nils-herrmann/clean_scirepeval huggingface dataset:
Bipartite Ranking through Minimization of Univariate Loss

Minimization of the rank loss or, equivalently, maximization of the AUC in bipartite ranking calls for minimizing the number of disagreements between pairs of instances. Since the complexity of this problem is inherently quadratic in the number of training examples, it is tempting to ask how much is actually lost by minimizing a simple univariate loss function, as done by standard classification methods, as a surrogate. In this paper, we first note that minimization of 0/1 loss is not an option, as it may yield an arbitrarily high rank loss. We show, however, that better results can be achieved by means of a weighted (cost-sensitive) version of 0/1 loss. Yet, the real gain is obtained through marginbased loss functions, for which we are able to derive proper bounds, not only for rank risk but, more importantly, also for rank regret. The paper is completed with an experimental study in which we address specific questions raised by our theoretical analysis.
A branch and bound algorithm for extracting smallest minimal unsatisfiable subformulas

Explaining the causes of infeasibility of Boolean formulas has practical applications in numerous fields, such as artificial intelligence (repairing inconsistent knowledge bases), formal verification (abstraction refinement and unbounded model checking), and electronic design (diagnosing and correcting infeasibility). Minimal unsatisfiable subformulas (MUSes) provide useful insights into the causes of infeasibility. An unsatisfiable formula often has many MUSes. Based on the application domain, however, MUSes with specific properties might be of interest. In this paper, we tackle the problem of finding a smallest-cardinality MUS (SMUS) of a given formula. An SMUS provides a succinct explanation of infeasibility and is valuable for applications that are heavily affected by the size of the explanation. We present (1) a baseline algorithm for finding an SMUS, founded on earlier work for finding all MUSes, and (2) a new branch-and-bound algorithm called Digger that computes a strong lower bound on the size of an SMUS and splits the problem into more tractable subformulas in a recursive search tree. Using two benchmark suites, we experimentally compare Digger to the baseline algorithm and to an existing incomplete genetic algorithm approach. Digger is shown to be faster in nearly all cases. It is also able to solve far more instances within a given runtime limit than either of the other approaches.
HDML: Habit Detection with Machine Learning

All people can not do as they plan, it happens because of their habits. Therefore, habits and moods may affect their productivity. Hence, the habits and moods are the important parts of person's life. Such habits may be analyzed with various machine learning techniques as available nowadays. Now the question of analyzing the Habits and moods of a person with a goal of increasing one's productivity comes to mind. This paper discusses one such technique called HDML (Habit Detection with Machine Learning). HDML model analyses the mood which helps us to deal with a bad mood or a state of unproductivity, through suggestions about such activities that alleviate our mood. The overall accuracy of the model is about 87.5 %.
Continuous Probability Distribution Prediction of Image Emotions via Multitask Shared Sparse Regression

Previous works on image emotion analysis mainly focused on predicting the dominant emotion category or the average dimension values of an image for affective image classification and regression. However, this is often insufficient in various real-world applications, as the emotions that are evoked in viewers by an image are highly subjective and different. In this paper, we propose to predict the continuous probability distribution of image emotions which are represented in dimensional valence-arousal space. We carried out large-scale statistical analysis on the constructed Image-Emotion-Social-Net dataset, on which we observed that the emotion distribution can be well-modeled by a Gaussian mixture model. This model is estimated by an expectation-maximization algorithm with specified initializations. Then, we extract commonly used emotion features at different levels for each image. Finally, we formalize the emotion distribution prediction task as a shared sparse regression (SSR) problem and extend it to multitask settings, named multitask shared sparse regression (MTSSR), to explore the latent information between different prediction tasks. SSR and MTSSR are optimized by iteratively reweighted least squares. Experiments are conducted on the Image-Emotion-Social-Net dataset with comparisons to three alternative baselines. The quantitative results demonstrate the superiority of the proposed method.
Enhanced Experience Replay Generation for Efficient Reinforcement Learning

Applying deep reinforcement learning (RL) on real systems suffers from slow data sampling. We propose an enhanced generative adversarial network (EGAN) to initialize an RL agent in order to achieve faster learning. The EGAN utilizes the relation between states and actions to enhance the quality of data samples generated by a GAN. Pre-training the agent with the EGAN shows a steeper learning curve with a 20% improvement of training time in the beginning of learning, compared to no pre-training, and an improvement compared to training with GAN by about 5% with smaller variations. For real time systems with sparse and slow data sampling the EGAN could be used to speed up the early phases of the training process.
Clearance Pricing Optimization for a Fast-Fashion Retailer

