nils-herrmann/clean_scirepeval huggingface dataset:
Pesan dakwah pada media sosial: studi analisis semiotika Roland Barthes dalam video akun Instagram @nunuzoo Periode 2018
Year: 2019

Berkembangnya teknologi saat ini disebabkan oleh dorongan dari manusia yang ingin berinteraksi dan mencari informasi dengan cepat dan mudah seiring dengan kebutuhan manusia, salah satunya jejaring sosial media. Segala bentuk informasi dapat dengan mudahnya diakses. Salah satu media sosial yang banyak digemari masyarakat Indonesia adalah Instagram yang merupakan peluang besar untuk dijadikan sebagai media dakwah, karena Instagram bisa dijangkau oleh semua penjuru dan pengguna Instagram adalah semua kalangan tidak mengenal batas usia bahkan kebanyakan remaja yang aktifitasnya tidak lepas dari sosial media sedangkan mendatangi kajian keislaman terkadang di anggap sebagai sesuatu hal yang kuno untuk dihadiri remaja. Salah satu akun yang digunakan untuk aktivitas dakwah adalah akun Instagram @nunuzoo lewat video video berdurasi pendek yang merupakan video komedi bermuatan pesan dakwah. Penelitian ini bertujuan untuk mengetahui makna denotasi, konotasi dan mitos pesan dakwah pada video Instagram akun @nunuzoo periode 2018. Teori yang digunakan pada penelitian ini adalah teori semiotika Roland Barthes yang dikenal dengan konsep "two order signification" yakni mengkaji tentang makna atau simbol dalam bahasa atau tanda untuk mengetahui makna denotasi, konotasi dan mitos atau bergantung pada konteks tertentu. Metode yang digunakannya adalah deskriptif dengan pendekatan kualitatif yang bertujuan untuk menganalisis dan mengetahui makna dari tanda tanda yang ada dalam video akun Instagram @nunuzoo yanng meliputi denotasi (makna nyata yang sebenarnya) makna konotasi (makna lain yang tersirat) yang disebut signifikasi tahap pertama dan makna aspek lain dari penandaan yakni mitos yang dianggap mampu untuk mengenali tanda dalam pesan yang tersembunyi pada video durasi pendek akun Instagram @nunuzoo. Hasil penelitian menunjukkan bahwa pesan dakwah pada video Instagram @nunuzoo periode 2018 dibagi menjadi 3 yakni makna denotasi keseluruhan dalam adegan adegan yaitu pengambilan gambarnya lebih banyak menggunakan medium shot, close up karena untuk mempertegas ekspresi dari setiap video yang diperankan dan memperjelas pesan yang ingin disampaikan kepada penonton, makna konotasi keseluruhan dalam adegan adegan yakni diperlihatkan banyak makna konotasi juga menggambarkan bahwa sebuah video dapat dipahami tidak hanya dengan melihat makna nyata dari dialog dan ekspresi yang ditunjukkan tetapi ada makna lain yang tersirat. Makna mitos keseluruhan yakni makna mitos yang berkaitan dengan kebiasaan masyarakat dalam melakukan hal yang sebenarnya merupakan budaya barat dan kebiasaan zaman dulu.
Compressive sensing based image hashing
Venue: 2009 16th IEEE International Conference on Image Processing (ICIP)
Year: 2009

In this paper, a new image hashing scheme satisfying robustness and security is proposed. We exploit the property of dimensionality reduction inherent in compressive sensing/sampling (CS) for image hash design. The gained benefits include (1) the hash size can be kept small and (2) the CS based hash is computationally secure. We study the use of visual information fidelity (VIF) for hash comparison under Stirmark attacks. We further derive the relationships between the hash of an image and both of its MSE distortion and visual quality measured by VIF, respectively. Hence, based on hash comparisons, both the distortion and visual quality of a query image can be approximately estimated without accessing its original version. We also derive the minimum distortion for manipulating an image to be unauthentic to measure the security of our scheme.
Detection of Bacterial DNA During the Process of Sake Production Using Sokujo Moto
Venue: Current Microbiology
Year: 2018

There are two types of starter cultures used in Japanese rice wine (sake) fermentation, namely, sokujo moto and yamahai moto. Analyses of microbiota changes during sake production using yamahai moto have already been reported. In this study, we analyzed microbiota changes during sake production using sokujo moto. In addition, we sequenced bacterial DNA from the water used in sake production. The Lactobacillus DNA sequences, which are frequently detected during sake production using yamahai moto, were not detected during sake production using sokujo moto, indicating that the Lactobacillus DNA detected in sakes made from yamahai moto is from the fermentation starter. Most bacterial DNA sequences detected in water were not found in the production samples of sake suggesting that these bacteria do not proliferate during sake production. Thus, most of the bacterial DNA sequences detected during the production may be from the bacterial contamination during the production process.
Peltier Cooling in Molecular Junctions,Weakly Trapped, Charged, and Free Excitons in Single Layer MoS2 in the Presence of Defects, Strain, and Charged Impurities,Fabrication of Gold Nano Crystal Arrays for Molecular Electronics: High Frequency Molecular Rectifiers and p p Inter Molecular Interaction
Year: 2018

