ANALYZING RESPONSES TO OPEN ENDED QUESTIONS FOR SPIRIT USING ASPECT ORIENTED SENTIMENT ANALYSIS

Open ended questions provide an effective way of measuring the attitude and perception of respondents towards a topic. Surprising Possibilities Imagined and Realized through Information Technology (SPIRIT) was a program (2008-2012) that employed open-ended questions to gauge program participants\u27 attitudes related to computing. SPIRIT sought to increase the interest of high school students, especially female students, towards computing courses and careers. Pre- and post-attitude surveys were used during the program to measure the changes in attitudes of the participants towards IT and also to analyze the impact different sessions had on different demographic groups of participants. The open-ended survey questions from SPIRIT provide the data needed for this study\u27s analysis. SPIRIT\u27s external evaluator employed the constant comparison method to analyze the participant data. This study analyzed those same responses using aspect-oriented sentiment analysis to make reporting and decision making for such programs easier and more objective than human evaluation. The approach identified the aspect of each phrase or statement made in the responses and then quantitatively classified the sentiment of each aspect. Thus, the study\u27s approach not only solves the problem of objectively analyzing the open-ended responses of participants of short term educational programs similar to SPIRIT but also may help mine new information from the surveys that would help make decisions in order to make future programs have a better impact on the participants

Whose Click Fraud Data Do You Trust? Effect Of Click Fraud On Advertiser’s Trust And Sponsored Search Advertising Decisions

Online sponsored search has emerged as a dominant business model for majority of search engines and as a popular advertising mechanism for online retailers. However, sponsored search advertising is being negatively impacted by click fraud which involves the intentional clicking on sponsored links with the purpose of gaining undue monetary returns for the search engine or harming a particular advertiser by depleting its advertising budget. While search engines tend to compensate advertisers to an extent for click frauds, it still leaves an element of uncertainty in the minds of advertisers whether search engine is being faithful in reporting the click fraud numbers. Armed with additional data available from third party click fraud audit companies, advertisers may have more reasons to suspect click fraud numbers reported by search engines if there is a discrepancy between the numbers reported by two sources (search engines and third party click fraud audit companies). While the phenomenon of click fraud has been acknowledged to exist, its effect on sponsored search advertisers’ trust and their decision to advertise with a particular search engine has not been given sufficient attention in the literature. As an initial step, in this research in progress study, we develop a theoretical model to examine the effect of click fraud on advertiser’s trust in search engine and its subsequent impact on advertiser’s decision to adjust advertising spend for different search engines. In this paper, we also outline the proposed experimental design to validate the theoretical model subsequently in future. Broadly, the research suggests that sponsored search advertisers are likely to adjust their advertising spend based on level of trust they have in search engine, click fraud numbers discrepancy, and return on investment obtained from advertising on that particular search engine

Addressing Memory Bottlenecks for Emerging Applications

There has been a recent emergence of applications from the domain of machine learning, data mining, numerical analysis and image processing. These applications are becoming the primary algorithms driving many important user-facing applications and becoming pervasive in our daily lives. Due to their increasing usage in both mobile and datacenter workloads, it is necessary to understand the software and hardware demands of these applications, and design techniques to match their growing needs. This dissertation studies the performance bottlenecks that arise when we try to improve the performance of these applications on current hardware systems. We observe that most of these applications are data-intensive, i.e., they operate on a large amount of data. Consequently, these applications put significant pressure on the memory. Interestingly, we notice that this pressure is not just limited to one memory structure. Instead, different applications stress different levels of the memory hierarchy. For example, training Deep Neural Networks (DNN), an emerging machine learning approach, is currently limited by the size of the GPU main memory. On the other spectrum, improving DNN inference on CPUs is bottlenecked by Physical Register File (PRF) bandwidth. Concretely, this dissertation tackles four such memory bottlenecks for these emerging applications across the memory hierarchy (off-chip memory, on-chip memory and physical register file), presenting hardware and software techniques to address these bottlenecks and improve the performance of the emerging applications. For on-chip memory, we present two scenarios where emerging applications perform at a sub-optimal performance. First, many applications have a large number of marginal bits that do not contribute to the application accuracy, wasting unnecessary space and transfer costs. We present ACME, an asymmetric compute-memory paradigm, that removes marginal bits from the memory hierarchy while performing the computation in full precision. Second, we tackle the contention in shared caches for these emerging applications that arise in datacenters where multiple applications can share the same cache capacity. We present ShapeShifter, a runtime system that continuously monitors the runtime environment, detects changes in the cache availability and dynamically recompiles the application on the fly to efficiently utilize the cache capacity. For physical register file, we observe that DNN inference on CPUs is primarily limited by the PRF bandwidth. Increasing the number of compute units in CPU requires increasing the read ports in the PRF. In this case, PRF quickly reaches a point where latency could no longer be met. To solve this problem, we present LEDL, locality extensions for deep learning on CPUs, that entails a rearchitected FMA and PRF design tailored for the heavy data reuse inherent in DNN inference. Finally, a significant challenge facing both the researchers and industry practitioners is that as the DNNs grow deeper and larger, the DNN training is limited by the size of the GPU main memory, restricting the size of the networks which GPUs can train. To tackle this challenge, we first identify the primary contributors to this heavy memory footprint, finding that the feature maps (intermediate layer outputs) are the heaviest contributors in training as opposed to the weights in inference. Then, we present Gist, a runtime system, that uses three efficient data encoding techniques to reduce the footprint of DNN training.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146016/1/anijain_1.pd

