nils-herrmann/clean_scirepeval huggingface dataset:
T-Finder: A Recommender System for Finding Passengers and Vacant Taxis

This paper presents a recommender system for both taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers' mobility patterns and 2) taxi drivers' picking-up/dropping-off behaviors learned from the GPS trajectories of taxicabs. First, this recommender system provides taxi drivers with some locations and the routes to these locations, toward which they are more likely to pick up passengers quickly (during the routes or in these locations) and maximize the profit of the next trip. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we learn the above-mentioned knowledge (represented by probabilities) from GPS trajectories of taxis. Then, we feed the knowledge into a probabilistic model that estimates the profit of the candidate locations for a particular driver based on where and when the driver requests the recommendation. We build our system using historical trajectories generated by over 12,000 taxis during 110 days and validate the system with extensive evaluations including in-the-field user studies.
Bipolar Fuzzy Trees

Connectivity has an important role in different discipline s of computer science including computer network. In the des ign of a network, it is important to analyze connections by the le v ls. The structural properties of bipolar fuzzy graphs pro vide a tool that allows for the solution of operations research problems. In this paper, we introduce various types of bipolar fuzzy brid ges, bipolar fuzzy cut-vertices, bipolar fuzzy cycles and bipolar fuzzy trees in bipolar fuzzy graphs, and investigate some of their prope rties. Most of these various types are defined in terms of levels. We also describe omparison of these types.
AI's War on Manipulation: Are We Winning?

big day: Citizens of Bitotia would once and for all establish which byte order was better, big-endian (B) or little-endian (L). Little Bit Timmy was a big supporter of little endian because that would give him the best position in the word. However, the population was split quite evenly between L and B, with a small minority of Bits who still remembered the single-tape Turing machine and preferred unary encoding (U), without any of this endianness business. Nonetheless, about half of the Bits preferred big-endian (B > L > U), and about half were the other way round (L > B > U). The voting rule was simple enough: You gave 2 points to your top choice, 1 point to your second-best, and 0 points to the worst. As Timmy was about to fall asleep, a sudden realization struck him: Why vote L > B > U and give the point to B, when U is not winning anyway? Immediately, Timmy knew: He would vote L > U > B! The next day brought some of the most sensational news in the whole history of Bitotia: Unary system had won! There were 104 votes L > U > B, 98 votes B > U > L, and 7 votes U > B > L. (Bitotia is a surprisingly small country.) U had won with 216 points, while B had 203 and L had 208. Apparently, Timmy was not the only one who found the trick. Naturally, Bitotians wanted to find out if they could avoid such situations in the future, but ... since they have to use unary now, we will have to help them!
How Elite Partisan Polarization Affects Public Opinion Formation

Competition is a defining element of democracy. One of the most noteworthy events over the last quarter-century in U.S. politics is the change in the nature of elite party competition: The parties have become increasingly polarized. Scholars and pundits actively debate how these elite patterns influence polarization among the public (e.g., have citizens also become more ideologically polarized?). Yet, few have addressed what we see as perhaps more fundamental questions: Has elite polarization altered the way citizens arrive at their policy opinions in the first place and, if so, in what ways? We address these questions with a theory and two survey experiments (on the issues of drilling and immigration). We find stark evidence that polarized environments fundamentally change how citizens make decisions. Specifically, polarization intensifies the impact of party endorsements on opinions, decreases the impact of substantive information and, perhaps ironically, stimulates greater confidence in those—less substantively grounded—opinions. We discuss the implications for public opinion formation and the nature of democratic competition.
Online Transfer Learning in Reinforcement Learning Domains

This paper proposes an online transfer framework to capture the interaction among agents and shows that current transfer learning in reinforcement learning is a special case of online transfer. Furthermore, this paper re-characterizes existing agents-teaching-agents methods as online transfer and analyze one such teaching method in three ways. First, the convergence of Q-learning and Sarsa with tabular representation with a finite budget is proven. Second, the convergence of Qlearning and Sarsa with linear function approximation is established. Third, the we show the asymptotic performance cannot be hurt through teaching. Additionally, all theoretical results are empirically validated.
Design of a Biologically Accurate Prosthetic Hand

