nils-herrmann/clean_scirepeval huggingface dataset:
A path following algorithm for Sparse Pseudo-Likelihood Inverse Covariance Estimation (SPLICE)

Given n observations of a p-dimensional random vector, the covariance matrix and its inverse (precision matrix) are needed in a wide range of applications. Sample covariance (e.g. its eigenstructure) can misbehave when p is comparable to the sample size n. Regularization is often used to mitigate the problem. In this paper, we proposed an `1 penalized pseudo-likelihood estimate for the inverse covariance matrix. This estimate is sparse due to the `1 penalty, and we term this method SPLICE. Its regularization path can be computed via an algorithm based on the homotopy/LARS-Lasso algorithm. Simulation studies are carried out for various inverse covariance structures for p = 15 and n = 20, 1000. We compare SPLICE with the `1 penalized likelihood estimate and a `1 penalized Cholesky decomposition based method. SPLICE gives the best overall performance in terms of three metrics on the precision matrix and ROC curve for model selection. Moreover, our simulation results demonstrate that the SPLICE estimates are positive-definite for most of the regularization path even though the restriction is not enforced.
Multi-Relational Learning at Scale with ADMM

The complex graph structure of the Web – with different relations or edge types – has motivated a large body of research tackling the challenge of mining multi-relational data in the presence of noise, partial inconsistencies, ambiguities, or duplicate entities. State-of-the-art advances in this field are relevant to many applications such as link prediction , Resource Description Framework (RDF) mining [2], entity linking , recommender systems [4], and natural language processing . However, new paradigms are still needed for statistical and computational inference for very large multi-relational datasets, like the ones produced at massive scale in projects such as the Google’s Knowledge Graph , YAGO [7], and in Semantic Web initiatives such as DBpedia [8]. Factorization models are considered state-of-the-art approaches for Statistical Relational Learning (SRL) in which they have exhibited a high predictive performance , , [11]. Factorization models for multi-relational data associate entities and relations with latent feature vectors and model predictions about unknown relationships through operations on these vectors (e.g., dot products). Optimizing the predictions for a number of relations can be seen as a prediction task with multiple target variables. For example, multi-target models can support information retrieval tasks in Linked Open Data bases like DBPedia by providing estimates of facts, that are neither explicitly stated in the knowledge base nor can be inferred from logical entailment, enabling probabilistic queries on such databases , . Another example in the context of social web recommender systems, is that such services are not only interested in recommending, for instance, news items to a user but also recommending other users as potential new friends. State-of-the-art factorization models approach the multitarget prediction task by sharing the parameters used for all target relations. Instances of such approaches are RESCAL , Multiple Order Factorization  and SME , which share entity specific parameters among all relations in the
Wrapper Induction for Information Extraction

Many Internet information resources present relational data|telephone directories, product catalogs, etc. Because these sites are formatted for people, mechanically extracting their content is di cult. Systems using such resources typically use hand-coded wrappers, procedures to extract data from information resources. We introduce wrapper induction, a method for automatically constructing wrappers, and identify hlrt, a wrapper class that is e ciently learnable, yet expressive enough to handle 48% of a recently surveyed sample of Internet resources. We use PAC analysis to bound the problem's sample complexity, and show that the system degrades gracefully with imperfect labeling knowledge.
Low-RCS and Polarization-Reconfigurable Antenna Using Cross-Slot-Based Metasurface

This letter presents a polarization-reconfigurable compact slot antenna with reduced radar cross section (RCS) using an asymmetric cross-shaped metasurface (MS). The proposed MS can reconfigure the polarization of the slot antenna between right-hand circular polarization (RHCP), left-hand circular polarization (LHCP), and linear polarization (LP) by rotating it with respect to the center of the slot antenna. In addition, the MS reduces the RCS of the slot antenna significantly in all polarization states. The cross-slot MS is placed just over the planar slot antenna without any air gap. The simulated monostatic RCS of -19.5 dBsm is observed at 4.4 GHz for LHCP and RHCP cases and -17.0 dBsm for LP mode of operation. Antenna performance in terms of its input matching, far-field parameters, monostatic RCS, and axial ratio are measured at its three polarization states, which are in agreement with simulated results.
Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding

