Unleashing the Potential of Crowd Work: The Need for a Post-Taylorism Crowdsourcing Model

Paid crowdsourcing connects task requesters to a globalized, skilled workforce that is available 24/7. In doing so, this new labor model promises not only to complete work faster and more efficiently than any previous approach but also to harness the best of our collective capacities. Nevertheless, for almost a decade now, crowdsourcing has been limited to addressing rather straightforward and simple tasks. Large-scale innovation, creativity, and wicked problem-solving are still largely out of the crowd’s reach. In this opinion paper, we argue that existing crowdsourcing practices bear significant resemblance to the management paradigm of Taylorism. Although criticized and often abandoned by modern organizations, Taylorism principles are prevalent in many crowdsourcing platforms, which employ practices such as the forceful decomposition of all tasks regardless of their knowledge nature and the disallowing of worker interactions, which diminish worker motivation and performance. We argue that a shift toward post-Taylorism is necessary to enable the crowd address at scale the complex problems that form the backbone of today’s knowledge economy. Drawing from recent literature, we highlight four design rules that can help make this shift, namely, endorsing social crowd networks, encouraging teamwork, scaffolding ownership of one’s work within the crowd, and leveraging algorithm-guided worker self-coordination.

Ethical problems in the use of algorithms in data management and in a free market economy

The problem that I present in this paper concerns the issue of ethical evaluation of algorithms, especially those used in social media and which create profiles of users of these media and new technologies that have recently emerged and are intended to change the functioning of technologies used in data management. Systems such as Overton, SambaNova or Snorkel were created to help engineers create data management models, but they are based on different assumptions than the previous approach in machine learning and deep learning. There is a need to analyze both deep learning algorithms and new technologies in database management in terms of their actions towards a person who leaves their digital footprints, on which these technologies work. Then, the possibilities of applying the existing deep learning technology and new Big Data systems in the economy will be shown. The opportunities offered by the systems mentioned above seem to be promising for many companies and—if implemented on a larger scale—they will affect the functioning of the free market.

METODE TRANSFER PRICING: HIERARCHY ATAU THE MOST APPROPRIATE?

Penentuan metode transfer pricing yang dikenal saat ini dilakukan dengan pendekatan “yang paling tepat” (the most appropriate). Pendekatan hirarki yang sebelumnya digunakan dianggap kurang memenuhi kaidah logika dan sistematika yang baik. Penelitian ini bertujuan untuk menginvestigasi bagaimana pendekatan hierarchy dan the most appropriate berdasarkan studi literatur. Hasil penelitian menunjukkan bahwa sebetulnya tidak ada yang perlu dipertentangkan antara kedua pendekatan tersebut. Pendekatan the most appropriate tetap memiliki unsur hirarki dalam pelaksanaannya. Determination of the transfer pricing method that is currently known is carried out using “the most appropriate" approach. The previous approach (hierarchy) was widely is considered unfamiliar with good logical and unsystematical. This study aims to investigate how the hierarchical and most appropriate approach is based on the literature study. The results of the study show that there is actually nothing to contradict the two approaches. The most appropriate approach still has an element of hierarchy in its implementation.

ATMOSPHERIC RADIOCARBON FOR THE PERIOD 1950–2019

ABSTRACT This paper presents a compilation of atmospheric radiocarbon for the period 1950–2019, derived from atmospheric CO2 sampling and tree rings from clean-air sites. Following the approach taken by Hua et al. (2013), our revised and extended compilation consists of zonal, hemispheric and global radiocarbon (14C) data sets, with monthly data sets for 5 zones (Northern Hemisphere zones 1, 2, and 3, and Southern Hemisphere zones 3 and 1–2). Our new compilation includes smooth curves for zonal data sets that are more suitable for dating applications than the previous approach based on simple averaging. Our new radiocarbon dataset is intended to help facilitate the use of atmospheric bomb 14C in carbon cycle studies and to accommodate increasing demand for accurate dating of recent (post-1950) terrestrial samples.

Improving Search Engine Efficiency through Contextual Factor Selection

Learning to rank (LTR) is an important artificial intelligence (AI) approach supporting the operation of many search engines. In large-scale search systems, the ranking results are continually improved with the introduction of more factors to be considered by LTR. However, the more factors being considered, the more computation resources required, which in turn, results in increased system response latency. Therefore, removing redundant factors can significantly improve search engine efficiency. In this paper, we report on our experience incorporating our Contextual Factor Selection (CFS) deep reinforcement learning approach into the Taobao e-commerce platform to optimize the selection of factors based on the context of each search query to simultaneously maintaining search result quality while significantly reducing latency. Online deployment on Taobao.com demonstrated that CFS is able to reduce average search latency under everyday use scenarios by more than 40% compared to the previous approach with comparable search result quality. Under peak usage during the Single’s Day Shopping Festival (November 11th) in 2017, CFS reduced the average search latency by 20% compared to the previous approach.

