nils-herrmann/clean_scirepeval huggingface dataset:
Microstrip patch antenna miniaturization using artificial magnetic conductor

This paper deals with an alternative technique for microstrip patch antenna miniaturization is investigated. The technique employs a 2 layer structures with an artificial magnetic conductor (AMC) structure used as the bottom layer replacing the ground plane. The AMC structure is constructed using a textured high impedance surface (HIS) that takes form of a doubly periodic square patches on top of a dielectric substrate. The miniaturization effect that is introduced especially in the reduction of patch size by use of this 2 layer structures will be numerically investigated. The proposed antenna is designed to have a resonant frequency of 2.45MHz suitable for wireless local area network (WLAN) application and is deployed on an FR-4 Epoxy substrate. To obtain the optimum design, numerical analysis is carried out using 3D simulation software. To verify the miniaturization effect, the proposed antenna will be compared to some reference antenna resonates at the same resonant frequency placed on a 2 layers FR-4 Epoxy substrate with the same thickness but without AMC structure. From the result, it is shown that the incorporation of AMC structure can reduce the size of antenna patch by more than 31%.
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems

Language understanding is a key component in a spoken dialogue system. In this paper, we investigate how the language understanding module influences the dialogue system performance by conducting a series of systematic experiments on a task-oriented neural dialogue system in a reinforcement learning based setting. The empirical study shows that among different types of language understanding errors, slot-level errors can have more impact on the overall performance of a dialogue system compared to intent-level errors. In addition, our experiments demonstrate that the reinforcement learning based dialogue system is able to learn when and what to confirm in order to achieve better performance and greater robustness.
Empirical, theoretical, and practical advantages of the HEXACO model of personality structure.

The authors argue that a new six-dimensional framework for personality structure--the HEXACO model--constitutes a viable alternative to the well-known Big Five or five-factor model. The new model is consistent with the cross-culturally replicated finding of a common six-dimensional structure containing the factors Honesty-Humility (H), Emotionality (E), eExtraversion (X), Agreeableness (A), Conscientiousness (C), and Openness to Experience (O). Also, the HEXACO model predicts several personality phenomena that are not explained within the B5/FFM, including the relations of personality factors with theoretical biologists' constructs of reciprocal and kin altruism and the patterns of sex differences in personality traits. In addition, the HEXACO model accommodates several personality variables that are poorly assimilated within the B5/FFM.
Subdue: compression-based frequent pattern discovery in graph data

A majority of the existing algorithms which mine graph datasets target complete, frequent sub-graph discovery. We describe the graph-based data mining system Subdue which focuses on the discovery of sub-graphs which are not only frequent but also compress the graph dataset, using a heuristic algorithm. The rationale behind the use of a compression-based methodology for frequent pattern discovery is to produce a fewer number of highly interesting patterns than to generate a large number of patterns from which interesting patterns need to be identified. We perform an experimental comparison of Subdue with the graph mining systems gSpan and FSG on the Chemical Toxicity and the Chemical Compounds datasets that are provided with gSpan. We present results on the performance on the Subdue system on the Mutagenesis and the KDD 2003 Citation Graph dataset. An analysis of the results indicates that Subdue can efficiently discover best-compressing frequent patterns which are fewer in number but can be of higher interest.
Predicate Expression Cost Functions to Guide Evolutionary Search for Test Data

Several researchers are using evolutionary search methods to search for test data with which to test a program. The fitness or cost function depends on the test goal but almost invariably an important component of the cost function is an estimate of the cost of satisfying a predicate expression as might occur in branches, exception conditions, etc. This paper reviews the commonly used cost functions and points out some deficiencies. Alternative cost functions are proposed to overcome these deficiencies. The evidence from an experiment is that they are more reliable.
Data Augmentation by Pairing Samples for Images Classification