E x p e ri m e n t a l s e tup for the measurement of Peltier cooling in molecular junctions.  Thermoelectric cooling is based on the Peltier effect that consists in the
Sketch a Classifier: Sketch Based Photo Classifier Generation
Venue: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018

Contemporary deep learning techniques have made image recognition a reasonably reliable technology. However training effective photo classifiers typically takes numerous examples which limits image recognition's scalability and applicability to scenarios where images may not be available. This has motivated investigation into zero shot learning, which addresses the issue via knowledge transfer from other modalities such as text. In this paper we investigate an alternative approach of synthesizing image classifiers: almost directly from a user's imagination, via freehand sketch. This approach doesn't require the category to be nameable or describable via attributes as per zero shot learning. We achieve this via training a model regression network to map from free hand sketch space to the space of photo classifiers. It turns out that this mapping can be learned in a category agnostic way, allowing photo classifiers for new categories to be synthesized by user with no need for annotated training photos. We also demonstrate that this modality of classifier generation can also be used to enhance the granularity of an existing photo classifier, or as a complement to name based zero shot learning.
Comparative Analysis of Deep Neural Network and Texture Based Classifiers for Recognition of Acute Stroke using Non Contrast CT Images
Venue: 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)
Year: 2020

This work presents a computer technology for automatic recognition of acute stroke using non contrast computed tomography brain images. The early diagnosis of acute stroke is of primary importance for deciding on a method for further treatment, and the developed system aims at assisting a radiology specialist in the decision making process. We consider deep neural network and texture based classifiers in order to compare their efficiency on a moderate sized sample of patients with acute stroke. We use U net as a basic architecture of the neural network, and Haralick textural features, extracted from images, for kNN, SVM, Random Forest and Adaboost classifiers. Experiments with real CT images using cross validation technique show that deep neural network outperforms the considered texture based classifiers; however, the latter are faster in training. We demonstrate that texture based approach is able to give potentially useful additional information for stroke recognition, such as estimates of textural features importance; visualization of differences in positive and negative class distributions.
Principals' Encouragement of Teacher Collaboration and Support for Peer Coaching in Government Secondary Schools, South West Region, of Cameroon
Year: 2019

Copyright (c) 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal.  Within an education culture striving for continuous improvement, there is a constant need to ensure the appropriate skills, knowledge and actions of staff match the changing needs of the system. Coaching can assist in this process of 'upskilling' There is great interest in educational leadership in the early part of the 21st century because of the widespread belief that the quality of leadership makes a significant difference to the school and student outcomes. There is also increasing recognition that schools require effective leaders and managers if they are to provide the best possible education for their learners. Schools need trained and committed teachers but they, in turn, need the leadership of highly effective principals and support from other senior and m idle managers. The purpose of this study was to provide answers to the following questions: Do principals encourage a collaborative work place culture among teachers? Do principals provide release time to enable teachers to work with each other as coaches? Do principals ensure that teachers have the needed material and financial support for peer coaching? A descriptive survey research design was used. Data was collected from 288 teachers in 8 schools and 4 divisions of the South West Region using a questionnaire. Collected data were analyzed using SPSS version 12.0 for windows and reported using frequencies, percentages and means. The results of the data analysis revealed as follows: Principals of government secondary school in Cameroon do not encourage teacher collaboration. Of the 12 questionnaire items designed to answer this question, none of the items had a mean of 3.2 which is the cut off mean. It is recommended that Principals of government secondary schools in Cameroon should take up courses in Educational Administration in the area of Educational Leadership at University level in order to update their knowledge, skills and attitudes in this area. Principals should work in collaboration with the Faculty of Education of the University of Buea and organize seminars to acquaint teachers with the practice of peer coaching. Principals should invest in buying relevant books in educational leadership (peer coaching) and create time to read them in order to keep abreast with current school leadership practices.
A Combination of Lexicon and Machine Learning Approaches for Sentiment Analysis on Facebook
Year: 2020

