nils-herrmann/clean_scirepeval huggingface dataset:
Characterization of phosphate solubilizing bacteria isolated from heavy metal contaminated soils and their potential for lead immobilization.
Venue: Journal of environmental management
Year: 2019

Phosphate solubilizing bacteria (PSB) were isolated from heavy metal contaminated soils and their potentials for lead (Pb) immobilization in soil were studied in this paper. 53 PSB strains were isolated and their minimum inhibitory concentrations (MICs) for lead were determined. The results showed that strains B3, B4, B10, F2 1 and L1 5 had relatively high lead resistant capabilities with MICs ranged from 0.5 to 8 mM, and were identified as species of Leclercia adecarboxylata and Pseudomonas putida by 16S rRNA. The most efficient PSB strains could solubilize 200 mg L 1 of P, and phosphate solubilizing capacity of those PSBs was related to the concentrations of organic acids, acid phosphatase activity and pH. Furthermore, the growth pattern of L1 5 strain with the presence of Pb(NO3)2 was observed and a mechanism of biomineralization of lead ions in bacteria biomass was determined by FT IR and XRD analyses. Results showed that high concentration of lead can inhibit the growth of PSBs, and L1 5 isolate can transform lead ions into lead hydroxyapatite and pyromorphite. The PSBs, which possessing the properties of immobilizing lead through phosphate solubilization and biomineralization could be exploited for bioremediation of Pb polluted soils in future.
Sim to Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
Venue: 2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2020

Deep} reinforcement learning has recently seen huge success across multiple areas in the robotics domain. Owing to the limitations of gathering real world data, i.e. sample inefficiency and the cost of collecting it, simulation environments are utilized for training the different agents. This not only aids in providing a potentially infinite data source, but also alleviates safety concerns with real robots. Nonetheless, the gap between the simulated and real worlds degrades the performance of the policies once the models are transferred into real robots. Multiple research efforts are therefore now being directed towards closing this sim toreal gap and accomplish more efficient policy transfer. Recent years have seen the emergence of multiple methods applicable to different domains, but there is a lack, to the best of our knowledge, of a comprehensive review summarizing and putting into context the different methods. In this survey paper, we cover the fundamental background behind sim to real transfer in deep reinforcement learning and overview the main methods being utilized at the moment: domain randomization, domain adaptation, imitation learning, meta learning and knowledge distillation. We categorize some of the most relevant recent works, and outline the main application scenarios. Finally, we discuss the main opportunities and challenges of the different approaches and point to the most promising directions.
Blindfolded for Science: An Integration of Dance as Therapy for Visually Impaired or Blind Individuals
Year: 2018

Sight is used by our brain as a connecting bridge between other sensory input and stimuli from the world. Since humans are visual creatures, we heavily rely on vision to interact with our environments. Because of this role, impaired vision can diminish a person's confidence in movement, introducing an increased fear of falling ultimately impacting posture. Previous research suggests that these limitations can be overcome through the use of Dance/Movement Therapy, a current form of psychological therapy. Although beneficial to the mental health of participants in the American Dance Therapy Association, the physical effects that dance can have on the human body when visually impaired are not as thoroughly examined. This project investigated the sport of dance and compared the effects that vision state and dance experience had on orientation and posture. Students with minimal dance experience were compared to students with extensive dance experience both before and after one month of blindfolded dance training. They were also compared to students with minimal dance experience who were sighted during the dance training. Although not significant, the blindfolded dance training appeared to improve posture and orientation and is promising as a form of physical therapy for visually impaired or blind individuals, however further research is necessary. Blindfolded for Science page 3 of 46
Response Time Analysis for Non Preemptive Global Scheduling with FIFO Spin Locks
Venue: 2020 IEEE Real Time Systems Symposium (RTSS)
Year: 2020

