nils-herrmann/clean_scirepeval huggingface dataset:
Reinforcement learning algorithms for solving classification problems

We describe a new framework for applying reinforcement learning (RL) algorithms to solve classification tasks by letting an agent act on the inputs and learn value functions. This paper describes how classification problems can be modeled using classification Markov decision processes and introduces the Max-Min ACLA algorithm, an extension of the novel RL algorithm called actor-critic learning automaton (ACLA). Experiments are performed using 8 datasets from the UCI repository, where our RL method is combined with multi-layer perceptrons that serve as function approximators. The RL method is compared to conventional multi-layer perceptrons and support vector machines and the results show that our method slightly outperforms the multi-layer perceptron and performs equally well as the support vector machine. Finally, many possible extensions are described to our basic method, so that much future research can be done to make the proposed method even better.
Memristor Model Comparison

Since the 2008-dated discovery of memristor behavior at the nano-scale, Hewlett Packard is credited for, a large deal of efforts have been spent in the research community to derive a suitable model able to capture the nonlinear dynamics of the nano-scale structures. Despite a considerable number of models of different complexity have been proposed in the literature, there is an ongoing debate over which model should be universally adopted for the investigation of the unique opportunities memristors may offer in integrated circuit design. In order to shed some light into this passionate discussion, this paper compares some of the most noteworthy memristor models present in the literature. The strength of the Pickett?s model stands in its experiment-based development and in its ability to describe some physical mechanism at the origin of memristor dynamics. Since its parameter values depend on the excitation of the memristor and/or on the circuit employing the memristor, it may be assumed as a reference for comparison only in those scenarios for which its parameters were reported in the literature. In this work various noteworthy memristor models are fitted to the Pickett's model under one of such scenarios. This study shows how three models, Biolek's model, the Boundary Condition Memristor model and the Threshold Adaptive Memristor model, outperform the others in the replica of the dynamics observed in the Pickett's model. In the second part of this work the models are used in a couple of basic circuits to study the variance between the dynamical behaviors they give rise to. This analysis intends to make the circuit designers aware of the different behaviors which may occur in memristor-based circuits according to the memristor model under use.
Gradient and Curvature from Photometric Stereo Including Local Confidence Estimation

Photometric stereo is one technique for 3D shape determination that has been implemented in a variety of experimental settings and that has produced consistently good results. The idea is to use intensity values recorded from multiple images obtained from the same viewpoint but under different conditions of illumination. The resulting radiometric constraint makes it possible to obtain local estimates of both surface orientation and surface curvature without requiring global smoothness assumptions and without requiring prior image segmentation. This paper moves photometric stereo one step closer to practical viability by describing an experimental setting in which surface gradient estimation is achieved on full frame video data at near video frame rates (i.e., 15Hz). The implementation uses commercially available hardware. Reflectance is modeled empirically using measurements obtained from a calibration sphere. Estimation of the gradient, (p, q), requires only simple table lookup. Curvature estimation uses, in addition, the reflectance map, R(p, q). The required lookup table and reflectance maps are derived during calibration. Because reflectance is modeled empirically, no prior physical model of the reflectance characteristics of the objects to be analyzed is assumed. At the same time, if a good physical model is available, it can be retrofit to the method for implementation purposes. Photometric stereo is subject to error in the presence of cast shadows and interreflection. No purely local technique can succeed since these phenomena are inherently non-local. Nevertheless, this paper demonstrates that one can exploit the redundancy in three light source photometric stereo to, in most cases, locally detect the presence of cast shadows and interreflection. Detection is facilitated by explicitly including a local confidence estimate in the lookup table used for gradient estimation.
A machine learning approach for personalized autonomous lane change initiation and control

We study an algorithm that allows a vehicle to autonomously change lanes in a safe but personalized fashion without the driver's explicit initiation (e.g. activating the turn signals). Lane change initiation in autonomous driving is typically based on subjective rules, functions of the positions and relative velocities of surrounding vehicles. This approach is often arbitrary, and not easily adapted to the driving style preferences of an individual driver. Here we propose a data-driven modeling approach to capture the lane change decision behavior of human drivers. We collect data with a test vehicle in typical lane change situations and train classifiers to predict the instant of lane change initiation with respect to the preferences of a particular driver. We integrate this decision logic into a model predictive control (MPC) framework to create a more personalized autonomous lane change experience that satisfies safety and comfort constraints. We show the ability of the decision logic to reproduce and differentiate between two lane changing styles, and demonstrate the safety and effectiveness of the control framework through simulations.
Probabilistic Matrix Factorization