The increase of user generated content (UGC) on the Internet has led previous studies to propose various sentiment analysis approaches to understand public opinion. The primary goal is to enhance engagement through social media by analyzing various feedback. Sentiment analysis is performed based on two approaches i.e. machine learning and lexicon based. Since approaches based on machine learning require costly preparation of training dataset and the approaches based on lexicon produce unsatisfactory performance, in this paper, both approaches are combined to perform sentiment analysis on Facebook comments. The importance of a lexicon based approach to automatically construct the labeled data for machine learning sentiment classification is discussed in this paper. Experiments performed using the Universiti Utara Malaysia (UUM) Facebook posts show that using the combined lexicon based and machine learning approach on two classifiers i.e. Naive Bayes and Support Vector Machine outperform the single approaches to produce more accurate sentiment classifications.
Democratization of AI to Small Scale Farmers, Albeit Food Harvesting Citizen Data Scientists, that Are at the Bottom of the Economic Pyramid
Venue: IHSI
Year: 2020

Climate change is impacting milk production worldwide. For instance, increased heat stress in cows is causing average sized dairy farms losing thousands of milk gallons each year; drastic climate change, especially in developing countries, pushing small farmers, farmers with less than 10 to 25 cattle, below the poverty line and is triggering suicides due to economic stress and social stigma. It's profoundly clear that current dairy agriculture practices are falling short to counter the impacts of climate change. What we need are innovative and intelligent dairy farming techniques that employ best of traditional practices with data infused insights to counter negative effects of climate change. To achieve innovative and intelligent dairy farming techniques, we need to disseminate state of art data science to masses. In other word, provide the state of art data science algorithms to every food harvesting citizen data scientist, farmer. The democratization of artificial intelligence to farmers, importantly, is not only empowers farmers to understand the patterns and signatures of climate change but also provides the ability to forecast the impending climate change adverse events and recommends data driven insights to counter the negative effects of climate change. With the availability of new data tools, farmers can not only improve their standard of life but also conquer, importantly, perennial "climate change related suicide" issue. It's our staunch believe that the gold standard for the success of the democratization of artificial intelligence is no farmer life loss due to negative effects of climate change. In this paper, we propose an innovative machine learning Sensor edge approach that considers the impact of climate change and develops artificial intelligent (AI) models that is validated globally but enables localized solution to thwart impacts of climate change. The paper presents prototyping dairy IoT sensor solution design as well as its application and certain experimental results.
Liking, wanting, and the incentive sensitization theory of addiction.
Venue: The American psychologist
Year: 2016

Rewards are both "liked" and "wanted," and those 2 words seem almost interchangeable. However, the brain circuitry that mediates the psychological process of "wanting" a particular reward is dissociable from circuitry that mediates the degree to which it is "liked." Incentive salience or "wanting," a form of motivation, is generated by large and robust neural systems that include mesolimbic dopamine. By comparison, "liking," or the actual pleasurable impact of reward consumption, is mediated by smaller and fragile neural systems, and is not dependent on dopamine. The incentive sensitization theory posits the essence of drug addiction to be excessive amplification specifically of psychological "wanting," especially triggered by cues, without necessarily an amplification of "liking." This is because of long lasting changes in dopamine related motivation systems of susceptible individuals, called "neural sensitization." A quarter century after its proposal, evidence has continued to grow in support the incentive sensitization theory. Further, its scope is now expanding to include diverse behavioral addictions and other psychopathologies.
Disease Modeling via Large Scale Network Analysis Report Title
Year: 2015

Disease Modeling via Large Scale Network Analysis Report Title A central goal of genetics is to learn how the genotype of an organism determines its phenotype. We address the implicit problem of predicting the association of genes with phenotypes or traits. Our primary goal is to develop pragmatic data analytic methods for linking specific genes to traits and diseases, especially polygenic traits, which are the most challenging. We are also interested in developing theoretical guarantees for the methods. In the past, we have developed predictive methods general enough to apply to potentially any genetic trait, varying from plant traits relevant to desirable agricultural properties to important human diseases. Our methods, Katz on heterogeneous network and CATAPULT[1] for predicting gene disease associations were published during the last project period in the PLOS One journal. The biological problem has also led us to pursue a significant problem in machine learning. One of the fundamental questions in machine learning relating to the classification problem is if we can efficiently learn classifiers that can provably achieve low misclassification rates in the presence of certain type of random label noise in the training data. We have answered the question in affirmative in our recent paper[2] and in particular, developed important theoretical results and robust algorithms for dealing with random label noise in classification. Our CATAPULT system employs "biased" support vector machines (SVM) to cope with the lack of negative examples. Biased SVMs have been empirically shown to be successful for similar tasks, but there has not been any theoretical study previously. In the last project period, we were able to show that they exhibit a certain noise tolerance property, a novel result in machine learning. We have also developed a novel matrix completion method called Inductive Matrix Completion to the problem of predicting genedisease associations  that combines multiple types of features for diseases and genes for discovering new gene disease associations. While the primary objective is the biological problem, the mathematical models and techniques developed for the problem are expected to shed light on some open problems related to PU(Positive Unlabeled) learning, such as (a) multiple sources in PU learning, (b) multi task PU learning, and (c) theoretical guarantees for PU learning algorithms. (a) Papers published in peer reviewed journals (N/A for none) Enter List of papers submitted or published that acknowledge ARO support from the start of the project to the date of this printing. List the papers, including journal references, in the following categories:
Why do women prefer home births in Ethiopia?
Venue: BMC Pregnancy and Childbirth
Year: 2013

