nils-herrmann/clean_scirepeval huggingface dataset:
Social Structure, Anomie, and National Levels of Homicide
Venue: International journal of offender therapy and comparative criminology

Merton's "Social Structure and Anomie" seeks to explain how "socio cultural" influences exert "definite pressures" to engage in non conformity. Despite having a significant influence, few studies have assessed the degree to which Merton's propositions explain cross national variation in levels of crime. Using data on national levels of homicide, data from the World Values Survey, and other structural controls, the present study assesses the degree to which deinstitutionalization, demoralization, and blocked opportunity interact to explain crime cross nationally. Results provide a high degree of support for Merton's assertion that societal types characterized by relatively high levels of materialism and/or demoralization or deinstitutionalization suffer from higher levels of homicide. However, there is less support for Merton's assertion that inequality interacts with various societal patterns of means/ends integration in a meaningful way. Findings and implications for the utility of classical anomie as a general macro level theory are discussed.
Automated Extraction of Conceptual Models from User Stories via NLP
Venue: 2016 IEEE 24th International Requirements Engineering Conference (RE)

Natural language (NL) is still the predominant notation that practitioners use to represent software requirements. Albeit easy to read, NL does not readily highlight key concepts and relationships such as dependencies and conflicts. This contrasts with the inherent capability of graphical conceptual models to visualize a given domain in a holistic fashion. In this paper, we propose to automatically derive conceptual models from a concise and widely adopted NL notation for requirements: user stories. Due to their simplicity, we hypothesize that our approach can improve on the low accuracy of previous works. We present an algorithm that combines state of the art heuristics and that is implemented in our Visual Narrator tool. We evaluate our work on two case studies wherein we obtained promising precision and recall results (between 80% and 92% The creators of the user stories perceived the generated models as a useful artifact to communicate and discuss the requirements, especially for team members who are not yet familiar with the project.
QUANTIFIABLE DIVERSITY IN THE ARTS: HOW HISTORICALLY BLACK COLLEGES AND UNIVERSITIES (HBCU) CAN INCREASE DIVERSITY IN ARTS ADMINISTRATION

Title of Thesis: QUANTIFIABLE DIVERSITY IN THE ARTS: HOW HISTORICALLY BLACK COLLEGES AND UNIVERSITIES (HBCU) CAN INCREASE DIVERSITY IN ARTS ADMINISTRATION Degree Candidate: Glenda Estelle Wooden Degree and Year: Master of Arts in Arts Administration, 2016 Major Paper Directed by: Margie Reese Welsh Center for Graduate and Professional Studies Goucher College To elevate the diversity dialogue among students, the arts community, and Historically Black Colleges and Universities (HBCU) with the intent purpose to combat rhetoric that prolongs, or even proliferates the diversity gap, this paper explores what preemptive role HBCUs might play in proactively addressing the need to create a diverse pipeline to the field of arts administration. Through academic preparation, HBCUs can ensure African American candidates acquire proficiencies to enter and make measurable gains in arts administration. The first argument asserts that arts organizations continue to postulate that the lack of African American candidates in professional and leadership roles is inextricably related to the insufficient number of qualified and capable African American candidates from which to recruit. The second argument asserts that arts organizations that have adopted satisfactory recruitment efforts, practices, and policies that value diversity yet remains homogenous reinforce assumptions that emerging African American arts administrators are in short supply, lack interest in the field, or have inadequate formal arts education or experience. The third argument asserts that HBCUs, while limited in financial and physical resources, graduate an equal or greater amount of African American students than predominantly white institutions (PWI) This implies that accompanying factors unique to the HBCU, such as early development of faculty and student relationships, alumni networks, extended family environment, and relevance to the African American experience, are key to academic, social, and professional development. These findings suggest that unless reversed, the education gap will directly attribute to a greater underrepresentation of African Americans in arts administration. QUANTIFIABLE DIVERSIFICATION IN THE ARTS: HOW HISTORICALLY BLACK COLLEGES AND UNIVERSITIES (HBCU) CAN INCREASE DIVERSITY IN ARTS ADMINISTRATION Glenda Estelle Wooden Major paper Submitted to the faculty of Goucher College in partial fulfillment of the requirements for the degree of Master of Arts in Arts Administration 2016 Readers' Committee Margie Reese, MFA, Chair __________________________________ Ramona Baker, MFA __________________________________ Greg Lucas, MBA __________________________________ Dulcie Willis, MAAA __________________________________ Copyright by Glenda Estelle Wooden 2016
Palestinian Children Forging National Identity through the Social and Spatial Practices of Territoriality

