements. The inferred location of the finest granular level is assigned to a tweet, based on a novel location assignment rule. The location assigned by the location inference process is considered to be the inferred location of a tweet, and is compared with the geotagged coordinates as the ground truth of the study. The results show that this method is able to successfully infer the location of 87% of the tweets at the average distance error of 12.2 km and the median distance error of 4.5 km, which is a significant improvement compared with that of the current methods that can predict the location with much larger distance errors or at a city-level resolution at best.

Responsive affirmative action in school choice

School choice programs aim to give students the option to choose their school. At the same time, underrepresented minority students should be favored to close the opportunity gap. A common way to achieve this is to have a majority quota at each school, and to require that no school be assigned more majority students than its majority quota. An alternative way is to reserve some seats at each school for the minority students, and to require that a reserve seat at a school be assigned to a majority student only if no minority student prefers that school to her assignment. However, fair rules based on either type of affirmative action suffer from a common problem: a stronger affirmative action may not benefit any minority student and hurt some minority students. First, we show that this problem is pervasive. Then, we uncover the root of this problem: for some minority students, treating them as minority students does not benefit them, but possibly hurts other minority students. We propose a new assignment rule that treats such minority students as majority students, achieves affirmative action, and never hurts a minority student without benefiting another minority student.

Causal effects of mental health treatment on education outcomes for youth in the justice system

This study assesses whether mental health interventions can improve academic outcomes for justice-involved youth. Only a limited number of studies have linked justice policies to outcomes beyond crime, particularly education, which carries large monetary and non-monetary benefits. The current study relies on detailed administrative data and unique policy rules under which youth are assigned to behavioral treatment programs. The administrative data allow for a rich set of controls for observed family- and youth-specific heterogeneity. In addition, the treatment assignment rules create discontinuous thresholds among youth who are deemed eligible or not eligible for treatment, rules which the study exploits empirically to address the non-random selection bias in estimating plausibly causal effects of treatment eligibility and treatment receipt. Estimates indicate that certain types of intensive mental health intervention can lower dropout and increase high-school completion for justice-involved youth. Effects on grades are negative or not significant, possibly due to the greater retention of less academically-skilled students. We also assess heterogeneity in the treatment effects, and find that the effects on dropout tend to be greater among youth believed to be less academically engaged prior to treatment.

Impacts of classifying New York City students as overweight

US schools increasingly report body mass index (BMI) to students and their parents in annual fitness "report cards." We obtained 3,592,026 BMI reports for New York City public school students for 2007-2012. We focus on female students whose BMI puts them close to their age-specific cutoff for categorization as overweight. Overweight students are notified that their BMI "falls outside a healthy weight" and they should review their BMI with a health care provider. Using a regression discontinuity design, we compare those classified as overweight but near to the overweight cutoff to those whose BMI narrowly earned them a "healthy" BMI grouping. We find that overweight categorization generates small impacts on girls' subsequent BMI and weight. Whereas presumably an intent of BMI report cards was to slow BMI growth among heavier students, BMIs and weights did not decline relative to healthy peers when assessed the following academic year. Our results speak to the discrete categorization as overweight for girls with BMIs near the overweight cutoff, not to the overall effect of BMI reporting in New York City.

Quantum probability assignment limited by relativistic causality.

Quantum theory has nonlocal correlations, which bothered Einstein, but found to satisfy relativistic causality. Correlation for a shared quantum state manifests itself, in the standard quantum framework, by joint probability distributions that can be obtained by applying state reduction and probability assignment that is called Born rule. Quantum correlations, which show nonlocality when the shared state has an entanglement, can be changed if we apply different probability assignment rule. As a result, the amount of nonlocality in quantum correlation will be changed. The issue is whether the change of the rule of quantum probability assignment breaks relativistic causality. We have shown that Born rule on quantum measurement is derived by requiring relativistic causality condition. This shows how the relativistic causality limits the upper bound of quantum nonlocality through quantum probability assignment.

Panel Asymptotics and Statistical Decision Theory

This paper develops some applications of asymptotic statistical decision theory in econometrics, focusing on settings where the data are organized into groups or cells with heterogeneous parameters. Even if the groups are of different sizes, local asymptotic normality holds under suitable regularity conditions, and this can greatly simplify analysis of different types of econometric problems. We apply these results to the analysis of treatment assignment rules, and to estimators of cell-specific parameters that employ shrinkage towards parametric models.

