nils-herrmann/clean_scirepeval huggingface dataset:
A multi-objective artificial bee colony algorithm

This work presents a multi-objective optimization method based on the artificial bee colony, called the MOABC, for optimizing problems with multiple objectives. The MOABC uses a grid-based approach to adaptively assess the Pareto frontmaintained in an external archive. The external archive is used to control the flying behaviours of the individuals and structuring the bee colony. The employed bees adjust their trajectories based on the non-dominated solutionsmaintained in the external archive. On the other hand, the onlooker bees select the food sources advertised by the employed bees to update their positions. The qualities of these food sources are computed based on the Pareto dominance notion. The scout bees are used by the MOABC to get rid of food sources with poor qualities. The proposed algorithm was evaluated on a set of standard test problems in comparison with other state-of-the-art algorithms. Experimental results indicate that the proposed approach is competitive compared to other algorithms considered in this work.
Modeling ITIL Business Motivation Model in ArchiMate

According to Enterprise Architecture (EA) approaches, organizations have motivational concepts that are used to model the motivations that underlie its design or change, which represent the organization’s Business Motivation Model (BMM). Likewise, this BMM is also present in the organizations who provide IT services. ITIL has become a reference for IT service providers, but is commonly modeled as a process-oriented approach to IT Service Management, often disregarding the remaining EA domains or its motivational elements. Conversely, we believe that like EA, ITIL has an important motivation model that should be formally represented.
QuickCut: An Interactive Tool for Editing Narrated Video

We present QuickCut, an interactive video editing tool designed to help authors efficiently edit narrated video. QuickCut takes an audio recording of the narration voiceover and a collection of raw video footage as input. Users then review the raw footage and provide spoken annotations describing the relevant actions and objects in the scene. QuickCut time-aligns a transcript of the annotations with the raw footage and a transcript of the narration to the voiceover. These aligned transcripts enable authors to quickly match story events in the narration with semantically relevant video segments and form alignment constraints between them. Given a set of such constraints, QuickCut applies dynamic programming optimization to choose frame-level cut points between the video segments while maintaining alignments with the narration and adhering to low-level film editing guidelines. We demonstrate QuickCut's effectiveness by using it to generate a variety of short (less than 2 minutes) narrated videos. Each result required between 14 and 52 minutes of user time to edit (i.e. between 8 and 31 minutes for each minute of output video), which is far less than typical authoring times with existing video editing workflows.
Nanosecond Delay Floating High Voltage Level Shifters in a 0.35 $\mu$m HV-CMOS Technology

We present novel circuits for high-voltage digital level shifting with zero static power consumption. The conventional topology is analysed, showing the strong dependence of speed and dynamic power on circuit area. Novel techniques are shown to circumvent this and speed up the operation of the conventional level-shifter architecture by a factor of 5-10 typically and 30-190 in the worst case. In addition, these circuits use 50% less silicon area and exhibit a factor of 20-80 lower dynamic power consumption typically. Design guidelines and equations are given to make the design robust over process corners, ensuring good production yield. The circuits were fabricated in a 0.35 high-voltage CMOS process and verified. Due to power and IO speed limitation on the test chip, a special ring oscillator and divider structure was used to measure inherent circuit speed.
Group and Individual Time Management Tools: What You Get is Not What You Need

Some studies of diaries and scheduling systems have considered how individuals use diaries with a view to proposing requirements for computerised time management tools. Others have focused on the criteria for success of group scheduling systems. Few have paid attention to how people use a battery of tools as an ensemble. This interview study reports how users exploit paper, personal digital assistants (PDAs) and a group scheduling system for their time management. As with earlier studies, we find many shortcomings of different technologies, but studying the ensemble rather than individual tools points towards a different conclusion: rather than aiming towards producing electronic time management tools that replace existing paper-based tools, we should be aiming to understand the relative strengths and weaknesses of each technology and look towards more seamless integration between tools. In particular, the requirements for scheduling and those for more responsive, fluid time management conflict in ways that demand different kinds of support.
CellNet: Network Biology Applied to Stem Cell Engineering

Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering.
Automating Content Extraction of HTML Documents