(C-f)- Weak Contraction in Cone Metric Spaces

The purpose of this article  is to introduced the concept of  weak contraction in cone metric space and also establish a coincidence and common fixed point result for  weak contractions in cone metric spaces. Our result proper generalizes the results of Sintunavarat and Kumam [7]. We also give an example in support of our result. Keywords :- Cone metric spaces, weak contraction,  weak contraction, coincidence point, common fixed point

Colonic ulcerations may predict steroid-refractory course in patients with ipilimumab-mediated enterocolitis

To investigate management of patients who develop ipilimumab-mediated enterocolitis, including association of endoscopic findings with steroid-refractory symptoms and utility of infliximab as second-line therapy

DDPMnet: All-Digital Pulse Density-Based DNN Architecture with 228 Gate Equivalents/MAC Unit, 28-TOPS/W and 1.5-TOPS/mm2 in 40nm

Relentless advances in DNN accelerator energy and area efficiency are demanded in low-cost edge devices [1]-[8]. Both directly benefit from the reduction in the complexity of MAC units (neurons), thanks to the reduction in area and energy of computations and the interconnect fabric. Unfortunately, such area and energy cost per neuron further increases in practical cases where flexibility is needed (e.g., precision scaling), ultimately limiting cost and power reductions. In this work, the all-digital DDPMnet architecture for DNN acceleration based on a pulse density data representation is introduced to reduce the gate count/MAC unit from the thousand range to few hundreds . The proposed architecture removes any arithmetic block from MAC units (e.g., multipliers), while retaining the advantages of standard cell based design

Transcending Dualities of Gender: Mapping factors influencing the life of a trans person in India

The transgender community is one of the oldest communities in India, and includes hijras, eunuchs, kothis, and aravanis. All members face severe discrimination and harassment in all respects in contemporary India, having been subjected to unfair treatments such as verbal abuse, physical and sexual violence, and victimisation in multiple settings (family, educational institutions, workplace, health care settings, and public spaces). The project aimed to understand and represent the challenges faced by the trans community in India and explore possibilities of design intervention in the system. According to UN Sustainable Development Goals 5–Gender Equality, many developing and developed countries have raised concerns about gender-related inequalities at various levels, validating the relevance of the project. As an outcome of the project, the gigamap serves as a concise and comprehensive visual document on trans-individuals in India and is also aimed as an education tool for parents, teachers, educational institutions, policymakers, designers, changemakers, and the general public. The gigamap addresses the fundamental question of who a trans individual is, covering the basics of sex, gender and gender identities. It further delves into the history of trans-individuals in India, highlighting two important bills passed by the government and examining the ground reality. The core of the gigamap revolves around the life journeys of trans-individuals, mapping their emotional states. It encapsulates our findings around gender dysphoria, coming out, and transitioning, ensuring readers gain in-depth knowledge on the pivotal points of their lives. It directly connects to the synthesised causal map, emphasising the key factors influencing the life of a trans person in India. This section also incorporates illustration bubbles as empathy builders, narrating everyday emotions shared by the community. The last section gives a system overview in the form of loops and a stakeholder map, highlighting groups of individuals with high impact and influence. It concludes with nine areas for intervention and encourages the viewer to make a difference in their own spheres of influence. Throughout the ten-week project, systemic design mapping helped us navigate the complexities faced while dealing with such sensitive topics. Gigamapping as a tool and a deliverable also helped us as designers to consider products and services

Evaluation of Preclinical Task Based Learning program in Medical Education [version 1; peer review: 1 approved, 2 approved with reservations]

The conventional curriculum in preclinical medical education has a need for early clinical exposure programs that help in correlation of basic science data with clinical skills. This is helpful to develop clinical reasoning skills, problem-solving abilities, team work, communication skills and overall attitudes and behaviour relevant for a healthcare provider. Preclinical task based learning (TskBL) is an active learning strategy in which the focus for the first year medical student is a real task done by a doctor. In this strategy the student-doctors undergo a standardized patient encounter and discuss the learning issues related to the task in the first year of medical school. The current study is focussed on the student perception of the effectiveness of task based learning module.The TskBL was conducted among first year medical students for nine topics that are commonly encountered in the clinics. After TskBL was planned and implemented the evaluation of the modules was done using focus group discussions. The students highlighted the importance of standardized patients in the TskBL strategy in providing early clinical exposure in preclinical medical education. They reported its usefulness gaining essential knowledge, skills and attitudes for medical learning. They reported positive outcomes of module design and processes and activities in TskBL. Based on the negative aspects of the modules, future improvement was suggested in improving the usefulness of standardized patient encounter. This study showed the novice learners’ outlook of the potency of TskBL for several other topics of clinical relevance to provide early clinical exposure in medical schools