Many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings. In this paper we present the Probabilistic Matrix Factorization (PMF) model which scales linearly with the number of observations and, more importantly, performs well on the large, sparse, and very imbalanced Netflix dataset. We further extend the PMF model to include an adaptive prior on the model parameters and show how the model capacity can be controlled automatically. Finally, we introduce a constrained version of the PMF model that is based on the assumption that users who have rated similar sets of movies are likely to have similar preferences. The resulting model is able to generalize considerably better for users with very few ratings. When the predictions of multiple PMF models are linearly combined with the predictions of Restricted Boltzmann Machines models, we achieve an error rate of 0.8861, that is nearly 7% better than the score of Netflix’s own system.
Generating Responses Expressing Emotion in an Open-domain Dialogue System

Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances. The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion. In this paper we present neural models that learn to express a given emotion in the generated response. We propose four models and evaluate them against 3 baselines. An encoder-decoder framework-based model with multiple attention layers provides the best overall performance in terms of expressing the required emotion. While it does not outperform other models on all emotions, it presents promising results in most cases.
Methods and protocols of modern solid phase Peptide synthesis.

The purpose of this article is to delineate strategic considerations and provide practical procedures to enable non-experts to synthesize peptides with a reasonable chance of success. This article is not encyclopedic but rather devoted to the Fmoc/tBu approach of solid phase peptide synthesis (SPPS), which is now the most commonly used methodology for the production of peptides. The principles of SPPS with a review of linkers and supports currently employed are presented. Basic concepts for the different steps of SPPS such as anchoring, deprotection, coupling reaction and cleavage are all discussed along with the possible problem of aggregation and side-reactions. Essential protocols for the synthesis of fully deprotected peptides are presented including resin handling, coupling, capping, Fmoc-deprotection, final cleavage and disulfide bridge formation.
Stock price prediction using LSTM, RNN and CNN-sliding window model

Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the daily closing price. The proposed method is a model independent approach. Here we are not fitting the data to a specific model, rather we are identifying the latent dynamics existing in the data using deep learning architectures. In this work we use three different deep learning architectures for the price prediction of NSE listed companies and compares their performance. We are applying a sliding window approach for predicting future values on a short term basis. The performance of the models were quantified using percentage error.
Dual-band bandpass filter using helical resonators

This paper demonstrates non-uniform pitch helical resonators for realization of dual-band bandpass filters. By modifying the pitch and coil diameter of conventional uniform pitch helical resonators, the desired dual-band frequencies can be obtained. The external coupling and inter-resonator coupling structure are also illustrated. A non-uniform pitch helical resonator was designed and fabricated with unloaded quality-factor (>900) for both passbands. A 2nd order Butterworth filter that has passbands at 840 MHz and 2590 MHz has been designed. The simulation results show that the filter has a good selectivity with 6% and 4.3% fractional bandwidth at the passbands, respectively. The good power handling capability also suggests that the filter is applicable for microcell/picocell LTE base stations.
Internet Traffic Measurement

This tutorial article discusses the role of network traffic measurement in the design, testing, and evaluation of Internet protocols and applications. The article begins with some background information on Internet traffic measurement, and then proceeds to discuss the “tools of the trade”, including examples of both hardwarebased and software-based approaches to network traffic measurement. The article concludes with a summary of the main observations from the past fifteen years of network measurement research, along with pointers to the relevant literature for more information.
Towards a Maturity Model for the Adoption of Social Media as a Means of Organizational Innovation

Social Media applications as a contemporary phenomenon attract the attention of organizations and researchers. While these technologies are mainstream in a private setting, there is still uncertainty among organizations of operating them. The corporate perspective aims at deploying these technologies for value creation, i.e. improvement and even innovation. Conceptual guidance for a proper management to professionalize Social Media management is missing. Maturity models provide support to this challenge. This paper deals with the development of a maturity model for the organizational adoption of Social Media. By means of a design science research approach, the model is defined along the dimensions strategy, processes, IT systems, culture, and governance and measured against five maturity stages. Thereby a contribution is made to the knowledge base of corporate Social Media usage as well as to the development of a maturity model along a defined design methodology.
Web Service framework to translate text into sign language