This chapter explores the development of national identity for Palestinian children and their families through the lens of territoriality. It suggests that the development of national identity is a process to which territoriality contributes in the forms of both social and spatial practices that delineate who is included and excluded in a particular space. It includes social practices as a collective experience passed down with the historical memory and present oppression of the occupation. Territoriality includes spatial practices, because it is reproduced and enacted through the construction and regulation of everyday spaces, creating regimes of inclusion and exclusion that become central to children's national identity construction. Ultimately, territoriality is a politically contested process that enacts particular geographies that are experienced and contested at multiple sites (Harker 2011)
Semi active vibration control of laminated composite sandwich plate an experimental study

In this study, the vibration analysis of fully and partially treated laminated composite Magnetorheological (MR) fluid sandwich plates has been investigated experimentally. The natural frequencies of fully and partially treated laminated composite MR fluid sandwich plates have been measured at various magnetic field intensities under two different boundary conditions. The variations of natural frequencies with appliedmagnetic field, boundary conditions and location ofMRfluid pocket have been explored. Further, a comparison of natural frequencies of fully and partially treated MR fluid sandwich structure has been made at various magnetic field intensities.
Unmanned Aerial Vehicles for High Throughput Phenotyping and Agronomic Research
Venue: PloS one

Advances in automation and data science have led agriculturists to seek real time, high quality, high volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher resolution crop data collection leads to greater capability for scientists and growers to improve precision agriculture practices on increasingly larger farms; e.g. site specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1 the summer 2015 and winter 2016 growing seasons of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high quality, high volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.
Memes and the evolution of religion: We need memetics, too
Venue: Behavioral and Brain Sciences

In their analysis, Norenzayan et al. completely ignore memetics, which, unlike other theories, treats memes as replicators and looks to memetic as well as genetic advantage. Now that memes are evolving ever faster, genetic advantage is less relevant. So when religious and secular values are at odds, we need a memetic analysis to understand what is going on.
La Modernisation de l'action publique en question

Resume Lancee apres 2012, la politique de Modernisation de l'action publique (MAP) entendait se demarquer, par ses methodes et par ses orientations, de la Revision generale des politiques publiques precedemment entreprise. Passant par plusieurs etapes successives, elle a connu une serie d'inflexions concernant tant son pilotage que ses priorites. Si elles ne sont pas negligeables, les innovations qu'elle comporte se situent cependant dans une large mesure dans le prolongement de ce qui avait ete deja engage.
Une approche spatiale des instruments de l'action publique l'exemple des reformes du secteur de la sante en Inde

Dans de nombreux pays du monde, les systemes de soins sont affectes par des reformes de nature politique, organisationnelle et financiere. Ces dernieres contribuent a renforcer la concurrence, multiplier les interactions entre acteurs publics et prives et entre le local et le global. A partir de l'exemple du systeme de soins public indien, cet article insiste sur les difficultes de l'analyse des paysages sanitaires contemporains et presente les avantages d'une approche centree sur l'etude des instruments de l'action publique (Lascoumes P. Le Gales P. 2005) et de leurs spatialites. Ces reflexions sont developpees a partir de l'etude du role des Organisations Non Gouvernementales (ONG) dans les transformations du systeme de soins public. Une methodologie est presentee et appliquee a l'etude de quatre ONG.
Design of smart GE11 PLGA/PEG PLGA blend nanoparticulate platforms for parenteral administration of hydrophilic macromolecular drugs: synthesis, preparation and in vitro/ex vivo characterization.
Venue: International journal of pharmaceutics