Data augmentation is a widely used technique in many machine learning tasks, such as image classification, to virtually enlarge the training dataset size and avoid overfitting. Traditional data augmentation techniques for image classification tasks create new samples from the original training data by, for example, flipping, distorting, adding a small amount of noise to, or cropping a patch from an original image. In this paper, we introduce a simple but surprisingly effective data augmentation technique for image classification tasks. With our technique, named SamplePairing, we synthesize a new sample from one image by overlaying another image randomly chosen from the training data (i.e., taking an average of two images for each pixel). By using two images randomly selected from the training set, we can generate N new samples from N training samples. This simple data augmentation technique significantly improved classification accuracy for all the tested datasets; for example, the top-1 error rate was reduced from 33.5% to 29.0% for the ILSVRC 2012 dataset with GoogLeNet and from 8.22% to 6.93% in the CIFAR-10 dataset. We also show that our SamplePairing technique largely improved accuracy when the number of samples in the training set was very small. Therefore, our technique is more valuable for tasks with a limited amount of training data, such as medical imaging tasks.
Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians

Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a "unidirectional" bottom-up feed-forward fashion. However, practical experience and biological evidence tells us that feedback plays a crucial role, particularly for detailed spatial understanding tasks. This work explores "bidirectional" architectures that also reason with top-down feedback: neural units are influenced by both lower and higher-level units. We do so by treating units as rectified latent variables in a quadratic energy function, which can be seen as a hierarchical Rectified Gaussian model (RGs) . We show that RGs can be optimized with a quadratic program (QP), that can in turn be optimized with a recurrent neural network (with rectified linear units). This allows RGs to be trained with GPU-optimized gradient descent. From a theoretical perspective, RGs help establish a connection between CNNs and hierarchical probabilistic models. From a practical perspective, RGs are well suited for detailed spatial tasks that can benefit from top-down reasoning. We illustrate them on the challenging task of keypoint localization under occlusions, where local bottom-up evidence may be misleading. We demonstrate state-of-the-art results on challenging benchmarks.
Combining static analysis with probabilistic models to enable market-scale Android inter-component analysis

Static analysis has been successfully used in many areas, from verifying mission-critical software to malware detection. Unfortunately, static analysis often produces false positives, which require significant manual effort to resolve. In this paper, we show how to overlay a probabilistic model, trained using domain knowledge, on top of static analysis results, in order to triage static analysis results. We apply this idea to analyzing mobile applications. Android application components can communicate with each other, both within single applications and between different applications. Unfortunately, techniques to statically infer Inter-Component Communication (ICC) yield many potential inter-component and inter-application links, most of which are false positives. At large scales, scrutinizing all potential links is simply not feasible. We therefore overlay a probabilistic model of ICC on top of static analysis results. Since computing the inter-component links is a prerequisite to inter-component analysis, we introduce a formalism for inferring ICC links based on set constraints. We design an efficient algorithm for performing link resolution. We compute all potential links in a corpus of 11,267 applications in 30 minutes and triage them using our probabilistic approach. We find that over 95.1% of all 636 million potential links are associated with probability values below 0.01 and are thus likely unfeasible links. Thus, it is possible to consider only a small subset of all links without significant loss of information. This work is the first significant step in making static inter-application analysis more tractable, even at large scales.
A Logic for Uncertain Probabilities

In standard logic, propositions are considered to be either true or false. However, a fundamental aspect of the human condition is that nobody can ever determine with absolute certainty whether a proposition about the world is true or false. In addition, whenever the truth of a proposition is assessed, it is always done by an individual, and it can never be considered to represent a general and objective belief. This indicates that important aspects are missing in the way standard logic captures our perception of reality, and that it is more designed for an idealised world than for the subjective world in which we are all living. Several alternative calculi and logics which take uncertainty and ignorance into consideration have been proposed and quite successfully applied to practical problems where conclusions have to be made based on insufficient evidence (see for example Hunter 1996 or Motro & Smets 1997 for an analysis of some uncertainty logics and calculi). Although including uncertainty in the belief model is a significant step forward, it only goes half the way in realising the real nature of human beliefs. It is also necessary to take into account that beliefs always are held by individuals and that beliefs for this reason are fundamentally subjective. In this paper we describe subjective logic (see Jøsang 1997 for an earlier version) as a logic which operates on subjective beliefs about the world, and use the term opinion to denote the representation of a subjective belief. Subjective logic operates on opinions and contains standard logical operators in addition to some non-standard operators which specifically depend on belief ownership. An opinion can be interpreted as a probability measure containing secondary uncertainty, and as such subjective logic can be seen as an extension of both probability calculus and binary logic.
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising

Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled sub-images, achieving a good trade-off between inference speed and denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including: 1) the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network; 2) the ability to remove spatially variant noise by specifying a non-uniform noise level map; and 3) faster speed than benchmark BM3D even on CPU without sacrificing denoising performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications.
A fully Bayesian approach to unsupervised part-of-speech tagging

Unsupervised learning of linguistic structure is a difficult problem. A common approach is to define a generative model and maximize the probability of the hidden structure given the observed data. Typically, this is done using maximum-likelihood estimation (MLE) of the model parameters. We show using part-of-speech tagging that a fully Bayesian approach can greatly improve performance. Rather than estimating a single set of parameters, the Bayesian approach integrates over all possible parameter values. This difference ensures that the learned structure will have high probability over a range of possible parameters, and permits the use of priors favoring the sparse distributions that are typical of natural language. Our model has the structure of a standard trigram HMM, yet its accuracy is closer to that of a state-of-the-art discriminative model (Smith and Eisner, 2005), up to 14 percentage points better than MLE. We find improvements both when training from data alone, and using a tagging dictionary.
Dense Semi-rigid Scene Flow Estimation from RGBD Images

Scene flow is defined as the motion field in 3D space, and can be computed from a single view when using an RGBD sensor. We propose a new scene flow approach that exploits the local and piecewise rigidity of real world scenes. By modeling the motion as a field of twists, our method encourages piecewise smooth solutions of rigid body motions. We give a general formulation to solve for local and global rigid motions by jointly using intensity and depth data. In order to deal efficiently with a moving camera, we model the motion as a rigid component plus a non-rigid residual and propose an alternating solver. The evaluation demonstrates that the proposed method achieves the best results in the most commonly used scene flow benchmark. Through additional experiments we indicate the general applicability of our approach in a variety of different scenarios.
Training a Ranking Function for Open-Domain Question Answering

In recent years, there have been amazing advances in deep learning methods for machine reading. In machine reading, the machine reader has to extract the answer from the given ground truth paragraph. Recently, the stateof-the-art machine reading models achieve human level performance in SQuAD which is a reading comprehension-style question answering (QA) task. The success of machine reading has inspired researchers to combine information retrieval with machine reading to tackle open-domain QA. However, these systems perform poorly compared to reading comprehension-style QA because it is difficult to retrieve the pieces of paragraphs that contain the answer to the question. In this study, we propose two neural network rankers that assign scores to different passages based on their likelihood of containing the answer to a given question. Additionally, we analyze the relative importance of semantic similarity and word level relevance matching in open-domain QA.
FINET: Context-Aware Fine-Grained Named Entity Typing

We propose FINET, a system for detecting the types of named entities in short inputs—such as sentences or tweets—with respect to WordNet’s super fine-grained type system. FINET generates candidate types using a sequence of multiple extractors, ranging from explicitly mentioned types to implicit types, and subsequently selects the most appropriate using ideas from word-sense disambiguation. FINET combats data scarcity and noise from existing systems: It does not rely on supervision in its extractors and generates training data for type selection from WordNet and other resources. FINET supports the most fine-grained type system so far, including types with no annotated training data. Our experiments indicate that FINET outperforms state-of-the-art methods in terms of recall, precision, and granularity of extracted types.
Authorship analysis: identifying the author of a program

Authorship analysis on computer software is a difficult problem. In this paper we explore the classification of programmers’ style, and try to find a set of characteristics that remain constant for a significant portion of the programs that this programmer might produce. Our goal is to show that it is possible to identify the author of a program by examining programming style characteristics. Within a closed environment, the results of this paper support the conclusion that, for a specific set of programmers, it is possible to identify the author of any individual program. Also, based on previous work and our observations during the experiments described herein we believe that the probability of finding two programmers who share exactly those same characteristics should be very small.
Named entity recognition for Amharic using deep learning

