nils-herrmann/clean_scirepeval huggingface dataset:
Online power system state estimation using alternating direction method of multipliers

The convex relaxation approaches for power system state estimation (PSSE) offer robust alternatives to the conventional PSSE algorithms, by avoiding local optima and providing guaranteed convergence, critical especially when the states deviate significantly from the nominal conditions. On the other hand, the associated semidefinite programming problem may be computationally demanding. In this work, a variable splitting technique called alternating direction method of multipliers is employed to reduce the complexity, and also efficiently accommodate a regularizer promoting desired low-rank matrix solutions. Both static and online formulations are developed. Numerical tests verify the efficacy of the proposed techniques.
Chatbot Enhanced Algorithms: A Case Study on Implementation in Bahasa Malaysia Human Language

Chatbot is one of a technology that tried to encounter the question that popped into computer science field in 1950 which is ”Can machines think?” . Proposed by mathematician Alan Turing, the question later becomes the pinnacle reference for researchers in artificial intelligence discipline. Turing later also introduces ”The Imitation Game” that now known as ”Turing Test” where the idea of the test is to examine whether machine can fool a judge into thinking that they are having a conversation with an actual human. The technology back then was great but in rapid evolution of computer science, it can become even better. Evolution is computer scripting language, application design model, and so on, clearly have its advantage towards enabling more complex features in developing a computer program. In this paper, we propose an enhanced algorithm of a chatbot by taking advantages of relational database model to design the whole chatbot architecture that enable several features that cannot or difficult to be done in previous state of computer science programming technique. Started with some literature of a previous developed chatbot, then a detailed description of each new enhanced algorithm together with testing and results from the implementation of these new algorithms that can be used in development of a modern chatbot. These several new algorithms will enable features that will extend chatbot capabilities in responding to the conversation. These algorithm is actually implemented in design and development of chatbot that specifically deal with Bahasa Malaysia language, but taking to account that language in chatbot is really about the data in chatbot knowledge-based, the algorithm is seems transferable wherever it fits into another human language.
Seminal quality prediction using optimized artificial neural network with genetic algorithm

Infertility problem is an important issue in recent decades. Semen analysis is one of the principle tasks to evaluate male partner fertility potential. It has been seen in many researches that life habits and health status affect semen quality. Data mining as a decision support system can help to recognize this effect. The artificial neural network (ANN) is a powerful data mining tool that can be used for this goal. The performance of ANN depends heavily on network structure. It is a very difficult task to determine the appropriate structure and is a discussable matter. This paper utilizes a genetic algorithm to optimize the structure of artificial neural network to classify the semen samples. These samples usually suffer from unbalancing problem. Thus, this paper attempts to resolve it by using the bootstrap method. The performance of the proposed algorithm is significantly better than the previous works. We achieve accuracy equal to 93.86% in our experiments on a real fertility diagnosis dataset that is a good improvement compared with other classification methods.
A Large-Scale, Long-Term Analysis of Mobile Device Usage Characteristics

Today, mobile devices like smartphones and tablets have become an indispensable part of people's lives, posing many new questions e.g., in terms of interaction methods, but also security. In this paper, we conduct a large scale, long term analysis of mobile device usage characteristics like session length, interaction frequency, and daily usage in locked and unlocked state with respect to location context and diurnal pattern. Based on detailed logs from 29,279 mobile phones and tablets representing a total of 5,811 years of usage time, we identify and analyze 52.2 million usage sessions with some participants providing data for more than four years.
 Our results show that context has a highly significant effect on both frequency and extent of mobile device usage, with mobile phones being used twice as much at home compared to in the office. Interestingly, devices are unlocked for only 46 % of the interactions. We found that with an average of 60 interactions per day, smartphones are used almost thrice as often as tablet devices (23), while usage sessions on tablets are three times longer, hence are used almost for an equal amount of time throughout the day. We conclude that usage session characteristics differ considerably between tablets and smartphones. These results inform future approaches to mobile interaction as well as security.
An open annotation ontology for science on web 3.0

There is currently a gap between the rich and expressive collection of published biomedical ontologies, and the natural language expression of biomedical papers consumed on a daily basis by scientific researchers. The purpose of this paper is to provide an open, shareable structure for dynamic integration of biomedical domain ontologies with the scientific document, in the form of an Annotation Ontology (AO), thus closing this gap and enabling application of formal biomedical ontologies directly to the literature as it emerges.



Initial requirements for AO were elicited by analysis of integration needs between biomedical web communities, and of needs for representing and integrating results of biomedical text mining. Analysis of strengths and weaknesses of previous efforts in this area was also performed. A series of increasingly refined annotation tools were then developed along with a metadata model in OWL, and deployed for feedback and additional requirements the ontology to users at a major pharmaceutical company and a major academic center. Further requirements and critiques of the model were also elicited through discussions with many colleagues and incorporated into the work.



