nils-herrmann/clean_scirepeval huggingface dataset:
Time-Frequency Analysis via Ramanujan Sums

Research in signal processing shows that a variety of transforms have been introduced to map the data from the original space into the feature space, in order to efficiently analyze a signal. These techniques differ in their basis functions, that is used for projecting the signal into a higher dimensional space. One of the widely used schemes for quasi-stationary and non-stationary signals is the time-frequency (TF) transforms, characterized by specific kernel functions. This work introduces a novel class of Ramanujan Fourier Transform (RFT) based TF transform functions, constituted by Ramanujan sums (RS) basis. The proposed special class of transforms offer high immunity to noise interference, since the computation is carried out only on co-resonant components, during analysis of signals. Further, we also provide a 2-D formulation of the RFT function. Experimental validation using synthetic examples, indicates that this technique shows potential for obtaining relatively sparse TF-equivalent representation and can be optimized for characterization of certain real-life signals.
Shade matching in restorative dentistry: the science and strategies.

Closely matching natural teeth with an artificial restoration can be one of the most challenging procedures in restorative dentistry. Natural teeth vary greatly in color and shape. They reveal ample information about patients' background and personality. Dentistry provides the opportunity to restore unique patient characteristics or replace them with alternatives. Whether one tooth or many are restored, the ability to assess and properly communicate information to the laboratory can be greatly improved by learning the language of color and light characteristics. It is only possible to duplicate in ceramic what has been distinguished, understood, and communicated in the shade-matching process of the natural dentition. This article will give the reader a better understanding of what happens when incident light hits the surface of a tooth and give strategies for best assessing and communicating this to the dental laboratory.
Customer churn analysis for a software-as-a-service company

SaaS companies generate revenues by charging recurring subscription fees for using their software services. The fast growth of SaaS companies is usually accompanied with huge upfront costs in marketing expenses targeted at their potential customers. Customer retention is a critical issue for SaaS companies because it takes twelve months on average to break-even with the expenses for a single customer. This study describes a methodology for helping SaaS companies manage their customer relationships. We investigated the time-dependent software feature usage data, for example, login numbers and comment numbers, to predict whether a customer would churn within the next three months. Our study compared model performance across four classification algorithms. The XGBoost model yielded the best results for identifying the most important software usage features and for classifying customers as either churn type or non-risky type. Our model achieved a 10-fold cross-validated mean AUC score of 0.7941. Companies can choose to move along the ROC curve to accommodate to their marketing capability. The feature importance output from the XGBoost model can facilitate SaaS companies in identifying the most significant software features to launch more effective marketing campaigns when facing prospective customers.
Advances in Artificial Intelligence and Applications

Preface All around the world, every year, scientists and practitioners discuss at numerous conferences dedicated to Artificial Intelligence the novel achievements and challenges. Such conferences are organized also in Poland, one of them is the International Symposium Advances in Artificial Intelligence and Applications (AAIA), organized annually within the International Multiconference on Computer Science and Information Technology framework. Selected participants of that Symposium have been invited to submit their papers to this issue of Fundamenta Informaticae. Two papers have been written especially to this issue of Fundamenta journal, three other were presented at the Symposium and are considerably extended. The first paper, entitled " Measuring Semantic Closeness of Ontologically Demarcated Resources " is written by two teams: from Poland and Korea. Fusion of their experiences has allowed to develop an agent-based system, aimed to support workers in an organization. One of key functionalities of this system is ontological matchmaking, understood as a way of establishing closeness between resources. The system recommends which, among available resources, are relevant / of interest to the worker. Authors approach to measuring semantic closeness between ontologically demarcated information objects is discussed in the paper. A Duty Trip Support application is used as a case study. It is worth mentioning that, in computer science, ontology is a representation of a set of concepts and relationships between them within a given domain. The second paper, " Designing Model Based Classifiers by Emphasizing Soft Targets " , deals with training classifiers. Classification task is very popular in real-life problems. A number of different classification methods exists, each reveals some advantages and weakness. The authors explore the effectiveness of using emphasized soft targets with generative models, such as Gaussian Mixture Models (GMM), and Gaussian Processes (GP). Their approach seems to produce better performance and is less sensitive for parameters values. The third paper, Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem , uses classifiers (a family of classifiers) for automatic annotation of images. It is an important research topic in pattern recognition area. In the paper, a generic approach to find correct word frequencies is proposed. The Optimizer can be used with different automatic image annotators, based on various machine learning paradigms. In the paper, a new, improved authors former method, Greedy Resulted Word Counts Optimization is proposed. The proposed method is more intuitive and it reduces
Composing Relationships with Translations