Scheduling the Main Professional Football League of Argentina

In this paper, we describe our work in scheduling Argentina’s First Division professional football league, the Superliga. Following existing work in sports scheduling, we develop an integer programming model for the Superliga season schedule and then, solve it using a decomposition approach. Unlike previous work, this scheme is based on the creation and assignment of cluster patterns, which take advantage of the model’s geographically driven handling of sporting fairness. We also model the assignment of matches to specific dates and time slots while simultaneously considering various conditions relating to or imposed by game broadcasters, the government, and international tournament calendars. Our work was implemented to schedule the Superliga’s 2018–2019 and 2019–2020 seasons, achieving clear improvements on a number of criteria over the previous approach.

Comparison of Twelve Types of Rough Approximations Based on j-Neighborhood Space and j-Adhesion Neighborhood Space

In this paper, we generalize six kinds of rough set models based on j-neighborhood space (i.e., reflexive 1 j-neighborhood rough set, reflexive 2 j-neighborhood rough set, reflexive 3 j-neighborhood rough set, similarity 4 j-neighborhood rough set, similarity 5 j-neighborhood rough set, and similarity 6 j-neighbor\\hood rough set), and investigate some of their basic properties. Further, we propose a new neighborhood space called j-adhesion neighborhood based on six types of rough set models (i.e., reflexive 7 j-adhesion neighborhood rough set, reflexive 8 j-adhesion neighborhood rough set, reflexive 9 j-adhesion neighborhood rough set, similarity 10 j-adhesion neighborhood rough set, similarity 11 j-adhesion neighborhood rough set, and similarity 12 j-neighbor\\hood rough set) to reduce the boundary region and the accuracy. The fundamental properties of approximation operators based on j-adhesion neighborhood space are investigated. The relationship between the properties of these types is explained. Finally, we give comparisons between the proposed approach with the previous approach (i.e., Abo-Tabl's approach and Dai et al.'s approach) from six types of rough set models. Consequently, the accuracy from the proposed approach is improved.

Congenital aural atresia surgery: anterior mastoid and transmastoid approaches

Background Aural atresia (CAA) is a congenital abnormality with hypoplasia or aplasia of the external auditory canal. Surgical procedures of CAA is not only difficult but has still not been embraced by surgeons. Many surgeons are doubtful to perform a repair due to poor hearing results and the risk of new canal stenosis. This article describes the writers’ expertise with surgical management of aural atresia. A retrospective study of 16 cases of CAA was undertaken in the Hearing and Speech Institute between 2015 and 2018. The study consisted of 16 patients, 8 patients for each group, 10 males and 6 females between 4 and 18 years of age with a median age of 6 years. Ten patients had bilateral atresia, and 6 had unilateral atresia. Postoperatively, patients were tested for hearing recovery and follow-up lasting up to 2 years to record any complications. Purpose of the study was to compare hearing effects and risks of anterior and transmastoid approaches to external and middle ear restoration in patients with CAA. Results All patients were satisfied with the surgery by improving the hearing up to 35 dB or less after 12 months. Four patients (25%) developed soft tissue meatal stenosis, with 2 patients in each group. There were no cases of bony canal stenosis. TM perforation was seen in two patients (12.51%), one patient in each group. Lateralization of the graft was seen in one patient in the transmastoid group, and no ossicular chain refixation. Five cases had postoperative otorrhea, one in the anterior mastoid group and four in the transmastoid group. There were no other complications. Conclusions Although the findings of the hearing are close. The previous approach, due to fewer postoperative complications, is now our favorite technique. Proper alignment and soft-tissue strategies are keys to the effective correction of the congenitally atretic ear canal.

Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning

Medulloblastoma (MB) is a primary central nervous system tumor and the most common malignant brain cancer among children. Neuropathologists perform microscopic inspection of histopathological tissue slides under a microscope to assess the severity of the tumor. This is a timeconsuming task and often infused with observer variability. Recently, pre-trained convolutional neural networks (CNN) have shown promising results for MB subtype classification. Typically, high-resolution images are divided into smaller tiles for classification, while the size of the tiles has not been systematically evaluated. We study the impact of tile size and input strategy and classify the two major histopathological subtypes-Classic and Desmoplastic/Nodular. To this end, we use recently proposed EfficientNets and evaluate tiles with increasing size combined with various downsampling scales. Our results demonstrate using large input tiles pixels followed by intermediate downsampling and patch cropping significantly improves MB classification performance. Our top-performing method achieves the AUC-ROC value of 90.90% compared to 84.53% using the previous approach with smaller input tiles.

MEDITERRANEAN SURFACE GEOSTROPHIC CIRCULATION FROM SATELLITE DATA

In this work, we provide an updated geodetic approach to the Mediterranean Surface Geostrophic circulation based onsatellite data. We follow same methodology as in a previous approach by Vigo et al. (2018), but here both the Sea SurfaceHeight (SSH) and the Geoid (N) have been updated by enhanced solutions, and the time period covered has beenextended to 23 years, from 1993 to 2015. The main general pattern of circulation is confirmed with respect to previousapproach, but the new estimation provides enhanced resolution of the details, and higher variations in the climatology.When compare both satellite data-based approaches to the Mediterranean Surface Geostrophic Circulation (SGC) withMercator model simulations that assimilates in-situ measurements, our new estimate shows clearly better agreement thanthe earlier approach. The mean circulation for the studied period, and the climatology of the SGC for the MediterraneanSea are presented in the context of previous literature.