nils-herrmann/clean_scirepeval huggingface dataset:
Using Reinforcement Learning for Autonomic Resource Allocation in Clouds: Towards a Fully Automated Workflow

Dynamic and appropriate resource dimensioning is a crucial issue in cloud computing. As applications go more and more 24/7, online policies must be sought to balance performance with the cost of allocated virtual machines. Most industrial approaches to date use ad hoc manual policies, such as thresholdbased ones. Providing good thresholds proved to be tricky and hard to automatize to fit every application requirement. Research is being done to apply automatic decision-making approaches, such as reinforcement learning. Yet, they face a lot of problems to go to the field: having good policies in the early phases of learning, time for the learning to converge to an optimal policy and coping with changes in the application performance behavior over time. In this paper, we propose to deal with these problems using appropriate initialization for the early stages as well as convergence speedups applied throughout the learning phases and we present our first experimental results for these. We also introduce a performance model change detection on which we are currently working to complete the learning process management. Even though some of these proposals were known in the reinforcement learning field, the key contribution of this paper is to integrate them in a real cloud controller and to program them as an automated workflow. Cloud computing; virtual machine allocation; reinforcement learning; autonomic computing.
Dermoscopy of lymphomatoid papulosis.

L YMPHOMATOID PAPULOSIS (LYP) IS CHARACterized by papular, papulonecrotic, and/or nodular skin lesions at different stages of development, predominantly on the trunk and limbs. We prospectively evaluated 8 patients using dermoscopy and histopathologic correlations. The aim of this pilot study was to define dermoscopic criteria associated with the different stages of the disease (erythematous papule, ulcerated and/or necrotic papule, and cicatricial lesion). The dermoscopic pattern was similar in all patients and varied with the different stages of the disease. The initial inflammatory papular lesion (Figure 1A) showed essentially a vascular pattern of tortuous irregular vessels radiating from the center to the periphery of the lesion . A white structureless area was seen around the vessels (Figure 1B, inset). On histopathologic evaluation, dilated vessels were observed on the upper dermis (Figure 1C and D, arrow) among the tumoral dense infiltrate of large CD30 cells (for type A LyP). Intravascular thrombosis was noted on the small vessels, usually at the beginning of the ischemic process (Figure 1E, arrow). On more mature lesions (hyperkeratotic papules), a whitish structureless area was seen at the center of the papule (Figure 2A). The vessel pattern was still observed, but it spared the middle of the lesion  This central pattern was correlated with the onset of a hyperkeratosis on histologic samples and the onset of fibrinoid necrosis (Figure 2C, arrow) associated with intravascular thrombosis on the upper dermis. In the third stage, the necrotic ulceration replaced the whitish structureless area (Figure 3A). We observed centrally a brown-gray structureless area corresponding to the fibrinocruoric material; the vessel pattern was strictly limited to the extreme periphery of the lesion (Figure 3B). On histopathologic analysis , a crust was seen on the center of the lesion, while the tumoral infiltrate and vessels were only seen deeper and at the periphery. Finally, in the cicatricial phase (Figure 4A), we observed no more residual vessel pattern; only a browngray structureless mark suggesting postinflammatory pigmentation persisted . Histopathologically, findings corresponded to the dermal fibrosis noted in scarring LyP but without residual tumoral infiltrate (Figure 4C). As LyP lesions mature, their histopathologic changes can be well correlated with morphologic features seen on dermoscopy.
Ripple-Based Control of Switching Regulators—An Overview

Switching regulators with ripple-based control (i.e., ¿ripple regulators¿) are conceptually simple, have fast transient responses to both line and load perturbations, and some versions operate with a switching frequency that is proportional to the load current under the discontinuous conduction mode. These characteristics make the ripple regulators well-suited, especially for power management applications in computers and portable electronic devices. Ripple regulators also have some drawbacks, including (in some versions) a poorly defined switching frequency, noise-induced jitter, inadequate dc regulation, and a tendency for fast-scale instability. This paper presents an overview of the various ripple-based control techniques, discusses their merits and limitations, and introduces techniques for reducing the noise sensitivity and the sensitivity to capacitor parameters, improving the frequency stability and the dc regulation, and avoiding fast-scale instability.
Sponge: portable stream programming on graphics engines