Performing link prediction in Knowledge Bases (KBs) with embedding-based models, like with the model TransE (Bordes et al., 2013) which represents relationships as translations in the embedding space, have shown promising results in recent years. Most of these works are focused on modeling single relationships and hence do not take full advantage of the graph structure of KBs. In this paper, we propose an extension of TransE that learns to explicitly model composition of relationships via the addition of their corresponding translation vectors. We show empirically that this allows to improve performance for predicting single relationships as well as compositions of pairs of them.
Information Flow Control with System Dependence Graphs - Improving Modularity, Scalability and Precision for Object Oriented Languages

This work is concerned with the field of static program analysis —in particular with analyses aimed to guarantee certain security properties of programs, like confidentiality and integrity. Our approach uses socalled dependence graphs to capture the program behavior as well as the information flow between the individual program points. Using this technique, we can guarantee for example that a program does not reveal any information about a secret password. In particular we focus on techniques that improve the dependence graph computation —the basis for many advanced security analyses. We incorporated the presented algorithms and improvements into our analysis tool Joana and published its source code as open source. Several collaborations with other researchers and publications using Joana demonstrate the relevance of these improvements for practical research. This work consists essentially of three parts. Part 1 deals with improvements in the computation of the dependence graph, Part 2 introduces a new approach to the analysis of incomplete programs and Part 3 shows current use cases of Joana on concrete examples. In the first part we describe the algorithms used to compute a dependence graph, with special attention to the problems and challenges that arise when analyzing object-oriented languages such as Java. For example we present an analysis that improves the precision of detected control flow by incorporating the effects of exceptions. The main improvement concerns the way side effects —caused by communication over methods boundaries— are modelled. Dependence graphs capture side effects —memory locations read or changed by a method— in the form of additional nodes called parameter nodes. We show that the structure and computation of these nodes have a huge impact on both the precision and scalability of the entire analysis. The so-called parameter model describes the algorithms used to compute these nodes. We explain the weakness of the old parameter model based on object-trees and present our improvements in form of a new model using object-graphs. The new graph structure merges redundant information of multiple nodes into a single node and thus reduces the number of overall parameter nodes
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network

The deep convolutional neural networks have achieved significant improvements in accuracy and speed for single image super-resolution. However, as the depth of network grows, the information flow is weakened and the training becomes harder and harder. On the other hand, most of the models adopt a single-stream structure with which integrating complementary contextual information under different receptive fields is difficult. To improve information flow and to capture sufficient knowledge for reconstructing the high-frequency details, we propose a cascaded multi-scale cross network (CMSC) in which a sequence of subnetworks is cascaded to infer high resolution features in a coarse-to-fine manner. In each cascaded subnetwork, we stack multiple multi-scale cross (MSC) modules to fuse complementary multi-scale information in an efficient way as well as to improve information flow across the layers. Meanwhile, by introducing residual-features learning in each stage, the relative information between high-resolution and low-resolution features is fully utilized to further boost reconstruction performance. We train the proposed network with cascaded-supervision and then assemble the intermediate predictions of the cascade to achieve high quality image reconstruction. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed method over state-of-the-art superresolution methods.
Dimensions of DevOps

DevOps has been identified as an important aspect in the continuous deployment paradigm in practitioner communities and academic research circles. However, little has been presented to describe and formalize what it constitutes. The absence of such understanding means that the phenomenon will not be effectively communicated and its impact not understood in those two communities. This study investigates the elements that characterize the DevOps phenomenon using a literature survey and interviews with practitioners actively involved in the DevOps movement. Four main dimensions of DevOps are identified: collaboration, automation, measurement and monitoring. An initial conceptual framework is developed to communicate the phenomenon to practitioners and the scientific community as well as to facilitate input for future research.
Organizational Divisions in BIM-Enabled Commercial Construction

