nils-herrmann/clean_scirepeval huggingface dataset:
Categorization, analysis, and visualization of computer-mediated communication and electronic markets

Computer mediated communication (CMC) and electronic markets have seen tremendous growth due to the fast propagation of the internet. In spite of the numerous benefits of electronic communication, it is not without its pitfalls. Two characteristics of computer mediated communication have proven to be particularly problematic: online anonymity and the enormity of data present in cyber communities. This dissertation follows the design science research paradigm in MIS, by addressing issues pertaining to the design and development of an important IT artifact capable of alleviating the two aforementioned CMC concerns. We present 8 essays related to the creation of CMC systems that can provide improved text analysis capabilities by incorporating a richer set of textual information types. Using Systemic Functional Linguistic Theory (SFLT) as a kernel theory, emphasis is placed on developing techniques for analyzing textual and ideational information. A rich set of features are used to represent textual (e.g., style, genres, social cues etc.) and ideational (topics, sentiments, affects, etc.) information. The research revolves around a core set of algorithms utilized for feature selection, categorization, analysis, and visualization of CMC text. The dissertation is arranged in three parts. The first two parts attempt to develop a set of features and techniques that can effectively represent textual and ideational information. In Chapters 2-5, we leverage information types related to the textual meta-function of SFLT for enhanced identity and institutional trust. Experiments are conducted on various CMC modes prevalent in organizational settings, including email, instant messaging, forums, program code, and websites. Chapters 6-8 consider two important information
Quality physical education: a commentary on effective physical education teaching.

In my commentary in response to the 3 articles (McKenzie & Lounsbery, 2013;  2013; Ward, 2013), I focus on 3 areas: (a) content knowledge, (b) a holistic approach to physical education, and (c) policy impact. I use the term quality teaching rather than "teacher effectiveness." Quality teaching is a term with the potential to move our attention beyond a focus merely on issues of effectiveness relating to the achievement of prespecified objectives. I agree with Ward that teacher content knowledge is limited in physical education, and I argue that if the student does not have a connection to or relationship with the content, this will diminish their learning gains. I also argue for a more holistic approach to physical education coming from a broader conception. Physical educators who teach the whole child advocate for a plethora of physical activity, skills, knowledge, and positive attitudes that foster healthy and active playful lifestyles. Play is a valuable educational experience. I also endorse viewing assessment from different perspectives and discuss assessment through a social-critical political lens. The 3 articles also have implications for policy. Physical education is much broader than just physical activity, and we harm the future potential of our field if we adopt a narrow agenda. Looking to the future, I propose that we broaden the kinds of research that we value, support, and appreciate in our field.
Optimization at Container Terminals : Status , Trends and Perspectives

International sea-freight container transportation has grown dramatically over the last years and container terminals represent nowadays a key actor in the global shipping network. Terminal managers have to face with an increasing competitiveness among terminals, which require more and more efficiency in container operations both along the quayside and within the yard: the objective is usually to minimize ship’s turnaround time, one of the main indicators of the terminal performance for the shipping companies. Moreover, the minimization of operational costs directly entails the achievement of competitive terminal fares, thus increasing the attractiveness for new customers. Operations research methods and techniques are therefore worth being used in optimizing terminal operations. In this work, we firstly give an overview of decision problems which arise in the management of a container terminal (e.g. berth allocation, quay crane scheduling, storage policies and strategies, transfer operations, ship stowage planning) and provide a review of recent papers in the OR literature. Then, starting from a collaboration with some of the busiest ports in Europe, we identify some critical issues: in particular, we discuss the impact that gate and transshipment operations have on the yard. We also focus on competition and cooperation issues among port market players and decision makers. Finally, we conclude by suggesting possible research tracks and open issues.
The Word Game: The ontology of an undefinable object

In this paper we address the problem of defining games formally, following Wittgenstein's dictum that games cannot be defined adequately as a formal category. Several influential attempts at definitions will be evaluated and shown to be inadequate. As an alternative, we propose a descriptive model of the definable supercategory that games belong to, cybermedia, that is pragmatic, open, and capable of meeting the needs of the diverse, intensely interdisciplinary field of game studies for a uniting conceptuallization of its main phenomenon. Our approach, the Cybermedia model, consisting of Player, Sign, Mechanical System, and Material Medium, offers a medium-independent, flexible and analytically useful way to contrast different approaches in games research and to determine which aspect of the phenomenon one is talking about when the word ‘game’ is used.
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs

In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs. We experiment with six different ranking models and propose a novel self-attention based slot matching model which exploits the inherent structure of query graphs, our logical form of choice. Our proposed model generally outperforms the other models on two QA datasets over the DBpedia knowledge graph, evaluated in different settings. In addition, we show that transfer learning from the larger of those QA datasets to the smaller dataset yields substantial improvements, effectively offsetting the general lack of training data.
Dropout: a simple way to prevent neural networks from overfitting

Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different “thinned” networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Globally Normalized Reader

Rapid progress has been made towards question answering (QA) systems that can extract answers from text. Existing neural approaches make use of expensive bidirectional attention mechanisms or score all possible answer spans, limiting scalability. We propose instead to cast extractive QA as an iterative search problem: select the answer’s sentence, start word, and end word. This representation reduces the space of each search step and allows computation to be conditionally allocated to promising search paths. We show that globally normalizing the decision process and back-propagating through beam search makes this representation viable and learning efficient. We empirically demonstrate the benefits of this approach using our model, Globally Normalized Reader (GNR), which achieves the second highest single model performance on the Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster than bi-attention-flow. We also introduce a data-augmentation method to produce semantically valid examples by aligning named entities to a knowledge base and swapping them with new entities of the same type. This method improves the performance of all models considered in this work and is of independent interest for a variety of NLP tasks.
Business process model and notation: The current state of affairs

With business process modelling, companies and organizations can gain explicit control over their processes. Currently, there are many notations in the area of business process modelling, where Business Process Model and Notation (BPMN) is denoted as the de facto standard.  The aim of this research is to provide the state-of-the-art results addressing the acceptance of BPMN, while also examining the purposes of its usage. Furthermore, the advantages, disadvantages and other interests related to BPMN were also investigated.  To achieve these objectives, a Systematic Literature Review (SLR) and a semantic examination of articles’ citations was conducted.  After completing SLR, out of a total of 852 articles, 31 were deemed relevant. The majority of the articles analyzed the notation and compared it with other modelling techniques. The remainder evaluated general aspects of the notation, e.g. history and versions of the standard, usage of the notation or tools.  Our findings demonstrate that there are empirical insights about the level of BPMN acceptance. They suggest that BPMN is still widely perceived as the de facto standard in the process modelling domain and its usage is everincreasing. However, many studies report that only a limited set of elements are commonly used and to this end, several extensions were proposed. The main purpose of BPMN remains the description of business processes.
Leadership Theory and Research in the New Millennium: Current Theoretical Trends and Changing Perspectives

Scholarly research on the topic of leadership has witnessed a dramatic increase over the last decade, resulting in the development of diverse leadership theories. To take stock of established and developing theories since the beginning of the new millennium, we conducted an extensive qualitative review of leadership theory across 10 top-tier academic publishing outlets that included The Leadership Quarterly, Administrative Science Quarterly, American Psychologist, Journal of Management, Academy of Management Journal, Academy of Management Review, Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Organizational Science, and Personnel Psychology. We then combined two existing frameworks (Gardner, Lowe, Moss, Mahoney, & Cogliser, 2010; Lord & Dinh, 2012) to provide a processoriented framework that emphasizes both forms of emergence and levels of analysis as a means to integrate diverse leadership theories. We then describe the implications of the findings for future leadership research and theory.
Comprehending pronouns: a role for word-specific gender stereotype information.