BackgroundSkilled attendants during labor, delivery, and in the early postpartum period, can prevent up to 75% or more of maternal death. However, in many developing countries, very few mothers make at least one antenatal visit and even less receive delivery care from skilled professionals. The present study reports findings from a region where key challenges related to transportation and availability of obstetric services were addressed by an ongoing project, giving a unique opportunity to understand why women might continue to prefer home delivery even when facility based delivery is available at minimal cost.MethodsThe study took place in Ethiopia using a mixed study design employing a cross sectional household survey among 15 49 year old women combined with in depth interviews and focus group discussions.ResultsSeventy one percent of mothers received antenatal care from a health professional (doctor, health officer, nurse, or midwife) for their most recent birth in the one year preceding the survey. Overall only 16% of deliveries were assisted by health professionals, while a significant majority (78% was attended by traditional birth attendants. The most important reasons for not seeking institutional delivery were the belief that it is not necessary (42% and not customary (36% followed by high cost (22% and distance or lack of transportation (8% The group discussions and interviews identified several reasons for the preference of traditional birth attendants over health facilities. Traditional birth attendants were seen as culturally acceptable and competent health workers. Women reported poor quality of care and previous negative experiences with health facilities. In addition, women's low awareness on the advantages of skilled attendance at delivery, little role in making decisions (even when they want) and economic constraints during referral contribute to the low level of service utilization.ConclusionsThe study indicated the crucial role of proper health care provider client communication and providing a more client centered and culturally sensitive care if utilization of existing health facilities is to be maximized. Implications of findings for maternal health programs and further research are discussed.
Supervised Topic Models for Microblog Classification
Venue: 2015 IEEE International Conference on Data Mining
Year: 2015

In this paper we present a topic model based approach for classifying micro blog posts into a given topics of interests. The short nature of micro blog posts make them challenging for directly learning a classification model. To overcome this limitation, we use content of the links embedded in these posts to improve the topic learning. The hypothesis is that since the link content is far richer than the content of the post itself, using link content along with the content of the post will help learning. However, how this link content can be used to construct features for classification remains a challenging issue. Furthermore, in previous methods, user based information is utilized in an ad hoc manner that only work for certain type of classification, such as characterizing content of microblogs. In this paper, we propose supervised topic model, User Labeled LDA and its nonparametric variant that can avoid the ad hoc feature construction task and model the topics in a discriminative way. Our experiments on a Twitter dataset shows that modeling user interests and link information helps in learning quality topics for sparse tweets as well as helps significantly in classification task. Our experiments further show that modeling this information in a principled way through topic models helps more than simply adding this information through features.
Separate effects of surface roughness, wettability, and porosity on the boiling critical heat flux
Year: 2013

The separate effects of surface wettability, porosity, and roughness on the critical heat flux (CHF) of water were examined using engineered surfaces. Values explored were 0, 5, 10, and 15 mm for Rz (roughness) 110deg for static contact angle (wettability) and 0 and 50% for pore volume fraction. The porous hydrophilic surface enhanced CHF by 50% 60% while the porous hydrophobic surface resulted in a reduction of CHF by 97% Wettability had little effect on the smooth non porous surface CHF. Surface roughness (Ra, Rq, Rz) had no effect on CHF within the limit of this database.
Supervising the self driving car: situation awareness and fatigue during automated driving
Venue: AutomotiveUI
Year: 2019

The capacity for human drivers to resume control from an automated vehicle remains a central focus of human factors research. Physiological measures promise to allow the vehicle system to determine when a driver is in a ready state for transition of control, particularly for level 3 automation and above. We employ an adapted measure of Situation Awareness (SA) to assess the quality of driver SA following an extended period of simulated level 3 automated driving. It is hypothesised that a within subjects design will demonstrate increasing passive fatigue to be predictive of reduced SA following a takeover request. Participants were also randomly allocated to one of two separate conditions in which supervising drivers were either permitted to, or prohibited from the use of non driving related tasks (NDRT) during automated driving, to investigate a potential avenue for targeted SA enhancement through deliberate NDRT engagement. Preliminary results provide tentative support for our hypotheses.