nils-herrmann/clean_scirepeval huggingface dataset:
Diagram Rewriting for Orthogonal Matrices: A Study of Critical Peaks

One of the rules, which is similar to the Yang-Baxter equatio n, nvolves a map h : [0, π[ → [0, π[. To study the algebraic properties of h, we use the confluence of critical peaks in our rewrite system, and we introduce parametric diagramsdescribing the calculation of angles of rotations generated b y rewriting. In particular, h satisfies thetetrahedron equation(also called Zamolodchikov equation).
Learning, innovation, and sustainability among mobile phone repairers in Dhaka, Bangladesh

Acts of technology maintenance and repair constitute important and often overlooked moments in the operation of complex interactive systems. They also provide fresh insight on a series of problems -- innovation, learning, and sustainability -- long core to HCI concern. This paper builds on original ethnographic fieldwork in the repair markets of Dhaka, Bangladesh to advance three basic arguments: first, that repair activities in such locations reveal novel and significant forms of craft-based knowledge and innovation; second, that repair work is embedded in local and transnational flows that connect local practices to global networks and institutions; and third, that taking repair work seriously can cast new light on problems of learning and sustainability in the design and operation of complex interactive systems. We conclude with observations that relate our repair-based findings back to problems in interactive systems research and design.
A Review on Cloud Computing Development

Cloud computing can provide critical services for business management, reducing IT costs and maintenance costs of hardware and software effectively. In the meanwhile, it can enable enterprises to access to professional IT solutions with less IT investment. Cloud computing is of great significance for the ICT industry of each country. It is now bringing enormous impact to the human society, especially the business world. In this paper, the basic concepts and the development of cloud computing were introduced, and then the current situation and development of cloud computing research from two aspects of technology and business were illustrated. Finally future trends of cloud computing were discussed.
Optimal Beaconing Control for Epidemic Routing in Delay-Tolerant Networks

Owing to the uncertainty of transmission opportunities between mobile nodes, the routing in delay-tolerant networks (DTNs) exploits the mechanism of opportunistic forwarding. Energy-efficient algorithms and policies for DTN are crucial to maximizing the message delivery probability while reducing the delivery cost. In this contribution, we investigate the problem of energy-efficient optimal beaconing control in a DTN. We model the message dissemination under variable beaconing rate with a continuous-time Markov model. Based on this model, we then formulate the optimization problem of the optimal beaconing control for epidemic routing and obtain the optimal threshold policy from the solution of this optimization problem. Furthermore, through extensive numerical results, we demonstrate that the proposed optimal threshold policy significantly outperforms the static policy with constant beaconing rate in terms of system energy consumption savings.
Advantage of CNTFET characteristics over MOSFET to reduce leakage power

In this paper we compare and justify the advantage of CNTFET devices over MOSFET devices in nanometer regime. Thereafter we have analyzed the effect of chiral vector, and temperature on threshold voltage of CNTFET device. After simulation on HSPICE tool we observed that the high threshold voltage can be achieved at low chiral vector pair. It is also observed that the effect of temperature on threshold voltage of CNTFET is negligibly small. After analysis of channel length variation and their impact on threshold voltage of CNTFET as well as MOSFET devices, we found an anomalous result that the threshold voltage increases with decreasing channel length in CNTFET devices, this is quite contrary to the well known short channel effect. It is observed that at below 10 nm channel length the threshold voltage is increased rapidly in case of CNTFET device whereas in case of MOSFET device the threshold voltage decreases drastically below 10 nm channel length.
Missing data analyses: a hybrid multiple imputation algorithm using Gray System Theory and entropy based on clustering

Researchers and practitioners who use databases usually feel that it is cumbersome in knowledge discovery or application development due to the issue of missing data. Though some approaches can work with a certain rate of incomplete data, a large portion of them demands high data quality with completeness. Therefore, a great number of strategies have been designed to process missingness particularly in the way of imputation. Single imputation methods initially succeeded in predicting the missing values for specific types of distributions. Yet, the multiple imputation algorithms have maintained prevalent because of the further promotion of validity by minimizing the bias iteratively and less requirement on prior knowledge to the distributions. This article carefully reviews the state of the art and proposes a hybrid missing data completion method named Multiple Imputation using Gray-system-theory and Entropy based on Clustering (MIGEC). Firstly, the non-missing data instances are separated into several clusters. Then, the imputed value is obtained after multiple calculations by utilizing the information entropy of the proximal category for each incomplete instance in terms of the similarity metric based on Gray System Theory (GST). Experimental results on University of California Irvine (UCI) datasets illustrate the superiority of MIGEC to other current achievements on accuracy for either numeric or categorical attributes under different missing mechanisms. Further discussion on real aerospace datasets states MIGEC is also applicable for the specific area with both more precise inference and faster convergence than other multiple imputation methods in general.
Autonomous Aerobatic Maneuvering of Miniature Helicopters by Vladislav Gavrilets

