Like trainer, like bot? Inheritance of bias in algorithmic content moderation

The internet has become a central medium through which `networked publics' express their opinions and engage in debate. Offensive comments and personal attacks can inhibit participation in these spaces. Automated content moderation aims to overcome this problem using machine learning classifiers trained on large corpora of texts manually annotated for offence. While such systems could help encourage more civil debate, they must navigate inherently normatively contestable boundaries, and are subject to the idiosyncratic norms of the human raters who provide the training data. An important objective for platforms implementing such measures might be to ensure that they are not unduly biased towards or against particular norms of offence. This paper provides some exploratory methods by which the normative biases of algorithmic content moderation systems can be measured, by way of a case study using an existing dataset of comments labelled for offence. We train classifiers on comments labelled by different demographic subsets (men and women) to understand how differences in conceptions of offence between these groups might affect the performance of the resulting models on various test sets. We conclude by discussing some of the ethical choices facing the implementers of algorithmic moderation systems, given various desired levels of diversity of viewpoints amongst discussion participants.Comment: 12 pages, 3 figures, 9th International Conference on Social Informatics (SocInfo 2017), Oxford, UK, 13--15 September 2017 (forthcoming in Springer Lecture Notes in Computer Science

Progestins Related to Progesterone and Testosterone Elicit Divergent Human Endometrial Transcriptomes and Biofunctions.

Progestins are widely used for the treatment of gynecologic disorders and alone, or combined with an estrogen, are used as contraceptives. While their potencies, efficacies and side effects vary due to differences in structures, doses and routes of administration, little is known about their effects on the endometrial transcriptome in the presence or absence of estrogen. Herein, we assessed the transcriptome and pathways induced by progesterone (P4) and the three most commonly used synthetic progestins, medroxyprogesterone acetate (MPA), levonorgestrel (LNG), and norethindrone acetate (NETA), on human endometrial stromal fibroblasts (eSF), key players in endometrial physiology and reproductive success. While there were similar transcriptional responses, each progestin induced unique genes and biofunctions, consistent with their structural similarities to progesterone (P4 and MPA) or testosterone (LNG and NETA), involving cellular proliferation, migration and invasion. Addition of estradiol (E2) to each progestin influenced the number of differentially expressed genes and biofunctions in P4 and MPA, while LNG and NETA signatures were more independent of E2. Together, these data suggest different mechanisms of action for different progestins, with progestin-specific altered signatures when combined with E2. Further investigation is warranted for a personalized approach in different gynecologic disorders, for contraception, and minimizing side effects associated with their use

Automating rolling stock diagramming and platform allocation

Rolling stock allocation is the process of assigning timetable schedules to physical train units. This is primarily done by connecting together schedules at their terminal locations (known as schedule associations). Platforming allocation is the process of assigning those associations to particular platforms. A simple last-in, first-legal-out algorithm is used for rolling stock allocation that performs comparably to the traditional manual approach but only takes a few seconds as opposed to days or weeks in many manual cases. A simple stochastic hill-climbing approach is used for assigning associations to platforms to provide a conflict-free platform allocation within a few seconds. These two approaches are tested on real train planning problems with excellent results that would allow an expert to rapidly produce optimal or near optimal solutions. The time saving using these approaches can be used by the train planner to try out various options or have greater checking of robustness of the solutions created

A Concept for Extending the Spotify Organisational Model to Cater for Platform Organisations

Modern platform organizations together with modular producers operate in an ecosystem with various role-players. These role-players all form part of a product or service that is delivered to the customer. Currently, organizations are implementing organizational models of the Spotify type to enhance their agility. This Spotify model works if the organization is an island by itself but within an ecosystem, this is not sufficient. Although the Spotify Organizational Model has been used in many agile contexts to increase flow and collaboration, it has not been adapted for platform organizations and the ecosystems they are part of. We propose a conceptual model that addresses this deficiency. Our conceptual model builds on the theory of the flow of work and the Spotify organizational model. We then extend this model to resolve the tensions created by the notion of a platform organization

DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE

Detection and classification of vegetation is a crucial technical task in the management of natural resources since vegetation serves as a foundation for all living things and has a significant impact on climate change such as impacting terrestrial carbon dioxide (CO2). Traditional approaches for acquiring vegetation covers such as field surveys, map interpretation, collateral and data analysis are ineffective as they are time consuming and expensive.  In this paper vegetation regions are automatically detected by applying simple but effective vegetation indices Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) on red(R) and near infrared (NIR) bands of Landsat-8 satellite image. Remote sensing technology makes it possible to analyze vegetation cover across wide areas in a cost-effective manner. Using remotely sensed images, the mapping of vegetation requires a number of factors, techniques, and methodologies. The rapid improvement of remote sensing technologies broadens possibilities for image sources making remotely sensed images more accessible. The dataset used in this paper is the R and NIR bands of Level-1 Tier 1 Landsat-8 optical remote sensing image acquired on 6th September 2013, is processed and made available to users on 2nd May 2017. The pre-processing involving sub-setting operation is performed using the ERDAS Imagine tool on R and NIR bands of Landsat-8 image. The NDVI and SAVI are utilized to extract vegetation features automatically by using python language. Finally by establishing a threshold, vegetation cover of the research area is detected and then classified

ePhysio: A Wearables-Enabled Platform for the Remote Management of Musculoskeletal Diseases

Technology advancements in wireless communication and embedded computing are fostering their evolution from standalone elements to smart objects seamlessly integrated in the broader context of the Internet of Things. In this context, wearable sensors represent the building block for new cyber-physical social systems, which aim at improving the well-being of people by monitoring and measuring their activities and provide an immediate feedback to the users. In this paper, we introduce ePhysio, a large-scale and flexible platform for sensor-assisted physiotherapy and remote management of musculoskeletal diseases. The system leverages networking and computing tools to provide real-time and ubiquitous monitoring of patients. We propose three use cases which differ in scale and context and are characterized by different human interactions: single-user therapy, indoor group therapy, and on-field therapy. For each use case, we identify the social interactions, e.g., between the patient and the physician and between different users and the performance requirements in terms of monitoring frequency, communication, and computation. We then propose three related deployments, highlighting the technologies that can be applied in a real system. Finally, we describe a proof-of-concept implementation, which demonstrates the feasibility of the proposed solution

The Impact of Innovations on the Business Model: Exploratory Analysis of a Small Travel Agency

AbstractThe objective of this exploratory paper is to analyse the innovations introduced by a small travel agency and their impact on the firm's business model in the specific context of a salt mine in Romania. In particular, the purpose of this paper is to analyse the business model of the small firm that did not introduce a single innovation. A case study provides rich data on “unbundling” made by the entrepreneur and the trade-offs decisions that affect the business model. The paper highlights the implications of the entrepreneur's decision to implement in a short period of time a “package” of product, process, marketing and organizational innovations and their impact upon the business model, and describes the transformation of the company due to changes in the environment (content and relationships among the building blocks of the business model)

VE-cadherin RGD motifs promote metastasis and constitute a potential therapeutic target in melanoma and breast cancers

13 p.-6 fig.We have investigated the role of vascular-endothelial (VE)-cadherin in melanoma and breast cancer metastasis. We found that VE-cadherin is expressed in highly aggressive melanoma and breast cancer cell lines. Remarkably, inactivation of VEcadherin triggered a significant loss of malignant traits (proliferation, adhesion, invasion and transendothelial migration) in melanoma and breast cancer cells. These effects, except transendothelial migration, were induced by the VE-cadherin RGD motifs. Co-immunoprecipitation experiments demonstrated an interaction between VE-cadherin and α2β1 integrin, with the RGD motifs found to directly affect β1 integrin activation. VE-cadherin-mediated integrin signaling occurred through specific activation of SRC, ERK and JNK, including AKT in melanoma. Knocking down VEcadherin suppressed lung colonization capacity of melanoma or breast cancer cells inoculated in mice, while pre-incubation with VE-cadherin RGD peptides promoted lung metastasis for both cancer types. Finally, an in silico study revealed the association of high VE-cadherin expression with poor survival in a subset of melanoma patients and breast cancer patients showing low CD34 expression. These findings support a general role for VE-cadherin and other RGD cadherins as critical regulators of lung and liver metastasis in multiple solid tumours. These results pave the way for cadherin-specific RGD targeted therapies to control disseminated metastasis in multiple cancers.BEP was an FPI fellow from Ministry of Economy and Competitiveness (MINECO). This research was supported by grants BIO2012-31023 and BIO2015-66849 from MINECO and PRB2 (IPT13/0001-ISCIII-SGEFI/FEDER) to JIC.Peer reviewe