Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification

Many real world problems can now be effectively solved using supervised machine learning. A major roadblock is often the lack of an adequate quantity of labeled data for training. A possible solution is to assign the task of labeling data to a crowd, and then infer the true label using aggregation methods. A well-known approach for aggregation is the Dawid-Skene (DS) algorithm, which is based on the principle of Expectation-Maximization (EM). We propose a new simple, yet effective, EM-based algorithm, which can be interpreted as a `hard' version of DS, that allows much faster convergence while maintaining similar accuracy in aggregation. We show the use of this algorithm as a quick and effective technique for online, real-time sentiment annotation. We also prove that our algorithm converges to the estimated labels at a linear rate. Our experiments on standard datasets show a significant speedup in time taken for aggregation - upto $\sim$8x over Dawid-Skene and $\sim$6x over other fast EM methods, at competitive accuracy performance. The code for the implementation of the algorithms can be found at https://github.com/GoodDeeds/Fast-Dawid-SkeneComment: 8 pages, 5 tables, 1 figure, KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) 201

DANTE: Deep AlterNations for Training nEural networks

We present DANTE, a novel method for training neural networks using the alternating minimization principle. DANTE provides an alternate perspective to traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convexity to cast training a neural network as a bi-quasi-convex optimization problem. We show that for neural network configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations effectively in this formulation. DANTE can also be extended to networks with multiple hidden layers. In experiments on standard datasets, neural networks trained using the proposed method were found to be promising and competitive to traditional backpropagation techniques, both in terms of quality of the solution, as well as training speed.Comment: 19 page

Impact of district mental health care plans on symptom severity and functioning of patients with priority mental health conditions: the Programme for Improving Mental Health Care (PRIME) cohort protocol

Background: The Programme for Improving Mental Health Care (PRIME) sought to implement mental health care plans (MHCP) for four priority mental disorders (depression, alcohol use disorder, psychosis and epilepsy) into routine primary care in five low- and middle-income country districts. The impact of the MHCPs on disability was evaluated through establishment of priority disorder treatment cohorts. This paper describes the methodology of these PRIME cohorts. Methods: One cohort for each disorder was recruited across some or all five districts: Sodo (Ethiopia), Sehore (India) , Chitwan (Nepal), Dr. Kenneth Kaunda (South Africa) and Kamuli (Uganda), comprising 17 treatment cohorts in total (N = 2182). Participants were adults residing in the districts who were eligible to receive mental health treatment according to primary health care staff, trained by PRIME facilitators as per the district MHCP. Patients who screened positive for depression or AUD and who were not given a diagnosis by their clinicians (N = 709) were also recruited into comparison cohorts in Ethiopia, India, Nepal and South Africa. Caregivers of patients with epilepsy or psychosis were also recruited (N = 953), together with or on behalf of the person with a mental disorder, depending on the district. The target sample size was 200 (depression and AUD), or 150 (psychosis and epilepsy) patients initiating treatment in each recruiting district. Data collection activities were conducted by PRIME research teams. Participants completed follow-up assessments after 3 months (AUD and depression) or 6 months (psychosis and epilepsy), and after 12 months. Primary outcomes were impaired functioning, using the 12-item World Health Organization Disability Assessment Schedule 2.0 (WHODAS), and symptom severity, assessed using the Patient Health Questionnaire (depression), the Alcohol Use Disorder Identification Test (AUD), and number of seizures (epilepsy). Discussion: Cohort recruitment was a function of the clinical detection rate by primary health care staff, and did not meet all planned targets. The cross-country methodology reflected the pragmatic nature of the PRIME cohorts: while the heterogeneity in methods of recruitment was a consequence of differences in health systems and MHCPs, the use of the WHODAS as primary outcome measure will allow for comparison of functioning recovery across sites and disorders

Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification,

Many real world problems can now be effectively solved using supervised machine learning. A major roadblock is often the lack of an adequate quantity of labeled data for training. A possible solution is to assign the task of labeling data to a crowd, and then infer the true label using aggregation methods. A well-known approach for aggregation is the Dawid-Skene (DS) algorithm, which is based on the principle of Expectation-Maximization (EM). We propose a new simple, yet effective, EM-based algorithm, which can be interpreted as a `hard' version of DS, that allows much faster convergence while maintaining similar accuracy in aggregation. We show the use of this algorithm as a quick and effective technique for online, real-time sentiment annotation. We also prove that our algorithm converges to the estimated labels at a linear rate. Our experiments on standard datasets show a significant speedup in time taken for aggregation - upto $\sim$8x over Dawid-Skene and $\sim$6x over other fast EM methods, at competitive accuracy performance. The code for the implementation of the algorithms can be found at this https UR

Egalitarian Resource Sharing Over Multiple Rounds

It is often beneficial for agents to pool their resources in order to better accommodate fluctuations in individual demand. Many multi-round resource allocation mechanisms operate in an online manner: in each round, the agents specify their demands for that round, and the mechanism determines a corresponding allocation. In this paper, we focus instead on the offline setting in which the agents specify their demand for each round at the outset. We formulate a specific resource allocation problem in this setting, and design and analyze an associated mechanism based on the solution concept of lexicographic maximin fairness. We present an efficient implementation of our mechanism, and prove that it is envy-free, non-wasteful, resource monotonic, population monotonic, and group strategyproof. We also prove that our mechanism guarantees each agent at least half of the utility that they can obtain by not sharing their resources. We complement these positive results by proving that no maximin fair mechanism can improve on the aforementioned factor of one-half.Comment: 25 page

An analysis of executable size reduction by LLVM passes

The formidable increase in the number of smaller and smarter embedded devices has compelled programmers to develop more and more specialized application programs for these systems. These resource intensive programs that have to be executed on limited memory systems make a strong case for compiler optimizations that reduce the executable size of programs. Standard compilers (like LLVM) offer an out-of-the-box -Oz optimization option—just a series of compiler optimization passes—that is specifically targeted for the reduction of the generated executable size. In this paper, we aim to analyze the effects of optimizations of LLVM compiler on the reduction of executable size. Specifically, we take the size of the executable as a metric and attempt to divide the -Oz series into logical groups and study their individual effects; while also study the effect of their combinations. Our preliminary study over SPEC CPU 2017 benchmarks gives us an insight into the comparative effect of the groups of passes on the executable size. Our work has potential to enable the user to tailor a custom series of passes so as to obtain the desired executable size