nils-herrmann/clean_scirepeval huggingface dataset:
On the number of response regions of deep feed forward networks with piece-wise linear activations

This paper explores the complexity of deep feedforward networks with linear presynaptic couplings and rectified linear activations. This is a contribution to the growing body of work contrasting the representational power of deep and shallow network architectures. In particular, we offer a framework for comparing deep and shallow models that belong to the family of piecewise linear functions based on computational geometry. We look at a deep rectifier multi-layer perceptron (MLP) with linear outputs units and compare it with a single layer version of the model. In the asymptotic regime, when the number of inputs stays constant, if the shallow model has kn hidden units and n0 inputs, then the number of linear regions is O(k0n0). For a k layer model with n hidden units on each layer it is Ω(bn/n0c n0). The number bn/n0c grows faster than k0 when n tends to infinity or when k tends to infinity and n ≥ 2n0. Additionally, even when k is small, if we restrict n to be 2n0, we can show that a deep model has considerably more linear regions that a shallow one. We consider this as a first step towards understanding the complexity of these models and specifically towards providing suitable mathematical tools for future analysis.
Deep Learning with K-Means Applied to Community Detection in Networks

Identifying communities, or clusters, in graphs is a task of great importance when analyzing network structures. A telecommunications provider, for instance, would like to identify communities of customers that place a large amount of calls to each other, in order to create more effective, directed marketing campaigns. Another example would be a financial institution, trying to identify and understand communities of customers that have a high amount of financial transactions between each other. There is also a wide applicability of community detection in life sciences research as well: for example, when studying protein-protein interaction networks. There is a wide range of algorithms and methods that can be applied, in order to identify communities in network structures, such as Spectral Clustering, Modularity Maximization, and Hierarchical Clustering. Spectral Clustering is one of the traditional algorithms that is suitable for network clustering because it first map the original data points into a lowerdimensional space, where the clustering properties of the graph tend to be much more evident, allowing for a subsequent application of standard clustering techniques such as K-Means. Despite its good results, Spectral Clustering presents some computational challenges when applied to very large networks. Recent research shows that techniques and algorithms from Deep Learning, such as layered neural networks-based autoencoders, are suitable for the task of mapping data points into lower-dimensional spaces, which can be useful for a variety of further tasks such as data clustering and classification. There is also recent research showing that a K-Means based approach is also suitable for a lower-dimensional data representation that can replace traditional neural networksbased auto-encoders in a Deep Learning pipeline. In this project we will create a new processing pipeline for nonoverlapping community detection in network structures based entirely on K-Means. We will show that this approach is similar to a traditional Deep Learning auto-encoder in its ability to learn useful representations of the original data in a lowerdimensional space, making the data clustering task easier to accomplish. We will then test its applicability for the specific challenges of community detection in networks and compare its performance with the traditional Spectral Clustering approach.
The characteristics of persistent sexual offenders: a meta-analysis of recidivism studies.

A meta-analysis of 82 recidivism studies (1,620 findings from 29,450 sexual offenders) identified deviant sexual preferences and antisocial orientation as the major predictors of sexual recidivism for both adult and adolescent sexual offenders. Antisocial orientation was the major predictor of violent recidivism and general (any) recidivism. The review also identified some dynamic risk factors that have the potential of being useful treatment targets (e.g., sexual preoccupations, general self-regulation problems). Many of the variables commonly addressed in sex offender treatment programs (e.g., psychological distress, denial of sex crime, victim empathy, stated motivation for treatment) had little or no relationship with sexual or violent recidivism.
Semisupervised Autoencoder for Sentiment Analysis

In this paper, we investigate the usage of autoencoders in modeling textual data. Traditional autoencoders suffer from at least two aspects: scalability with the high dimensionality of vocabulary size and dealing with task-irrelevant words. We address this problem by introducing supervision via the loss function of autoencoders. In particular, we first train a linear classifier on the labeled data, then define a loss for the autoencoder with the weights learned from the linear classifier. To reduce the bias brought by one single classifier, we define a posterior probability distribution on the weights of the classifier, and derive the marginalized loss of the autoencoder with Laplace approximation. We show that our choice of loss function can be rationalized from the perspective of Bregman Divergence, which justifies the soundness of our model. We evaluate the effectiveness of our model on six sentiment analysis datasets, and show that our model significantly outperforms all the competing methods with respect to classification accuracy. We also show that our model is able to take advantage of unlabeled dataset and get improved performance. We further show that our model successfully learns highly discriminative feature maps, which explains its superior performance.
Intangible benefits valuation in ERP projects