Towards semi-automated assignment of software change requests

Change Requests (CRs) are key elements to software maintenance and evolution. Finding the appropriate developer to a CR is crucial for obtaining the lowest, economically feasible, fixing time. Nevertheless, assigning CRs is a labor-intensive and time consuming task. In this paper, we report on a questionnaire-based survey with practitioners to understand the characteristics of CR assignment, and on a semi-automated approach for CR assignment which combines rule-based and machine learning techniques. In accordance with the results of the survey, the proposed approach emphasizes the use of contextual information, essential to effective assignments, and puts the development team in control of the assignment rules, toward making its adoption easier. The assignment rules can be either extracted from the assignment history or created from scratch. An empirical validation was performed through an offline experiment with CRs from a large software project. The results pointed out that the approach is up to 46.5% more accurate than other approaches which relying solely on machine learning techniques. This indicates that a rule-based approach is a viable and simple method to leverage CR assignments.

Multiple rules with game theoretic analysis for flexible flow shop scheduling problem with component altering times

This paper studies the flexible flow shop scheduling problem with component altering times (FFSP-CAT), which is a specific form of the flexible flow shop scheduling problem with sequence dependent setup times in the practical scenario. Dealing with FFSP-CAT includes the jobs' machine assignment determination and the globe optimisation. We develop six rules for jobs' machine assignment, and since these rules will easily conflict with one another if they are used with the same priorities, we construct a repeated cooperative model and provide a game theoretical analysis, then derive a Nash equilibrium machine assignment scheme (NEMAS) to effectively manage these rules for jobs' machines assignment at each stage. Furthermore, to achieve the global optimum, we design an advanced compact genetic algorithm (ACGA). By combining NEMAS and ACGA, an approach named advanced compact genetic algorithm with Nash equilibrium machine assignment scheme (ACGA-NEMAS) is used for minimising the makespan of FFSP-CAT. Through extensive comparison experiments with different scales of instances, we show that the algorithm with NEMAS acquires 56.85% improvement over the algorithm without NEMAS, and ACGA-NEMAS performs 80.28% better than genetic algorithm.

Why a "Women in Combat Symposium"?

INTRODUCTION Women have served as combatants in the U.S. Armed Forces since the Revolutionary War, when they avoided being detected as females by disguising their appearance and changing their names. Likewise, hundreds of women presented themselves as men to fight in the Civil War. More than 12,000 women enlisted in the U.S. Navy and Marine Corps during World War I, and some 400 died. Approximately 350,000 women served in the U.S. Armed Forces in World War II, assigned to every theater of combat, many in positions that had been newly created to meet emerging military scientific and technological needs. Some women were taken prisoners of war, some earned the highest honors, and many died in service to their country. However, women (other than nurses) were only permitted to serve in temporary, emergency capacities; they were then discharged without benefits. The military changed after World War II, when General Dwight Eisenhower and other military leaders supported integration of women as regular service members. General Eisenhower told Congress that although he had originally opposed women’s military service (“I was violently against it.”), “Every phase of the record they compiled during the war convinced me of the error of my first reaction.” Interestingly, Eisenhower also believed that few women would remain members long enough to earn retirement benefits. He believed that “after an enlistment or 2 enlistments they will ordinarily – and thank God – they will get married.” Today, women comprise more than 200,000 members (about 15%) of the U.S. active duty military. As of 2014, 2 million of the nation’s military veterans were women, approximately one-third of whom were enrolled in Veterans Health Administration health care. In 1948, President Harry S. Truman signed the Women’s Armed Services Integration Act, officially allowing women to serve as full and permanent members of the Army, Navy, Marine Corps, and Air Force. However, the law capped women’s total military membership at 2%. It also limited promotions, prohibited service in combat positions, and allowed for involuntary discharge because of pregnancy or parenthood. Despite marked sociocultural changes and legal efforts to expand women’s access to military service opportunities, these conditions applied to female members of the U.S. military until 1991, when Congress modified exclusions of women from Navy and Marine Corps assignments and repealed prohibitions that had prevented women being assigned to combat aircraft. In 1994, the Department of Defense (DoD) adopted the Direct Ground Combat Definition and Assignment Rule, which specifically excluded women from assignments to or co-location with units or organizations whose primary mission was to engage in direct combat on the ground. This policy prevented women from serving in infantry, artillery, armor, combat engineer, and special operation units below the level of brigade. However, this was difficult to apply in the Afghanistan and Iraq wars, where enemy tactics obscured the traditional distinction between combat and noncombat environments and where, to achieve mission critical objectives, female military members were instead “attached” (not assigned) to combat units. Whereas among post-1990 female veterans 24% report having been exposed to combat, a 2009 study by the Defense Advisory Committee on Women in the Services found that most military women deployed to Afghanistan and Iraq were involved in combat functions and/or exposed to hostile fire. In January 2013, in response to recommendations of the Joint Chiefs of Staff (JCS), the Secretary of Defense rescinded the ground combat exclusion rule. This action opened the door for women to serve in more than 14,000 additional occupations and assignments involving exposure to direct ground combat. The JCS directed the Services to develop integration plans—with full implementation to occur no later than January 1, 2016 (unless SECDEF approved an exception to keep certain positions closed)—in keeping with JCS guidelines and objectives as follows:

