Michele Banko, Michael J. Cafarella, S. Soderland
et al.
Traditionally, Information Extraction (IE) has focused on satisfying precise, narrow, pre-specified requests from small homogeneous corpora (e.g., extract the location and time of seminars from a set of announcements). Shifting to a new domain requires the user to name the target relations and to manually create new extraction rules or hand-tag new training examples. This manual labor scales linearly with the number of target relations. This paper introduces Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input. The paper also introduces TEXTRUNNER, a fully implemented, highly scalable OIE system where the tuples are assigned a probability and indexed to support efficient extraction and exploration via user queries. We report on experiments over a 9,000,000 Web page corpus that compare TEXTRUNNER with KNOWITALL, a state-of-the-art Web IE system. TEXTRUNNER achieves an error reduction of 33% on a comparable set of extractions. Furthermore, in the amount of time it takes KNOWITALL to perform extraction for a handful of pre-specified relations, TEXTRUNNER extracts a far broader set of facts reflecting orders of magnitude more relations, discovered on the fly. We report statistics on TEXTRUNNER’s 11,000,000 highest probability tuples, and show that they contain over 1,000,000 concrete facts and over 6,500,000more abstract assertions.
A. Luccioni, Christopher Akiki, Margaret Mitchell
et al.
As machine learning-enabled Text-to-Image (TTI) systems are becoming increasingly prevalent and seeing growing adoption as commercial services, characterizing the social biases they exhibit is a necessary first step to lowering their risk of discriminatory outcomes. This evaluation, however, is made more difficult by the synthetic nature of these systems' outputs: common definitions of diversity are grounded in social categories of people living in the world, whereas the artificial depictions of fictive humans created by these systems have no inherent gender or ethnicity. To address this need, we propose a new method for exploring the social biases in TTI systems. Our approach relies on characterizing the variation in generated images triggered by enumerating gender and ethnicity markers in the prompts, and comparing it to the variation engendered by spanning different professions. This allows us to (1) identify specific bias trends, (2) provide targeted scores to directly compare models in terms of diversity and representation, and (3) jointly model interdependent social variables to support a multidimensional analysis. We leverage this method to analyze images generated by 3 popular TTI systems (Dall-E 2, Stable Diffusion v 1.4 and 2) and find that while all of their outputs show correlations with US labor demographics, they also consistently under-represent marginalized identities to different extents. We also release the datasets and low-code interactive bias exploration platforms developed for this work, as well as the necessary tools to similarly evaluate additional TTI systems.
Maduri Mallareddy, Ramasamy Thirumalaikumar, Padmaanaban Balasubramanian
et al.
Rice is a water-guzzling crop cultivated mostly through inefficient irrigation methods which leads to low water use efficiency and many environmental problems. Additionally, the export of virtual water through rice trading and the looming water crisis poses significant threats to the sustainability of rice production and food security. There are several alternative rice production methods to improve water use efficiency. These include aerobic rice, direct-seeded rice (DSR), alternate wetting and drying (AWD), saturated soil culture (SSC), drip-irrigated rice, a system of rice intensification (SRI), and smart irrigation with sensors and the Internet of Things (IoT). However, each method has its own advantages and disadvantages. For example, drip-irrigated rice and IoT-based automated irrigation are not feasible for poor farmers due to the high production costs associated with specialized machinery and tools. Similarly, aerobic rice, drip-irrigated rice, and the SRI are labor-intensive, making them unsuitable for areas with a shortage of labor. On the other hand, DSR is suitable for labor-scarce areas, provided herbicides are used to control weeds. In this article, the suitability of different water-saving rice production methods is reviewed based on factors such as climate, soil type, labor, energy, and greenhouse gas emissions, and their prospects and challenges are evaluated. Additionally, the article examines how cultural practices, such as seed treatment, weed control, and nutrition management, contribute to enhancing water use efficiency in rice production.
Kamer-Ainur Aivaz, Daniel Teodorescu, Oana Roxana Radu
The rapid development of artificial intelligence (AI) has intensified debates regarding its impact on the labor market, specifically concerning the potential for replacement versus the augmentation of human labor. While the existing literature highlights both the opportunities and risks associated with AI, research conducted by faculty in academic settings focuses predominantly on academic integrity, paying limited attention to AI readiness and/or anxiety related to labor market entry. This study aims to compare the perceptions of students and faculty in Romania regarding the impact of AI on employment, exploring the role of personal and organizational readiness in shaping these attitudes. The research is based on an empirical approach utilizing a questionnaire applied to a sample of 271 respondents, consisting of 197 students and 74 faculty members. Data analysis included descriptive and inferential methods, such as Chi-square tests and binary logistic regression, and was theoretically grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT). The results indicate significant differences between students and faculty regarding general attitudes toward AI, with students manifesting higher levels of concern regarding job replacement. However, both groups converge in their functional definition of AI as a major factor in labor transformation, suggesting an evaluative rather than a cognitive difference. Multivariate analyses show that personal readiness and the perception of organizational readiness are the primary predictors of a positive attitude toward AI, while demographic variables lose statistical significance when these dimensions are controlled. This study contributes to the literature by highlighting that AI-related anxiety is not inherently determined by demographic characteristics but represents a malleable state shaped by individual competencies and institutional conditions. The findings underscore the strategic role of universities in reducing perceptions of replacement and facilitating the transition to an AI-augmented labor market through training policies, adequate infrastructure, and transparent institutional communication.