This paper presents Annotation Ontology (AO), an open ontology in OWL-DL for annotating scientific documents on the web. AO supports both human and algorithmic content annotation. It enables "stand-off" or independent metadata anchored to specific positions in a web document by any one of several methods. In AO, the document may be annotated but is not required to be under update control of the annotator. AO contains a provenance model to support versioning, and a set model for specifying groups and containers of annotation. AO is freely available under open source license at http://purl.org/ao/, and extensive documentation including screencasts is available on AO's Google Code page: http://code.google.com/p/annotation-ontology/ .



The Annotation Ontology meets critical requirements for an open, freely shareable model in OWL, of annotation metadata created against scientific documents on the Web. We believe AO can become a very useful common model for annotation metadata on Web documents, and will enable biomedical domain ontologies to be used quite widely to annotate the scientific literature. Potential collaborators and those with new relevant use cases are invited to contact the authors.
An Alternative Definition and Model for Knowledge Visualization

The term knowledge visualization has been used in many different fields with many different definitions. In this paper, we propose a new definition of knowledge visualization specifically in the context of visual analysis and reasoning. Our definition begins with the differentiation of knowledge as either explicit and tacit knowledge. We then present a model for the relationship between the two through the use visualization. Instead of directly representing data in a visualization, we first determine the value of the explicit knowledge associated with the data based on a cost/benefit analysis and display the knowledge in accordance to its importance. We propose that the displayed explicit knowledge leads us to create our own tacit knowledge through visual analytical reasoning and discovery.
Narrow Band Imaging with Magnification Endoscopy for Celiac Disease: Results from a Prospective, Single-Center Study

In celiac disease (CD), the intestinal lesions can be patchy and partial villous atrophy may elude detection at standard endoscopy (SE). Narrow Band Imaging (NBI) system in combination with a magnifying endoscope (ME) is a simple tool able to obtain targeted biopsy specimens. The aim of the study was to assess the correlation between NBI-ME and histology in CD diagnosis and to compare diagnostic accuracy between NBI-ME and SE in detecting villous abnormalities in CD. Forty-four consecutive patients with suspected CD undergoing upper gastrointestinal endoscopy have been prospectively evaluated. Utilizing both SE and NBI-ME, observed surface patterns were compared with histological results obtained from biopsy specimens using the k-Cohen agreement coefficient. NBI-ME identified partial villous atrophy in 12 patients in whom SE was normal, with sensitivity, specificity, and accuracy of 100%, 92.6%, and 95%, respectively. The overall agreement between NBI-ME and histology was significantly higher when compared with SE and histology (kappa score: 0.90 versus 0.46; P = 0.001) in diagnosing CD. NBI-ME could help identify partial mucosal atrophy in the routine endoscopic practice, potentially reducing the need for blind biopsies. NBI-ME was superior to SE and can reliably predict in vivo the villous changes of CD.
A Supervised Learning Method for Prediction Citation Count of Scientists in Citation Networks

Predicting the future impacts of scientists and their publications is an important task for future scientific studies. Studies predicting scientific effects in the academia are mostly based on estimating citation numbers of publications. However, there are few studies focusing on predicting the impact of scientists. Determining the potential influence of scientists is useful for future scientific studies and organizations. In this respect, we propose a new approach for predicting future impacts of scientists by considering the number of citations that each scientist individually has. A great number of studies predicting citation count of publications use content based information such as title, abstract and keywords of publication, institution of scientists, journal impact factor of publisher, etc. and they don't benefit from graph structures in citation networks. However, citations among scientific publications create more complex networks which have the potential for mining scientific impacts. So, we formulated the problem of predicting future citation counts of a given scientist, as a link prediction problem for directed, weighted and dynamic citation networks. The proposed link prediction method is able to predict links and its weights. Firstly, we introduce a dynamic metric based on increasing and decreasing trends during transitions time frames in citation networks. Then, we use the introduced dynamic proximity metric with some simple topological features in classifiers to predict citation count. Experiments are conducted on 12 sampled datasets which are extracted from two citation networks. The experimental results show that the proposed approach performs well when considering the problem of prediction links with its weights is firstly addressed in the context of link prediction problem in directed, weighted and dynamic networks.
Developing a predictive model of quality of experience for internet video