Sorting Around the Discontinuity Threshold: The Case of a Neighbourhood Investment Programme

This paper investigates the empirical validity of the setup of a large-scale government neighbourhood investment programme in the Netherlands. Selection of neighbourhoods into the programme was determined by their score on a predetermined index. At first sight this is a textbook example for the application of a regression discontinuity (RD) design to estimate the causal effect of the programme on neighbourhood outcomes. However, at the discontinuity threshold we find a large gap in the share of non-Western immigrants. In addition, the pattern of non-compliance with the assignment rule is consistent with investing in neighbourhoods with a high share of non-Western immigrants. Finally, the way of selecting neighbourhoods into the programme could be a likely explanation for the imbalance at the discontinuity threshold. This case illustrates that RD designs can become invalid even when treatment and control groups have no influence on the assignment.

Causal Effects of Mental Health Treatment on Education Outcomes for Youth in the Justice System

This study assesses whether mental health interventions can improve academic outcomes for justice-involved youth. Only a limited number of studies have linked justice policies to outcomes beyond crime, particularly education, which carries large monetary and non-monetary benefits. The current study relies on detailed administrative data and unique policy rules under which youth are assigned to behavioral treatment programs. The administrative data allow for a rich set of controls for observed family- and youth-specific heterogeneity. In addition, the treatment assignment rules create discontinuous thresholds among youth who are deemed eligible or not eligible for treatment, rules which the study exploits empirically to address the non-random selection bias in estimating plausibly causal effects of treatment eligibility and treatment receipt. Estimates indicate that certain types of intensive mental health intervention can lower dropout and increase high-school completion for justice-involved youth. Effects on grades are negative or not significant, possibly due to the greater retention of less academically-skilled students. We also assess heterogeneity in the treatment effects, and find that the effects on dropout tend to be greater among youth believed to be less academically engaged prior to treatment. The positive effects on education, and the earnings returns alone, are large enough to produce benefits in excess of the cost for the more intensive of the mental health interventions.

Bayesian predictive modeling for genomic based personalized treatment selection.

Efforts to personalize medicine in oncology have been limited by reductive characterizations of the intrinsically complex underlying biological phenomena. Future advances in personalized medicine will rely on molecular signatures that derive from synthesis of multifarious interdependent molecular quantities requiring robust quantitative methods. However, highly parameterized statistical models when applied in these settings often require a prohibitively large database and are sensitive to proper characterizations of the treatment-by-covariate interactions, which in practice are difficult to specify and may be limited by generalized linear models. In this article, we present a Bayesian predictive framework that enables the integration of a high-dimensional set of genomic features with clinical responses and treatment histories of historical patients, providing a probabilistic basis for using the clinical and molecular information to personalize therapy for future patients. Our work represents one of the first attempts to define personalized treatment assignment rules based on large-scale genomic data. We use actual gene expression data acquired from The Cancer Genome Atlas in the settings of leukemia and glioma to explore the statistical properties of our proposed Bayesian approach for personalizing treatment selection. The method is shown to yield considerable improvements in predictive accuracy when compared to penalized regression approaches.

Assignment Rules of a Single Indivisible Object Under the Full Preference Domain

We consider the allocation problem of a single indivisible object to one of several agents under the full preference domain when monetary transfers are not allowed. Our central requirement is strategy-proofness. The additional properties we seek are Pareto optimality, non-dictatorship, and non-bossiness. We provide characterizations of strategy-proof rules that satisfy two out of three additional properties: Pareto optimality and non-bossiness; non-dictatorship and non-bossiness; and Pareto optimality and non-dictatorship. As a consequence of these characterizations, we show that a strategy-proof rule cannot satisfy these three additional properties simultaneously.

First-mover advantage in best-of series: an experimental comparison of role-assignment rules

Kingston (1976) and Anderson (1977) show that the probability that a given contestant wins a best-of-2k 1 series of asymmetric, zero-sum, binary-outcome games is, for a large class of assignment rules, independent of which contestant is assigned the advantageous role in each component game. We design a laboratory experiment to test this hypothesis for four simple role-assignment rules. Despite the fact that play does not uniformly conform to the equilibrium, our results show that the four assignment rules are observationally equivalent at the series level: the fraction of series won by a given contestant and all other series outcomes do not differ across the four rules.