This study examines the historical evolution and contemporary manifestations of slavery and human trafficking in the Muslim world by integrating historical analysis, Islamic legal interpretation, and modern human rights perspectives. The research addresses the central problem of the persistence of exploitative practices such as forced labor, child exploitation, and coercive religious or educational systems despite the ethical and legal principles within Islam that emphasize human dignity and freedom. Using a qualitative documentary method, the study analyzes pre-Islamic practices, early Islamic reforms, classical legal traditions, and current forms of exploitation in Muslim-majority societies. Findings reveal that slavery in pre-Islamic Arabia was deeply embedded within regional social structures, but Islam introduced significant moral and legal reforms that promoted humane treatment and encouraged emancipation. However, variations in cultural and political contexts across regions allowed certain practices to persist. In the modern era, new forms of human trafficking continue to challenge both legal frameworks and societal norms. The study concludes that Islamic teachings, when understood through their historical and ethical trajectory, provide a strong foundation for eliminating all forms of human exploitation. It also highlights the importance of contemporary legal interpretations that reject slavery entirely and call for greater alignment between Islamic principles and modern human rights standards.
Harry Navasca, Aliasghar Bazrafkan, Françoise Dalprá Dariva
et al.
Abstract Accurately estimating days to maturity (DTM) is essential for assessing local adaptation and yield potential in field pea (Pisum sativum L.) breeding programs. However, traditional manual scoring of DTM is labor‐intensive and inefficient for large‐scale, multi‐environment trials. To address this challenge, we developed a high‐throughput, low‐cost phenotyping framework using uncrewed aerial systems (UASs) equipped with red‐green‐blue cameras, implemented within the North Dakota State University Pulse Crop Breeding Program. This study aimed to (1) compare aerial and manual phenotyping for DTM estimation, (2) identify the optimal assessment time point, and (3) detect significant loci associated with DTM in a panel of 300 genetically diverse pea accessions. Image‐derived vegetation indices (VIs) collected 71 days after planting exhibited strong correlations with manually assessed DTM. Notably, vegetation indices demonstrated higher heritability (H2 = 0.91) compared to traditional DTM scores (H2 = 0.84). eXtreme Gradient Boosting models identified the visible atmospherically resistant index (31%), modified green‐red vegetation index (17%), and redness index (13%) as the most predictive VIs. Genome‐wide association mapping using these indices revealed three significant single nucleotide polymorphisms on chromosomes 3 and 5—variants not detected using traditional maturity data—highlighting the potential enhanced detection power of image‐derived traits. This work demonstrates the utility of low‐cost UAS platforms for scalable, non‐destructive maturity estimation and illustrates their potential to uncover genetic components of economically important traits, offering new avenues for addressing missing heritability in legume breeding.
Longtemps en marge des institutions du marché du travail traditionnelles, les plateformes de travail numériques sont confrontées à une offensive réglementaire. L’Espagne a adopté une série de mesures législatives – en particulier la loi Rider – qui la place aux avant-postes de ce mouvement mondial. À partir d’une étude de cas élargie conduite durant 18 mois auprès de divers acteurs, l’auteur évalue si elle est parvenue à réencastrer les livreurs travaillant via une plateforme dans les relations d’emploi classiques. Il brosse un tableau en demi-teinte: les livreurs ont désormais accès à un salaire fixe, aux congés payés et à la protection sociale, mais leurs espoirs ont été déçus s’agissant du traitement équitable, de l’autonomie et de la confiance mutuelle en raison de pratiques comme l’externalisation, le temps partiel subi et l’intensification de la surveillance. L’inefficacité de la disposition légale qui garantit l’accès des représentants des travailleurs aux algorithmes des plateformes en dit long sur l’ampleur des défis à relever.
