nils-herrmann/clean_scirepeval huggingface dataset:
The Role of Mathematics in interdisciplinary STEM education
Venue: ZDM
Year: 2019

In times of rapid technological innovation and global challenges, the development of science, technology, engineering and mathematics (STEM) competencies becomes important. They improve the personal scientific literacy of citizens, enhance international economic competitiveness and are an essential foundation for responsible citizenship, including the ethical custodianship of our planet. The latest programme for international student assessment results, however, indicate that even in economically mature countries such as those in Europe, and the USA and Australia, approximately 20% of students lack sufficient skills in mathematics or science. This trend serves to highlight the urgent need for action in relation to STEM education. While it is widely acknowledged that mathematics underpins all other STEM disciplines, there is clear evidence it plays an understated role in integrated STEM education. In this article, we address an element of this concern by examining the role of mathematics within STEM education and how it might be advanced through three interdisciplinary approaches: (1) twenty first century skills; (2) mathematical modelling; and (3) education for responsible citizenship. At the end of the paper we discuss the potential for research in relation to these three aspects and point to what work needs to be done in the future.
Biofabricating Functional Soft Matter Using Protein Engineering to Enable Enzymatic Assembly.
Venue: Bioconjugate chemistry
Year: 2018

Biology often provides the inspiration for functional soft matter, but biology can do more: it can provide the raw materials and mechanisms for hierarchical assembly. Biology uses polymers to perform various functions, and biologically derived polymers can serve as sustainable, self assembling, and high performance materials platforms for life science applications. Biology employs enzymes for site specific reactions that are used to both disassemble and assemble biopolymers both to and from component parts. By exploiting protein engineering methodologies, proteins can be modified to make them more susceptible to biology's native enzymatic activities. They can be engineered with fusion tags that provide (short sequences of amino acids at the C and/or N termini) that provide the accessible residues for the assembling enzymes to recognize and react with. This "biobased" fabrication not only allows biology's nanoscale components (i.e. proteins) to be engineered, but also provides the means to organize these components into the hierarchical structures that are prevalent in life.
Emotions and the Spatialisation of Social Relations in Text Based Computer Mediated Communication
Year: 2013

This paper advances our knowledge of emotions in virtual teams using text based computer mediated communication. The literature's preoccupation with the absence of physical cues of emotion has meant we lack both an understanding of how emotions are co constructed through interaction and an explanation of their role in the social relations of virtual teams. Adopting a communicative view of emotion, we present the findings of a longitudinal study of a virtual team within a transnational collaborative project. We present three aspects of interaction that demonstrate how team members' experience and understanding of the emotions expressed through, and suppressed from, text based messages are influenced by the styles and patterns of interaction enabled by technology. Where our three aspects tend towards stasis, we argue that emotion provides a temporal dimension to a process of 'spatialising' social relations by connoting what should change, or what should endure, between people.
Real Time Implementation of Convolutional Neural Network to Detect Plant Diseases Using Internet of Things
Venue: VDAT
Year: 2019

In this paper, we have described the real time implementation of machine learning on identification of plant disease. This is done by using the concept of transfer learning with Convolutional Neural Networks (CNNs) on self designed drone. In order to achieve high crop yield, the time management and robust diagnosis of the plant disease is a crucial factor along with the inspection of their nutrient deficiencies. We have designed and trained a novel real time system using machine learning with deep convolutional networks to identify the plant diseases for the diagnosis. This is based on a concept of leaf image classification. The trained system is processed on an open source development board and it is mounted on self design automated drone and uses Internet of Things (IoT) to transfer the captured images to a local database which is connected to a global Server.
Evolution of Fashion Marketing and Communication: Systematic Literature Review
Year: 2021

The research objectiveis to analyze the development and evolution of digital tools in luxury fashion companies.  A search for specific criteria and boolean algorithms is carried out in Web of Science and Scopus on "Marketing digital" [AND] "Fashion" once the results are obtained, a selection of the most cited is made, and critical and analytical reading is carried out.  The relationship between digital marketing and fashion companies is reviewed from a perspective of economic growth and, more specifically, from the industrial sector's communication and marketing described above. However, the review clarifies that using social networks as virtual channels in managing reputation and engagement is not sufficiently addressed. In this sense, in a pandemic environment such as the one currently experienced, luxury fashion companies have assumed and understood the role of digital channels in building image, reputation, and desire to purchase by consumers in an environment that allows them to generate know the trends of stakeholders.  Reviewing the evolution of digital tools in luxury fashionto build trendsbecomes essential with the increased use of social networks. There is little research with a targeted aroach to the evolution of digital marketing in luxury fashion. For this reason, the study attempts to compile the theoretical and reflective discussion in this regard, contributing to the understanding of consumer behavior in the digital environment today.
Venipuncture activates the cerebral cortex in children with intellectual disability
Venue: Archives of Disease in Childhood
Year: 2020