The research investigated how word-specific gender stereotype information can be used during language comprehension. In a rating study, the gender stereotypes for 405 noun and noun compounds were assessed. From these norms, 32 words strongly stereotyped to refer mostly to males and 32 words strongly stereotyped to refer mostly to females were selected and used in a reading experiment. Comprehenders read pairs of sentences. The subject of the first sentence was a gender stereotyped word (e.g., executive or secretary). The subject of the second sentence was either the pronoun he or she. Reading time was significantly longer when gender of the pronoun mismatched the gender stereotype of the antecedent than when the genders of pronoun and antecedent matched. This gender mismatch effect occurred on the two regions immediately following the region containing the pronoun. The results indicated that word-specific gender stereotypes can influence coreference resolution of pronouns.
Fog computing through public-resource computing and storage

Fog computing is a model in which data and processing are concentrated on devices at the edge of the network, rather than almost entirely in the cloud. This new paradigm tries to solve the latency and bandwidth issues that the current cloud systems have to deal with, and also addresses the scalability problems caused by the exponential growth of the number of smart devices due to the success of the Internet of Things. In this paper we introduce the idea of using public-resource computing and storage techniques in order to process part of the workload of the current cloud systems so as to avoid saturating the cloud. This idea proposes the use of devices working as participants, which form a data center between the cloud service providers and the end-clients. A participant can be any type of device, from a traditional PC (Personal Computer), to a smartphone or tablet or even a smart TV. We have evaluated two different case studies by simulating the use of participating nodes in video-transfer applications. The results of the simulations demonstrate that public-resource computing and storage can be used to solve the latency and bandwidth issues that affect the current cloud systems. Therefore, our results represent a feasible solution for applications that process or store public data.
The story of the Therac-25 in LOTOS

We consider the use of formal speciication and veriication techniques for proving the safety, or otherwise, of an abstraction of a safety-critical medical application: the Therac-25 radiation machine. This machine was responsible for several patient deaths in the late 1980s. The speciication is given in LOTOS and we consider trace analysis, property testing, and temporal logic for reasoning about the safe and unsafe behaviour of the speciied machine. The testing tool LOLA is used for rigorous veriicationn with LOLA, two signiicant design errors are uncovered. The work reported herein is part of a case study on the practical use of formal methods in safety-critical softwaree the speciication is based only on an informal description of part of the ma-chine's behaviour, and does not constitute a speciication of the entire machine.
Stick to the script: Monitoring the policy compliance of SDN data plane

Software defined networks provide new opportunities for automating the process of network debugging. Many tools have been developed to verify the correctness of network configurations on the control plane. However, due to software bugs and hardware faults of switches, the correctness of control plane may not readily translate into that of data plane. To bridge this gap, we present VeriDP, which can monitor "whether actual forwarding behaviors are complying with network configurations". Given that policies are well-configured, operators can leverage VeriDP to monitor the correctness of the network data plane. In a nutshell, VeriDP lets switches tag packets that they forward, and report tags together with headers to the verification server before the packets leave the network. The verification server pre-computes all header-to-tag mappings based on the configuration, and checks whether the reported tags agree with the mappings. We prototype VeriDP with both software and hardware OpenFlow switches, and use emulation to show that VeriDP can detect common data plane fault including black holes and access violations, with a minimal impact on the data plane.
Dynamic Modeling and Compensation of Robot Six-Axis Wrist Force/Torque Sensor

The dynamic mathematical models of six channels of six-axis wrist force/torque sensor for robots are built up by the system identification method based on the experimental data of step-response calibrations. The performance indexes of frequency domain are determined, and the dynamic characteristics of the wrist force sensor are described accurately. In order to speed the dynamic responses of the wrist force sensor, the dynamic compensating devices are designed using the method of functional link artificial neural network, and a digital-signal-processor-based real-time dynamic compensation system is developed, which includes six compensating devices. The experimental results show that the adjusting time of dynamic response of the wrist force sensor is reduced to less than 25%.
Considering the role of presence in the conceptual design of interior architectural environments

This paper reports on an educational setting used to explore presence within the interior architecture discipline through an interior design studio environment. An undergraduate studio subject was developed to integrate a hybrid design approach, where the main focus was to examine a means to heighten a designer’s appreciation of experience in virtual environments. Initially the focus of the unit and its objectives will be outlined before describing the projects. Attention will be focused on the main project and the hybrid design process before discussing the outcomes, student feedback and potential for further research in the field. The outcome of the study aims to contribute to current research on presence and its potential role in the design process and end product in interior architecture.  Presence, Interior Architecture.
LipNet: Sentence-level Lipreading