Proponents claim that the adoption of building information modeling BIM will lead to greater efficiencies through increased collaboration. In this paper, we present research that examines the use of BIM technologies for mechanical, electrical, plumbing, and fire life safety systems often referred to as MEP coordination and how the introduction of BIM influences collaboration and communication. Using data from over 12 months of ethnographic observations of the MEP coordination process for two commercial construction projects and interviews with 65 industry leaders across the United States, we find that BIM-enabled projects are often tightly coupled technologically, but divided organizationally. This means that while BIM makes visible the connections among project members, it is not fostering closer collaboration across different companies. We outline the competing obligations to scope, project, and company as one cause for this division. Obligations to an individual scope of work or to a particular company can conflict with project goals. Individual leadership, especially that of the MEP coordinator in the teams we studied, often substitutes for stronger project cohesion and organization. Organizational forces and structures must be accounted for in order for BIM to be implemented successfully.  Database subject headings: Integrated systems; Construction industry; Leadership; Information systems; Information technology; Communication; Constructability; Mechanical systems; Electrical systems.  Integrated systems; Construction industry; Leadership; Information systems; Information technology; Communication; Constructability; Mechanical systems; Electrical systems.
Optimal consumption and investment strategies with stochastic interest rates

We characterize the solution to the consumption and investment problem of a power utility investor in a continuous-time dynamically complete market with stochastic changes in the opportunity set. Under stochastic interest rates the investor optimally hedges against changes in the term structure of interest rates by investing in a coupon bond, or portfolio of bonds, with a payment schedule that equals the forward-expected (i.e. certainty equivalent) consumption pattern. Numerical experiments with two different specifications of the term structure dynamics (the Vasicek model and a three-factor non-Markovian Heath–Jarrow–Morton model) suggest that the hedge portfolio is more sensitive to the form of the term structure than to the dynamics of interest rates.
Information security policies: A review of challenges and influencing factors

Organisations increasingly perceive their employees as a great asset that needs to be cared for; however, at the same time, they view employees as one of the biggest potential threats to their cyber security. Employees are widely acknowledged to be responsible for security breaches in organisations, and it is important that these are given as much attention as are technical issues. A significant number of researchers have argued that non-compliance with information security policy is one of the major challenges facing organisations. This is primarily considered to be a human problem rather than a technical issue. Thus, it is not surprising that employees are one of the major underlying causes of breaches in information security. In this paper, academic literature and reports of information security institutes relating to policy compliance are reviewed. The objective is to provide an overview of the key challenges surrounding the successful implementation of information security policies. A further aim is to investigate the factors that may have an influence upon employees' behaviour in relation to information security policy. As a result, challenges to information security policy have been classified into four main groups: security policy promotion; noncompliance with security policy; security policy management and updating; and shadow security. Furthermore, the factors influencing behaviour have been divided into organisational and human factors. Ultimately, this paper concludes that continuously subjecting users to targeted awareness raising and dynamically monitoring their adherence to information security policy should increase the compliance level.
The role of intermediaries in electronic marketplaces: developing a contingency model

Early research in electronic markets seemed to suggest that E-Commerce transactions would result in decreased costs for buyers and sellers alike, and would therefore ultimately lead to the elimination of intermediaries from electronic value chains. However, a careful analysis of the structure and functions of electronic marketplaces reveals a different picture. Intermediaries provide many value-adding functions that cannot be easily substituted or ‘internalised’ through direct supplier-buyer dealings, and hence mediating parties may continue to play a significant role in the E-Commerce world. In this paper we provide an analysis of the potential roles of intermediaries in electronic markets and we articulate a number of hypotheses for the future of intermediation in such markets. Three main scenarios are discussed: the disintermediation scenario where market dynamics will favour direct buyer-seller transactions, the reintermediation scenario where traditional intermediaries will be forced to differentiate themselves and reemerge in the electronic marketplace, and the cybermediation scenario where wholly new markets for intermediaries will be created. The analysis suggests that the likelihood of each scenario dominating a given market is primarily dependent on the exact functions that intermediaries play in each case. A detailed discussion of such functions is presented in the paper, together with an analysis of likely outcomes in the form of a contingency model for intermediation in electronic markets.
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning

The focus in machine learning has branched beyond training classifiers on a single task to investigating how previously acquired knowledge in a source domain can be leveraged to facilitate learning in a related target domain, known as inductive transfer learning. Three active lines of research have independently explored transfer learning using neural networks. In weight transfer, a model trained on the source domain is used as an initialization point for a network to be trained on the target domain. In deep metric learning, the source domain is used to construct an embedding that captures class structure in both the source and target domains. In few-shot learning, the focus is on generalizing well in the target domain based on a limited number of labeled examples. We compare state-of-the-art methods from these three paradigms and also explore hybrid adapted-embedding methods that use limited target-domain data to fine tune embeddings constructed from sourcedomain data. We conduct a systematic comparison of methods in a variety of domains, varying the number of labeled instances available in the target domain (k), as well as the number of target-domain classes. We reach three principal conclusions: (1) Deep embeddings are far superior, compared to weight transfer, as a starting point for inter-domain transfer or model re-use (2) Our hybrid methods robustly outperform every few-shot learning and every deep metric learning method previously proposed, with a mean error reduction of 34% over state-of-the-art. (3) Among loss functions for discovering embeddings, the histogram loss (Ustinova & Lempitsky, 2016) is most robust. We hope our results will motivate a unification of research in weight transfer, deep metric learning, and few-shot learning.
Open Problems in Universal Induction & Intelligence

Specialized intelligent systems can be found everywhere: finger print, handwriting, speech, and face recognition, spam filtering, chess and other game programs, robots, et al. This decade the first presumably complete mathematical theory of artificial intelligence based on universal induction-predictiondecision-action has been proposed. This information-theoretic approach solidifies the foundations of inductive inference and artificial intelligence. Getting the foundations right usually marks a significant progress and maturing of a field. The theory provides a gold standard and guidance for researchers working on intelligent algorithms. The roots of universal induction have been laid exactly half-a-century ago and the roots of universal intelligence exactly one decade ago. So it is timely to take stock of what has been achieved and what remains to be done. Since there are already good recent surveys, I describe the state-of-the-art only in passing and refer the reader to the literature. This article concentrates on the open problems in universal induction and its extension to universal intelligence.
Artificial intelligence in medicine: the challenges ahead.

The modern study of artificial intelligence in medicine (AIM) is 25 years old. Throughout this period, the field has attracted many of the best computer scientists, and their work represents a remarkable achievement. However, AIM has not been successful-if success is judged as making an impact on the practice of medicine. Much recent work in AIM has been focused inward, addressing problems that are at the crossroads of the parent disciplines of medicine and artificial intelligence. Now, AIM must move forward with the insights that it has gained and focus on finding solutions for problems at the heart of medical practice. The growing emphasis within medicine on evidence-based practice should provide the right environment for that change.
Deep Brain Stimulation for Neurologic and Neuropsychiatric Disorders

In the 1960s, ablative stereotactic surgery was employed for a variety of movement disorders and psychiatric conditions. Although largely abandoned in the 1970s because of highly effective drugs, such as levodopa for Parkinson's disease (PD), and a reaction against psychosurgery, the field has undergone a virtual renaissance, guided by a better understanding of brain circuitry and the circuit abnormalities underlying movement disorders such as PD and neuropsychiatric conditions, such as obsessive compulsive disorder. High-frequency electrical deep brain stimulation (DBS) of specific targets, introduced in the early 1990s for tremor, has gained widespread acceptance because of its less invasive, reversible, and adjustable features and is now utilized for an increasing number of brain disorders. This review summarizes the rationale behind DBS and the use of this technique for a variety of movement disorders and neuropsychiatric diseases.
Epistasis and quantitative traits: using model organisms to study gene–gene interactions

The role of epistasis in the genetic architecture of quantitative traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype–phenotype map. This controversy arises because most genetic variation for quantitative traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex traits.