To evaluate the pattern of cortical activation during a painful procedure, such as a venipuncture, in children with intellectual disability and compare it with that of cognitively healthy children.  A cohort study was conducted and cortical activation was assessed by multichannel cerebral near infrared spectroscopy to monitor variations in oxyhaemoglobin and deoxyhaemoglobin (Hbb) in children with and without intellectual disability during a venipuncture for blood sampling with topical anaesthesia. Pain and distress were assessed as well using different validated pain scales (visual analogue scale and Non Communicating Children's Pain Checklist Postoperative Version for children with intellectual disability) and compared between groups. Participants 16 children with severe to profound intellectual disability and 20 cognitively healthy peers (age range: 4 17 years)  When Hbb was analysed, children with intellectual disability exhibited a bilateral activation of the somatosensory (p<0.006) and right motor cortex (p=0.0045) whereas cognitively healthy peers never showed a cortical activation. Children with intellectual disability also showed more pain than controls (p=0.001) Conclusions When subjected to a painful procedure, only children with intellectual disability show an activation of the cerebral cortex, even if topical anaesthesia is applied, and express more pain than cognitively healthy peers. The role of other issues in painful procedures, such as anxiety, fear or physical restraint, deserves further investigation.
SPME/GC/MS and sensory flavour profile analysis for estimation of authenticity of thyme honey
Year: 2007

Volatiles of six commercially obtained thyme honey samples and a pure thyme honey sample were extracted and analysed by Solid phase micro extraction (SPME)/Gas chromatography (GC)/Mass spectrometry (MS) procedure. Excess amount of volatiles such as thymol (trace) and carvacrole (0.66% that originate from thyme plants indicated adulteration by thyme essential oil in one of the commercial samples. Sensory flavour profile analysis showed that the flavour of pure thyme honey sample consisted of: sweet, honey, lilac, bitter almond, thyme, violet, waxy, sour, ginger, caramel and rose characters. Adulterated honey was detected to be the sweetest sample involving intense thyme flavour without honey character. 3,4,5 Trimethoxybenzaldehyde which is a volatile not found in other unifloral honeys, seems to be a possible marker, but further studies with certified thyme honey samples are necessary in order to confirm the utility of this compound in estimation of authenticity.
ELT EAPR: Expected Life Time of Energy Aware Parent Routing for IoT Networks
Year: 2018

Power consumption is a crucial issue in designing the routing protocols for Internet of Things (IoT) Routing is responsible to choose the best available path towards the root. Therefore, routing protocol should be energy efficient as the IoT devices are energy constraint in nature. This paper proposes Expected Life Time of Energy Aware Parent Routing (ELTEAPR) for parent nodes. The expected life time of the parent cum network is calculated using neural network predictor. Keywords IoT; Routing; Energy Efficiency; Energy Aware Parent; Neural Network; RPL;
Appraisal of Cloud Multi Tenant Database Infrastructure Usability in Public Safety and Emergency Delivery Services
Year: 2019

In this era of multiplicity of security challenges, information technology (IT) techniques and infrastructures could enhance public safety services. Cloud computing models can serve hundreds of thousands of customers using sharable resource (database) to reduce costs and offer performance. With these models, organizations no longer have to buy, build and manage costly computing resources to provide for security via public safety networking. IaaS and SaaS models made IT more affordable than Traditional IT. Data activities (processing, storage, management) and collaboration over 'encapsulated' Internet environment is enabled for optimal performance. This study critically appraises multi tenant database as IT infrastructure and/or software for public safety networking where robust, fail safe mission critical communication deliveries are expedient. Emergency situations and daily routine of public safety agencies require more broadband services for life saving information to be securely/reliably transmitted between control centers and field responders using separate databases. Through virtualization, database is separated from infrastructure but is also conceptualized as infrastructure for users/customers to benefit as tenants and multi tenants (MTs) in data centres. Using decision tree and Bayesian classifier models, database is classified into public utility (PU) or emergency need (EN) servicing two defined customer segments: low workload and high workload tenants. A confusion matrix detailed the service requirement instances' analysis of MT infrastructure for performance while the research concludes by iterating MT database excellence for collaborative work. Cloud concept, IaaS and SaaS as power station and core modules leverage MT database as infrastructure and software to increase usability and provide trade off between storage space (cost) and performance. It helps overcome challenges of continuous changing and higher frequency data refreshing needs of both emergent and autonomous computing.
How do I teach you? An examination of multiple intelligences and the impact on communication in the classroom
Year: 2020