Lipreading is the task of decoding text from the movement of a speaker’s mouth. Traditional approaches separated the problem into two stages: designing or learning visual features, and prediction. More recent deep lipreading approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman, 2016a). All existing works, however, perform only word classification, not sentence-level sequence prediction. Studies have shown that human lipreading performance increases for longer words (Easton & Basala, 1982), indicating the importance of features capturing temporal context in an ambiguous communication channel. Motivated by this observation, we present LipNet, a model that maps a variable-length sequence of video frames to text, making use of spatiotemporal convolutions, an LSTM recurrent network, and the connectionist temporal classification loss, trained entirely end-to-end. To the best of our knowledge, LipNet is the first lipreading model to operate at sentence-level, using a single end-to-end speaker-independent deep model to simultaneously learn spatiotemporal visual features and a sequence model. On the GRID corpus, LipNet achieves 93.4% accuracy, outperforming experienced human lipreaders and the previous 79.6% state-of-the-art accuracy.
PDFFigures 2.0: Mining figures from research papers

Figures and tables are key sources of information in many scholarly documents. However, current academic search engines do not make use of figures and tables when semantically parsing documents or presenting document summaries to users. To facilitate these applications we develop an algorithm that extracts figures, tables, and captions from documents called "PDFFigures 2.0." Our proposed approach analyzes the structure of individual pages by detecting captions, graphical elements, and chunks of body text, and then locates figures and tables by reasoning about the empty regions within that text. To evaluate our work, we introduce a new dataset of computer science papers, along with ground truth labels for the locations of the figures, tables, and captions within them. Our algorithm achieves impressive results (94% precision at 90% recall) on this dataset surpassing previous state of the art. Further, we show how our framework was used to extract figures from a corpus of over one million papers, and how the resulting extractions were integrated into the user interface of a smart academic search engine, Semantic Scholar (www.semanticscholar.org). Finally, we present results of exploratory data analysis completed on the extracted figures as well as an extension of our method for the task of section title extraction. We release our dataset and code on our project webpage for enabling future research (http://pdffigures2.allenai.org).
Artificial intelligence based smart diagnosis of alzheimer's disease and mild cognitive impairment

Modern pattern recognition and artificial intelligence systems can help in providing better health care and medical solutions. The performance of human diagnosis degrades due to fatigue, cognitive biases, systems faults, and distractions. However, artificial intelligence based diagnosis systems are less error prone and give safe support to clinicians in detection and decision making. This work presents a smart and reliable way of diagnosing Alzheimer's disease (AD) and its possible early stage i.e., mild cognitive impairment. Alzheimer's is a neurodegenerative disease and leads to severe memory loss and inability to cope with daily life tasks. The diagnosis of AD from structural images requires great skill and is challenging for human diagnostics. The presented framework is based on deep learning and detects Alzheimer's and its initial stages accurately from structural MRI scans. The framework analyzes four different classes simultaneously in a single setup. The testing accuracy of diagnosis obtained by the method is 98.88%. Experiments are also performed on binary data and transfer learning is applied for multiclass classification achieving 99.7% accuracy. Once a good trained model is obtained, the decision for an unseen test scan is given within a few seconds. These models are free from the factors that are responsible for causing human diagnostic errors. Therefore, these models are dependable and can provide much faster diagnosis.
Information Systems Opportunities in Brain–Machine Interface Decoders

Brain-machine interface (BMI) systems convert neural signals from motor regions of the brain into control signals to guide prosthetic devices. The ultimate goal of BMIs is to improve the quality of life for people with paralysis by providing direct neural control of prosthetic arms or computer cursors. While considerable research over the past 15 years has led to compelling BMI demonstrations, there remain several challenges to achieving clinically viable BMI systems. In this review, we focus on the challenge of increasing BMI performance and robustness. We review and highlight key aspects of intracortical BMI decoder design, which is central to the conversion of neural signals into prosthetic control signals, and discuss emerging opportunities to improve intracortical BMI decoders. This is one of the primary research opportunities where information systems engineering can directly impact the future success of BMIs.