Motivated by the lack of response time analyses for non preemptive global scheduling that consider shared resources, this paper provides such an analysis for global job level fixed priority (JLFP) scheduling policies and FIFO ordered spin locks. The proposed analysis computes response time bounds for a set of resource sharing jobs subject to release jitter and execution time uncertainties by implicitly exploring all possible execution scenarios using state abstraction and state pruning techniques. A large scale empirical evaluation of the proposed analysis shows it to be substantially less pessimistic than simple execution time inflation methods, thanks to the explicit modeling of contention for shared resources and scenario aware blocking analysis.
Searching for activation functions using a self adaptive evolutionary algorithm
Venue: GECCO Companion
Year: 2020

The introduction of the ReLU function in neural network architectures yielded substantial improvements over sigmoidal activation functions and allowed for the training of deep networks. Ever since, the search for new activation functions in neural networks has been an active research topic. However, to the best of our knowledge, the design of new activation functions has mostly been done by hand. In this work, we propose the use of a self adaptive evolutionary algorithm that searches for new activation functions using a genetic programming approach, and we compare the performance of the obtained activation functions to ReLU. We also analyze the shape of the obtained activations to see if they have any common traits such as monotonicity or piece wise linearity, and we study the effects of the self adaptation to see which operators perform well in the context of a search for new activation functions. We perform a thorough experimental study on datasets of different sizes and types, using different types of neural network architectures. We report favorable results obtained from the mean and standard deviation of the performance metrics over multiple runs.
HUBUNGAN POLA MAKAN DENGAN KEJADIAN GASTRITIS PADA MAHASISWA TINGKAT II DI SEKOLAH TINGGIN ILMU KESEHATAN ABDI NUSANTARA JAKARTA TAHUN 2018
Year: 2020

ABSTRAKLatar Belakang :Timbulnya suatu penyakit berpengaruh terhadap perubahan gaya hidup dan pola konsumsi makanan yang merangsang peningkatan asam lambung seperti asinan, cuka, sambal,serta kebiasaan merokok dan minum alkohol, sehingga banyak timbul masalah kesehatan, salah satunya gangguan pada lambung seperti gastritis. Gastritis adalah suatu peradangan mukosa lambung yang bersifat akut, kronik, difus atau local, dengan karakteristik anoreksia, perasaan penuh diperut (tengah) tidak nyaman pada epigastrium, mual, dan muntah (Adriansyah, 2013) Untuk mengetahui hubungan pola makan dengan kejadian gastritis pada mahasiswa tingkat 2 Sekolah Tinggi Ilmu Kesehatan Abdi Nusantara Jakarta tahun 2018.Metode Penelitian :Metode penelitian yang digunakan dalam penelitian ini adalah analitik yang bertujuan untuk melihat adanya hubungan dngan menggunakan metode "cross sectional'" yaitu jenis variabel sebab (independent) maupun variabel akibat (dependent) diukur dalam waktu bersamaanHasil Penelitian Dari hasil cross tabulasi antara variabel pola makan dengan kejadian gastritis menunjukan hasil statistic Continuity Correction diperoleh nilai P= 0.073 (P.Value>0.05) artinya HA ditolak dan HO diterima, yang berarti ada hubungan antara variabel pola makan dengan kejadian gastritis.Kesimpulan dan Saran Diketahui dari 65 responden kejadian terbanyak terdapat pada kategori mahasiswa yang mengalami gastritis yaitu sebanyak 36 orang (55.4% responden. Sedangkan responden yang tidak mengalami gastritis terdapat 29 orang (44.6% responden. diharapkan penelitian ini dapat digunakan sebagai acuan atau referensi bagi peneliti lain yang akan melakukan penelitian serupa.
An Algorithmic Approach based on Principal Component Analysis for Aspect based Opinion Summarization
Venue: 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom)
Year: 2019