In this thesis, I present an experimentally proven control methodology for the autonomous execution of aerobatic maneuvers with small-scale helicopters, and a low-order dynamic model which adequately describes a miniature helicopter in a wide range of flight conditions, including aerobatics. The control laws consist of steady-state trim trajectory controllers, used prior to, and upon exit from the maneuvers; and a maneuver execution logic inspired by human pilot strategies. In order to test the control laws, a miniature helicopter was outfitted with a custom digital avionics system, and a hardware-in-the-loop simulation was developed. The logic was tested with several aerobatic maneuvers and maneuver sequences, which demonstrated smooth maneuver entry, automatic recovery to a steady-state trim trajectory, and robustness of the trim-trajectory control system toward measurement and modeling errors. Based on these results, I further propose a simplified hybrid model for a helicopter under such closed loop control. The model can be utilized in the development of computationally tractable motion-planning algorithms for agile vehicles.   Associate Professor of Aeronautics and Astronautics Thesis Supervisor: Munther Dahleh Title: Professor of Electrical Engineering Thesis Supervisor: David Vos Title: Chief Technology Officer, Athena Technologies Thesis Supervisor: Carlos Cesnik Title: Associate Professor of Aerospace Engineering, University of Michigan
The Function of Fiction is the Abstraction and Simulation of Social Experience.

Fiction literature has largely been ignored by psychology researchers because its only function seems to be entertainment, with no connection to empirical validity. We argue that literary narratives have a more important purpose. They offer models or simulations of the social world via abstraction, simplification, and compression. Narrative fiction also creates a deep and immersive simulative experience of social interactions for readers. This simulation facilitates the communication and understanding of social information and makes it more compelling, achieving a form of learning through experience. Engaging in the simulative experiences of fiction literature can facilitate the understanding of others who are different from ourselves and can augment our capacity for empathy and social inference.
HypeBIOS : Enforcing VM Isolation with Minimized and Decomposed Cloud TCB

Virtualization has made cloud computing a popular trend by virtue of its elastic “data anywhere” and “computing anywhere”. However, traditional virtualization architectures usually have three drawbacks: 1) being vulnerable to many known attacks targeting at the large software stacks; 2) endowing too much power to cloud providers, who can fully control the Virtual Machine Monitor (VMM) and the management Virtual Machine (VM); and 3) lacking trusted isolation between VMs. In this paper, we propose HypeBIOS to provide isolation of VMs based on a verifiable thin virtualization Trusted Computing Base (TCB). Unlike the traditional architectures, HypeBIOS excludes unnecessary initialization components in the boot chain and shifts the control VM (management VM) out of the TCB. The reduced TCB is further decomposed into two layers. The master layer works in the System Management Mode (SMM) and contains crucial handlers. The slave layer resides in the legacy virtualization host mode to cooperate with the master layer. We build a prototype of HypeBIOS on the x86 platform. The experiments show that HypeBIOS only introduces moderate overhead.
Psychological aspects of natural language. use: our words, our selves.