Active drug targeting and controlled release of hydrophilic macromolecular drugs represent crucial points in designing efficient polymeric drug delivery nanoplatforms. In the present work EGFR targeted polylactide co glycolide (PLGA) nanoparticles were made by a blend of two different PLGA based polymers. The first, GE11 PLGA, in which PLGA was functionalized with GE11, a small peptide and EGFR allosteric ligand, able to give nanoparticles selective targeting features. The second polymer was a PEGylated PLGA (PEG PLGA) aimed at improving nanoparticles hydrophilicity and stealth features. GE11 and GE11 PLGA were custom synthetized through a simple and inexpensive method. The nanoprecipitation technique was exploited for the preparation of polymeric nanoparticles composed by a 1:1weight ratio between GE11 PLGA and PEG PLGA, obtaining smart nanoplatforms with proper size for parenteral administration (143.9+ 5.0nm) In vitro cellular uptake in EGFR overexpressing cell line (A549) demonstrated an active internalization of GE11 functionalized nanoparticles. GE11 PLGA/PEG PLGA blend nanoparticles were loaded with Myoglobin, a model hydrophilic macromolecule, reaching a good loading (2.42% respect to the theoretical 4.00% w/w) and a prolonged release over 60days. GE11 PLGA/PEG PLGA blend nanoparticles showed good in vitro stability for 30days in physiological saline solution at 4degC and for 24h in pH 7.4 or pH 5.0 buffer at 37degC respectively, giving indications about potential storage and administration conditions. Furthermore ex vivo stability study in human plasma using fluorescence Single Particle Tracking (fSPT) assessed good GE11 PLGA/PEG PLGA nanoparticles dimensional stability after 1 and 4h. Thanks to the versatility in polymeric composition and relative tunable nanoparticles features in terms of drug incorporation and release, GE11 PLGA/PEG PLGA blend NPs can be considered highly promising as smart nanoparticulate platforms for the treatment of diseases characterized by EGFR overexpression by parenteral administration
Vegetable Oil Loaded Nanocapsules: Innovative Alternative for Incorporating Drugs for Parenteral Administration.
Venue: Journal of nanoscience and nanotechnology

An innovative nanocapsule formulation for parenteral administration using selected vegetable oils (mango, jojoba, pequi, oat, annatto, calendula, and chamomile) was developed that has the potential to encapsulate various drugs. The vegetable oil loaded nanocapsules were prepared by interfacial deposition and compared with capric/caprylic triglyceride loaded lipid core nanocapsules. The major objective was to investigate the effect of vegetable oils on particle size distribution and physical stability and to determine the hemolytic potential of the nanocapsules, considering their applicability for intravenous administration. Taking into account the importance of accurately determining particle size for the selected route of administration, different size characterization techniques were employed, such as Laser Diffraction, Dynamic Light Scattering, Multiple Light Scattering, Nanoparticle Tracking Analysis, and Transmission Electronic Microscopy. Laser diffraction studies indicated that the mean particle size of all nanocapsules was below 300 nm. For smaller particles, the laser diffraction and multiple light scattering data were in agreement (D[3,2] 130 nm) Dynamic light scattering and nanoparticle tracking analysis, two powerful techniques that complement each other, exhibited size values between 180 and 259 nm for all nanoparticles. Stability studies demonstrated a tendency of particle creaming for jojoba nanocapsules and sedimentation for the other nanoparticles; however, no size variation occurred over 30 days. The hemolysis test proved the hemocompatibility of all nanosystems, irrespective of the type of oil. Although all developed nanocapsules presented the potential for parenteral administration, jojoba oil loaded nanocapsules were selected as the most promising nanoformulation due to their low average size and high particle size homogeneity.
Errors in preparation and administration of parenteral drugs in neonatology: evaluation and corrective actions.
Venue: La Tunisie medicale

The medication iatrogenic risk is quite unevaluated in neonatology Objective: Assessment of errors that occurred during the preparation and administration of injectable medicines in a neonatal unit in order to implement corrective actions to reduce the occurrence of these errors.  A prospective, observational study was performed in a neonatal unit over a period of one month. The practice of preparing and administering injectable medications were identified through a standardized data collection form. These practices were compared with summaries of the characteristics of each product (RCP) and the bibliography.  One hundred preparations were observed of 13 different drugs. 85 errors during preparations and administration steps were detected. These errors were divided into preparation errors in 59% of cases such as changing the dilution protocol (32% the use of bad solvent (11% and administration errors in 41% of cases as errors timing of administration (18% or omission of administration (9%  This study showed a high rate of errors during stages of preparation and administration of injectable drugs. In order to optimize the care of newborns and reduce the risk of medication errors, corrective actions have been implemented through the establishment of a quality assurance system which consisted of the development of injectable drugs preparation procedures, the introduction of a labeling system and staff training.
Energy Consumption in Onion and Potato Production within the Province of El Hajeb (Morocco) Towards Energy Use Efficiency in Commercialized Vegetable Production

