nils-herrmann/clean_scirepeval huggingface dataset:
Simulation in software engineering training

Simulation is frequently used for training in many application areas like aviation and economics, but not in software engineering. We present the SESAM project which focuses on software engineering education using simulation. In the SESAM project a simulator was developed. Using this simulator, a student can take the role of a software project manager. The simulated software project can be finished within a couple of hours because it is simulated in “quick-motion” mode.
In this paper, the background and goals of the SESAM project are presented. A new simulation model, the so called QA model, is introduced. The model behavior is demonstrated by investigating and comparing different strategies for software development. The results of experiments based on the QA model are reported. Finally, conclusions are drawn from the experiments and future work is outlined.
A comparative study on content-based music genre classification

Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs. DWCHs stands for Daubechies Wavelet Coefficient Histograms. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.
Language Shapes Thought : Rethinking on Linguistic Relativity

We reviewed the researches on linguistic relativity in color, space and time domains, and rethink the relationship between language and perception to support the idea that language interact with other cognitive processes and shape the perception and thought of human. Language, perception and action are not separated systems, but are closely interconnected and highly interacted to some extent. And language plays a constructive role in object affordances, which do not only require low-level processes of action and motor control, but depend on the language
Geometry-Based Symbolic Approximation for Fast Sequence Matching on Manifolds

In this paper, we consider the problem of fast and efficient indexing techniques for sequences evolving in non-Euclidean spaces. This problem has several applications in the areas of human activity analysis, where there is a need to perform fast search, and recognition in very high dimensional spaces. The problem is made more challenging when representations such as landmarks, contours, and human skeletons etc. are naturally studied in a non-Euclidean setting where even simple operations are much more computationally intensive than their Euclidean counterparts. We propose a geometry and data adaptive symbolic framework that is shown to enable the deployment of fast and accurate algorithms for activity recognition, dynamic texture recognition, motif discovery. Toward this end, we present generalizations of key concepts of piece-wise aggregation and symbolic approximation for the case of non-Euclidean manifolds. We show that one can replace expensive geodesic computations with much faster symbolic computations with little loss of accuracy in activity recognition and discovery applications. The framework is general enough to work across both Euclidean and non-Euclidean spaces, depending on appropriate feature representations without compromising on the ultra-low bandwidth, high speed and high accuracy. The proposed methods are ideally suited for real-time systems and low complexity scenarios.
Automatic Service Discovery of IP Cameras over Wide Area Networks with NAT Traversal

A novel framework for remote service discovery and access of IP cameras with Network address Translation (NAT) traversal is presented in this paper. The proposed protocol, termed STDP (Service Trader Discovery Protocol), is a hybrid combination of Zeroconf and SIP (Session Initial Protocol). The Zeroconf is adopted for the discovery and/or publication of local services; whereas, the SIP is used for the delivery of local services to the remote nodes. In addition, both the SIP-ALG (Application Layer Gateway) and UPnP (Universal Plug and Play)-IGD (Internet Gateway Device) protocols are used for NAT traversal. The proposed framework is well-suited for high mobility applications where the fast deployment and low administration efforts of IP cameras are desired.
Design of Novel Reconfigurable Reflectarrays With Single-Bit Phase Resolution for Ku-Band Satellite Antenna Applications

Reconfigurable reflectarray antennas operating in Ku-band are presented in this paper. First, a novel multilayer unit-cell based on polarization turning concept is proposed to achieve the single-bit phase shift required for reconfigurable reflectarray applications. The principle of the unit-cell is discussed using the current model and the space match condition, along with simulations to corroborate the design and performance criteria. Then, an offset-fed configuration is developed to verify performance of the unit-cell in antenna application, and its polarization transformation property is elaborated. Finally, an offset-fed reflectarray with 10 × 10 elements is developed and fabricated. The dual-polarized antenna utilizes the control code matrices to accomplish a wide angle beam-scanning. A full-wave analysis is applied to the reflectarray, and detailed results are presented and discussed. This electronically steerable reflectarray antenna has significant potential for satellite applications, due to its wide operating band, simple control and beam-scanning capability.
Handling imbalanced datasets: A review