Improving users' quality of experience (QoE) is crucial for sustaining the advertisement and subscription based revenue models that enable the growth of Internet video. Despite the rich literature on video and QoE measurement, our understanding of Internet video QoE is limited because of the shift from traditional methods of measuring video quality (e.g., Peak Signal-to-Noise Ratio) and user experience (e.g., opinion scores). These have been replaced by new quality metrics (e.g., rate of buffering, bitrate) and new engagement centric measures of user experience (e.g., viewing time and number of visits). The goal of this paper is to develop a predictive model of Internet video QoE. To this end, we identify two key requirements for the QoE model: (1) it has to be tied in to observable user engagement and (2) it should be actionable to guide practical system design decisions. Achieving this goal is challenging because the quality metrics are interdependent, they have complex and counter-intuitive relationships to engagement measures, and there are many external factors that confound the relationship between quality and engagement (e.g., type of video, user connectivity). To address these challenges, we present a data-driven approach to model the metric interdependencies and their complex relationships to engagement, and propose a systematic framework to identify and account for the confounding factors. We show that a delivery infrastructure that uses our proposed model to choose CDN and bitrates can achieve more than 20\% improvement in overall user engagement compared to strawman approaches.
Commenting on Writing Typology and Perceived Helpfulness of Comments from Novice Peer Reviewers and Subject Matter Experts

How do comments on student writing from peers compare to those from subject-matter experts? This study examined the types of comments that reviewers produce as well as their perceived helpfulness. Comments on classmates’ papers were collected from two undergraduate and one graduate-level psychology course. The undergraduate papers in one of the courses were also commented on by an independent psychology instructor experienced in providing feedback to students on similar writing tasks. The comments produced by students at both levels were shorter than the instructor’s. The instructor’s comments were predominantly directive and rarely summative. The undergraduate peers’ comments were more mixed in type; directive and praise comments were the most frequent. Consistently, undergraduate peers found directive and praise comments helpful. The helpfulness of the directive comments was also endorsed by a writing expert.
Antimicrobial and antioxidant properties of pigments synthesized from microorganisms

Scientists and consumers preference focused on natural colorants due to the emergence of negative health effects of synthetic colorants which is used for many years in foods. Interest in natural colorants is increasing with each passing day as a consequence of their antimicrobial and antioxidant effects. The biggest obstacle in promotion of natural colorants as food pigment agents is that it requires high investment. For this reason, the R&D studies related issues are shifted to processes to reduce cost and it is directed to pigment production from microorganisms with fermentation. Nowadays, there is pigments obtained by commercially microorganisms or plants with fermantation. These pigments can be use for both food colorant and food supplement. In this review, besides colourant and antioxidant properties, antimicrobial properties of natural colorants are discussed.
A parallel butterfly algorithm

The butterfly algorithm is a fast algorithm which approximately evaluates a discrete analogue of the integral transform ∫ Rd K(x, y)g(y)dy at large numbers of target points when the kernel, K(x, y), is approximately low-rank when restricted to subdomains satisfying a certain simple geometric condition. In d dimensions with O(Nd) source and target points, when each appropriate submatrix of K is approximately rank-r, the running time of the algorithm is at most O(r2Nd logN). A parallelization of the butterfly algorithm is introduced which, assuming a message latency of α and per-process inverse bandwidth of β, executes in at most O ( r2 N d p logN + ( βr d p + α ) log p ) time using p processes. This parallel algorithm was then instantiated in the form of the opensource DistButterfly library for the special case where K(x, y) = exp(iΦ(x, y)), where Φ(x, y) is a black-box, sufficiently smooth, real-valued phase function. Experiments on Blue Gene/Q demonstrate impressive strong-scaling results for important classes of phase functions. Using quasi-uniform sources, hyperbolic Radon transforms and an analogue of a 3D generalized Radon transform were respectively observed to strong-scale from 1-node/16-cores up to 1024-nodes/16,384-cores with greater than 90% and 82% efficiency, respectively. These experiments at least partially support the theoretical argument that, given p = O(Nd) processes, the running-time of the parallel algorithm is O ( (r2 + βr + α) logN ) .
Multidomain Document Layout Understanding using Few Shot Object Detection

We try to address the problem of document layout understanding using a simple algorithm which generalizes across multiple domains while training on just few examples per domain. We approach this problem via supervised object detection method and propose a methodology to overcome the requirement of large datasets. We use the concept of transfer learning by pre-training our object detector on a simple artificial (source) dataset and fine-tuning it on a tiny domain specific (target) dataset. We show that this methodology works for multiple domains with training samples as less as 10 documents. We demonstrate the effect of each component of the methodology in the end result and show the superiority of this methodology over simple object detectors.
Simultaneous assay of pigments, carbohydrates, proteins and lipids in microalgae.

