nils-herrmann/clean_scirepeval huggingface dataset:
Green Criminology and Environmental Crime: Criminology That Matters in the Age of Global Ecological Collapse
Venue: Journal of White Collar and Corporate Crime
Year: 2019

An overview of green criminology (GC) is provided. That substantial literature is not easily summarized, and here, some core issues are reviewed: defining green crimes, the scope of GC, measuring green crimes, and empirical studies of green crimes. Special attention is paid to political economic approaches to GC, which was the foundation for GC. Connections are made to environmental sociology, theories of metabolic rift and unequal ecological exchange, and scientific evidence on planetary boundaries and ecological footprints. Examples of widespread human and nonhuman being green victimization are reviewed. In an era of anthropogenic driven global ecological collapse, academic disciplines must pay increased attention to ecological disorganization, ecosystem destruction, and excessive production/consumption, which is the proper subject matter of GC.
Inter organizational collaboration for energy efficiency in the maritime sector: the case of a database project
Year: 2019

This article explores the complexities of establishing knowledge sharing practices between organizations through a case study of the creation of a database for energy efficiency measures relevant to the shipping sector. As researchers and policy makers tend to point towards knowledge sharing and collaboration as means towards a more energy efficient society, there is a need to better understand the knowledge sharing practices in such initiatives. The study is based upon extensive fieldwork where the first author was recruited to a collaborative network on energy efficiency in the shipping sector, to aid in the development of the collaboration while carrying out participatory observational research in an ethnographic tradition. The study highlights the need to maintain realistic expectations for new knowledge sharing collaborations, and the necessity to allow such arrangements to develop over time.
IoT based Smart Baby Cradle System using Raspberry Pi B+
Venue: 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC)
Year: 2020

This paper presents IoT based smart system that act as baby cradle monitoring system for engaged or working parent so that they can manage properly, and also for proper care and safety of the infant. Parent can recognize baby's movement, sound like crying and video output of baby's present position and motion will be visible on a screen monitor so the parent or any person can watch the infant even while away from baby. This cradle system is useful for monitoring or detecting movement and crying condition of the child automatically. The Raspberry pi B module is used to have control on the entire hardware, condenser MIC is implemented for baby cry detection, PIR motion sensor is designed to identify baby's motion and pi camera is capturing the infant condition of motion and the display is used to show video output of sleeping baby. This smart baby monitoring system have n number of parameters such as live video and sound, set down audio and leisure movement of infant, measuring the room temperature and the humidity indicates if the baby is sleepless, and the most important characteristic is the ability to listen to the baby noise with cry detection feature.
Exploring Parameter Space with Structured Noise for Meta Reinforcement Learning
Venue: IJCAI
Year: 2020

Efficient exploration is a major challenge in Reinforcement Learning (RL) and has been studied extensively. However, for a new task existing methods explore either by taking actions that maximize task agnostic objectives (such as information gain) or applying a simple dithering strategy (such as noise injection) which might not be effective enough. In this paper, we investigate whether previous learning experiences can be leveraged to guide exploration of current new task. To this end, we propose a novel Exploration with Structured Noise in Parameter Space (ESNPS) approach. ESNPS utilizes meta learning and directly uses meta policy parameters, which contain prior knowledge, as structured noises to perturb the base model for effective exploration in new tasks. Experimental results on four groups of tasks: cheetah velocity, cheetah direction, ant velocity and ant direction demonstrate the superiority of ESNPS against a number of competitive baselines.
Averaged DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Venue: ICML
Year: 2017

Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged DQN is a simple extension to the DQN algorithm, based on averaging previously learned Q values estimates, which leads to a more stable training procedure and improved performance by reducing approximation error variance in the target values. To understand the effect of the algorithm, we examine the source of value function estimation errors and provide an analytical comparison within a simplified model. We further present experiments on the Arcade Learning Environment benchmark that demonstrate significantly improved stability and performance due to the proposed extension.
Development of a responsive optimisation framework for decision making in precision agriculture
Venue: Comput. Chem. Eng.
Year: 2019