Learning classifiers from imbalanced or skewed datasets is an important topic, arising very often in practice in classification problems. In such problems, almost all the instances are labelled as one class, while far fewer instances are labelled as the other class, usually the more important class. It is obvious that traditional classifiers seeking an accurate performance over a full range of instances are not suitable to deal with imbalanced learning tasks, since they tend to classify all the data into the majority class, which is usually the less important class. This paper describes various techniques for handling imbalance dataset problems. Of course, a single article cannot be a complete review of all the methods and algorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.
From Movement to Thought: Executive Function, Embodied Cognition, and the Cerebellum

This paper posits that the brain evolved for the control of action rather than for the development of cognition per se. We note that the terms commonly used to describe brain–behavior relationships define, and in many ways limit, how we conceptualize and investigate them and may therefore constrain the questions we ask and the utility of the “answers” we generate. Many constructs are so nonspecific and over-inclusive as to be scientifically meaningless. “Executive function” is one such term in common usage. As the construct is increasingly focal in neuroscience research, defining it clearly is critical. We propose a definition that places executive function within a model of continuous sensorimotor interaction with the environment. We posit that control of behavior is the essence of “executive function,” and we explore the evolutionary advantage conferred by being able to anticipate and control behavior with both implicit and explicit mechanisms. We focus on the cerebellum's critical role in these control processes. We then hypothesize about the ways in which procedural (skill) learning contributes to the acquisition of declarative (semantic) knowledge. We hypothesize how these systems might interact in the process of grounding knowledge in sensorimotor anticipation, thereby directly linking movement to thought and “embodied cognition.” We close with a discussion of ways in which the cerebellum instructs frontal systems how to think ahead by providing anticipatory control mechanisms, and we briefly review this model's potential applications.
community2vec: Vector representations of online communities encode semantic relationships

Vector embeddings of words have been shown to encode meaningful semantic relationships that enable solving of complex analogies. This vector embedding concept has been extended successfully to many different domains and in this paper we both create and visualize vector representations of an unstructured collection of online communities based on user participation. Further, we quantitatively and qualitatively show that these representations allow solving of semantically meaningful community analogies and also other more general types of relationships. These results could help improve community recommendation engines and also serve as a tool for sociological studies of community relatedness.
A Taxonomy of Scheduling in General-Purpose Distributed Computing Systems

One measure of usefulness of a general-purpose distributed computing system is the system’s ability to provide a level of performance commensurate to the degree of multiplicity of resources present in the system. Many different approaches and metrics of performance have been proposed in an attempt to achieve this goal in existing systems. In addition, analogous problem formulations exist in other fields such as control theory, operations research, and production management. However, due to the wide variety of approaches to this problem, it is difficult to meaningfully compare different systems since there is no uniform means for qualitatively or quantitatively evaluating them. It is difficult to successfully build upon existing work or identify areas worthy of additional effort without some understanding of the relationships between past efforts. In this paper, a taxonomy of approaches to the resource management problem is presented in an attempt to provide a common terminology and classification mechanism necessary in addressing this problem. The taxonomy, while presented and discussed in terms of distributed scheduling, is also applicable to most types of resource management. As an illustration of the usefulness of the taxonomy an annotated bibliography is given which classifies a large number of distributed scheduling approaches according to the taxonomy.
Improving Preemptive Scheduling with Application-Transparent Checkpointing in Shared Clusters

Modern data center clusters are shifting from dedicated single framework clusters to shared clusters. In such shared environments, cluster schedulers typically utilize preemption by simply killing jobs in order to achieve resource priority and fairness during peak utilization. This can cause significant resource waste and delay job response time.
 In this paper, we propose using suspend-resume mechanisms to mitigate the overhead of preemption in cluster scheduling. Instead of killing preempted jobs or tasks, our approach uses a system level, application-transparent checkpointing mechanism to save the progress of jobs for resumption at a later time when resources are available. To reduce the preemption overhead and improve job response times, our approach uses adaptive preemption to dynamically select appropriate preemption mechanisms (e.g., kill vs. suspend, local vs. remote restore) according to the progress of a task and its suspend-resume overhead. By leveraging fast storage technologies, such as non-volatile memory (NVM), our approach can further reduce the preemption penalty to provide better QoS and resource efficiency. We implement the proposed approach and conduct extensive experiments via Google cluster trace-driven simulations and applications on a Hadoop cluster. The results demonstrate that our approach can significantly reduce the resource and power usage and improve application performance over existing approaches. In particular, our implementation on the next generation Hadoop YARN platform achieves up to a 67% reduction in resource wastage, 30% improvement in overall job response time times and 34% reduction in energy consumption over the current YARN scheduler.
Upgrading Wireless Home Routers for Enabling Large-Scale Deployment of Cloudlets