The words people use in their daily lives can reveal important aspects of their social and psychological worlds. With advances in computer technology, text analysis allows researchers to reliably and quickly assess features of what people say as well as subtleties in their linguistic styles. Following a brief review of several text analysis programs, we summarize some of the evidence that links natural word use to personality, social and situational fluctuations, and psychological interventions. Of particular interest are findings that point to the psychological value of studying particles-parts of speech that include pronouns, articles, prepositions, conjunctives, and auxiliary verbs. Particles, which serve as the glue that holds nouns and regular verbs together, can serve as markers of emotional state, social identity, and cognitive styles.
A Fractal-based Algorithm of Emotion Recognition from EEG using Arousal-Valence Model

Emotion recognition from EEG could be used in many applications as it allows us to know the “inner” emotion regardless of the human facial expression, behaviour, or verbal communication. In this paper, we proposed and described a novel fractal dimension (FD) based emotion recognition algorithm using an Arousal-Valence emotion model. FD values calculated from the EEG signal recorded from the corresponding brain lobes are mapped to the 2D emotion model. The proposed algorithm allows us to recognize emotions that could be defined by arousal and valence levels. Only 3 electrodes are needed for the emotions recognition. Higuchi and box-counting algorithms were used for the EEG analysis and comparison. Support Vector Machine classifier was applied for arousal and valence levels recognition. The proposed method is a subject dependent one. Experiments with music and sound stimuli to induce human emotions were realized. Sound clips from the International Affective Digitized Sounds (IADS) database were used in the experiments.
Character-Level neural networks for short text classification

Since short text is characterized of the short length, sparse features and strong context dependency, the traditional models have a limited precision. Motivated by this, this article offers an empirical exploration on a character-level model which implements a combination of convolutional neural network(CNN) and recurrent neural networks(RNN) for short text classification. Including the highway networks framework so that we can address the difficult of training and improve the accuracy of classification. The evaluations on several datasets showed that our proposed model outperforms the standard CNN and traditional models on short text classification mission.
A New Mechanism of Hook Formation during Continuous Casting of Ultra-Low-Carbon Steel Slabs

The initial stages of solidification near the meniscus during continuous casting of steel slabs involve many complex inter-related transient phenomena, which cause periodic oscillation marks (OMs), subsurface hooks, and related surface defects. This article presents a detailed mechanism for the formation of curved hooks and their associated OMs, based on a careful analysis of numerous specially etched samples from ultra-low-carbon steel slabs combined with previous measurements, observations, and theoretical modeling results. It is demonstrated that hooks form by solidification and dendritic growth at the liquid meniscus during the negative strip time. Oscillation marks form when molten steel overflows over the curved hook and solidifies by nucleation of undercooled liquid. The mechanism has been justified by its explanation of several plant observations, including the variability of hook and OM characteristics under different casting conditions, and the relationships with mold powder consumption and negative/positive strip times.
Dridex: Analysis of the traffic and automatic generation of IOCs

In this paper we present a framework that generates network Indicators of Compromise (IOC) automatically from a malware sample after dynamic runtime analysis. The framework addresses the limitations of manual Indicator of Compromise generation and utilises sandbox environment to perform the malware analysis in. We focus on the generation of network based IOCs from captured traffic files (PCAPs) generated by the dynamic malware analysis. The Cuckoo Sandbox environment is used for the analysis and the setup is described in detail. Accordingly, we discuss the concept of IOCs and the popular formats used as there is currently no standard. As an example of how the proof-of-concept framework can be used, we chose 100 Dridex malware samples and evaluated the traffic and showed what can be used for the generation of network-based IOCs. Results of our system confirm that we can create IOCs from dynamic malware analysis and avoid the legitimate background traffic originating from the sandbox system. We also briefly discuss the sharing of, and application of the generated IOCs and the number of systems that can be used to share them. Lastly we discuss how they can be useful in combating cyber threats.
Measures of Fit for Logistic Regression

One of the most common questions about logistic regression is “How do I know if my model fits the data?” There are many approaches to answering this question, but they generally fall into two categories: measures of predictive power (like R-square) and goodness of fit tests (like the Pearson chi-square). This presentation looks first at R-square measures, arguing that the optional R-squares reported by PROC LOGISTIC might not be optimal. Measures proposed by McFadden and Tjur appear to be more attractive. As for goodness of fit, the popular Hosmer and Lemeshow test is shown to have some serious problems. Several alternatives are considered.
Micro bubble fluidics by EWOD and ultrasonic excitation for micro bubble tweezers

Recently, we envisioned so called micro bubble tweezers where EWOD (electrowetting-on-dielectric) actuated bubbles can manipulate micro objects such as biological cells by pushing or pulling them. Besides, oscillating (shrinking and expanding) bubbles in the presence of ultrasonic wave act as a to deliver drugs and molecules into the cells. In this paper, as a great stride in our quest for micro bubble tweezers, we present (1) full realization of two critical bubble operations (generating of bubbles in an on-chip and on-demand manner and splitting of single bubbles) and (2) two possible applications of mobile bubbles oscillating under acoustic excitation (a mobile vortex generator and micro particle carrier).
Detection of rare events within industrial datasets by means of data resampling and specific algorithms

