nils-herrmann/clean_scirepeval huggingface dataset:
c-Through: part-time optics in data centers

Data-intensive applications that operate on large volumes of data have motivated a fresh look at the design of data center networks. The first wave of proposals focused on designing pure packet-switched networks that provide full bisection bandwidth. However, these proposals significantly increase network complexity in terms of the number of links and switches required and the restricted rules to wire them up. On the other hand, optical circuit switching technology holds a very large bandwidth advantage over packet switching technology. This fact motivates us to explore how optical circuit switching technology could benefit a data center network. In particular, we propose a hybrid packet and circuit switched data center network architecture (or HyPaC for short) which augments the traditional hierarchy of packet switches with a high speed, low complexity, rack-to-rack optical circuit-switched network to supply high bandwidth to applications. We discuss the fundamental requirements of this hybrid architecture and their design options. To demonstrate the potential benefits of the hybrid architecture, we have built a prototype system called c-Through. c-Through represents a design point where the responsibility for traffic demand estimation and traffic demultiplexing resides in end hosts, making it compatible with existing packet switches. Our emulation experiments show that the hybrid architecture can provide large benefits to unmodified popular data center applications at a modest scale. Furthermore, our experimental experience provides useful insights on the applicability of the hybrid architecture across a range of deployment scenarios.
Bayesian Statistics in Software Engineering: Practical Guide and Case Studies

Statistics comes in two flavors: frequentist and Bayesian. For a variety of historical and technical reasons, frequentist statistics has dominated data analysis in the past; but Bayesian statistics is making a comeback at the forefront of science. In this paper, we give a practical overview of Bayesian statistics for the kinds of analyses that are common in empirical software engineering, and illustrate its main advantages over frequentist statistics. We also practically demonstrate Bayesian statistics by applying it to empirical data from previous research investigating the performance of programming languages, agile vs. structured development processes, and random testing of object-oriented programs. In addition to being three case studies of how Bayesian analysis can be applied in practice, the analyses provide new insights beyond the results in the original publications (which used frequentist statistics), thus showing the practical value brought by Bayesian statistics.
Control synthesis and verification for a perching UAV using LQR-Trees

We consider the problem of designing a feedback controller for a fixed-wing unmanned aerial vehicle (UAV) to execute a dynamic post-stall maneuver and land accurately on a perch. This controller must deal with the nonlinearity of the post-stall dynamics and inherently limited control authority of the actuators. Using a recently proposed algorithm called “LQR-Trees”, here we demonstrate that we can generate formal guarantees (using time-varying Lyapunov functions) which verify that a controller generated with trajectory optimization and local linear feedback can achieve the desired perching tolerance from a range of initial conditions. These locally valid controllers are then combined into an library of controllers which cover a space of initial conditions with verified controllers. We describe some of the details of the implementation, which requires formulating and solving large semidefinite programs, and compare the verified regions with numerical samples of the system's true performance. This perching problem represents the most high-dimensional and numerically challenging application of the LQR Trees algorithm reported to date.
Hilbert-Huang transform based physiological signals analysis for emotion recognition

This paper presents a feature extraction technique based on the Hilbert-Huang Transform (HHT) method for emotion recognition from physiological signals. Four kinds of physiological signals were used for analysis: electrocardiogram (ECG), electromyogram (EMG), skin conductivity (SC) and respiration changes (RSP). Each signal is decomposed into a finite set of AM-FM mono components (fission process) by the Empirical Mode Decomposition (EMD) which is the key part of the HHT. The information components of interest are then combined to create feature vectors (fusion process) for the next classification stage. In addition, classification is performed by using Support Vector Machines (SVM). The classification scores show that HHT based methods outperform traditional statistical techniques and provide a promising framework for both analysis and recognition of physiological signals in emotion recognition.
Lichens as a potential natural source of bioactive compounds: a review

Biological activity of material whether known in folk medicine or observed in planned screening program has been the starting point in the drug research. The general pattern is the isolation of active principles, elucidation their structures, followed by attempts for modulation of its activity potential by chemical modification. Lichens are valuable plant resources and are used as medicine, food, fodder, perfume, spice, dyes and for miscellaneous purposes throughout the world. Lichens are well known for the diversity of secondary metabolites that they produce. Compounds isolated from various lichen species have been reported to display diverse biological activities. Here we review the medicinal efficacy of lichen substances, which intends to explore the pharmaceutical potential of lichen substances.
A Students Attendance System Using QR Code