Oluwatosin S. Atitebi, Kalpana Dumre, Erick C. Jones
The clean energy transition is a paradigm shift from a carbon-intensive energy system to a renewable energy one. The new energy system requires large amounts of critical minerals, including lithium. However, the mining and extraction of these minerals introduces environmental challenges. Recycling critical minerals, a critical step for a circular economy, is a potential solution that could reduce the need for new mining, lowering the overall environmental impact. In this experimentally based work, we evaluate the lithium recycling labor- and cost-intensive preprocessing stage that is currently performed by large-scale recycling systems, reducing the efficiency and raising the costs of the downstream stages. We investigate multiple inexpensive and distributed alternatives to the preprocessing tasks that produce black mass (separation, grinding, and shredding techniques) in order to identify methods that improve the efficiency of the downstream recycling process. This work finds that shredding and grinding end-of-life batteries with equipment that can be purchased for under USD 1000 produces viable black mass for a fraction of the cost. Therefore, this work contributes toward the goal of a circular economy for battery energy storage by identifying the technical requirements and measuring the efficacy of redistributing the labor- and time-intensive preprocessing tasks to small-scale recyclers in order to enhance the efficiency of the downstream stages in the lithium-ion battery recycling reverse supply chain.
Abstract Accurate tumor delineation is foundational to radiotherapy. In the era of deep learning, the automation of this labor-intensive and variation-prone process is increasingly tractable. We developed a deep neural network model to segment gross tumor volumes (GTVs) in the lung and propagate them across 4D CT images to generate an internal target volume (ITV), capturing tumor motion during respiration. Using a multicenter cohort-based registry from 9 clinics across 2 health systems, we trained a 3D UNet model (iSeg) on pre-treatment CT images and corresponding GTV masks (n = 739, 5-fold cross-validation) and validated it on two independent cohorts (n = 161; n = 102). The internal cohort achieved a median Dice (DSC) of 0.73 [IQR: 0.62–0.80], with comparable performance in external cohorts (DSC = 0.70 [0.52–0.78] and 0.71 [0.59–79]), indicating multi-site validation. iSeg matched human inter-observer variability and was robust to image quality and tumor motion (DSC = 0.77 [0.68–0.86]). Machine-generated ITVs were significantly smaller than physician delineated contours (p < 0.0001), indicating more precise delineation. Notably, higher false positive voxel rate (regions segmented by the machine but not the human) were associated with increased local failure (HR: 1.01 per voxel, p = 0.03), suggesting the clinical relevance of these discordant regions. These results mark a leap in automated target volume segmentation and suggest that machine delineation can enhance the accuracy, reproducibility, and efficiency of this core task in radiotherapy.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Women’s participation in the labor market plays a crucial role in advancing economic growth and fostering gender equality, yet it continues to encounter persistent social and structural obstacles. This research seeks to examine the determinants of female employment absorption in Central Java from 2019 to 2023. The analysis applies panel data regression with a Fixed Effect Model (FEM) to capture the influence of each explanatory variable. The study incorporates the Information and Communication Technology Development Index (ICTDI), Provincial Minimum Wage (PMW), Average Years of Schooling (AYS), investment, labor force, and the presence of vocational training centers. Findings reveal that PMW, AYS, labor force, and vocational training centers exert a significant impact on female labor absorption, whereas ICTDI and investment show no meaningful effect. In light of these results, it is recommended that policymakers emphasize equitable wage systems, broaden educational opportunities, encourage women’s participation in the workforce, and reinforce the contribution of vocational training institutions to sustainably enhance competitiveness and the integration of women into the labor market.
Keywords: Labor Absorption; Information; Provincial Minimum Wage; Labor Force; Communication and Technology Development Index (IP-ICT); Level of Education; Vocational Training Institutions
Financial crises of 2007-08 were the worst crisis the world has ever seen. These crisis engulfed the world and affected almost every part of it. Conventional economic patterns are major causes of such crises. In this economic system, profit maximization objective of corporate sector compress the wages as well as size of labor which finally cause two evils: widening rich poor gap and unemployment simultaneously. These evils beget other evils e.g. increasing trend of crime, illiteracy, unavailability of health facilities, moral and ethical diseases, social and economical injustice and insecurity etc. In this scenario, an obvious resemblance can be seen between modern economic system and no system of jahiliyyah when there were no institutions, no systems and no knowledge. Looking at actions and consequences, one can say that modern economic system is nothing other than replica of no system of old jahiliyyah but in relatively modern worst forms. Unlike to conventional economic system, Islamic economic System emerged claiming that it would enhance the socio-economic welfare of the mankind, but this dream is getting faded looking at the current practices in Islamic finance. Conventional finance is accused of high cost to client, but unfortunately current Islamic finance is not exception to this as well. It has become need of the time to look back the early Islamic revolutionary socio economic pattern to get how Islam revolutionized the principle-less pre-Islamic economic environment and how it developed principle abiding economic system to achieve the goals of Socio-Economic welfare of mankind. It will make possible to compare the modern prevailing economic and financial system with the pre-Islamic said system, and it will provide a true pattern through which modern age can effectively enhance its Socio-Economic welfare.