The development, implementation and ownership of information systems, especially large-scale systems such as enterprise resource planning (ERP), has become progressively longer in duration and more cost intensive. As a result, IS managers are being required to justify projects financially based on their return. Historically, information systems have been difficult to quantify in monetary terms because of the intangible nature of many of the derived benefits, e.g. improved customer service. Using the case study methodology, this paper examines an attempt by a large computer manufacturer to incorporate intangibles into traditional cost–benefit analysis in an ERP project. The paper reviews the importance of intangibles, lists intangible benefits that are important in ERP projects and demonstrates the use of a scheme through which they can be incorporated into traditional evaluation techniques.
Multi-agent reinforcement learning approaches for distributed job shop scheduling problems

Decentralized decision-making has become an active research topic in artificial intelligence. In a distributed system, a number of individually acting agents coexist. If they strive to accomplish a common goal, i.e. if the multi-agent system is a cooperative one, then the establishment of coordinated cooperation between the agents is of utmost importance. With this in mind, our focus is on multi-agent reinforcement learning methods which allow for automatically acquiring cooperative policies based solely on a specification of the desired joint behavior of the whole system. Research in distributed systems has pointed out that the decentralization of the control of the system and of the observation of the system among independent agents has a significant impact on the complexity of solving a given problem. Therefore, we address the intricacy of learning and acting in multi-agent systems by the following complementary approaches. Many practical problems exhibit some structure whose exploitation may ease the task of finding solutions. For this reason, we are going to identify a subclass of general decentralized decision-making problems that features regularities in the way the agents interact with one another. We will show that the complexity of optimally solving a problem instance from this class is provably lower than solving a general one. Even though a lower complexity class may be entered by sticking to certain subclasses of a general multi-agent problem, the computational complexity may be still so high that optimally solving it is infeasible. This holds, in particular, when intending to tackle problems of larger size that are of relevance for practical problems. Given these facts, our goal will be not to develop optimal solution algorithms that are applicable to small problems only, but to look for techniques capable of quickly obtaining approximate solutions in the vicinity of the optimum. To this end, we will develop and utilize various model-free reinforcement learning approaches. In contrast to offline planning algorithms which aim at finding optimal solutions in a modelbased manner, reinforcement learning allows for employing independently learning agents and, hence, for a full decentralization of the problem. As a matter of fact, many large-scale applications are well-suited to be formulated in terms of spatially or functionally distributed entities. Thus, multi-agent approaches are of high relevance to various real-world problems. Job-shop scheduling is one such application stemming from the field of factory optimization and manufacturing control. It is our particular goal to interpret job-shop scheduling problems as distributed sequential decision-making problems, to employ the multi-agent reinforcement learning algorithms we will propose for solving such problems, and, moreover, to evaluate the performance of our learning approaches in the scope of various established scheduling benchmark problems.
An Edge Driven Wavelet Frame Model for Image Restoration

Wavelet frame systems are known to be effective in capturing singularities from noisy and degraded images. In this paper, we introduce a new edge driven wavelet frame model for image restoration by approximating images as piecewise smooth functions. With an implicit representation of image singularities sets, the proposed model inflicts different strength of regularization on smooth and singular image regions and edges. The proposed edge driven model is robust to both image approximation and singularity estimation. The implicit formulation also enables an asymptotic analysis of the proposed models and a rigorous connection between the discrete model and a general continuous variational model. Finally, numerical results on image inpainting and deblurring show that the proposed model is compared favorably against several popular image restoration models.
Assessment of ROCPAD Relay for Islanding Detection in Distributed Generation