A high fidelity biomimetic hand actuated by 9 stepper motors packaged within forearm casing was manufactured for less than 350 USD; it has 18 mechanical degrees of freedom, is 38 cm long, weighs 2.2 kg. The hand model has 3D printed replicas of human bones and laser cut tendons and ligaments. The user intent is deduced from EEG and EMG signals, obtained by Neurosky and Myoware commercial sensors, respectively. Three distinct EEG patterns trigger pinch, hook, and point actions. EMG signals are used for finer motor control, e.g. strength of grip. A pilot test study on three subjects showed that EMG can actuate the hand with an 80% success rate, while EEG allows for a 68% success rate. The system proved its robustness at the 2017 Cambridge Science Festival, using EEG signals alone. Out of approximately 30 visitors the majority could generate a “peace” sign after 1 to 2 minutes.
Location-Based Services with iBeacon Technology

iBeacons are a new way to interact with hardware. An iBeacon is a Bluetooth Low Energy device that only sends a signal in a specific format. They are like a lighthouse that sends light signals to boats. This paper explains what an iBeacon is, how it works and how it can simplify your daily life, what restriction comes with iBeacon and how to improve this restriction., as well as, how to use Location-based Services to track items. E.g., every time you touchdown at an airport and wait for your suitcase at the luggage reclaim, you have no information when your luggage will arrive at the conveyor belt. With an iBeacon inside your suitcase, it is possible to track the luggage and to receive a push notification about it even before you can see it. This is just one possible solution to use them. iBeacon can create a completely new shopping experience or make your home smarter. This paper demonstrates the luggage tracking use case and evaluates its possibilities and restrictions.
An overview of continuous improvement : from the past to the present

To provide an overview of the history, evolution, and existing research on continuous improvement.  Extensive review of the literature.  This paper provides an overview of continuous improvement, its inception, how it evolved into sophisticated methodologies used in organizations today, and existing research in this field in the literature.  It does not provide an exhaustive review of the existing literature, or an exhaustive list of all continuous improvement programs, only the most well known.  This paper traces how organizations have used various tools and techniques to address the need for improvement on various levels. The paper also presents research conducted in this field. It should be of value to practitioners of continuous improvement programs and to academics who are interested in how continuous improvement has evolved, and where it is today. To the authors’ knowledge, no recent papers have provided an historical perspective of continuous improvement. Furthermore, our paper also discusses the existing research in this field.
Studying Context : A Comparison of Activity Theory , Situated Action Models , and Distributed Cognition

It has been recognized that system design will benefit from explicit study of the context in which users work. The unaided individual divorced from a social group and from supporting artifacts is no longer the model user. But with this realization about the importance of context come many difficult questions. What exactly is context? If the individual is no longer central, what is the correct unit of analysis? What are the relations between artifacts, individuals, and the social groups to which they belong? This chapter compares three approaches to the study of context: activity theory, situated action models, and distributed cognition. I consider the basic concepts each approach promulgates and evaluate the usefulness of each for the design of technology.
Machine Teaching: A New Paradigm for Building Machine Learning Systems

The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them. We believe that in order to meet this growing demand for machine learning systems we must significantly increase the number of individuals that can teach machines. We postulate that we can achieve this goal by making the process of teaching machines easy, fast and above all, universally accessible. While machine learning focuses on creating new algorithms and improve the accuracy of "learners", the machine teaching discipline focuses on the efficacy of the "teachers". Machine teaching as a discipline is a paradigm shift that follows and extends principles of software engineering and programming languages. We put a strong emphasis on the teacher and the teacher’s interaction with data, as well as crucial components such as techniques and design principles of interaction and visualization. In this paper, we present our position regarding the discipline of machine teaching and articulate fundamental machine teaching principles. We also describe how, by decoupling knowledge about machine learning algorithms from the process of teaching, we can accelerate innovation and empower millions of new uses for machine learning models.
Defining the healthy "core microbiome" of oral microbial communities