Emerging digital technologies and data advances (e.g. smart machinery, remote sensing) not only enable Agriculture 4.0 to envisage interconnected agro ecosystems and precision agriculture but also demand responsive decision making. This study presents a mathematical optimisation model to bring real time data and information to precision decision support and to optimise short term farming operation. To achieve responsive decision support, we proposed two meta heuristic algorithms i.e. a tailored genetic algorithm and a hybrid genetic tabu search algorithm for solving the deterministic optimisation. The developed responsive optimisation framework has been applied to a hypothetical case study to optimise sugarcane harvesting in the KwaZulu Natal region in South Africa. In comparison with the optimal solutions derived from the exact algorithm, the proposed meta heuristic methods lead to near optimal solutions (less than 5% from optimality) and significantly reduced computational time by over 95% Our results suggest that the tailored genetic algorithm enables rapid solution searching but the solution quality on sugarcane harvesting cannot compete with the exact method. The hybrid genetic tabu search algorithm achieved a good trade off between computational time reduction and solution optimality, demonstrating the potential to enhance responsive decision making in precision sugarcane farming. Our research highlights the development of the responsive optimisation framework combining mixed integer linear programming and hybrid meta heuristic search algorithms and its applications in real time decision making under Agriculture 4.0 vision.
ADOPTION OF SOCIAL MEDIA AND MANAGEMENT OF LESSONS LEARNED IN IT PROJECT
Year: 2019

This research explores the gap in the management of lessons learned (MLL) in an Information Technology project (IT) validating a model (named Target) with the support of wiki platform in a medium sized company in the IT industry. This model supports the following MLL processes: awareness, collection, verification, storage, dissemination and reuse. This study adopts the paradigm of the Design Science Research and the Technical Action Research method to instantiate the Target model in the implementation phase of an IT project. The practical implications can be seen in the company's learning to introduce the MLL, improve design productivity, increase employee collaboration and better disseminate knowledge.
The ties that bind: Internet communication technologies, networked authoritarianism, and 'voice' in the Syrian diaspora
Year: 2018

Internet communication technologies (ICTs) enable diasporas to act transnationally by facilitating ties to their places of origin and providing low cost ways to mobilize against home country regimes. However, studies neglect to address how ICTs globalize regimes' methods of social control and impact anti regime diasporas. In order to investigate the operation and effects of what I call digitally enabled transnational repression, this study analyzes data that include original interviews with pro revolution Syrian activists based in the US and Britain. The findings demonstrate that the presence and tactics of pro regime agents online during the onset of Syria's 2011 uprising (i) eroded respondents' transnational ties and (ii) deterred many from using ICTs to contest the Assad regime. The study shows how networked authoritarianism mitigates diaspora members' voices and tactics during periods of violent unrest, which is precisely when ICT enabled activism can aid home country movements in significant ways. I conclude by discussing implications for the study of authoritarian regimes, diasporas, and transnational dynamics of contention.
Dividend policy in Turkey: Survey evidence from Borsa Istanbul firms
Year: 2018

This study investigates the views of managers of firms listed on the Borsa Istanbul (BIST) on dividend policy. The survey evidence provides general support for Lintner's partial adjustment model, signaling theory, catering, firm life cycle, and bird in the hand hypotheses for explaining cash dividends. The results do not support the agency cost theory, substitution model of dividends, tax related explanations, transaction cost theory, and residual dividend policy. The findings suggest that after implementing major economic and structural reforms and abolishing a mandatory dividend payment requirement, BIST managers follow similar dividend policy factors and patterns of dividend policy as managers in more developed countries.
Identification and Specification of the Mouse Skeletal Stem Cell
Venue: Cell
Year: 2015

How are skeletal tissues derived from skeletal stem cells? Here, we map bone, cartilage, and stromal development from a population of highly pure, postnatal skeletal stem cells (mouse skeletal stem cells, mSSCs) to their downstream progenitors of bone, cartilage, and stromal tissue. We then investigated the transcriptome of the stem/progenitor cells for unique gene expression patterns that would indicate potential regulators of mSSC lineage commitment. We demonstrate that mSSC niche factors can be potent inducers of osteogenesis, and several specific combinations of recombinant mSSC niche factors can activate mSSC genetic programs in situ, even in nonskeletal tissues, resulting in de novo formation of cartilage or bone and bone marrow stroma. Inducing mSSC formation with soluble factors and subsequently regulating the mSSC niche to specify its differentiation toward bone, cartilage, or stromal cells could represent a paradigm shift in the therapeutic regeneration of skeletal tissues.
Numerical investigation of asphaltene fouling growth in crude oil preheat trains using multi fluid approach
Year: 2020