The paper presents a new approach for islanding detection in distributed generation (DG) using rate of change of phase angle difference (ROCPAD). The process starts with retrieving the voltage and current signals at the DG end and estimating the phasors (amplitude, phase, and frequency) using synchronous transformation based algorithm. The next step is to compute the phase angle difference and the ROCPAD for registering islanding detection. The proposed algorithm is compared with the widely used rate of change of frequency relays (ROCOF) and found working effectively in the situations where ROCOF fails. The proposed ROCPAD relay is tested on standard microgrid structures with possible variations in operating parameters and the results indicate that the ROCPAD relay can reliably detect islanding conditions in large microgrids.
Is Information Security Under Control?: Investigating Quality in Information Security Management

Over the past decade, organizations have sought to become more efficient and productive by adopting information and communication technologies. Organizations are consequently more aware of information security risks and the need to take appropriate action. Previous studies of organizations' use of information security controls have focused on the presence or absence of controls, rather than their quality. We designed and conducted a survey as an initial step toward meeting this challenge. To do this, we benchmarked how organizations manage information security by implementating various controls. Although security surveys are nothing new, our method aims to uncover specific details of control implementation and focus on implementation quality. With a more precise understanding of current practices, information security management can begin to properly pursue effective strategies to improve quality and lower risk
A descent modified

In this paper, we propose a modified Polak–Ribière–Polyak (PRP) conjugate gradient method. An attractive property of the proposed method is that the direction generated by the method is always a descent direction for the objective function. This property is independent of the line search used. Moreover, if exact line search is used, the method reduces to the ordinary PRP method. Under appropriate conditions, we show that the modified PRP method with Armijo-type line search is globally convergent. We also present extensive preliminary numerical experiments to show the efficiency of the proposed method.
Generalized Zero-Shot Recognition based on Visually Semantic Embedding

We propose a novel Generalized Zero-Shot learning (GZSL) method that is agnostic to both unseen images and unseen semantic vectors during training. Prior works in this context propose to map high-dimensional visual features to the semantic domain, which we believe contributes to the semantic gap. To bridge the gap, we propose a novel low-dimensional embedding of visual instances that is “visually semantic.” Analogous to semantic data that quantifies the existence of an attribute in the presented instance, components of our visual embedding quantifies existence of a prototypical part-type in the presented instance. In parallel, as a thought experiment, we quantify the impact of noisy semantic data by utilizing a novel visual oracle to visually supervise a learner. These factors, namely semantic noise, visual-semantic gap and label noise lead us to propose a new graphical model for inference with pairwise interactions between label, semantic data, and inputs. We tabulate results on a number of benchmark datasets demonstrating significant improvement in accuracy over state-of-art under both semantic and visual supervision.
Exploring Students' Computational Thinking Skills in Modeling and Simulation Projects: a Pilot Study

Computational Thinking (CT) is gaining a lot of attention in education. We explored how to discern the occurrences of CT in the projects of 12th grade high school students in the computer science (CS) course. Within the projects, they constructed models and ran simulations of phenomena from other (STEM) disciplines. We examined which CT aspects occurred in students' activities and how to assess students' CT accomplishments. For this purpose we employed a framework based on CT characterizations by Wing [14, 15],  and Comer et al. . We analyzed students' project documentation, survey results and interviews with individual students. The findings indicate that this framework is suitable for detection of occurrences of CT aspects in students' data. Moreover, our preliminary results suggest that the framework is useful in assessment of the quality of the students' CT performance.
iMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos

Fig. 1. We present iMapper, a method that reasons about the interactions of humans with objects, to recover both a plausible scene arrangement and human motions, that best explain an input monocular video (see inset). We fit characteristic interactions called scenelets (e.g., A, B, C) to the video and use them to reconstruct a plausible object arrangement and human motion path (left). The key challenge is that reliable fitting requires information about occlusions, which are unknown (i.e., latent). (Right) We show an overlay (from top-view) of our result over manually annotated groundtruth object placements. Note that object meshes are placed based on estimated object category, location, and size information.
BUILDING BETTER CAUSAL THEORIES : A FUZZY SET APPROACH TO TYPOLOGIES IN ORGANIZATION RESEARCH