Most studies examining the commensal human oral microbiome are focused on disease or are limited in methodology. In order to diagnose and treat diseases at an early and reversible stage an in-depth definition of health is indispensible. The aim of this study therefore was to define the healthy oral microbiome using recent advances in sequencing technology (454 pyrosequencing). We sampled and sequenced microbiomes from several intraoral niches (dental surfaces, cheek, hard palate, tongue and saliva) in three healthy individuals. Within an individual oral cavity, we found over 3600 unique sequences, over 500 different OTUs or "species-level" phylotypes (sequences that clustered at 3% genetic difference) and 88 - 104 higher taxa (genus or more inclusive taxon). The predominant taxa belonged to Firmicutes (genus Streptococcus, family Veillonellaceae, genus Granulicatella), Proteobacteria (genus Neisseria, Haemophilus), Actinobacteria (genus Corynebacterium, Rothia, Actinomyces), Bacteroidetes (genus Prevotella, Capnocytophaga, Porphyromonas) and Fusobacteria (genus Fusobacterium). Each individual sample harboured on average 266 "species-level" phylotypes (SD 67; range 123 - 326) with cheek samples being the least diverse and the dental samples from approximal surfaces showing the highest diversity. Principal component analysis discriminated the profiles of the samples originating from shedding surfaces (mucosa of tongue, cheek and palate) from the samples that were obtained from solid surfaces (teeth). There was a large overlap in the higher taxa, "species-level" phylotypes and unique sequences among the three microbiomes: 84% of the higher taxa, 75% of the OTUs and 65% of the unique sequences were present in at least two of the three microbiomes. The three individuals shared 1660 of 6315 unique sequences. These 1660 sequences (the "core microbiome") contributed 66% of the reads. The overlapping OTUs contributed to 94% of the reads, while nearly all reads (99.8%) belonged to the shared higher taxa. We obtained the first insight into the diversity and uniqueness of individual oral microbiomes at a resolution of next-generation sequencing. We showed that a major proportion of bacterial sequences of unrelated healthy individuals is identical, supporting the concept of a core microbiome at health.
Learning PHP and MySQL - step-by-step guide to creating database-driven web sites

Excellent book is always being the best friend for spending little time in your office, night time, bus, and everywhere. It will be a good way to just look, open, and read the book while in that time. As known, experience and skill don't always come with the much money to acquire them. Reading this book with the PDF learning php mysql step by step guide to creating database driven web sites will let you know more things.
Specialised Tools for Automating Data Mining for Hospital Management

This paper presents a research project which is directed to the partial automation of Data Mining (DM) in hospital information systems (HIS). We concentrate on hospital management applications and information systems such as emergencies and ward management, human resources (services, night duties, etc.), physical resources (beds, intervention theatres, etc.), etc. We have realised how the business objectives are usually the same across several hospitals and so is the information which is gathered in several HIS (even using different DBMS). This means that although the models extracted highly differ between hospitals, data mining processes are highly similar across different hospitals. We argue how a tool can be constructed in such a way that it automates many DM processes and that can be ported to other hospitals which could benefit more quickly of a first DM experience. Our work plan covers all the stages in the process of Knowledge Discovery from Databases (KDD): data cleansing, extraction and integration from the HIS and external data, construction of tasks and minable views, model generation, and finally a module to carry out and interpret their predictions. We also consider a module to perform simulations and to integrate the models extracted by the previous modules with other decision support systems as well as model monitoring.
Lexical Disambiguation in a Discourse Context