Web pages often contain clutter (such as unnecessary images and extraneous links) around the body of an article that distracts a user from actual content. Extraction of “useful and relevant” content from web pages has many applications, including cell phone and PDA browsing, speech rendering for the visually impaired, and text summarization. Most approaches to making content more readable involve changing font size or removing HTML and data components such as images, which takes away from a webpage’s inherent look and feel. Unlike “Content Reformatting,” which aims to reproduce the entire webpage in a more convenient form, our solution directly addresses “Content Extraction.” We have developed a framework that employs an easily extensible set of techniques. It incorporates advantages of previous work on content extraction. Our key insight is to work with DOM trees, a W3C specified interface that allows programs to dynamically access document structure, rather than with raw HTML markup. We have implemented our approach in a publicly available Web proxy to extract content from HTML web pages. This proxy can be used both centrally, administered for groups of users, as well as by individuals for personal browsers. We have also, after receiving feedback from users about the proxy, created a revised version with improved performance and accessibility in mind.
Latency arbitrage, market fragmentation, and efficiency: a two-market model

We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We propose a simple model of latency arbitrage in which a single security is traded on two exchanges, with aggregate information available to regular traders only after some delay. An infinitely fast arbitrageur profits from market fragmentation by reaping the surplus when the two markets diverge due to this latency in cross-market communication. We develop a discrete-event simulation system to capture this processing and information transfer delay, and using an agent-based approach, we simulate the interactions between high-frequency and zero-intelligence trading agents at the millisecond level. We then evaluate allocative efficiency and market liquidity arising from the simulated order streams, and we find that market fragmentation and the presence of a latency arbitrageur reduces total surplus and negatively impacts liquidity. By replacing continuous-time markets with periodic call markets, we eliminate latency arbitrage opportunities and achieve further efficiency gains through the aggregation of orders over short time periods.
A developmental, mentalization-based approach to the understanding and treatment of borderline personality disorder.

The precise nature and etiopathogenesis of borderline personality disorder (BPD) continues to elude researchers and clinicians. Yet, increasing evidence from various strands of research converges to suggest that affect dysregulation, impulsivity, and unstable relationships constitute the core features of BPD. Over the last two decades, the mentalization-based approach to BPD has attempted to provide a theoretically consistent way of conceptualizing the interrelationship between these core features of BPD, with the aim of providing clinicians with a conceptually sound and empirically supported approach to BPD and its treatment. This paper presents an extended version of this approach to BPD based on recently accumulated data. In particular, we suggest that the core features of BPD reflect impairments in different facets of mentalization, each related to impairments in relatively distinct neural circuits underlying these facets. Hence, we provide a comprehensive account of BPD by showing how its core features are related to each other in theoretically meaningful ways. More specifically, we argue that BPD is primarily associated with a low threshold for the activation of the attachment system and deactivation of controlled mentalization, linked to impairments in the ability to differentiate mental states of self and other, which lead to hypersensitivity and increased susceptibility to contagion by other people's mental states, and poor integration of cognitive and affective aspects of mentalization. The combination of these impairments may explain BPD patients' propensity for vicious interpersonal cycles, and their high levels of affect dysregulation and impulsivity. Finally, the implications of this expanded mentalization-based approach to BPD for mentalization-based treatment and treatment of BPD more generally are discussed.
Transactional events

Concurrent programs require high-level abstractions in order to manage complexity and enable compositional reasoning. In this paper, we introduce a novel concurrency abstraction, dubbed transactional events, which combines first-class synchronous messagepassing events with all-or-nothing transactions. This combination enables simple solutions to interesting problems in concurrent programming. For example, guarded synchronous receive can be implemented as an abstract transactional event, whereas in other languages it requires a non-abstract, non-modular protocol. Likewise, three-way rendezvous can also be implemented as an abstract transactional event, which is impossible using first-class events alone. Both solutions are easy to code and easy to reason about.The expressive power of transactional events arises from a sequencing combinator whose semantics enforces an all-or-nothing transactional property - either both of the constituent events synchronize in sequence or neither of them synchronizes. This sequencing combinator, along with a non-deterministic choice combinator, gives transactional events the compositional structure of a monad-with-plus. We provide a formal semantics for and a preliminary implementation of transactional events.
CompNet: Neural networks growing via the compact network morphism

