ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., "if X pays Y a compliment, then Y will likely return the compliment"). We propose nine if-then relation types to distinguish causes vs. effects, agents vs. themes, voluntary vs. involuntary events, and actions vs. mental states. By generatively training on the rich inferential knowledge described in ATOMIC, we show that neural models can acquire simple commonsense capabilities and reason about previously unseen events. Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.Comment: AAAI 2019 C

Response of coccinellid community to the dimethoate application in olive groves in northeastern Portugal

In this work we assessed the effects of the application of dimethoate on the coccinellid community. The field work was carried out on a weekly basis, in two different olive groves, from April to November of 2002 and 2003 and captured coccinellids were identified to species level. Principal response curves (PRC) method was used to analyse the effect of the dimethoate application on the abundance of coccinellid species. A total of 23 species were identified from the two olive groves. Nine species occurred in both olive groves and in the two years of the study. Scymnus interruptus was the dominant species in the control grove with 46.4% of the total Coccinellidae recovered while in the grove treated with dimethoate, Rhyzobius chrysomeloides represented 35.7% of the total number captured. PCR showed that the main effect of the treatment was a significant reduction of the abundance of the most common species of the coccinellid community (S. interruptus and Chilocorus bipustulatus) in the treated grove. This can also have implications on the preservation of ecological functions associated with coccinellids, namely their role as control agents of olive pests.En este trabajo se analizan los efectos de la aplicación de dimetoato en la comunidad de coccinélidos. El trabajo de campo se ha realizado en dos olivares, semanalmente, de abril a noviembre en 2002 y 2003, y los coccinélidos capturados se han identificado hasta el nivel de especie. Se han utilizado las principales curvas de respuesta (PRC) para analizar el efecto que produce la aplicación de dimetoato en la abundancia de especies de coccinélidos. Se encontraron un total de 23 especies de coccinélidos en los dos olivares, nueve de ellas comunes en los dos olivares y en los dos años de estudio. Scymnus interruptus fue la especie dominante en el olivar no tratado con dimetoato (46,4% del total de coccinélidos capturados), mientras que en el olivar tratado Rhyzobius chrysomeloides representó el 35,7% del total de las capturas. Las PRC demuestran que el principal efecto producido al aplicar dimetoato ha sido una reducción significativa de la abundancia de las especies más comunes de la comunidad de coccinélidos (S. interruptus y Chilocorus bipustulatus) en el olivar tratado. Esto puede tener implicaciones en la conservación de las funciones ecológicas asociadas a los coccinélidos como, por ejemplo, su papel como agentes de control de las plagas del olivo. Palabras clave adicionales: abundancia de especies, agricultura ecológica, Coccinellidae, curvas de respuesta principal, manejo integrado de plagas, Olea europaea

Event2Mind:Commonsense inference on events, intents, and reactions

We investigate a new commonsense inference task: given an event described in a short free-form text (“X drinks coffee in the morning”), a system reasons about the likely intents (“X wants to stay awake”) and reactions (“X feels alert”) of the event’s participants. To support this study, we construct a new crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations. We report baseline performance on this task, demonstrating that neural encoder-decoder models can successfully compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. In addition, we demonstrate how commonsense inference on people’s intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts

Analysis of Veal Shoulder Muscles for Chemical Attributes

The value of wholesale veal cuts varies; the rack, loin, and leg demand a premium price, while the shoulder brings little more per pound than the live animal. This study characterized the chemical properties of muscles from the veal shoulder for the potential to upgrade their value. The m. infraspinatus and m. rhomboideus fell in the intermediate or desirable groups for all traits. All nine muscles show promise in the ability to increase value

Development of a mechanism to facilitate the safety stock planning configuration in ERP

Safety stock planning in ERP in general is dependent upon the planner having the experience to simulate planning scenarios. This paper focuses on the development of a mechanism to calculate adequate safety stocks in accordance with required service levels while enabling efficient configuration of the ERP safety stock parameters. The proposed mechanism could be of great benefit to industrial firms as it offers the ability to classify demand patterns, proposes replenishment strategies that are consistent with the demand profile, calculates key parameters and identifies the changes required to the ERP master data. The associated real world application is able to identify potential to save approximately £1.2 M in stock reductions and, more importantly, allows targeted actions to be implemented at material level. These results demonstrated that the proposed mechanism can be considered as a valuable new development for manufacturing industry to gain the competitive advantage

The Strategy of Simulation of Powered Led Lamp System (Solar Cell + Diesel Engine) for Traditional Fishing Vessels in Makassar

This paper deals with explaining the importance of higher education research that is directed to the design the strategy of simulation of powered LED lamp system solar cell in future fishing vessels. The principal aim of this research is the utilization of renewable energy with the use of solar cell technology as a driver of lux system on fishing vessels. This research was a panel solar cell yields its power is 100 WP. It is applied to implement LED lamp with its power 100 WP. This wind energy is environmentally (clean energy), economically (cheapest), easy to operate and easy to maintain, also renewable energy. The method of analysis is quantitative approach using one way classification (analysis of variance or design of experiments). The finding of this research is accepted the null hypothesis or not differ significantly at 5% from each independent variable. The scenario and the parameters during the strategy simulation powered LED, solar cell as a power generated by The Fcount is higher than Ftable (3635,27 > 5,77), so H0 is rejected, it means at least there is one light intensity mean value that is produced by the different sun panel significantly on the real stage of 5%. It is expected to encourage and motivate the fisherman public in developing and applying this technology so that it can upgrade the fish production quality and increase the economic value of fishermen society

RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the effectiveness of controllable text generation algorithms at preventing such toxic degeneration. We create and release RealToxicityPrompts, a dataset of 100K naturally occurring, sentence-level prompts derived from a large corpus of English web text, paired with toxicity scores from a widely-used toxicity classifier. Using RealToxicityPrompts, we find that pretrained LMs can degenerate into toxic text even from seemingly innocuous prompts. We empirically assess several controllable generation methods, and find that while data- or compute-intensive methods (e.g., adaptive pretraining on non-toxic data) are more effective at steering away from toxicity than simpler solutions (e.g., banning "bad" words), no current method is failsafe against neural toxic degeneration. To pinpoint the potential cause of such persistent toxic degeneration, we analyze two web text corpora used to pretrain several LMs (including GPT-2; Radford et. al, 2019), and find a significant amount of offensive, factually unreliable, and otherwise toxic content. Our work provides a test bed for evaluating toxic generations by LMs and stresses the need for better data selection processes for pretraining.Comment: Findings in EMNLP 202