The purpose of this paper is to present an empirical study on gender bias in text. Current research in this field is focused on detecting and correcting for gender bias in existing machine learning models rather than approaching the issue at the dataset level. The underlying motivation is to create a dataset which could enable machines to learn to differentiate bias writing from non-bias writing. A taxonomy is proposed for structural and contextual gender biases which can manifest themselves in text. A methodology is proposed to fetch one type of structural gender bias, Gender Generalization. We explore the IMDB movie review dataset and 9 different corpora from Project Gutenberg. By filtering out irrelevant sentences, the remaining pool of candidate sentences are sent for human validation. A total of 6123 judgments are made on 1627 sentences and after a quality check on randomly selected sentences we obtain an accuracy of 75%. Out of the 1627 sentences, 808 sentence were labeled as Gender Generalizations. The inter-rater reliability amongst labelers was of 61.14%.

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Dynamic and temporal graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks. In this paper, we focus on change point detection in dynamic graphs and address two main challenges associated with this problem: I) how to compare graph snapshots across time, II) how to capture temporal dependencies. To solve the above challenges, we propose Laplacian Anomaly Detection (LAD) which uses the spectrum of the Laplacian matrix of the graph structure at each snapshot to obtain low dimensional embeddings. LAD explicitly models short term and long term dependencies by applying two sliding windows. In synthetic experiments, LAD outperforms the state-of-the-art method. We also evaluate our method on three real dynamic networks: UCI message network, US senate co-sponsorship network and Canadian bill voting network. In all three datasets, we demonstrate that our method can more effectively identify anomalous time points according to significant real world events. CCS CONCEPTS • Computing methodologies → Anomaly detection; Temporal reasoning; Spectral methods; • Mathematics of computing → Spectra of graphs; • Theory of computation → Dynamic graph algorithms.

Porous concrete with a high void content has gained popularity in urban environments as it allows water to spread through its pores. Permeability is a desired characteristic when developing media for plant growth as it enables roots to spread and anchor themselves. This leads to the aim of this study, developing a porous concrete substrate for plant growth with a pH lower than that of standard concrete. The substrate incorporates recovered industrial byproducts from blast furnaces. The materials used in the design consist of a blast furnace slag binder, two proprietary alkali activators, quartz aggregates (2.0–3.2 mm in size), a void content of 30%, and a water to binder ratio of 0.295. Tomato (Solanum lycopersicum), radish (Raphanus raphanistrum), and romaine lettuce (Lactuva sativa) were seeded onto the slag porous concrete for a 28-day hydroponic experiment. The treatments with porous concrete substrates differed in concentrations of the nutrient solution: Hoagland normal (1×), double Hoagland (2×), and quintuple Hoagland (5×). Rockwool with a 1× nutrient solution was selected as the control treatment, a hydroponic standard for plant growth. The dry mass values of the 2× treatment and the control treatment were similar (P > 0.05). The largest dry mass of all treatments investigated was the radish in the 2× treatment at 125.4% of the control radish dry mass.

Salinity negatively impacts crop productivity, yet neutral and alkali salt stresses are not often differentiated. To investigate these abiotic stresses separately, saline and alkaline solutions with identical concentrations of sodium (12 mM, 24 mM and 49 mM) were used to compare the seed germination, viability and biomass of four crop species. Commercial buffers containing NaOH were diluted to generate alkaline solutions. The sodic solutions tested contained the neutral salt NaCl. Romaine lettuce, tomato, beet, and radish were seeded and grown hydroponically for 14 days. A rapid germination was observed for alkaline solutions when compared to saline–sodic solutions. The highest plant viability recorded (90.0%) was for the alkaline solution, containing 12 mM Na+, and for the control treatment. Plant viability, with a value of 49 mM Na+ in saline–sodic and alkaline solutions, was the lowest (50.0% and 40.8% respectively), and tomato plants did not germinate. EC values were higher for the saline–sodic solutions than the alkaline solutions, yielding greater fresh mass per plant for all species, with the exception of beets grown in alkaline solution, with a value of 24 mM Na+. The fresh mass of romaine lettuce grown in the 24 mM Na+ saline–sodic solution was significantly greater than romaine lettuce grown in the alkaline solution with the same sodium concentration.