Summarization helps in reducing the text in the shortest way possible such that significant properties in the text remain preserved and important information can be gained from the text. A novel approach is developed and summaries are generated using extractive based technique using Principal Component Analysis. The advantages of the proposed method lie in greater computational efficiency, data understanding, robustness and handling sparse data. This paper discusses the aspect based opinion summarization problem by proposing a novel unsupervised method by combining theories of rational awareness with sentence dependency trees to identify aspects. The results are carried out on dissimilar datasets consisting of numerous opinions and comparison with the previous based approaches demonstrates the success of the work. The results on Opinosis dataset are reported by measuring using ROUGE tool. The three random individuals are contacted for reference summaries which are compared with the system generated gold summaries for conducting subjective evaluation.
Effective inventory control policies with a minimum order quantity and batch ordering
Year: 2015

In this paper, we consider a single item periodic review stochastic inventory system with both minimum order quantity (MOQ) and batch ordering requirements. In each time period, the firm can order either none or at least as much as the MOQ. At the same time, if an order is placed, the order quantity is required to be an integral multiple of a given specific batch size. We first adopt a heuristic policy which is specified by two parameters (s,t) Applying a discrete time Markov chain approach, we compute the system cost and optimize this (s,t) policy under the long run average cost criterion. We also consider a simpler one parameter policy, the so called S policy, which is a special case of the (s,t) policy. In an intensive numerical study, we find that (1) both policies perform well in comparison with other policies and (2) the S policy also performs well and is compatible with the (s,t) policy; only in a few cases where demand variation is small, the latter outperforms the former significantly. We also evaluate the effects of some important parameters on system performance.
Infrastructuralization of Tik Tok: transformation, power relationships, and platformization of video entertainment in China
Year: 2020

The article contributes to complicate the issue of 'infrastructuralization of platform' through an empirical study on China's video streaming platform, Tik Tok. It aims at exploring how video streaming platform will transform in its infrastructural process and changing powering relationships between different actors in the platform ecosystem. After scrutinizing its technique features, business model, platform discourse, and power relationships with government, it argues that the infrastructural process of Tik Tok is not only embodied in its transformation from an entertainment community to an integrated platform, including services in e commerce, online education, propaganda, and tourism, but more in its growing power of indispensability entrenched in our society. The main argument will be elaborated in two layers: first, Tik Tok attains its power on commercial monetization, content distribution, and acquisition of data sources through its infrastructural ambition of building a 'video encyclopedia' that can be salable, ranked, and archived. Second, Tik Tok has started to engage in fields of propaganda and tourism for city branding in alignment with central and local government. Tik Tok thereby wins its legitimacy in content management and government in turn plays as a role of stakeholder sharing the dividend from its contribution for state's development goals. Therefore, the article not only fills in the gap of a case study on infrastructural video streaming platform but also intends to highlight changing power relationships between government and platform in the process of infrastructuralization.
A Study on the Relationship between Scientific Reasoning and Achievement in Chemistry of Secondary School Students
Year: 2017

Reasoning in psychological perspective may be defined as set of mental processes used to derive inferences or conclusions from premises (Samarapungavan, 2009) Reasoning helps us to generate new knowledge and to organize existing knowledge making it more usable for future mental work. Reasoning therefore becomes central to many forms of thought such as scientific, critical, and creative thinking, argumentation, problem solving, and decision making. Reasoning is an inevitable part of the various aspects of academic inquiry. In this paper, the Investigators tried to find out the relationship between Scientific reasoning and Achievement in Chemistry of Secondary school students for total sample and for the subsample Gender. They also tried to find out whether there exists any significant difference in the Scientific reasoning and Achievement in Chemistry of Secondary school students based on their Gender. Proper statistical techniques were used to collect, standardize and Analyze the data.
CacheShuffle: A Family of Oblivious Shuffles
Venue: ICALP
Year: 2018