Smartphones are becoming more preferred companions to users than desktops or notebooks. Knowing that smartphones are most popular with users at the age around 26, using smartphones to speed up the process of taking attendance by university instructors would save lecturing time and hence enhance the educational process. This paper proposes a system that is based on a QR code, which is being displayed for students during or at the beginning of each lecture. The students will need to scan the code in order to confirm their attendance. The paper explains the high level implementation details of the proposed system. It also discusses how the system verifies student identity to eliminate false registrations. Mobile Computing; Attendance System; Educational System; GPS
Classification of Android apps and malware using deep neural networks

Malware targeting mobile devices is a pervasive problem in modern life. The detection of malware is essentially a software classification problem based on information gathered from program analysis. We focus on classification of Android applications using system API-call sequences and investigate the effectiveness of Deep Neural Networks (DNNs) for such purpose. The ability of DNNs to learn complex and flexible features may lead to timely and effective detection of malware. We design a Convolutional Neural Network (CNN) for sequence classification and conduct a set of experiments on malware detection and categorization of software into functionality groups to test and compare our CNN with classifications by recurrent neural network (LSTM) and other n-gram based methods. Both CNN and LSTM significantly outperformed n-gram based methods. Surprisingly, the performance of our CNN is also much better than that of the LSTM, which is considered a natural choice for sequential data.
Supply Chain Contracting Under Competition : Bilateral Bargaining vs . Stackelberg

W e analyze contracting behaviors in a two-tier supply chain system consisting of competing manufacturers and competing retailers. We contrast the contracting outcome of a Stackelberg game, in which the manufacturers offer take-itor-leave-it contracts to the retailers, with that of a bargaining game, in which the firms bilaterally negotiate contract terms via a process of alternating offers. The manufacturers in the Stackelberg game possess a Stackelberg-leader advantage in that the retailers are not entitled to make counteroffers. Our analysis suggests that whether this advantage would benefit the manufacturers depends on the contractual form. With simple contracts such as wholesale-price contracts, which generally do not allow one party to fully extract the trade surplus, the Stackelberg game replicates the boundary case of the bargaining game with the manufacturers possessing all the bargaining power. In contrast, with sophisticated contracts such as two-part tariffs, which enable full surplus extraction, the two games lead to distinct outcomes. We further show that the game structure being Stackelberg or bargaining critically affects firms’ preferences over contract types and thus their equilibrium contract choices. These observations suggest that the Stackelberg game may not be a sufficient device to predict contracting behaviors in reality where bargaining is commonly observed.
Toward Self-Growing Soft Robots Inspired by Plant Roots and Based on Additive Manufacturing Technologies

In this article, we present a novel class of robots that are able to move by growing and building their own structure. In particular, taking inspiration by the growing abilities of plant roots, we designed and developed a plant root-like robot that creates its body through an additive manufacturing process. Each robotic root includes a tubular body, a growing head, and a sensorized tip that commands the robot behaviors. The growing head is a customized three-dimensional (3D) printer-like system that builds the tubular body of the root in the format of circular layers by fusing and depositing a thermoplastic material (i.e., polylactic acid [PLA] filament) at the tip level, thus obtaining movement by growing. A differential deposition of the material can create an asymmetry that results in curvature of the built structure, providing the possibility of root bending to follow or escape from a stimulus or to reach a desired point in space. Taking advantage of these characteristics, the robotic roots are able to move inside a medium by growing their body. In this article, we describe the design of the growing robot together with the modeling of the deposition process and the description of the implemented growing movement strategy. Experiments were performed in air and in an artificial medium to verify the functionalities and to evaluate the robot performance. The results showed that the robotic root, with a diameter of 50 mm, grows with a speed of up to 4 mm/min, overcoming medium pressure of up to 37 kPa (i.e., it is able to lift up to 6 kg) and bending with a minimum radius of 100 mm.
Boolean satisfiability with transitivity constraints