Multiple intelligences theory explores the idea that people learn and process information in different ways based on a combination of different areas of intelligence. In the areas of communication and education, this becomes important as a means to understand the listening process and creating class environments that offer instruction in more than just the typical linguistic or logical mathematical areas of intelligence. This paper will explore the literature on multiple intelligences as related to communication and instruction. The application of this theory will be discussed, presenting information the use of multiple intelligences in communicating in a class environment.
Methods and datasets on semantic segmentation: A review
Venue: Neurocomputing
Year: 2018

Semantic segmentation, also called scene labeling, refers to the process of assigning a semantic label (e.g. car, people, and road) to each pixel of an image. It is an essential data processing step for robots and other unmanned systems to understand the surrounding scene. Despite decades of efforts, semantic segmentation is still a very challenging task due to large variations in natural scenes. In this paper, we provide a systematic review of recent advances in this field. In particular, three categories of methods are reviewed and compared, including those based on hand engineered features, learned features and weakly supervised learning. In addition, we describe a number of popular datasets aiming for facilitating the development of new segmentation algorithms. In order to demonstrate the advantages and disadvantages of different semantic segmentation models, we conduct a series of comparisons between them. Deep discussions about the comparisons are also provided. Finally, this review is concluded by discussing future directions and challenges in this important field of research.
Near infrared six band polarization independent wide angle absorber based on metal cavity arrays filled with GaAs
Venue: Science China Information Sciences
Year: 2018

Dear editor, Since the first microwave metamaterial perfect absorber (MPA) was reported by Landy et al.  the study of metamaterial perfect absorbers has attracted attention because of their broad application prospects. Owing to the strong resonant nature of metamaterials, MPAs generally have a single narrow absorption band, which considerably affects their practical applications, particularly in infrared frequency selective detection, imaging, and bolometric applications. To increase the number of absorption bands, there are currently two typical ways, namely, coplanar multiple substructures with different geometric dimensions in a unit cell [2,3] and vertically stacked multilayer metal/dielectric structures  However, these designs are limited to triple band and quadrupleband perfect absorption and five or more absorption bands are rarely involved. Therefore, the design of multiband (higher than five bands) infrared absorbers is still a challenge.
Exploiting gold nanoparticles for diagnosis and cancer treatments
Venue: Nanotechnology
Year: 2021

Gold nanoparticles (AuNPs) represent a relatively simple nanosystem to be synthesised and functionalized. AuNPs offer numerous advantages over different nanomaterials, primarily due to highly optimized protocols for their production with sizes in the range 1 150 nm and shapes, spherical, nanorods (AuNRs) nanocages, nanostars or nanoshells (AuNSs) just to name a few. AuNPs possess unique properties both from the optical and chemical point of view. AuNPs can absorb and scatter light with remarkable efficiency. Their outstanding interaction with light is due to the conduction electrons on the metal surface undergoing a collective oscillation when they are excited by light at specific wavelengths. This oscillation, known as a localized surface plasmon resonance, causes the absorption and scattering intensities of AuNPs to be significantly higher than identically sized non plasmonic nanoparticles. In addition, AuNP absorption and scattering properties can be tuned by controlling the particle size, shape, and the local refractive index near the particle surface. By the chemical side, AuNPs offer the advantage of functionalization with therapeutic agents through covalent and ionic binding, which can be useful for biomedical applications, with particular emphasis on cancer treatments. Functionalized AuNPs exhibit good biocompatibility and controllable distribution patterns when delivered in cells and tissues, which make them particularly fine candidates for the basis of innovative therapies. Currently, major available AuNP based cancer therapeutic approaches are the photothermal therapy (PTT) or photodynamic therapy (PDT) PTT and PDT rely upon irradiation of surface plasmon resonant AuNPs (previously delivered in cancer cells) by light, in particular, in the near infrared range. Under irradiation, AuNPs surface electrons are excited and resonate intensely, and fast conversion of light into heat takes place in about 1 ps. The cancer cells are destroyed by the induced hyperthermia, i.e. the condition under which cells are subject to temperature in the range of 41 degC 47 degC for tens of minutes. The review is focused on the description of the optical and thermal properties of AuNPs that underlie their continuous and progressive exploitation for diagnosis and cancer therapy.
Regulation of addiction behavior by miRNA
Year: 2011