This research presents a new framework using web services to translate text into a sign language animated GIF. When users enter a sentence, the web service will analyzes the sentence using the longest words from a dictionary, then produces a series of images per word and integrates all images into an animated GIF. This will help the hearing impairment to access more facilities. In addition, the framework can support entrepreneurs to create content that is able to communicate with and accessible by people with hearing loss. This framework is provided by a Web Service (Representational State Transfer: REST) and sends information through JavaScript Object Notation (JSON). REST and JSON are convenient to use and can be utilized by many platforms and devices. Finally, this framework could be further developed to other basic services for the hearing impaired.
Unbalanced Three-Phase Optimal Power Flow for Smart Grids

Advanced distribution management system (DMS), an evolution of supervisory control and data acquisition obtained by extending its working principles from transmission to distribution, is the brain of a smart grid. Advanced DMS assesses smart functions in the distribution system and is also responsible for assessing control functions such as reactive dispatch, voltage regulation, contingency analysis, capability maximization, or line switching. Optimal power flow (OPF)-based tools can be suitably adapted to the requirements of smart distribution network and be employed in an advanced DMS framework. In this paper, the authors present a methodology for unbalanced three-phase OPF (TOPF) for DMS in a smart grid. In the formulation of the TOPF, control variables of the optimization problem are actual active load demand and reactive power outputs of microgenerators. The TOPF is based on a quasi-Newton method and makes use of an open-source three-phase unbalanced distribution load flow. Test results are presented on the IEEE 123-bus Radial Distribution Feeder test case.
Information-Driven Dynamic Sensor Collaboration for Tracking Applications

This article overviews the information-driven approach to sensor collaboration in ad hoc sensor networks. The main idea is for a network to determine participants in a " sensor collaboration " by dynamically optimizing the information utility of data for a given cost of communication and computation. A definition of information utility is introduced, and several approximate measures of the information utility are developed for reasons of computational tractability. We illustrate the use of this approach using examples drawn from tracking applications.  The technology of wirelessly networked micro-sensors promises to revolutionize the way we live, work, and interact with the physical environment. For example, tiny, inexpensive sensors can be " sprayed " onto roads, walls, or machines to monitor and detect a variety of interesting events such as highway traffic, wildlife habitat condition, forest fire, manufacturing job flow, and military battlefield situation.
A wideband unity-gain buffer in 0.13-μm CMOS

In this paper, an ultra wideband analog voltage-mode buffer is presented which can drive a load impedance of 50 Ω. The presented feedback-based buffer uses a compound amplifier which is a parallel combination of a high-DC gain operational amplifier and a operation transconductance amplifier to achieve a high unity gain bandwidth. A proof-of-concept prototype is designed and fabricated in a 0.13 μm CMOS process. The simulation and measurement results of the proposed buffer are in good agreement. The prototype buffer circuit consumes 7.34 mW from a 1.3-V supply, while buffering a 2 GHz sinusoidal input signal with a 0.4 V peak-to-peak (Vpp) amplitude and driving an AC-coupled 50-Ω load.
On rectified linear units for speech processing

Deep neural networks have recently become the gold standard for acoustic modeling in speech recognition systems. The key computational unit of a deep network is a linear projection followed by a point-wise non-linearity, which is typically a logistic function. In this work, we show that we can improve generalization and make training of deep networks faster and simpler by substituting the logistic units with rectified linear units. These units are linear when their input is positive and zero otherwise. In a supervised setting, we can successfully train very deep nets from random initialization on a large vocabulary speech recognition task achieving lower word error rates than using a logistic network with the same topology. Similarly in an unsupervised setting, we show how we can learn sparse features that can be useful for discriminative tasks. All our experiments are executed in a distributed environment using several hundred machines and several hundred hours of speech data.
Low cost Ka-band transmitter for CubeSat systems

CubeSat platforms grow increasingly popular in commercial ventures as alternative solutions for global Internet networks deep space exploration, and aerospace research endeavors. Many technology companies and system engineers plan to implement small satellite systems as part of global Low Earth Orbit (LEO) inter-satellite constellations. High performing low cost hardware is of key importance in driving these efforts. This paper presents the heterodyne architecture and performance of Ka-Band Integrated Transmitter Assembly (ITA) Module, which could be implemented in nano/microsatellite or other satellite systems as a low-cost solution for high data rate space communication systems. The module converts a 0.9 to 1.1 GHz IF input signal to deliver linear transmission of +29 dBm at 26.7 to 26.9 GHz frequency range with a built-in phase locked oscillator, integrated transmitter, polarizer, and lens corrected antenna.
Differentially private frequent subgraph mining