Biochemical compositional analysis of microbial biomass is a useful tool that can provide insight into the behaviour of an organism and its adaptational response to changes in its environment. To some extent, it reflects the physiological and metabolic status of the organism. Conventional methods to estimate biochemical composition often employ different sample pretreatment strategies and analytical steps for analysing each major component, such as total proteins, carbohydrates, and lipids, making it labour-, time- and sample-intensive. Such analyses when carried out individually can also result in uncertainties of estimates as different pre-treatment or extraction conditions are employed for each of the component estimations and these are not necessarily standardised for the organism, resulting in observations that are not easy to compare within the experimental set-up or between laboratories. We recently reported a method to estimate total lipids in microalgae (Chen, Vaidyanathan,  Acta, 724, 67-72). Here, we propose a unified method for the simultaneous estimation of the principal biological components, proteins, carbohydrates, lipids, chlorophyll and carotenoids, in a single microalgae culture sample that incorporates the earlier published lipid assay. The proposed methodology adopts an alternative strategy for pigment assay that has a high sensitivity. The unified assay is shown to conserve sample (by 79%), time (67%), chemicals (34%) and energy (58%) when compared to the corresponding assay for each component, carried out individually on different samples. The method can also be applied to other microorganisms, especially those with recalcitrant cell walls.
Crowd Research: Open and Scalable University Laboratories

Research experiences today are limited to a privileged few at select universities. Providing open access to research experiences would enable global upward mobility and increased diversity in the scientific workforce. How can we coordinate a crowd of diverse volunteers on open-ended research? How could a PI have enough visibility into each person's contributions to recommend them for further study? We present Crowd Research, a crowdsourcing technique that coordinates open-ended research through an iterative cycle of open contribution, synchronous collaboration, and peer assessment. To aid upward mobility and recognize contributions in publications, we introduce a decentralized credit system: participants allocate credits to each other, which a graph centrality algorithm translates into a collectively-created author order. Over 1,500 people from 62 countries have participated, 74% from institutions with low access to research. Over two years and three projects, this crowd has produced articles at top-tier Computer Science venues, and participants have gone on to leading graduate programs.
Cache invalidation strategies for internet-based mobile ad hoc networks

Internet-based mobile ad hoc network (IMANET) is an emerging technique that combines a mobile ad hoc network (MANET) and the Internet to provide universal information accessibility. Although caching frequently accessed data items in mobile terminals (MTs) improves the communication performance in an IMANET, it brings a critical design issue when data updates. In this paper, we analyze several push and pull-based cache invalidation strategies for IMANETS. A global positioning system (GPS) based connectivity estimation (GPSCE) scheme is first proposed to assess the connectivity of an MT for supporting cache invalidation mechanisms. Then, we propose a pull-based approach, called aggregate cache based on demand (ACOD) scheme that uses an efficient search algorithm for finding the queried data items. In addition, we modify two push-based cache invalidation strategies, proposed for cellular networks, to work in IMANETS. They are called modified timestamp (MTS) scheme and MTS with updated invalidation report (MTS + UIR) scheme, respectively. We compare the performance of all these schemes as a function of query interval, cache update interval, and cache size through extensive simulation. Simulation results indicate that the ACOD scheme provides high throughput, low query latency, and low communication overhead, and thus, is a viable approach for implementation in IMANETS.
A Closer Look at Amphetamine-Induced Reverse Transport and Trafficking of the Dopamine and Norepinephrine Transporters

Amphetamine (AMPH) and its derivatives are regularly used in the treatment of a wide array of disorders such as attention-deficit hyperactivity disorder (ADHD), obesity, traumatic brain injury, and narcolepsy (Prog Neurobiol 75:406–433, 2005;  1935;  2002; Neuron 43:261–269, 2004; Annu Rev Pharmacol Toxicol 47:681–698, 2007; Drugs Aging 21:67–79, 2004). Despite the important medicinal role for AMPH, it is more widely known for its psychostimulant and addictive properties as a drug of abuse. The primary molecular targets of AMPH are both the vesicular monoamine transporters (VMATs) and plasma membrane monoamine—dopamine (DA), norepinephrine (NE), and serotonin (5-HT)—transporters. The rewarding and addicting properties of AMPH rely on its ability to act as a substrate for these transporters and ultimately increase extracellular levels of monoamines. AMPH achieves this elevation in extracellular levels of neurotransmitter by inducing synaptic vesicle depletion, which increases intracellular monoamine levels, and also by promoting reverse transport (efflux) through plasma membrane monoamine transporters (; Med ; Neuron 19:1271–1283, 1997;  1958;  1998;  1987;  1995). This review will focus on two important aspects of AMPH-induced regulation of the plasma membrane monoamine transporters—transporter mediated monoamine efflux and transporter trafficking.