In 1994, Karl Sims' evolved virtual creatures showed the potential of evolutionary algorithms to produce natural, complex morphologies and behaviors [30]. One might assume that nearly 20 years of improvements in computational speed and evolutionary algorithms would produce far more impressive organisms, yet the creatures evolved in the field of artificial life today are not obviously more complex, natural, or intelligent. Fig. 2 demonstrates an example of similar complexity in robots evolved 17 years apart.
Attend More Times for Image Captioning

Most attention-based image captioning models attend to the image once per word. However, attending once per word is rigid and is easy to miss some information. Attending more times can adjust the attention position, find the missing information back and avoid generating the wrong word. In this paper, we show that attending more times per word can gain improvements in the image captioning task, without increasing the number of parameters. We propose a flexible two-LSTM merge model to make it convenient to encode more attentions than words. Our captioning model uses two LSTMs to encode the word sequence and the attention sequence respectively. The information of the two LSTMs and the image feature are combined to predict the next word. Experiments on the MSCOCO caption dataset show that our method outperforms the state-of-the-art. Using bottom up features and self-critical training method, our method gets BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE scores of 0.381, 0.283, 0.580, 1.261 and 0.220 on the Karpathy test split.
Pincer-Search: A New Algorithm for Discovering the Maximum Frequent Set

Discovering frequent itemsets is a key problem in important data mining applications, such as the discovery of association rules, strong rules, episodes, and minimal keys. Typical algorithms for solving this problem operate in a bottom-up breadth-rst search direction. The computation starts from frequent 1-itemsets (minimal length frequent itemsets) and continues until all maximal (length) frequent itemsets are found. During the execution, every frequent itemset is explicitly considered. Such algorithms perform reasonably well when all maximal frequent item-sets are short. However, performance drastically decreases when some of the maximal frequent itemsets are relatively long. We present a new algorithm which combines both the bottom-up and top-down directions. The main search direction is still bottom-up but a restricted search is conducted in the top-down direction. This search is used only for maintaining and updating a new data structure we designed, the maximum frequent candidate set. It is used to prune candidates in the bottom-up search. As a very important characteristic of the algorithm, it is not necessary to explicitly examine every frequent itemset. Therefore it performs well even when some maximal frequent itemsets are long. As its output, the algorithm produces the maximum frequent set, i.e., the set containing all maximal frequent itemsets, which therefore speciies immediately all frequent itemsets. We evaluate the performance of the algorithm using a well-known benchmark database. The improvements can be up to several orders of magnitude, compared to the best current algorithms.
Generation of Synthetic Training Data for an HMM-based Handwriting Recognition System

A perturbation model for generating synthetic textlines from existing cursively handwritten lines of text produced by human writers is presented. Our purpose is to improve the performance of an HMM-based off-line cursive handwriting recognition system by providing it with additional synthetic training data. Two kinds of perturbations are applied, geometrical transformations and thinning/thickening operations. The proposed perturbation model is evaluated under different experimental conditions.
A New Approach for Current Sensor Fault Diagnosis in PMSG Drives for Wind Energy Conversion Systems

Fault diagnosis is a mandatory feature in fault-tolerant systems, since it provides the information necessary for the fault isolation and system reconfiguration. Recently, permanent-magnet synchronous generator (PMSG) drives have achieved prominence in wind energy conversion systems (WECSs), due to their reliability and availability. In this paper, a new approach is proposed for current sensor fault diagnosis in PMSG drives for WECSs. As opposed to the conventional state-observer-based methods for current sensor faults, which require a system model and the respective parameters, the proposed diagnostic method uses the measured phase currents only. Thus, its main merits are simplicity and reliability in the diagnosis, making it suitable for real-time implementation and to trigger remedial procedures. In addition, current sensor and open-circuit faults can be distinguished, and the affected phase is effectively identified in both cases. The proposed diagnostic technique is applied to the two power converters of a conventional back-to-back topology, and its performance is analyzed by means of several experimental results.
Improving application security with data flow assertions