Fast-fashion retailers such as Zara offer continuously changing assortments and use minimal in-season promotions. Their clearance pricing problem is thus challenging because it involves comparatively more different articles of unsold inventory with less historical price data points. Until 2007, Zara used a manual and informal decision-making process for determining price markdowns. In collaboration with their pricing team, we designed and implemented since an alternative process relying on a formal forecasting model feeding a price optimization model. As part of a controlled field experiment conducted in all Belgian and Irish stores during the 2008 Fall-Winter season, this new process increased clearance revenues by approximately 6%. Zara is currently using this process worldwide for its markdown decisions during clearance sales.
Classification by Pairwise Coupling

We discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together. The coupling model is similar to the Bradley-Terry method for paired comparisons. We study the nature of the class probability estimates that arise, and examine the performance of the procedure in simulated datasets. The classifiers used include linear discriminants and nearest neighbors: application to support vector machines is also briefly described.
Synthetic Word Parsing Improves Chinese Word Segmentation

We present a novel solution to improve the performance of Chinese word segmentation (CWS) using a synthetic word parser. The parser analyses the internal structure of words, and attempts to convert out-of-vocabulary words (OOVs) into in-vocabulary fine-grained sub-words. We propose a pipeline CWS system that first predicts this fine-grained segmentation, then chunks the output to reconstruct the original word segmentation standard. We achieve competitive results on the PKU and MSR datasets, with substantial improvements in OOV recall.
Interactive topic modeling

Topic models are a useful and ubiquitous tool for understanding large corpora. However, topic models are not perfect, and for many users in computational social science, digital humanities, and information studies—who are not machine learning experts—existing models and frameworks are often a “take it or leave it” proposition. This paper presents a mechanism for giving users a voice by encoding users’ feedback to topic models as correlations between words into a topic model. This framework, interactive topic modeling (itm), allows untrained users to encode their feedback easily and iteratively into the topic models. Because latency in interactive systems is crucial, we develop more efficient inference algorithms for tree-based topic models. We validate the framework both with simulated and real users.
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes

Normalization techniques have only recently begun to be exploited in supervised learning tasks. Batch normalization exploits mini-batch statistics to normalize the activations. This was shown to speed up training and result in better models. However its success has been very limited when dealing with recurrent neural networks. On the other hand, layer normalization normalizes the activations across all activities within a layer. This was shown to work well in the recurrent setting. In this paper we propose a unified view of normalization techniques, as forms of divisive normalization, which includes layer and batch normalization as special cases. Our second contribution is the finding that a small modification to these normalization schemes, in conjunction with a sparse regularizer on the activations, leads to significant benefits over standard normalization techniques. We demonstrate the effectiveness of our unified divisive normalization framework in the context of convolutional neural nets and recurrent neural networks, showing improvements over baselines in image classification, language modeling as well as super-resolution.
Towards an Appropriate Receiver Beamforming Scheme for Millimeter Wave Communication: A Power Consumption Based Comparison

At millimeter wave (mmW) frequencies, beamforming and large antenna arrays are an essential requirement to combat the high path loss for mmW communication. Moreover, at these frequencies, very large bandwidths are available t o fulfill the data rate requirements of future wireless networks. However, utilization of these large bandwidths and of large antenna a rrays can result in a high power consumption which is an even bigger concern for mmW receiver design. In a mmW receiver, the analog-to-digital converter (ADC) is generally considered as the most power consuming block. In this paper, primarily focusing on the ADC power, we analyze and compare the total power consumption of the complete analog chain for Analog, Digita l and Hybrid beamforming (ABF, DBF and HBF) based receiver design. We show how power consumption of these beamforming schemes varies with a change in the number of antennas, the number of ADC bits (b) and the bandwidth (B). Moreover, we compare low power (as in ) and high power (as in ) ADC models, and show that for a certain range of number of antenna s, b and B, DBF may actually have a comparable and lower power consumption than ABF and HBF, respectively. In addition, we also show how the choice of an appropriate beamforming schem e depends on the signal-to-noise ratio regime.
Z-Forcing: Training Stochastic Recurrent Networks

Many efforts have been devoted to training generative latent variable models with autoregressive decoders, such as recurrent neural networks (RNN). Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech. We unify successful ideas from recently proposed architectures into a stochastic recurrent model: each step in the sequence is associated with a latent variable that is used to condition the recurrent dynamics for future steps. Training is performed with amortized variational inference where the approximate posterior is augmented with a RNN that runs backward through the sequence. In addition to maximizing the variational lower bound, we ease training of the latent variables by adding an auxiliary cost which forces them to reconstruct the state of the backward recurrent network. This provides the latent variables with a task-independent objective that enhances the performance of the overall model. We found this strategy to perform better than alternative approaches such as KL annealing. Although being conceptually simple, our model achieves state-of-the-art results on standard speech benchmarks such as TIMIT and Blizzard and competitive performance on sequential MNIST. Finally, we apply our model to language modeling on the IMDB dataset where the auxiliary cost helps in learning interpretable latent variables.
CMA-TWEANN: efficient optimization of neural networks via self-adaptation and seamless augmentation