Energy use efficiency is a key requirement for sustainability in agricultural production, but often overlooked. The aim of this study was to quantify the amount and efficiency of energy consumed in the production of onions and potatoes in El Hajeb province of Morocco. These estimates are of significant importance in informing contemporary policy discourse related to energy subsidy reform in Morocco, and more specifically within an ongoing national strategy for 'modernizing' the agricultural sector under the 'Green Morocco Plan' Data were collected through the administration of a direct questionnaire with 60 farmers and analyzed using PLANETE. Our results indicate that total energy consumption in onion production is 107483 MJ ha 1 with butane (79.5% as the main source of direct energy. Chemical fertilizers (61.53% and water for irrigation (30% were main sources of indirect energy. Energy indices related to energy efficiency ratios, energy profitability and energy productivity were estimated at 0.78, 0.22 and 0.54 kg MJ 1 respectively. Total energy consumption in potato production was estimated at 74,270 MJ ha 1 with direct energy consumption of 28,521 MJ ha 1 stemming from butane (70% and diesel (19.14% as primary sources. Indirect energy consumption was estimated at 45749 MJ ha 1 and generated principally through the use of fertilizers (60% Energy indices (efficiency, profitability and productivity) were estimated at 1.54, 0.54, and 0.45 kg MJ 1 respectively. GHG emissions were found to be 3.47 t CO 2eq ha 1 in the production of onions and 3.63 t CO 2eq ha 1 for potatoes. We find that within the study area, increases in the size of production plots are not necessarily consistent with increases in energy use efficiency.
Deep Quantization Network for Efficient Image Retrieval
Venue: AAAI

Hashing has been widely applied to approximate nearest neighbor search for large scale multimedia retrieval. Supervised hashing improves the quality of hash coding by exploiting the semantic similarity on data pairs and has received increasing attention recently. For most existing supervised hashing methods for image retrieval, an image is first represented as a vector of hand crafted or machine learned features, then quantized by a separate quantization step that generates binary codes. However, suboptimal hash coding may be produced, since the quantization error is not statistically minimized and the feature representation is not optimally compatible with the hash coding. In this paper, we propose a novel Deep Quantization Network (DQN) architecture for supervised hashing, which learns image representation for hash coding and formally control the quantization error. The DQN model constitutes four key components: (1) a sub network with multiple convolution pooling layers to capture deep image representations; (2) a fully connected bottleneck layer to generate dimension reduced representation optimal for hash coding; (3) a pairwise cosine loss layer for similarity preserving learning; and (4) a product quantization loss for controlling hashing quality and the quantizability of bottleneck representation. Extensive experiments on standard image retrieval datasets show the proposed DQN model yields substantial boosts over latest state of the art hashing methods.
A deep neural network based hashing for efficient image retrieval
Venue: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Learning valid similarities is a vital problem in hashing methods, especially in large scale image search. Similarity pertained hashing method is widely used in image retrieval for its high quality compact binary code mapping. The hashing scheme of most existing hashing methods is that the input data is encoded as a vector of visual features and hashed into binary hash codes via projection functions or quantization methods afterward. However, this separated pipeline may prone to lose accurate similarities of images, since the limited compatible domain between visual feature vectors generation and binary codes mapping process. Encouraged by the extraordinary image representation learning ability of deep neural networks in classification, we propose a structure that merges binary code generation process within deep neural networks for efficient image retrieval. The proposed architecture contains two fundamental blocks. The stacked convolution layers of Network In Network with global average pooling compute effective image representation and the embedded latent layer with binary activation functions learn binary hash codes simultaneously. Experiments show that the proposed method gains improvement over several state of the art hashing methods.
Deep Hashing Network for Efficient Similarity Retrieval
Venue: AAAI

Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest neighbor search for large scale multimedia retrieval. Supervised hashing, which improves the quality of hash coding by exploiting the semantic similarity on data pairs, has received increasing attention recently. For most existing supervised hashing methods for image retrieval, an image is first represented as a vector of hand crafted or machine learned features, followed by another separate quantization step that generates binary codes. However, suboptimal hash coding may be produced, because the quantization error is not statistically minimized and the feature representation is not optimally compatible with the binary coding. In this paper, we propose a novel Deep Hashing Network (DHN) architecture for supervised hashing, in which we jointly learn good image representation tailored to hash coding and formally control the quantization error. The DHN model constitutes four key components: (1) a subnetwork with multiple convolution pooling layers to capture image representations; (2) a fully connected hashing layer to generate compact binary hash codes; (3) a pairwise cross entropy loss layer for similarity preserving learning; and (4) a pairwise quantization loss for controlling hashing quality. Extensive experiments on standard image retrieval datasets show the proposed DHN model yields substantial boosts over latest state of the art hashing methods.