We consider oblivious two party protocols where a client outsources N blocks of private data to a server. The client wishes to access the data to perform operations in such a way that the access pattern does not leak information about the data and the operations. In this context, we consider oblivious shuffling with a focus on bandwidth efficient protocols for clients with small local memory. In the shuffling problem, the N outsourced blocks, B_1,,B_N, are stored on the server according to an initial permutation pi. The client wishes to reshuffle the blocks according to permutation sigma. Oblivious shuffling is a building block in several applications that hide patterns of data access. In this paper, we introduce a generalization of the oblivious shuffling problem, the K oblivious shuffling problem, and provide bandwidth efficient algorithms for a wide range of client storage requirements. The task of a K oblivious shuffling algorithm is to shuffle N encrypted blocks that were previously randomly allocated on the server in such a way that an adversarial server learns nothing about either the new allocation of blocks or the block contents. The security guarantee must hold when an adversary has partial information on the initial placement of a subset of K =N revealed blocks. The notion of oblivious shuffling is obtained for K=N. We first study the N oblivious shuffling problem and start by presenting CacheShuffleRoot, that is tailored for clients with O(sqrt{N} blocks of memory and uses approximately 4N blocks of bandwidth. CacheShuffleRoot is a 4x improvement over the previous best known N oblivious shuffle for practical sizes of N. We then generalize CacheShuffleRoot to CacheShuffle that can be instantiated for any client memory size S and requires O(N log_S N) blocks of bandwidth. Next, we present K oblivious shuffling algorithms that require 2N f(K,S) blocks of bandwidth for all K and a wide range of S. Any extra bandwidth above the 2N lower bound depends solely on K and S. Specifically, for clients with O(K) blocks of memory, we present KCacheShuffleBasic that uses exactly 2N blocks of bandwidth. For clients with memory S K, we present KCacheShuffle, that requires 2N O(K log_S K) blocks of bandwidth. Finally, motivated by applications to ORAMs, we consider the case where the server stores D dummy blocks whose contents are irrelevant in addition to the N real blocks. For this case, we design algorithm KCacheShuffleDummy that shuffles N+D blocks with K revealed blocks using O(K) blocks of client storage and approximately D+2N blocks of bandwidth.
Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration
Venue: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019

Our goal is to generate a policy to complete an unseen task given just a single video demonstration of the task in a given domain. We hypothesize that to successfully generalize to unseen complex tasks from a single video demonstration, it is necessary to explicitly incorporate the compositional structure of the tasks into the model. To this end, we propose Neural Task Graph (NTG) Networks, which use conjugate task graph as the intermediate representation to modularize both the video demonstration and the derived policy. We empirically show NTG achieves inter task generalization on two complex tasks: Block Stacking in BulletPhysics and Object Collection in AI2 THOR. NTG improves data efficiency with visual input as well as achieve strong generalization without the need for dense hierarchical supervision. We further show that similar performance trends hold when applied to real world data. We show that NTG can effectively predict task structure on the JIGSAWS surgical dataset and generalize to unseen tasks.
An Automated and Distributed Machine Learning Framework for Telecommunications Risk Management
Venue: ICAART
Year: 2020

Automation and scalability are currently two of the main challenges of Machine Learning (ML) This paper proposes an automated and distributed ML framework that automatically trains a supervised learning model and produces predictions independently of the dataset and with minimum human input. The framework was designed for the domain of telecommunications risk management, which often requires supervised learning models that need to be quickly updated by non ML experts and trained on vast amounts of data. Thus, the architecture assumes a distributed environment, in order to deal with big data, and Automated Machine Learning (AutoML) to select and tune the ML models. The framework includes several modules: task detection (to detect if classification or regression) data preprocessing, feature selection, model training and deployment. In this paper, we detail the model training module. In order to select the computational technologies to be used in this module, we first analyzed the capabilities of an initial set of five modern AutoML tools: Auto Keras, AutoSklearn, Auto Weka, H2O AutoML, and TransmogrifAI. Then, we performed a benchmarking of the only two tools that address a distributed ML (H2O AutoML and TransmogrifAI) Several comparison experiments were held using three real world datasets from the telecommunications domain (churn, event forecasting, and fraud detection) allowing to measure the computational effort and predictive capability of the AutoML tools.
Quantitative Harris type theorems for diffusions and McKean Vlasov processes
Year: 2016