It is often the case that the performance of a neural network can be improved by adding layers. In real-world practices, we always train dozens of neural network architectures in parallel which is a wasteful process. We explored CompNet, in which case we morph a well-trained neural network to a deeper one where network function can be preserved and the added layer is compact. The work of the paper makes two contributions: a). The modified network can converge fast and keep the same functionality so that we do not need to train from scratch again; b). The layer size of the added layer in the neural network is controlled by removing the redundant parameters with sparse optimization. This differs from previous network morphism approaches which tend to add more neurons or channels beyond the actual requirements and result in redundance of the model. The method is illustrated using several neural network structures on different data sets including MNIST and CIFAR10.
Word Semantic Representations using Bayesian Probabilistic Tensor Factorization

Many forms of word relatedness have been developed, providing different perspectives on word similarity. We introduce a Bayesian probabilistic tensor factorization model for synthesizing a single word vector representation and per-perspective linear transformations from any number of word similarity matrices. The resulting word vectors, when combined with the per-perspective linear transformation, approximately recreate while also regularizing and generalizing, each word similarity perspective. Our method can combine manually created semantic resources with neural word embeddings to separate synonyms and antonyms, and is capable of generalizing to words outside the vocabulary of any particular perspective. We evaluated the word embeddings with GRE antonym questions, the result achieves the state-ofthe-art performance.
Analysis on robust H∞ performance and stability for linear systems with interval time-varying state delays via some new augmented Lyapunov-Krasovskii functional

In this paper, the problem ofH1 performance and stability analysis for linear systems with interval time-varying delays is considered. First, by constructing a newly augmented Lyapunov–Krasovskii functional which has not been proposed yet, an improvedH1 performance criterion with the framework of linear matrix inequalities (LMIs) is introduced. Next, the result and method are extended to the problem of a delay-dependent stability criterion. Finally, numerical examples are given to show the superiority of proposed method.
Secure data deduplication

As the world moves to digital storage for archival purposes, there is an increasing demand for systems that can provide secure data storage in a cost-effective manner. By identifying common chunks of data both within and between files and storing them only once, deduplication can yield cost savings by increasing the utility of a given amount of storage. Unfortunately, deduplication exploits identical content, while encryption attempts to make all content appear random; the same content encrypted with two different keys results in very different ciphertext. Thus, combining the space efficiency of deduplication with the secrecy aspects of encryption is problematic.
 We have developed a solution that provides both data security and space efficiency in single-server storage and distributed storage systems. Encryption keys are generated in a consistent manner from the chunk data; thus, identical chunks will always encrypt to the same ciphertext. Furthermore, the keys cannot be deduced from the encrypted chunk data. Since the information each user needs to access and decrypt the chunks that make up a file is encrypted using a key known only to the user, even a full compromise of the system cannot reveal which chunks are used by which users.
Spanish Multicenter Normative Studies (NEURONORMA Project): norms for verbal span, visuospatial span, letter and number sequencing, trail making test, and symbol digit modalities test.

As part of the Spanish Multicenter Normative Studies (NEURONORMA project), we provide age- and education-adjusted norms for the following instruments: verbal span (digits), visuospatial span (Corsi's test), letter-number sequencing (WAIS-III), trail making test, and symbol digit modalities test. The sample consists of 354 participants who are cognitively normal, community-dwelling, and age ranging from 50 to 90 years. Tables are provided to convert raw scores to age-adjusted scaled scores. These were further converted into education-adjusted scaled scores by applying regression-based adjustments. The current norms should provide clinically useful data for evaluating elderly Spanish people. These data may be of considerable use for comparisons with other normative studies. Limitations of these normative data are mainly related to the techniques of recruitment and stratification employed.
Machine Learning Approaches for Failure Type Detection and Predictive Maintenance