Graphics processing units (GPUs) provide a low cost platform for accelerating high performance computations. The introduction of new programming languages, such as CUDA and OpenCL, makes GPU programming attractive to a wide variety of programmers. However, programming GPUs is still a cumbersome task for two primary reasons: tedious performance optimizations and lack of portability. First, optimizing an algorithm for a specific GPU is a time-consuming task that requires a thorough understanding of both the algorithm and the underlying hardware. Unoptimized CUDA programs typically only achieve a small fraction of the peak GPU performance. Second, GPU code lacks efficient portability as code written for one GPU can be inefficient when executed on another. Moving code from one GPU to another while maintaining the desired performance is a non-trivial task often requiring significant modifications to account for the hardware differences. In this work, we propose Sponge, a compilation framework for GPUs using synchronous data flow streaming languages. Sponge is capable of performing a wide variety of optimizations to generate efficient code for graphics engines. Sponge alleviates the problems associated with current GPU programming methods by providing portability across different generations of GPUs and CPUs, and a better abstraction of the hardware details, such as the memory hierarchy and threading model. Using streaming, we provide a write-once software paradigm and rely on the compiler to automatically create optimized CUDA code for a wide variety of GPU targets. Sponge's compiler optimizations improve the performance of the baseline CUDA implementations by an average of 3.2x.
Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System

A decision tree is a outstanding method for the data mining. In intrusion detection systems (IDSs), the data mining techniques are useful to detect the attack especially in anomaly detection. For the decision tree, we use the DARPA 98 Lincoln Laboratory Evaluation Data Set (DARPA Set) as the training data set and the testing data set. KDD 99 Intrusion Detection data set is also based on the DARPA Set. These three entities are widely used in IDSs. Hence, we describe the total process to generate the decision tree learned from the DARPA Sets. In this paper, we also evaluate the effective value of the decision tree as the data mining method for the IDSs, and the DARPA Set as the learning data set for the decision trees.
A Representation Language Language

The field of AI is strewn with knowledge representation languages. The language designer typically has one particular application domain in mind: as subsequent types of applications are tried, what had originally been US&II j2urure.y become undesirable limitations, and the language is overhauled or scrapped. One remedy to this bleak cycle might be to construct a representational scheme whose domain is the field of representational languages itself. Toward this end, we designed and implemented RLL, a frame-based Representation Languange Language. The components of representation languages in general (such ti slots and inheritance mechanisms) and of RLL itself are encoded declaratively as frames. Modifvinn these frames can change the of the RLL environment. semantics of RLL, by altering the
Experimental results for artificial intelligence-based self-organized 5G networks

Until now, mobile networks are still managed in a manual and semi-automatic manner, which are costly and time-consuming. For the forthcoming Fifth Generation (5G) system, its large-scale, heterogeneous, software-defined and virtualized infrastructure simply become unmanageable if no innovative managing paradigm is applied. Recently, artificial intelligence is proposed to be applied in the 5G system to realize intelligent management and highly self-organized networks. In this paper, proof-of-concept experiments, including the setup of an intelligent 5G testbed, its closed-loop control and enabling algorithms, are presented. The experimental results reveal that applying artificial intelligence to wireless network management is both feasible and effective.
Ideal Ratio Mask Estimation Using Deep Neural Networks for Monaural Speech Segregation in Noisy Reverberant Conditions

Monaural speech segregation is an important problem in robust speech processing and has been formulated as a supervised learning problem. In supervised learning methods, the ideal binary mask (IBM) is usually used as the target because of its simplicity and large speech intelligibility gains. Recently, the ideal ratio mask (IRM) has been found to improve the speech quality over the IBM. However, the IRM was originally defined in anechoic conditions and did not consider the effect of reverberation. In this paper, the IRM is extended to reverberant conditions where the direct sound and early reflections of target speech are regarded as the desired signal. Deep neural networks (DNNs) is employed to estimate the extended IRM in the noisy reverberant conditions. The estimated IRM is then applied to the noisy reverberant mixture for speech segregation. Experimental results show that the estimated IRM provides substantial improvements in speech intelligibility and speech quality over the unprocessed mixture signals under various noisy and reverberant conditions.
A cascaded Circular-Polarization-Selective Surface at K band