We consider a variant of the Boolean satisfiability problem where a subset ϵ of the propositional variables appearing in formula <i>F</i><inf>sat</inf> encode a symmetric, transitive, binary relation over <i>N</i> elements. Each of these <i>relational</i> variables, <i>e</i><inf><i>i,j</i></inf>, for 1 ≤ <i>i</i> < <i>j</i> ≤ <i>N</i>, expresses whether or not the relation holds between elements <i>i</i> and <i>j</i>. The task is to either find a satisfying assignment to <i>F</i><inf>sat</inf> that also satisfies all transitivity constraints over the relational variables (e.g., <i>e</i><inf>1,2</inf> ∧ <i>e</i><inf>2,3</inf> ⇒ <i>e</i><inf>1,3</inf>), or to prove that no such assignment exists. Solving this satisfiability problem is the final and most difficult step in our decision procedure for a logic of equality with uninterpreted functions. This procedure forms the core of our tool for verifying pipelined microprocessors.To use a conventional Boolean satisfiability checker, we augment the set of clauses expressing <i>F</i><inf>sat</inf> with clauses expressing the transitivity constraints. We consider methods to reduce the number of such clauses based on the sparse structure of the relational variables.To use Ordered Binary Decision Diagrams (OBDDs), we show that for some sets ϵ, the OBDD representation of the transitivity constraints has exponential size for all possible variable orderings. By considering only those relational variables that occur in the OBDD representation of <i>F</i><inf>sat</inf>, our experiments show that we can readily construct an OBDD representation of the relevant transitivity constraints and thus solve the constrained satisfiability problem.
AU ball on plate balancing robot

The ball on plate system is the extension of traditional ball on beam balancing problem in control theory. In this paper the implementation of a proportional-integral-derivative controller (PID controller) to balance a ball on a plate has been demonstrated. To increase the system response time and accuracy multiple controllers are piped through a simple custom serial protocol to boost the processing power, and overall performance. A single HD camera module is used as a sensor to detect the ball's position and two RC servo motors are used to tilt the plate to balance the ball. The result shows that by implementing multiple PUs (Processing Units) redundancy and high resolution can be achieved in real-time control systems.
HYPOSPADIAS. ANATOMY, EMBRYOLOGY, AND RECONSTRUCTIVE TECHNIQUES

Hypospadias is one of the most common congenital anomalies that can be treated with surgical reconstruction. The etiology in the majority of cases of hypospadias remains elusive. Androgens are clearly critical for penile development; however, defects in androgen metabolism and/or the androgen receptor explain only a small subset of patients with hypospadias. This paper reviews the present strategies to understanding the etiology of hypospadias. This is followed by a review of the anatomy of the male and female genitalia with an emphasis on reconstructive implications. Finally, current techniques for hypospadias repair are reviewed.
Improving video performance in VNC under high latency conditions

Virtual Network Computing, or VNC, is a popular thin client application used to access files and applications on remote computers. However, VNC can suffer from significant losses in throughput when there is high latency between the client and server. These losses become especially apparent in the case of video, where updates are both large and frequent. In this work, we present a Message Accelerator proxy for VNC. This simple but highly effective solution mitigates high latency network effects for video performance while maintaining the advantages of a client-pull system. By operating near/on the server, it can send updates to the client at a rate corresponding to proxyserver interactions which are faster than client-server interactions. When testing using video, our Message Accelerator design results in frame rates an order of magnitude higher than plain VNC when running under high latency conditions.
Progress In Incremental Machine Learning

We will describe recent developments in a system for machine learning that we’ve been working on for some time (Sol 86, Sol 89). It is meant to be a “Scientist’s Assistant” of great power and versatility in many areas of science and mathematics. It differs from other ambitious work in this area in that we are not so much interested in knowledge itself, as we are in how it is acquired how machines may learn. To start off, the system will learn to solve two very general kinds of problems. Most, but perhaps not all problems in science and engineering are of these two kinds. The first kind is Function Inversion. These are the P and NP problems of computational complexity theory. They include theorem proving, solution of equations, symbolic integration, etc. The second kind of problem is Time Limited Optimization. Inductive inference of all kinds, surface reconstruction, and image restoration are a few examples of this kind of problem. Designing an automobile in 6 months satisfying certain specifications and having minimal cost, is
Talking about the Moving Image: A Declarative Model for Image Schema Based Embodied Perception Grounding and Language Generation

We present a general theory and corresponding declarative model for the embodied grounding and natural language based analytical summarisation of dynamic visuo-spatial imagery. The declarative model —ecompassing spatiolinguistic abstractions, image schemas, and a spatio-temporal feature based language generator— is modularly implemented within Constraint Logic Programming (CLP). The implemented model is such that primitives of the theory, e.g., pertaining to space and motion, image schemata, are available as first-class objects with deep semantics suited for inference and query. We demonstrate the model with select examples broadly motivated by areas such as film, design, geography, smart environments where analytical natural language based externalisations of the moving image are central from the viewpoint of human interaction, evidence-based qualitative analysis, and sensemaking.
SDN/NFV-based end to end network slicing for 5G multi-tenant networks