Mining frequent subgraphs from a collection of input graphs is an important topic in data mining research. However, if the input graphs contain sensitive information, releasing frequent subgraphs may pose considerable threats to individual's privacy. In this paper, we study the problem of frequent subgraph mining (FGM) under the rigorous differential privacy model. We introduce a novel differentially private FGM algorithm, which is referred to as DFG. In this algorithm, we first privately identify frequent subgraphs from input graphs, and then compute the noisy support of each identified frequent subgraph. In particular, to privately identify frequent subgraphs, we present a frequent subgraph identification approach which can improve the utility of frequent subgraph identifications through candidates pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support derivation approach, where a series of methods has been proposed to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that our DFG algorithm satisfies ε-differential privacy. Extensive experimental results on real datasets show that the DFG algorithm can privately find frequent subgraphs with high data utility.
Combinatorial Creativity for Procedural Content Generation via Machine Learning

In this paper we propose the application of techniques from the field of creativity research to machine learned models within the domain of games. This application allows for the creation of new, distinct models without additional training data. The techniques in question are combinatorial creativity techniques, defined as techniques that combine two sets of input to create novel output sets. We present a survey of prior work in this area and a case study applying some of these techniques to pre-trained machine learned models of game
Planar inductive sensor for small displacement

This paper presents a meander type displacement inductive sensor, developed in printed circuit board (PCB) technology. It also describes design, realization and the input inductance measurement of sensor. The displacement in two directions (less than 0.5 mm) can be detected by using two sensor's elements (i.e. two pairs of meander coils). In each pair, one of the coils is fixed, and between its terminals the input inductance was measured, while other coil is short-circuited. If one coil is moved above the other in directions of x- and z-axes, coupling between coils will change, as well as input inductance, which serves as a measure of displacement. In order to achieve better linearity of the sensor, gap is inserted in the middle of each conductive segment of fixed coils. Four sets of inductive sensors were realized: without gap and with three different gaps: 0.25 mm, 0.51 mm and 0.76 mm. In addition, a simple model of the inductive sensor is proposed. Using this model, the input inductance of displacement sensor was calculated and compared with measured values. A good agreement was found.
Cyber–Physical Device Authentication for the Smart Grid Electric Vehicle Ecosystem

Entity authentication and related key management is an active research topic in smart grid security. However, existing works seem to have overlooked the significance that the smart grid is a cyber-physical system, which entails more considerations in the integration of its cyber and physical domains. Ignoring this could possibly undermine security since the effects of cyber authorization in the smart grid are usually extended into the physical domain. The substitution attack, a kind of the man-in-the-middle attack, has been demonstrated using this gap. This paper proposes a two-factor cyber-physical device authentication protocol to defend against coordinated cyber-physical attacks in the smart grid. The idea is to combine a novel contextual factor based on physical connectivity in the power grid with the conventional authentication factor in the challenge-response protocol, widely used in cybersecurity. The resulting protocol provides assurance on not only the digital identity of a device but also the device's controllability in the physical domain. While the design is for the electric vehicle ecosystem, the framework could be readily extended to other smart grid subsystems.
A single-phase photovoltaic inverter topology with a series-connected power buffer

Module integrated converters (MICs) have been under rapid developement for single-phase grid-tied photovoltaic applications. The capacitive energy storage implementation for the double-line-frequency power variation represents a differentiating factor among existing designs. This paper introduces a new topology that places the energy storage block in a series-connected path with the line interface block. This design provides independent control over the capacitor voltage, soft-switching for all semiconductor devices, and full four-quadrant operation with the grid. The proposed approach is analyzed and experimentally demonstrated.
CallRank: Combating SPIT Using Call Duration, Social Networks and Global Reputation

The growing popularity of IP telephony systems has made them attractive targets for spammers. Voice call spam, also known as Spam over Internet Telephony (SPIT), is potentially a more serious problem than email spam because of the real time processing requirements of voice packets. We explore a novel mechanism that uses duration of calls between users to combat SPIT. CallRank, the scheme proposed by us, uses call duration to establish social network linkages and global reputations for callers, based on which call recipients can decide whether the caller is legitimate or not. CallRank has been implemented within a VoIP system simulation and our results show that we are able to achieve a false negative rate of 10% and a false positive rate of 3% even in the presence of a significant fraction of spammers.