Circular-Polarization-Selective Surfaces (CPSS) are structures that reflect one sense of circular polarization and transmit the other. While known designs are made with vias that may be difficult to fabricate for high frequency structures, the proposed design is a simple cascade of two meander-line circular polarizers with a linear polarizer between them. Experimental results are compared with simulations for normal incidence. It is shown that the selected circular polarization transmission has a 3 dB axial ratio bandwidth of more than 5 GHz, with an insertion loss of 0.4 dB and an undesired polarization rejection of 22 dB at the center frequency of 19 GHz.
Musical genre classification of audio signals

Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. In this paper, the automatic classification of audio signals into an hierarchy of musical genres is explored. More specifically, three feature sets for representing timbral texture, rhythmic content and pitch content are proposed. The performance and relative importance of the proposed features is investigated by training statistical pattern recognition classifiers using real-world audio collections. Both whole file and real-time frame-based classification schemes are described. Using the proposed feature sets, classification of 61% for ten musical genres is achieved. This result is comparable to results reported for human musical genre classification.
An Interoperable Data Framework to Manipulate the Smart City Data using Semantic Technologies

During the last decade, enormous volumes of urban data have been produced by the Government agencies, the NGOs and the citizens. In such a scenario, we are presented with a diverse set of data which holds valuable information. This information can be extracted and analyzed and have a number of usages for the well-being of citizens. The major impediment to achieve this goal is the data itself, the available data are redundant, scattered and come with various legacy formats. Data interoperability, scalability and integration are paramount issues which could not be resolved unless the scattered data silos are accessible with a standard representation. In this paper, we propose a framework that resolves the data interoperability and associated challenges in the smart city environment. The framework takes the raw smart city data from several resources and stores them in a NoSQL database. The framework transforms the scattered data into machine-processable data. Besides, the database is linked with an API and simple dashboard for further analysis, which can be utilized to build big data applications based on urban data so that government agencies can get a summarized overview of resource distribution. Smart cities; Smart Data Integration; Big data; IOT; Software architecture
Modified Firefly Algorithm

Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time.
Software Architecture as a Set of Architectural Design Decisions

Software architectures have high costs for change, are complex, and erode during evolution. We believe these problems are partially due to knowledge vaporization. Currently, almost all the knowledge and information about the design decisions the architecture is based on are implicitly embedded in the architecture, but lack a first-class representation. Consequently, knowledge about these design decisions disappears into the architecture, which leads to the aforementioned problems. In this paper, a new perspective on software architecture is presented, which views software architecture as a composition of a set of explicit design decisions. This perspective makes architectural design decisions an explicit part of a software architecture. Consequently, knowledge vaporization is reduced, thereby alleviating some of the fundamental problems of software architecture.
Modeling and Simulating Apache Spark Streaming Applications

Stream processing systems are used to analyze big data streams with low latency. The performance in terms of response time and throughput is crucial to ensure all arriving data are processed in time. This depends on various factors such as the complexity of used algorithms and configurations of such distributed systems and applications. To ensure a desired system behavior, performance evaluations should be conducted to determine the throughput and required resources in advance. In this paper, we present an approach to predict the response time of Apache Spark Streaming applications by modeling and simulating them. In a preliminary controlled experiment, our simulation results suggest accurate prediction values for an upscaling scenario.
A hybrid model predictive controller for path planning and path following

The use of nonlinear model-predictive methods for path planning and following has the advantage of concurrently solving problems of obstacle avoidance, feasible trajectory selection, and trajectory following, while obeying constraints on control inputs and state values. However, such approaches are computationally intensive, and may not be guaranteed to return a result in bounded time when performing a non-convex optimization. This problem is an interesting application to cyber-physical systems due to their reliance on computation to carry out complex control. The computational burden can be addressed through model reduction, at a cost of potential (bounded) model error over the prediction horizon. In this paper we introduce a metric called uncontrollable divergence, and discuss how the selection of the model to use for the predictive controller can be addressed by evaluating this metric, which reveals the divergence between predicted and true states caused by return time and model mismatch. A map of uncontrollable divergence plotted over the state space gives the criterion to judge where reduced models can be tolerated when high update rate is preferred (e.g. at high speed and small steering angles), and where high-fidelity models are required to avoid obstacles or make tighter curves (e.g. at large steering angles). With this metric, we design a hybrid controller that switches at runtime between predictive controllers in which respective models are deployed.
A Bidirectional-Switch-Based Wide-Input Range High-Efficiency Isolated Resonant Converter for Photovoltaic Applications