In this paper we investigate how discourse structure a ects the meanings of words and how the meanings of words a ect discourse structure We integrate three ingredients a theory of discourse structure called sdrt which represents discourse in terms of rhetorical relations that glue together the propositions introduced by the text segments an accom panying theory of discourse attachment called dice which computes which rhetorical relations hold between the constituents on the basis of the reader s background infor mation and a formal language for specifying the lexical knowledge both syntactic and semantic called the lkb Through this integration we can model the information ow from words to discourse and discourse to words From words to discourse we show how the lkb permits the rules for computing rhetorical relations in dice to be generalised and simpli ed so that a single law applies to several semantically related lexical items From discourse to words we encode two novel heuristics for lexical disambiguation dis ambiguate words so that discourse incoherence is avoided and disambiguate words so that rhetorical connections are reinforced These heuristics enable us to tackle several cases of lexical disambiguation that have until now been outside the scope of theories of lexical processing
When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation

Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.
Evaluating the Stability of Embedding-based Word Similarities

Word embeddings are increasingly being used as a tool to study word associations in specific corpora. However, it is unclear whether such embeddings reflect enduring properties of language or if they are sensitive to inconsequential variations in the source documents. We find that nearest-neighbor distances are highly sensitive to small changes in the training corpus for a variety of algorithms. For all methods, including specific documents in the training set can result in substantial variations. We show that these effects are more prominent for smaller training corpora. We recommend that users never rely on single embedding models for distance calculations, but rather average over multiple bootstrap samples, especially for small corpora.
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies.



DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations.


Availability and implementation
DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy.



Supplementary data are available at Bioinformatics online.
Investigating users' perspectives on e-learning: An integration of TAM and IS success model

The purpose of this paper is to examine an integrated model of TAM and D&M to explore the effects of quality features, perceived ease of use, perceived usefulness on users’ intentions and satisfaction, alongside the mediating effect of usability towards use of e-learning in Iran. Based on the e-learning user data collected through a survey, structural equations modeling (SEM) and path analysis were employed to test the research model. The results revealed that ‘‘intention’’ and ‘‘user satisfaction’’ both had positive effects on actual use of e-learning. ‘‘System quality’’ and ‘‘information quality’’ were found to be the primary factors driving users’ intentions and satisfaction towards use of e-learning. At last, ‘‘perceived usefulness’’ mediated the relationship between ease of use and users’ intentions. The sample consisted of e-learning users of four public universities in Iran. Past studies have seldom examined an integrated model in the context of e-learning in developing countries. Moreover, this paper tries to provide a literature review of recent published studies in the field of e-learning.
An Experiment for Measuring Business Process Maturity with Different Maturity Models

Since the 2000s, Business Process Management (BPM) has evolved into a comprehensively studied discipline that goes beyond the boundaries of particular business processes. By also affecting enterprise-wide capabilities (such as an organisational culture and structure that support a processoriented way of working), BPM can now correctly be called Business Process Orientation (BPO). Meanwhile, various maturity models have been developed to help organisations adopt a processoriented way of working based on step-by-step best practices. The present article reports on a case study in which the process portfolio of an organisation is assessed by different maturity models that each cover a different set of process-oriented capabilities. The purpose is to reflect on how business process maturity is currently measured, and to explore relevant considerations for practitioners, scholars and maturity model designers. Therefore, we investigate a possible difference in maturity scores that are obtained based on model-related characteristics (e.g. capabilities, scale and calculation technique) and respondent-related characteristics (e.g. organisational function). For instance, based on an experimental design, the original maturity scores are recalculated for different maturity scales and different calculation techniques. Follow-up research can broaden our experiment from multiple maturity models in a single case to multiple maturity models in multiple cases.
Balanced Scorecard implementation in SMEs : reflection on literature and practice

Recent research has indicated that the degree of strategic planning in organisations is likely to have a direct impact on business performance and business evaluation. However, these findings leave small and medium-sized businesses (SMEs) in particular, with the challenge of matching the requirement for an improved strategic planning processes with the competitive advantage associated with being a “simple” and highly responsive organisation. In response to that challenge this paper discusses the potential benefits to SMEs in adopting the Balanced Scorecard methodology and the underlying management processes most relevant to SMEs. It also makes observations about how use and value may differ between Balanced Scorecard application in large and smaller enterprises.