The concept of network slicing opens the possibilities to address the complex requirements of multi-tenancy in 5G. To this end, SDN/NFV can act as technology enabler. This paper presents a centralised and dynamic approach for creating and provisioning network slices for virtual network operators' consumption to offer services to their end customers, focusing on an SDN wireless backhaul use case. We demonstrate our approach for dynamic end-to-end slice and service provisioning in a testbed.
Two Methods For Wild Variational Inference

Variational inference provides a powerful tool for approximate probabilistic inference on complex, structured models. Typical variational inference methods, however, require to use inference networks with computationally tractable probability density functions. This largely limits the design and implementation of variational inference methods. We consider wild variational inference methods that do not require tractable density functions on the inference networks, and hence can be applied in more challenging cases. As an example of application, we treat stochastic gradient Langevin dynamics (SGLD) as an inference network, and use our methods to automatically adjust the step sizes of SGLD, yielding significant improvement over the hand-designed step size schemes.
Social Network De-anonymization with Overlapping Communities: Analysis, Algorithm and Experiments

The advent of social networks poses severe threats on user privacy as adversaries can de-anonymize users' identities by mapping them to correlated cross-domain networks. Without ground-truth mapping, prior literature proposes various cost functions in hope of measuring the quality of mappings. However, there is generally a lacking of rationale behind the cost functions, whose minimizer also remains algorithmically unknown. We jointly tackle above concerns under a more practical social network model parameterized by overlapping communities, which, neglected by prior art, can serve as side information for de-anonymization. Regarding the unavailability of ground-truth mapping to adversaries, by virtue of the Minimum Mean Square Error (MMSE), our first contribution is a well-justified cost function minimizing the expected number of mismatched users over all possible true mappings. While proving the NP-hardness of minimizing MMSE, we validly transform it into the weighted-edge matching problem (WEMP), which, as disclosed theoretically, resolves the tension between optimality and complexity: (i) WEMP asymptotically returns a negligible mapping error in large network size under mild conditions facilitated by higher overlapping strength; (ii) WEMP can be algorithmically characterized via the convex-concave based de-anonymization algorithm (CBDA), finding the optimum of WEMP. Extensive experiments further confirm the effectiveness of CBDA under overlapping communities, in terms of averagely 90% re-identified users in the rare true cross-domain co-author networks when communities overlap densely, and roughly 70% enhanced reidentification ratio compared to non-overlapping cases.
Pairwise meta-rules for better meta-learning-based algorithm ranking

In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate Ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset.
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Artificial and Intercellular Lipid Nanotubes

In this thesis work, we investigated the formation and shape dynamics of artificially created lipid nanotubes, and explored the on-demand generation of intercellular nanotubes. Methods and techniques to produce networks of phospholipid vesicles and lipid nanotubes, as well as networks of biological cells and lipid nanotubes, are described in this thesis. We also describe means to transport membrane material and network-internalized molecules and particles through the nanotubes. The capabilities, limitations and experimental requirements of these methods were analyzed. Starting from simple networks interconnecting two giant liposomes, freefloating floating lipid nanotubes were produced by electrofission. After releasing a lipid nanotube of several hundreds of micrometers in length from its suspension points, shape transformations through different stages were observed and characterized, and the influence of membrane-embedded molecules on these transformations were established. A method was developed for on-demand generation of nanotubes between biological cells by means of micromanipulation and microinjection techniques originally developed for vesicle-nanotube networks. The new experimental model structures can greatly facilitate fundamental studies of cell-to-cell communication, the exchange of cell constituents and components, and the dynamics of biochemical reactions in native cell environments. Lastly, we investigated the cells' capability to probe free space in its immediate vicinity. We designed a micropatterning strategy for selective cell immobilization and directed cell growth. A new microfabrication protocol for high-resolution patterngeneration of Teflon AF was developed for that purpose. The new surfaces enabled cell growth in specific orientations to each other, which allowed us to determine the distance-requirements for tunneling nanotube-like conduit formation. The research results collected in this thesis represent a systematic approach towards on-demand generation and application of intercellular lipid nanotube connections, which is of importance for the understanding of, and eventually, full control over cellular communication networks.