The paper deals with the problem of the detection of rare patterns in unbalanced datasets coming from the industrial world. Such kind of patterns usually correspond to not frequent but very relevant events, such as the occurrence of product defects and machine faults. Within this work several approaches have been tested for the development of classifiers whose performance are able to meet the industrial requirements, i.e. a high rate of recognition of unfrequent patterns. Considered the unbalanced nature of the available datasets, most known techniques used for dealing with this kind of databases (i.e. resampling techniques and specific algorithms) have been investigated and assessed, subsequently the most promising ones have been combined in order to exploit their advantages. This latter combination led to satisfactory results which make the developed classifier usable in the industrial field.
Nachdiplom Lecture: Statistics Meets Optimization

A random vector xi ∈ R is said to be drawn from a spiked covariance model if it can written in the form xi = F ∗ √ Γξi + wi where F ∗ ∈ Rd×r is a fixed matrix with orthonormal columns; Γ = diagγ1, . . . , γr} is a diagonal matrix with γ1 ≥ γ2 ≥ · · · ≥ γr > 0; ξi ∈ R is a zero-mean random vector with identity covariance, and wi is a zero-mean random vector, independent of ξi, and with identity covariance matrix Id. Thus, the random vector xi ∈ R is zero-mean with a covariance matrix of the form Σ = (F ∗ √ Γ⊗ F ∗ √ Γ) + Id×d.
Nonlinear dimension reduction for EEG-based epileptic seizure detection

Approximately 0.1 percent of epileptic patients die from unexpected deaths. In general, for intractable seizures, it is crucial to have an algorithm to accurately and automatically detect the seizures and notify care-givers to assist patients. EEG signals are known as definitive diagnosis of seizure events. In this work, we utilize the frequency domain features (normalized in-band power spectral density) for the EEG channels. We applied a nonlinear data-embedding technique based on stochastic neighbor distance metric to capture the relationships among data elements in high dimension and improve the accuracy of seizure detection. This proposed data embedding technique not only makes it possible to visualize data in two or three dimensions, but also tackles the inherent difficulties regarding high dimensional data classification such as time complexity and memory requirement. We also applied a patient specific KNN classification to detect seizure and non-seizure events. The results indicate that our nonlinear technique provides significantly better visualization and classification efficiency (F-measure greater than 87%) compared to conventional dimension reduction approaches.
Formal Specification with the Java Modeling Language

This text is a general, self contained, and tool independent introduction into the Java Modeling Language, JML. It appears in a book about the KeY approach and tool, because JML is the dominating starting point of KeY style Java verification. However, this chapter does not depend on KeY, nor any other specific tool, nor on any specific verification methodology. With this text, the authors aim to provide, for the time being, the definitive, general JML tutorial. Other chapters in this book discuss the particular usage of JML in KeY style verification.1 In this chapter, however, we only refer to KeY in very few places, without relying on it. This introduction is written for all readers with an interest in formal specification of software in general, and anyone who wants to learn about the JML approach to specification in particular. A preliminary version of this chapter appeared as a technical report [Huisman et al., 2014].
Recent Advancements in Retinal Vessel Segmentation

Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.
Comparison of linear, nonlinear, and feature selection methods for EEG signal classification

The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.
Importance of a Search Strategy in Neural Dialogue Modelling

We investigate the impact of search strategies in neural dialogue modelling. We first compare two standard search algorithms, greedy and beam search, as well as our newly proposed iterative beam search. We further study the impact of incorporating a separate scoring function for selecting from candidate responses generated from one of these search algorithms. We evaluate these strategies based on full conversations with humans using model-based Bayesian calibration to address annotator bias using the PersonaChat dataset. These conversations are analyzed using two automatic metrics: log-probabilities assigned by the model and utterance diversity. Our experiments reveal that better search algorithms lead to higher rated conversations. However, incorporating a high quality scorer did not further improve performance, showing that effectively using a scoring mechanism is an open question.