Modular photovoltaic (PV) power conditioning systems (PCSs) require a high-efficiency dc-dc converter stage capable of regulation over a wide input voltage range for maximum power point tracking. In order to mitigate ground leakage currents and to be able to use a high-efficiency, nonisolated grid-tied inverter, it is also desirable for this microconverter to provide galvanic isolation between the PV module and the inverter. This paper presents a novel, isolated topology that is able to meet the high efficiency over a wide input voltage range requirement. This topology yields high efficiency through low circulating currents, zero-voltage switching (ZVS) and low-current switching of the primary side devices, ZCS of the output diodes, and direct power transfer to the load for the majority of switching cycle. This topology is also able to provide voltage regulation through basic fixed-frequency pulsewidth modulated (PWM) control. These features are able to be achieved with the simple addition of a secondary-side bidirectional ac switch to the isolated series resonant converter. Detailed analysis of the operation of this converter is discussed along with a detailed design procedure. Experimental results of a 300-W prototype are given. The prototype reached a peak power stage efficiency of 98.3% and a California Energy Commission (CEC) weighted power stage efficiency of 98.0% at the nominal input voltage.
Miniature Dual-Mode Microstrip Filters

In order to reduce the size of dual-mode microstrip filters by keeping excellent performance, novel filter geometry is proposed. The novel filter includes a dual-mode resonator based on a meander structure. The effect of input/output feed lines located along a straight line on filter response is investigated for the dual-mode filter. The coupling between degenerate modes of the proposed dual-mode microstrip resonator is discussed depending on the perturbation size. The two dual-mode microstrip bandpass filters with real axis and imaginary axis transmission zeros (TZs) are designed, fabricated, and measured to document the validity of the description of the positive and negative coupling coefficient
Attention with Intention for a Neural Network Conversation Model

In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the intention process. The decoder network is a recurrent network that produces responses to the input from the source side. It is a language model that is dependent on the intention and has an attention mechanism to attend to particular source side words, when predicting a symbol in the response. The model is trained end-toend without labeling data. Experiments show that this model generates natural responses to user inputs.
A Mathematical Theory of Evidence

This is an aptly titled effort to supplement probability theory as developed for chance/aleatory devices by a parallel, but distinct, epistemically oriented quantitative theory of evidence for, and evidential support of, our opinions, judgements of facts, and beliefs. That probability takes its meaning from and is used to describe such diverse phenomena as propensities for physical behavior, propositional attitudes of belief, logical relations of inductive support, and experimental outcomes under prescribed conditions of unlinked repetitions, has long been the source of much of the controversy and vitality in the development and application of probability theory and its associated concepts. Ian Hacking in his recent book The emergence of probability  attempted to trace and explain this intertwining of belief/knowledge and physical (objective) behavior in terms of a conceptual transformation of the categories of knowledge and opinion that was mainly completed by the early 18th century. Hacking's historical/philosophical analysis aims to explain what he holds to be our present dualistic conception of probability as being jointly epistemic (oriented towards assessment of knowledge/belief) and aleatory (oriented towards the objective description of the outcomes of 'random' experiments) with most of the present-day emphasis on the latter. Historically, however, the epistemic component was initially dominant in conceptions of probability. Probability through the Renaissance applied only to opinions/beliefs and was based upon authoritative testimony in support of these opinions/beliefs. The 19 year-old Leibniz writing in 1665 wished to formalize the evidential support for beliefs by a numerical assignment on a scale of [0, 1] of what he referred to as 'degrees of proof'. The object of this exercise was to be a rationalized jurisprudence. Key to such assignments was an analysis into equally possible (likely) cases. The growth of an aleatory notion of probability concerning inductive relations between physical signs and physical phenomena starts in the Renaissance. The extent to which the aleatory notion was dependent upon the epistemic notion (there was also a strong converse dependence) is apparent in the posthumously published (1713) A rs conjectandi of J. Bernoulli. In Part IV of the Ars  we find the first statement and proof of a law of large numbers, the first firm step on the road to the frequentist/aleatory concepts dominant today. Significantly though, J. Bernoulli was not a frequentist. For Bernoulli, frequency of occurrence was only a clue to the enumeration of the equally possible cases that was the basis of quantitative epistemic probability.