To introduce the research progresses in regulation of addiction behavior by miRNA.Methods The recent progresses in the study of mechanism of post transcriptional gene regulation in drug addiction were summarized in the paper. Drugs induce changes in miRNA level,and specific miRNAs were involved in the regulation of addictive behavior. Significant research progresses have been made in the recent years in the investigation into the role of miRNAs in regulation of drug addiction,which are of importance for revealing the pathological mechanisms for chronic drug dependence and other mental illness.
Renyi Differential Privacy of the Sampled Gaussian Mechanism
Venue: ArXiv
Year: 2019

The Sampled Gaussian Mechanism (SGM) a composition of subsampling and the additive Gaussian noise has been successfully used in a number of machine learning applications. The mechanism's unexpected power is derived from privacy amplification by sampling where the privacy cost of a single evaluation diminishes quadratically, rather than linearly, with the sampling rate. Characterizing the precise privacy properties of SGM motivated development of several relaxations of the notion of differential privacy. This work unifies and fills in gaps in published results on SGM. We describe a numerically stable procedure for precise computation of SGM's Renyi Differential Privacy and prove a nearly tight (within a small constant factor) closed form bound.
Identification of pruning branches in tall spindle apple trees for automated pruning
Year: 2014

Pruning is a labor intensive operation that constitutes a significant component of total apple production cost. As growers are adapting simpler, narrower, more accessible and productive (SNAP) tree architectures such as the tall spindle fruiting wall system, new opportunities have emerged to reduce pruning cost and labor through automated pruning. This work focused on identification of pruning branches on apple trees in a tall spindle architecture. A time of flight of light based three dimensional (ToF 3D) camera was used to construct 3D skeletons of apple trees. Pruning branches were identified in the reconstructed trees using a simplified two step pruning rule; (i) maintain specified branch spacing and (ii) maintain specified branch length. Performance of the algorithm was optimized using a training sample of 10 trees to achieve human worker's pruning level. With a selected branch spacing (28 cm) and branch length (20 cm) the algorithm achieved 19.5% branch removal with the training dataset and 19.8% of branch removal with the validation dataset (10 trees) compared to 22% average branch removal by workers. Root Mean Square Deviation (RMSD) between human and algorithm in number of branches identified for pruning was 10% for the training dataset and 13% for the validation dataset. The algorithm and the human pruning resulted in similar average branch spacing. The algorithm maintained an average spacing of 35.7 cm for validation set whereas the average spacing for three workers was 33.7 cm. RMSD in branch spacing between the algorithm and the workers was found to be 13% The algorithm removed 85% of long branches whereas the overlapping branch removal was only 69% With some additional work to improve the performance in terms of overlapping branch removal, it is expected that this work will provide a good foundation for automated pruning of tall spindle apple trees in the future.
Understanding Factors Influencing Faculty Engagement: A Study on Jesuit Higher Education Institutions in Tamil Nadu
Year: 2019

All organizations are not just retaining the talents but, finding a way to engaging them, capturing their minds and hearts at each stage of their work lives. A successful organization is built with the efficient employees who will move forward the organization into edge less environment. The present study attempted to capture the existing level of faculty engagement in Jesuit Higher Education Institutions in Tamil Nadu with sample respondents of 401 from nine Jesuit HE institutions. The research design was descriptive in nature and proportionate stratified random sampling was used to collect the primary data through pre tested questionnaire using five point Likert scale. The researcher used eight dimensions and 23 factors to elicit data from the sample respondents and the data was analysed So as to understand the present level of engagement among the faculty members.
One Match Ahead Forecasting in Two Team Sports with Stacked Bayesian Regressions
Venue: J. Artif. Intell. Soft Comput. Res.
Year: 2018

There is a growing interest in applying machine learning algorithms to real world examples by explicitly deriving models based on probabilistic reasoning. Sports analytics, being favoured mostly by the statistics community and less discussed in the machine learning community, becomes our focus in this paper. Specifically, we model two team sports for the sake of one match ahead forecasting. We present a pioneering modeling approach based on stacked Bayesian regressions, in a way that winning probability can be calculated analytically. Benefiting from regression flexibility and high standard of performance, Sparse Spectrum Gaussian Process Regression (SSGPR) an improved algorithm for the standard Gaussian Process Regression (GPR) was used to solve Bayesian regression tasks, resulting in a novel predictive model called TLGProb. For evaluation, TLGProb was applied to a popular sports event National Basketball Association (NBA) Finally, 85.28% of the matches in NBA 2014/2015 regular season were correctly predicted by TLGProb, surpassing the existing predictive models for NBA.
Integral Human Pose Regression
Venue: ECCV
Year: 2018

State of the art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time.