Neuroevolutionary algorithms are successful methods for optimizing neural networks, especially for learning a neural policy (controller) in reinforcement learning tasks. Their significant advantage over gradient-based algorithms is the capability to search network topology as well as connection weights. However, state-of-the-art topology evolving methods are known to be inefficient compared to weight evolving methods with an appropriately hand-tuned topology. This paper introduces a novel efficient algorithm called CMA-TWEANN for evolving both topology and weights. Its high efficiency is achieved by introducing efficient topological mutation operators and integrating a state-of-the-art function optimization algorithm for weight optimization. Experiments on benchmark reinforcement learning tasks demonstrate that CMA-TWEANN solves tasks significantly faster than existing topology evolving methods. Furthermore, it outperforms weight evolving techniques even when they are equipped with a hand-tuned topology. Additional experiments reveal how and why CMA-TWEANN is the best performing weight evolving method.
Weak leakage-resilient client-side deduplication of encrypted data in cloud storage

Recently, Halevi et al. (CCS '11) proposed a cryptographic primitive called proofs of ownership (PoW) to enhance security of client-side deduplication in cloud storage. In a proof of ownership scheme, any owner of the same file F can prove to the cloud storage that he/she owns file F in a robust and efficient way, in the bounded leakage setting where a certain amount of efficiently-extractable information about file F is leaked. Following this work, we propose a secure client-side deduplication scheme, with the following advantages: our scheme protects data confidentiality (and some partial information) against both outside adversaries and honest-but-curious cloud storage server, while Halevi et al. trusts cloud storage server in data confidentiality; our scheme is proved secure w.r.t. any distribution with sufficient min-entropy, while Halevi et al. (the last and the most practical construction) is particular to a specific type of distribution (a generalization of "block-fixing" distribution) of input files.
 The cost of our improvements is that we adopt a weaker leakage setting: We allow a bounded amount one-time leakage of a target file before our scheme starts to execute, while Halevi et al. allows a bounded amount multi-time leakage of the target file before and after their scheme starts to execute. To the best of our knowledge, previous works on client-side deduplication prior Halevi et al. do not consider any leakage setting.
Edge bundling in information visualization

The edge, which can encode relational data in graphs and multidimensional data in parallel coordinates plots, is an important visual primitive for encoding data in information visualization research. However, when data become very large, visualizations often suffer from visual clutter as thousands of edges can easily overwhelm the display and obscure underlying patterns. Many edge-bundling techniques have been proposed to reduce visual clutter in visualizations. In this survey, we briefly introduce the visual-clutter problem in visualizations. Thereafter, we review the cost-based, geometry-based, and image-based edge-bundling methods for graphs, parallel coordinates, and flow maps. We then describe the various visualization applications that use edge-bundling techniques and discuss the evaluation studies concerning the effectiveness of edge-bundling methods. An edge-bundling taxonomy is proposed at the end of this survey.
A Hybrid Model for Chinese Spelling Check

Spelling check for Chinese has more challenging difficulties than that for other languages. A hybrid model for Chinese spelling check is presented in this article. The hybrid model consists of three components: one graph-based model for generic errors and two independently trained models for specific errors. In the graph model, a directed acyclic graph is generated for each sentence, and the single-source shortest-path algorithm is performed on the graph to detect and correct general spelling errors at the same time. Prior to that, two types of errors over functional words (characters) are first solved by conditional random fields: the confusion of “在” (<i>at</i>) (pinyin is <i>zai</i> in Chinese), “再” (<i>again</i>, <i>more</i>, <i>then</i>) (pinyin: <i>zai</i>) and “的” (<i>of</i>) (pinyin: <i>de</i>), “地” (-<i>ly</i>, adverb-forming particle) (pinyin: <i>de</i>), and “得” (<i>so that</i>, <i>have to</i>) (pinyin: <i>de</i>). Finally, a rule-based model is exploited to distinguish pronoun usage confusion: “她” (<i>she</i>) (pinyin: <i>ta</i>), “他” (<i>he</i>) (pinyin: <i>ta</i>), and some other common collocation errors. The proposed model is evaluated on the standard datasets released by the SIGHAN Bake-off shared tasks, giving state-of-the-art results.
iBAT: detecting anomalous taxi trajectories from GPS traces

GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to reveal many hidden "facts" about the city dynamics and human behaviors. In this paper, we aim to discover anomalous driving patterns from taxi's GPS traces, targeting applications like automatically detecting taxi driving frauds or road network change in modern cites. To achieve the objective, firstly we group all the taxi trajectories crossing the same source destination cell-pair and represent each taxi trajectory as a sequence of symbols. Secondly, we propose an Isolation-Based Anomalous Trajectory (iBAT) detection method and verify with large scale taxi data that iBAT achieves remarkable performance (AUC>0.99, over 90% detection rate at false alarm rate of less than 2%). Finally, we demonstrate the potential of iBAT in enabling innovative applications by using it for taxi driving fraud detection and road network change detection.
A Critical Assessment of Null Hypothesis Significance Testing in Quantitative Communication Research

Null hypothesis significance testing (NHST) is the most widely accepted and frequently used approach to statistical inference in quantitative communication research. NHST, however, is highly controversial, and several serious problems with the approach have been identified. This paper reviews NHST and the controversy surrounding it. Commonly recognized problems include a sensitivity to sample size, the null is usually literally false, unacceptable Type II error rates, and misunderstanding and abuse. Problems associated with the conditional nature of NHST and the failure to distinguish statistical hypotheses from substantive hypotheses are emphasized. Recommended solutions and alternatives are addressed in a companion article.
The Asymmetric Traveling Salesman Problem: Algorithms, Instance Generators, and Tests

The purpose of this paper is to provide a preliminary report on the rst broad-based experimental comparison of modern heuristics for the asymmetric traveling salesmen problem (ATSP). There are currently three general classes of such heuristics: classical tour construction heuristics such as Nearest Neighbor and the Greedy algorithm, local search algorithms based on re-arranging segments of the tour, as exemplied by the Kanellakis-Papadimitriou algorithm [KP80], and algorithms based on patching together the cycles in a minimum cycle cover, the best of which are variants on an algorithm proposed by Zhang [Zha93]. We test implementations of the main contenders from each class on a variety of instance types, introducing a variety of new random instance generators modeled on real-world applications of the ATSP. Among the many tentative conclusions we reach is that no single algorithm is dominant over all instance classes, although for each class the best tours are found either by Zhang's algorithm or an iterated variant on KanellakisPapadimitriou.
Corona: Interactivity of Body Electrostatics in Mobile Scenarios Using Wearable High-Voltage Static Charger

We present Corona, a wearable device that allows constant high-voltage electrostatic charge to be continuously accumulated in the human body. We propose the usages of Corona for three basic functions; generating haptic sensations, generating electric power from body static charge and near-body electric field, and inducing physical force near the body. We describe detailed principle of operation, analysis of produced energy and force, discussion on safety issues, as well as demonstration of proof-of-concept applications for aforementioned basic functions. We conclude with discussion of our experiments using the prototype and applications, which also involves a study to gather user feedbacks. To the best of our knowledge, Corona is the first work to exploit continuous high-voltage static charge on the human body for Human-Computer Interaction purposes.
A Temperature Sensor With an Inaccuracy of ${-}{\hbox{1}}/{+}{\hbox{0.8}}\ ^{\circ}$ C Using 90-nm 1-V CMOS for Online Thermal Monitoring of VLSI Circuits

This paper proposes an accurate four-transistor temperature sensor designed, and developed, for thermal testing and monitoring circuits in deep submicron technologies. A previous three-transistor temperature sensor, which utilizes the temperature characteristic of the threshold voltage, shows highly linear characteristics at a power supply voltage of 1.8 V or more; however, the supply voltage is reduced to 1 V in a 90-nm CMOS process. Since the temperature coefficient of the operating point's current at a 1-V supply voltage is steeper than the coefficient at a 1.8-V supply voltage, the operating point's current at high temperature becomes quite small and the output voltage goes into the subthreshold region or the cutoff region. Therefore, the operating condition of the conventional temperature sensor cannot be satisfied at 1-V supply and this causes degradation of linearity. To improve linearity at a 1-V supply voltage, one transistor is added to the conventional sensor. This additional transistor, which works in the saturation region, changes the temperature coefficient gradient of the operating point's current and moves the operating points at each temperature to appropriate positions within the targeted temperature range. The sensor features an extremely small area of 11.6times4.1 mum2 and low power consumption of about 25 muW. The performance of the sensor is highly linear and the predicted temperature error is merely -1.0 to +0.8degC using a two-point calibration within the range of 50degC to 125degC. The sensor has been implemented in the ASPLA CMOS 90-nm 1P7M process and has been tested successfully with a supply voltage of 1 V.