Typologies are an important way of organizing the complex cause-effect relationships that are key building blocks of the strategy and organization literatures. Here, I develop a novel theoretical perspective on causal core and periphery, which is based on how elements of a configuration are connected to outcomes. Using data on hightechnology firms, I empirically investigate configurations based on the Miles and Snow typology using fuzzy set qualitative comparative analysis (fsQCA). My findings show how the theoretical perspective developed here allows for a detailed analysis of causal core, periphery, and asymmetry, shifting the focus to midrange theories of causal processes.
Sensorless vector control of asynchronous machine based on reduced order Kalman filter

The article describes sensorless vector control of the asynchronous machine based on Kalman filter. A control system block diagram is presented along with a functional scheme of the original speed observer using dynamic system state vector filter estimator. The synthesis of current regulator using machine parameters determined by inverse r model is shown. The method of a control matrix dimension reduction is proposed by removal the flux linkage calculations out of Kalman filter algorithms. Current and speed regulators, as well as a flux estimator, were implemented in the Mexbios Development Studio simulation environment. The robustness of a speed observer and the efficiency of the asynchronous machine sensorless control system were confirmed by the simulation results.
Renewing and improving the business model toward sustainability in theory and practice

The paper aims to analyse and discuss the evolution towards a sustainable business model and to focus on the motivations and the implications on the mission, the governance and the accountability of companies. Moreover, the theoretical framework of values-based, ethical and sustainable leadership has been used as interpretative key for the case-study analysis. After presenting the literature review, the second part of the work is based on the inductive approach applied to the analysis of the experience of an Italian medium-sized company (SGR Group) belonging to the energy sector (oil & gas). Recently this company has started a new project aimed at renewing its business model and deepening its sustainability orientation, which has already been experienced for years. The project is finalized at identifying and assessing vices and virtues which affect the governance and the leadership model, as well as the company’s stakeholders engagement processes. The case exemplifies a resilience capability derived from a sustainable business model activated through relationships among internal and external stakeholders and supported by an ethical-based transformational leadership model which, in turn, derives from and nurtures the coherence among the mission-governance and accountability model. Specifically, the analysis confirms the propositions drawn from the literature review relative to the fact that the coherence among mission, governance and accountability is a key driver for effective business model and that sustainable business models derive from and are characterized by sustainable leadership models, which include transformational, ethical-based and values-virtues-driven leadership.
An architecture pattern for safety critical automated driving applications: Design and analysis

Introduction of automated driving increases complexity of automotive systems. As a result, architecture design becomes a major concern for ensuring non-functional requirements such as safety, and modifiability. In the ISO 26262 standard, architecture patterns are recommended for system development. However, the existing architecture patterns may not be able to answer requirements of automated driving completely. When applying these patterns in the automated driving context, modification and analysis of these patterns are needed. In this paper, we present a novel architecture pattern for safety critical automated driving functions. In addition, we propose a generic approach to compare our pattern with a number of existing ones. The comparison results can be used as a basis for project specific architectural decisions. Our Safety Channel pattern is validated by its implementation for a real-life truck platooning application.
Plagiarism Meets Paraphrasing: Insights for the Next Generation in Automatic Plagiarism Detection

Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyze the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource that uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analyzed in the light of this annotation.The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarizing, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analyzed, providing critical insights for the improvement of automatic plagiarism detection systems.
Transformerless Capacitive Coupling of Gate Signals for Series Operation of Power MOS Devices

A reliable configuration for triggering a series string of power metal oxide semiconductor (MOS) devices without the use of transformer coupling is presented. A capacitor is inserted between the gate and ground of each metal oxide semiconductor field effect transistor (MOSFET), except for the bottom MOSFET in the stack. Using a single input voltage signal to trigger the bottom MOSFET, a voltage division across the network of device capacitance and inserted capacitances triggers the entire series stack reliably. Design formulas are presented and simple circuit protection is discussed. Simulation shows reliable operation and experimental verification is presented. Application of the method is applied to series insulated gate bipolar transistors (IGBTs).