In this study, the asphaltene deposition and the fouling layer growth are modeled using the multi fluid approach. The crude oil is considered as a multi species fluid which the asphaltene species can deposit on the heat transfer surfaces due to the volume reaction. The effective mechanisms of asphaltene particle transport are the molecular and eddy diffusions. Therefore the species modeling approach is utilized for particles transport by diffusion processes in the carrier phase. To model the fouling layer growth without considering an initial thickness for the deposited layer, extra terms are added to the momentum equations of primary and secondary phases. The results of the numerical model are validated against available experimental data and a good agreement is seen. Finally, the developed model is applied for a specific case and the effect of fouling layer growth on the fluid velocity, the temperature variations, and the heat transfer rate are investigated. The results show that the heat transfer rate decreases during the fouling and the developed model could predict accurately the physics of fouling layer growth.
INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH AND BIO SCIENCE
Year: 2014

Oxadiazole moiety and its various derivatives studied frequently in the past few decades and found potent in various pharmacological and pathological conditions. Oxadiazole is a versatile heterocyclic nucleus which has attracted a wide attention of the medicinal chemists in search for new therapeutic molecules. Oxadiazoles are five membered heterocyclic compounds with two nitrogen atoms and one oxygen atom. Oxadiazole possess a number of biological activities like anticancer, muscle relaxant, antimicrobial, anticonvulsant, antiviral, anti tubercular, anti amoebic, antiobesity, anti inflammatory activities etc. The present review describes the Chemistry, sources, synthetic review and pharmacological profile of 1,3,4 oxadiazole. The aim of the present paper is to review the available information on 1,3,4 oxadiazole.
Drug delivery and nanoparticles: Applications and hazards
Venue: International journal of nanomedicine
Year: 2008

The use of nanotechnology in medicine and more specifically drug delivery is set to spread rapidly. Currently many substances are under investigation for drug delivery and more specifically for cancer therapy. Interestingly pharmaceutical sciences are using nanoparticles to reduce toxicity and side effects of drugs and up to recently did not realize that carrier systems themselves may impose risks to the patient. The kind of hazards that are introduced by using nanoparticles for drug delivery are beyond that posed by conventional hazards imposed by chemicals in classical delivery matrices. For nanoparticles the knowledge on particle toxicity as obtained in inhalation toxicity shows the way how to investigate the potential hazards of nanoparticles. The toxicology of particulate matter differs from toxicology of substances as the composing chemical(s) may or may not be soluble in biological matrices, thus influencing greatly the potential exposure of various internal organs. This may vary from a rather high local exposure in the lungs and a low or neglectable exposure for other organ systems after inhalation. However, absorbed species may also influence the potential toxicity of the inhaled particles. For nanoparticles the situation is different as their size opens the potential for crossing the various biological barriers within the body. From a positive viewpoint, especially the potential to cross the blood brain barrier may open new ways for drug delivery into the brain. In addition, the nanosize also allows for access into the cell and various cellular compartments including the nucleus. A multitude of substances are currently under investigation for the preparation of nanoparticles for drug delivery, varying from biological substances like albumin, gelatine and phospholipids for liposomes, and more substances of a chemical nature like various polymers and solid metal containing nanoparticles. It is obvious that the potential interaction with tissues and cells, and the potential toxicity, greatly depends on the actual composition of the nanoparticle formulation. This paper provides an overview on some of the currently used systems for drug delivery. Besides the potential beneficial use also attention is drawn to the questions how we should proceed with the safety evaluation of the nanoparticle formulations for drug delivery. For such testing the lessons learned from particle toxicity as applied in inhalation toxicology may be of use. Although for pharmaceutical use the current requirements seem to be adequate to detect most of the adverse effects of nanoparticle formulations, it can not be expected that all aspects of nanoparticle toxicology will be detected. So, probably additional more specific testing would be needed.
Anti fog compositions and processes for making same
Year: 2014

Anti fog compositions comprising a primary film having opposing major planar surfaces and a central coplanar region, and processes for making such anti fog compositions. The central coplanar region is disposed between the opposing major planar surfaces. The primary film comprises cellulose acetate, plasticizer, and an anti blocking agent. The anti blocking agent has an average particle size ranging from 0.02 microns to 6 microns. The cellulose acetate has a degree of substitution that increases from the opposing major planar surfaces toward the central coplanar region or that is substantially constant throughout the thickness of the composition. The composition is formed by saponifying a precursor film to improve hydrophilicity.
Graph Neural Networks for Natural Language Processing: A Survey
Venue: ArXiv
Year: 2021