Smartphones become more and more popular over recent years due to their small form factors. However, such mobile systems are resource-constrained in view of computational power, storage and battery life. Offloading resource-intensive tasks (aka mobile cloud computing) to distant (e.g., cloud computing) or closely located data centers (e.g., cloudlet) overcomes these issues. Especially, cloudlets provide computational power with low latency for responsive applications due to their proximity to mobile users. However, a large-scale deployment of rangerestricted cloudlets is still an open challenge. In this paper, we propose a novel concept for a large-scale deployment of cloudlets by upgrading wireless home routers. Beside router’s native purpose of routing data packets through the network, it can now offer computing resources with low latency and high bandwidth without additional hardware. Proving our concept, we conducted comprehensive benchmark tests against existing concepts. As result, the feasibility of this concept is shown and provide a promising way to large-scale deploy cloudlets in existing infrastructures.
Wideband H-Plane Horn Antenna Based on Ridge Substrate Integrated Waveguide (RSIW)

A substrate integrated waveguide (SIW) H-plane sectoral horn antenna, with significantly improved bandwidth, is presented. A tapered ridge, consisting of a simple arrangement of vias on the side flared wall within the multilayer substrate, is introduced to enlarge the operational bandwidth. A simple feed configuration is suggested to provide the propagating wave for the antenna structure. The proposed antenna is simulated by two well-known full-wave packages, Ansoft HFSS and CST Microwave Studio, based on segregate numerical methods. Close agreement between simulation results is reached. The designed antenna shows good radiation characteristics and low VSWR, lower than 2.5, for the whole frequency range of 18-40 GHz.
Reinforcement learning for the unit commitment problem

In this work we solve the day-ahead unit commitment (UC) problem, by formulating it as a Markov decision process (MDP) and finding a low-cost policy for generation scheduling. We present two reinforcement learning algorithms, and devise a third one. We compare our results to previous work that uses simulated annealing (SA), and show a 27% improvement in operation costs, with running time of 2.5 minutes (compared to 2.5 hours of existing state-of-the-art).
How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing

We propose a new probabilistic graphical model that jointly models the difficulties of questions, the abilities of participants and the correct answers to questions in aptitude testing and crowdsourcing settings. We devise an active learning/adaptive testing scheme based on a greedy minimization of expected model entropy, which allows a more efficient resource allocation by dynamically choosing the next question to be asked based on the previous responses. We present experimental results that confirm the ability of our model to infer the required parameters and demonstrate that the adaptive testing scheme requires fewer questions to obtain the same accuracy as a static test scenario.
Adaptive hypertext navigation based on user goals and context

Hypertext systems allow flexible access to topics of information, but this flexibility has disadvantages. Users often become lost or overwhelmed by choices. An adaptive hypertext system can overcome these disadvantages by recommending information to users based on their specific information needs and preferences. Simple associative matrices provide an effective way of capturing these user preferences. Because the matrices are easily updated, they support the kind of dynamic learning required in an adaptive system. HYPERFLEX, a prototype of an adaptive hypertext system that learns, is described. Informal studies with HYPERFLEX clarify the circumstances under which adaptive systems are likely to be useful, and suggest that HYPERFLEX can reduce time spent searching for information by up to 40%. Moreover, these benefits can be obtained with relatively little effort on the part of hypertext authors or users. The simple models underlying HYPERFLEX's performance may offer a general and useful alternative to more sophisticated modelling techniques. Conditions under which these models, and similar adaptation techniques, might be most useful are discussed.
A Deep Learning Approach to Contract Element Extraction