With an increasing number of embedded sensing computer systems set up in production plants, machines, cars, etc., there are new possibilities to monitor and log the data from such systems. This development makes it possible to detect anomalies and predict the failures that affect maintenance plans. This thesis divides the field of failure type detection and predictive maintenance into subsections that focus on its realization by a machine learning technique, where each area of failure type detection and predictive maintenance explains and summarizes the most relevant research results in recent years. Each technique introduced is considered from a failure type detection and predictive maintenance perspective, highlighting its assets and drawbacks. An overview in tabular form shows the direction of the current research activities. It will be shown that many issues in the area of feature extraction, feature selection, and data labeling have not been researched so far. Zusammenfassung Die zunehmende Anzahl eingebetteter Sensorsysteme, die in Produktionsanlagen, Maschinen, Autos, usw. implementiert werden, eröffnet neue Möglichkeiten, um ein solches System zu überwachen und Objektdaten zu erfassen. Diese Entwicklung ermöglicht es, Anomalien zu detektieren und Fehler, die wiederum Auswirkungen auf Wartungspläne haben, vor dem eigentlichen Auftreten zu erkennen. Diese Arbeit teilt das Forschungsfeld der Fehlertypenkennung und prädiktiven Wartung in Teilgruppen auf und fokussiert sich auf Umsetzungen mithilfe maschineller Lernverfahren. Jeder Teilbereich der Fehlertypenkennung und prädiktiven Wartung wird erklärt und die Forschungsergebnisse der letzten Jahre in den einzelnen Bereichen zusammengefasst. Jede vorgestellte Technik wird mit deren Vorund Nachteile für die Fehlertypenkennung und prädiktiven Wartung betrachtet. Ein tabellarischer Überblick zeigt die aktuelle Forschungsrichtung der Teilbereiche. Weiter werden Problemstellungen in den Bereichen Feature-Extraction, Feature-Selection und Data-Labeling aufgezeigt, die bislang noch nicht untersucht worden sind. Acknowledgment At first, I would like to thank my thesis advisors, Prof. Johannes Fürnkranz and Dr. Frederik Janssen, who accepted my thesis proposal and made this work possible by offering their advice and support. Furthermore, I would like to express my gratitude to Dr. Immanuel Schweizer for the considerable amount of time that he invested in answering my early questions, as well as for the interesting discussions and organizational support. Moreover, I would like to thank Dr. Andrei Tolstikov,  Finally, I would like to thank my wife Kerstin and my children, Paul Maximilian and Hendrick Lennard, as well as my parents, who have always stood by me and dealt with my absence from many family occasions with a smile and never-ending support.
Comparing CORBA and Web-Services in view of a Service Oriented Architecture

The concept of Service Oriented Architecture revolves around registering services as tasks. These tasks are accomplished collectively by various disparate components seamlessly connected to one another. The task of interlinking these components may be considered amongst the most convoluted and difficult tasks currently faced by software practitioners. This paper attempts to show that although middleware technologies can be solely utilized to develop service oriented architecture, however such architecture would severely lack quality, interoperability and ease of implementation. In order to resolve these complexities and complications this paper proposes Web Services as an alternative to Middleware, for the realization of a fully functional interoperable and an automated SOA which conforms to the characteristics of a SOA. This paper provides an abstract implementation model of a SOA using both middleware and web services. It then attempts to point out the implementation and accepted benefits of the latter, especially when legacy applications are involved. Emphasize is laid out on the significance of interoperability since it assists in mobility and other corporate benefits. The paper concludes that when interoperability along with its benefits of mobility, expansion, costs, simplicity and enterprise integration are required in the construction of a SOA then web services should be the definite integration choice. The paper also highlights the importance of object oriented middleware, along with situations in which it might be preferred over web services.
Privacy and human behavior in the age of information

This Review summarizes and draws connections between diverse streams of empirical research on privacy behavior. We use three themes to connect insights from social and behavioral sciences: people’s uncertainty about the consequences of privacy-related behaviors and their own preferences over those consequences; the context-dependence of people’s concern, or lack thereof, about privacy; and the degree to which privacy concerns are malleable—manipulable by commercial and governmental interests. Organizing our discussion by these themes, we offer observations concerning the role of public policy in the protection of privacy in the information age.
Ontology Learning for the Semantic Web

The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck.  Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, refinement, and evaluation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general framework and architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, Text-ToOnto, such as ontology learning from free text, from dictionaries, or from legacy ontologies, and refer to some others that need to complement the complete architecture, such as reverse engineering of ontologies from database schemata or learning from XML documents. Ontologies for the Semantic Web Conceptual structures that define an underlying ontology are germane to the idea of machine processable data on the Semantic Web. Ontologies are (meta)data schemas, providing a controlled vocabulary of concepts, each with an explicitly defined and machine processable semantics. By defining shared and common domain theories, ontologies help both people and machines to communicate concisely,