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP) Although text inputs are typically represented as a sequence of tokens, there isa rich variety of NLP problems that can be best expressed with a graph structure. As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder decoder models. We further introducea large number of NLP applications that are exploiting the power of GNNs and summarize thecorresponding benchmark datasets, evaluation metrics, and open source codes. Finally, we discussvarious outstanding challenges for making the full use of GNNs for NLP as well as future researchdirections. To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.
A signal detection theory analysis of racial and ethnic disproportionality in the referral and substantiation processes of the U.S. child welfare services system
Year: 2014

Signal detection theory (SDT) was developed to analyze the behavior of a single judge but also can be used to analyze decisions made by organizations or other social systems. SDT quantifies the ability to distinguish between signal and noise by separating accuracy of the detection system from response bias the propensity to over warn (too many false positives) or under warn (too many misses) We apply SDT techniques to national and state level data sets to analyze the ability of the child welfare services systems to detect instances of child maltreatment. Blacks have higher rates of referral and the system is less accurate for them than for Whites or Hispanics. The incidence of false positives referrals leading to unsubstantiated findings is higher for Blacks than for other groups, as is the incidence of false negatives children for whom no referral was made but who are in fact neglected or abused. The rate of true positives children for whom a referral was made and for whom the allegation was substantiated is higher for Blacks. Values of d' (signal strength) are roughly the same for Whites, Blacks, and Hispanics but there are pronounced group differences in C (a measure of the location of the decision threshold) Analyses show that the child welfare services system treats Blacks differently from Hispanics and Whites in ways that cannot be justified readily in terms of objective measures of group differences. This study illustrates the potential for JDM techniques such as SDT to contribute to understanding of system level decision making processes.
The Schooling Decision: Family Preferences, Intergenerational Conflict, and Moral Hazard in the Brazilian Favelas
Venue: Journal of Political Economy
Year: 2012

This paper experimentally analyzes the schooling decisions of poor households in urban Brazil. We elicit parents' choices between monthly government transfers conditional on their adolescent child attending school and guaranteed, unconditional transfers of varying sizes. In the baseline treatment, an overwhelming majority of parents prefer conditional transfers to larger unconditional transfers. However, few parents prefer conditional payments if they are offered text message notifications whenever their child misses school. These findings suggest important intergenerational conflicts in these schooling decisions, a lack of parental control and observability of school attendance, and an additional rationale for conditional cash transfer programs the monitoring they provide.
Speech Enhancement: Theory and Practice
Year: 2007

With the proliferation of mobile devices and hearing devices, including hearing aids and cochlear implants, there is a growing and pressing need to design algorithms that can improve speech intelligibility without sacrificing quality. Responding to this need,  Theory and Practice, Second Edition introduces readers to the basic problems of speech enhancement and the various algorithms proposed to solve these problems. Updated and expanded, this second edition of the bestselling textbook broadens its scope to include evaluation measures and enhancement algorithms aimed at improving speech intelligibility. Fundamentals, Algorithms, Evaluation, and Future Steps Organized into four parts, the book begins with a review of the fundamentals needed to understand and design better speech enhancement algorithms. The second part describes all the major enhancement algorithms and, because these require an estimate of the noise spectrum, also covers noise estimation algorithms. The third part of the book looks at the measures used to assess the performance, in terms of speech quality and intelligibility, of speech enhancement methods. It also evaluates and compares several of the algorithms. The fourth part presents binary mask algorithms for improving speech intelligibility under ideal conditions. In addition, it suggests steps that can be taken to realize the full potential of these algorithms under realistic conditions. Whats New in This Edition Updates in every chapter A new chapter on objective speech intelligibility measures A new chapter on algorithms for improving speech intelligibility Real world noise recordings (on accompanying CD) MATLAB code for the implementation of intelligibility measures (on accompanying CD) MATLAB and C/C+ code for the implementation of algorithms to improve speech intelligibility (on accompanying CD) Valuable Insights from a Pioneer in Speech Enhancement Clear and concise, this book explores how human listeners compensate for acoustic noise in noisy environments. Written by a pioneer in speech enhancement and noise reduction in cochlear implants, it is an essential resource for anyone who wants to implement or incorporate the latest speech enhancement algorithms to improve the quality and intelligibility of speech degraded by noise. Includes a CD with Code and Recordings The accompanying CD provides MATLAB implementations of representative speech enhancement algorithms as well as speech and noise databases for the evaluation of enhancement algorithms.