We explore how deep learning methods can be used for contract element extraction. We show that a BILSTM operating on word, POS tag, and tokenshape embeddings outperforms the linear sliding-window classifiers of our previous work, without any manually written rules. Further improvements are observed by stacking an additional LSTM on top of the BILSTM, or by adding a CRF layer on top of the BILSTM. The stacked BILSTM-LSTM misclassifies fewer tokens, but the BILSTM-CRF combination performs better when methods are evaluated for their ability to extract entire, possibly multi-token contract elements.
Clustering cancer gene expression data: a comparative study

The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context. We present the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Our results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods. The data sets analyzed in this study are available at http://algorithmics.molgen.mpg.de/Supplements/CompCancer/ .
Observations of achievement and motivation in using cloud computing driven CAD: Comparison of college students with high school and vocational high school backgrounds

0747-5632/$ see front matter 2012  A http://dx.doi.org/10.1016/j.chb.2012.08.001 ⇑ Corresponding author. Tel.: +886 02 7734 3347; f E-mail address: joum@ntnu.edu.tw (M. Jou). Cloud computing technology has matured as it has been integrated with every kind of digitalization processes. It offers numerous advantages for data and software sharing, and thus making the management of complex IT systems much simpler. For education in engineering, cloud computing even provides students with versatile and ubiquitous access to software commonly used in the field without having to step into an actual computer lab. Our study analyzed learning attitudes and academic performances induced by the utilization of resources driven by cloud computing technologies. Comparisons were made between college students with high school and vocational high school backgrounds. One hundred and thirtytwo students who took the computer-aided designing (CAD) course participated in the study. Technology Acceptance Model (TAM) was used as the fundamental framework. Open-ended sets of questionnaires were designed to measure academic performance and causal attributions; the results indicated no significant differences in the cognitive domain between the two groups of students, though it is not so in both the psychomotor and the affective domains. College students with vocational high school background appeared to possess higher learning motivation in CAD applications.
Bernoulli Embeddings for Graphs

Just as semantic hashing (Salakhutdinov and Hinton 2009) can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying bias, continuous optimization techniques can be applied to the approximate expected loss. Embeddings optimized in this fashion consistently outperform the quantization of both spectral graph embeddings and various learned real-valued embeddings, on both ranking and pre-ranking tasks for a variety of datasets.
Violent Crime Rate Studies in Philosophical Context : A Destructive Testing Approach to Heat and Southern Culture ofViolence Effects

The logic behind the translation of conceptual hypotheses into testable propositions was illustrated with the heat hypothesis, The destructive testing philosophy was introduced and applied, This consists of first showing that a predicted empirical relation exists, then attempting to break that relation. by adding competitor variables. The key question in destructive testing is "How difficult was it to break the relation?'~ This approach was used to analyze the heat effect on violent crime rates (Study I ) and on White violent crime arrest rates (Study 2) in U.S. cities. One competitor variable was the particular focus of analysis: southern culture of violence. The heat hypothesis was supported by highly significant correlations between the warmth of a city and its violence rate. This heat effect survived multiple destructive tests. Some support for the southern culture effect was also found, but this effect was more easily broken.
Towards Seamless Integration of Data Analytics into Existing HPC Infrastructures

Customers of the High Performance Computing Center (HLRS) tend to execute more complex and data-driven applications, often resulting in large amounts of data of up to 1 Petabyte. The majority of our customers, however, is currently lacking the ability and knowledge to process this amount of data in a timely manner in order to extract meaningful information. We have therefore established a new project in order to support our users with the task of knowledge discovery by means of data analytics. We put the high performance data analytics system, a Cray Urika-GX, into operation to cope with this challenge. In this paper, we give an overview about our project and discuss immanent challenges in bridging the gap between HPC and data analytics in a production environment. The paper concludes with a case study about analyzing log files of a Cray XC40 to detect variations in system performance. We were able to identify successfully so-called aggressor jobs, which reduce significantly the performance of other simultaneously running