The paper describes a named entity recognition system for Amharic, an under-resourced language, using a recurrent neural network, a bi-directional long short term memory model to identify and classify tokens into six predefined classes: Person, Location, Organization, Time, Title, and Other (non-named entity tokens). Word vectors based on semantic information are built for all tokens using an unsupervised learning algorithm, word2vec. The word vectors were merged with a set of specifically developed language independent features and together fed to the neural network model to predict the classes of the words. When evaluated by 10-fold cross-validation, the created Amharic named entity recogniser achieved good average precision (77.2%), but did worse on recall (63.4%), for a 69.7% F1-score.
Flight Data Recorder: Monitoring Persistent-State Interactions to Improve Systems Management

Mismanagement of the persistent state of a system---all the executable files, configuration settings and other data that govern how a system functions---causes reliability problems, security vulnerabilities, and drives up operation costs. Recent research traces persistent state interactions---how state is read, modified, etc.---to help troubleshooting, change management and malware mitigation, but has been limited by the difficulty of collecting, storing, and analyzing the 10s to 100s of millions of daily events that occur on a single machine, much less the 1000s or more machines in many computing environments.
 We present the Flight Data Recorder (FDR) that enables always-on tracing, storage and analysis of persistent state interactions. FDR uses a domain-specific log format, tailored to observed file system workloads and common systems management queries. Our lossless log format compresses logs to only 0.5--0.9 bytes per interaction. In this log format, 1000 machine-days of logs---over 25 billion events---can be analyzed in less than 30 minutes. We report on our deployment of FDR to 207 production machines at MSN, and show that a single centralized collection machine can potentially scale to collecting and analyzing the complete records of persistent state interactions from 4000+ machines. Furthermore, our tracing technology is shipping as part of the Windows Vista OS.
Recent advances in fault diagnosis by Park's vector approach

This paper addresses recent advances in fault diagnosis by Park's Vector Approach, with special emphasis being given to the most recent developments concerning power converter fault diagnosis in variable speed ac drives. With the aim to demonstrate the suitability of such approach for power converters self-diagnostic, a detailed comparison of four algorithms intended for real-time diagnosis is also included. Their performance evaluation is accomplished by means of experimental results, analyzing some key features such as effectiveness, robustness against false alarms, detection speed, implementation effort, tuning effort and computational burden.
A Low-Voltage 77-GHz Automotive Radar Chipset

This paper presents the first complete 2.5 V, 77 GHz chipset for Doppler radar and imaging applications fabricated in 0.13 mum SiGe HBT technology. The chipset includes a voltage-controlled oscillator with -101.6 dBc/Hz phase noise at 1 MHz offset, an 25 dB gain low-noise amplifier, a novel low-voltage double-balanced Gilbert-cell mixer with two mm-wave baluns and IF amplifier achieving 12.8 dB noise figure and an OP1dB of +5 dBm, a 99 GHz static frequency divider consuming a record low 75 mW, and a power amplifier with 19 dB gain, +14.4 dBm saturated power, and 15.7% PAE. Monolithic spiral inductors and transformers result in the lowest reported 77 GHz receiver core area of only 0.45 mm times 0.30 mm. Simplified circuit topologies allow 77 GHz operation up to 125degC from 2.5 V/1.8 V supplies. Technology splits of the SiGe HBTs are employed to determine the optimum HBT profile for mm-wave performance.
Autonomous Learning of High-Level States and Actions in Continuous Environments

How can an agent bootstrap up from a low-level representation to autonomously learn high-level states and actions using only domain-general knowledge? In this paper, we assume that the learning agent has a set of continuous variables describing the environment. There exist methods for learning models of the environment, and there also exist methods for planning. However, for autonomous learning, these methods have been used almost exclusively in discrete environments. We propose attacking the problem of learning high-level states and actions in continuous environments by using a qualitative representation to bridge the gap between continuous and discrete variable representations. In this approach, the agent begins with a broad discretization and initially can only tell if the value of each variable is increasing, decreasing, or remaining steady. The agent then simultaneously learns a qualitative representation (discretization) and a set of predictive models of the environment. These models are converted into plans to perform actions. The agent then uses those learned actions to explore the environment. The method is evaluated using a simulated robot with realistic physics. The robot is sitting at a table that contains a block and other distractor objects that are out of reach. The agent autonomously explores the environment without being given a task. After learning, the agent is given various tasks to determine if it learned the necessary states and actions to complete them. The results show that the agent was able to use this method to autonomously learn to perform the tasks.