We consider \mathbb{R}^d$ valued diffusion processes of type \begin{align* dX_t\ b(X_t)dt\ dB_t.  Assuming a geometric drift condition, we establish contractions of the transitions kernels in Kantorovich $L^1$ Wasserstein) distances with explicit constants. Our results are in the spirit of Hairer and Mattingly's extension of Harris' Theorem. In particular, they do not rely on a small set condition. Instead we combine Lyapunov functions with reflection coupling and concave distance functions. We retrieve constants that are explicit in parameters which can be computed with little effort from one sided Lipschitz conditions for the drift coefficient and the growth of a chosen Lyapunov function. Consequences include exponential convergence in weighted total variation norms, gradient bounds, bounds for ergodic averages, and Kantorovich contractions for nonlinear McKean Vlasov diffusions in the case of sufficiently weak but not necessarily bounded nonlinearities. We also establish quantitative bounds for sub geometric ergodicity assuming a sub geometric drift condition.
Case Study: How Horses Helped a Teenager with Autism Make Friends and Learn How to Work
Venue: International journal of environmental research and public health
Year: 2019

I was born in 1947 and had autism with speech delay until age four. I am now a college professor of animal science. Horse activities enabled me to make friends through a shared interest in horses. This paper describes the benefits that I experienced from working with horses and my friendships and work skills. A close friendship developed with another student through both riding and horse craft projects. Keeping employment is a serious problem for many people with Autism Spectrum Disorder (ASD) The responsibility of caring for horses and cleaning stalls every day taught me good work skills. My experiences suggest that there were valuable outcomes from working with horses. This may be a beneficial intervention to include in programming for youth with ASD.
The Impact of Autocratic and Democratic Leadership Style on Job Satisfaction
Year: 2012

This current research investigates the impact of Autocratic and Democratic Leadership style on job satisfaction in private and public school. For this purpose a questionnaire was developed and validated. There were twenty three items in all. The items were based on five point scale from almost always to almost never).The data were collected from two hundred and five (205) teachers of both public and private schools (one hundred and five public school teachers and one hundred private school teachers) For statistical analysis Mean, Standard Deviation, T Test, ANOVA, Co relation and Regression Analysis were used. It was found that Leadership style has a positive impact on job satisfaction and public teachers have high level of job satisfaction rather than private teachers.
Longitudinal Study of Oral Microbiome Variation in Twins
Venue: Scientific Reports
Year: 2020

Humans are host to a multitude of microorganisms that rapidly populate the body at birth, subject to a complex interplay that is dependent on host genetics, lifestyle, and environment. The host associated microbiome, including the oral microbiome, presents itself in a complex ecosystem important to health and disease. As the most common chronic disease globally, dental caries is induced by host microbial dysbiosis in children and adults. Multiple biological and environmental factors are likely to impact disease predisposition, onset, progression, and severity, yet longitudinal studies able to capture these influences are missing. To investigate how host genetics and environment influenced the oral microbial communities over time, we profiled supragingival plaque microbiomes of dizygotic and monozygotic twins during 3 visits over 12 months. Dental plaque DNA samples were amplified by targeting the 16S rRNA gene V4 region, and microbial findings were correlated with clinical, diet and genetic metadata. We observed that the oral microbiome variances were shaped primarily by the environment when compared to host genetics. Among the environmental factors shaping microbial changes of our subjects, significant metadata included age of the subject, and the age by which subjects initiated brushing habits, and the types of actions post brushing. Relevant heritability of the microbiome included Actinomyces and Capnocytophaga in monozygotic twins and Kingella in dizygotic twins. Corynebacterium and Veillonella abundances were associated with age, whereas Aggregatibacter was associated with younger subjects. Streptococcus abundance showed an inverse association over time, and Selenomonas abundances increased with brushing frequency per day. Unraveling the exact biological mechanisms in caries has the potential to reveal novel host microbial biomarkers, pathways, and targets important to effective preventive measures, and early disease control in children.