Resin is a new language runtime that helps prevent security vulnerabilities, by allowing programmers to specify application-level data flow assertions. Resin provides policy objects, which programmers use to specify assertion code and metadata; data tracking, which allows programmers to associate assertions with application data, and to keep track of assertions as the data flow through the application; and filter objects, which programmers use to define data flow boundaries at which assertions are checked. Resin's runtime checks data flow assertions by propagating policy objects along with data, as that data moves through the application, and then invoking filter objects when data crosses a data flow boundary, such as when writing data to the network or a file.
 Using Resin, Web application programmers can prevent a range of problems, from SQL injection and cross-site scripting, to inadvertent password disclosure and missing access control checks. Adding a Resin assertion to an application requires few changes to the existing application code, and an assertion can reuse existing code and data structures. For instance, 23 lines of code detect and prevent three previously-unknown missing access control vulnerabilities in phpBB, a popular Web forum application. Other assertions comprising tens of lines of code prevent a range of vulnerabilities in Python and PHP applications. A prototype of Resin incurs a 33% CPU overhead running the HotCRP conference management application.
SINGO: A single-end-operative and genderless connector for self-reconfiguration, self-assembly and self-healing

Flexible and reliable connection is critical for self-reconfiguration, self-assembly, or self-healing. However, most existing connection mechanisms suffer from a deficiency that a connection would seize itself if one end malfunctions or is out of service. To mitigate this limitation on self-healing, this paper presents a new SINGO connector that can establish or disengage a connection even if one end of the connection is not operational. We describe the design and the prototype of the connector and demonstrate its performance by both theoretical analysis and physical experimentations.
Sexual harassment on the internet

Sexual harassment offline is a well-known, highly prevalent, extensively investigated, and intensively treated social problem. An accepted model classifies sexual harassment behaviors into the categories of gender harassment, unwanted sexual attention, and sexual coercion. Theory and research show that sexual harassment behavior occurs as a product of person × situation characteristics and has substantial personal and organizational costs. This article reviews the available information on sexual harassment in cyberspace, equates this phenomenon with what has been learned about sexual harassment offline, points to specific characteristics of online culture and technology that reinforce the behavior, and proposes ways of promoting prevention.
Controlling the False Discovery Rate : A Practical and Powerful Approach to Multiple Testing

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Generalized distributed rate limiting

The Distributed Rate Limiting (DRL) paradigm is a recently proposed mechanism for decentralized control of cloud-based services. DRL is a simple and efficient approach to resolve the issues of pricing and resource control/engineering of cloud based services. The existing DRL schemes focus on very specific performance metrics (such as loss rate and fair-share) and their design heavily depends on the assumption that the traffic is generated by elastic TCP sources. In this paper we tackle the DRL problem for general workloads and performance metrics and propose an analytic framework for the design of stable DRL algorithms. The closed-form nature of our results allows simple design rules which, together with extremely low communication overhead, makes the presented algorithms practical and easy to deploy with guaranteed convergence properties under a wide range of possible scenarios.
The security of modern password expiration: an algorithmic framework and empirical analysis

This paper presents the first large-scale study of the success of password expiration in meeting its intended purpose, namely revoking access to an account by an attacker who has captured the account's password. Using a dataset of over 7700 accounts, we assess the extent to which passwords that users choose to replace expired ones pose an obstacle to the attacker's continued access. We develop a framework by which an attacker can search for a user's new password from an old one, and design an efficient algorithm to build an approximately optimal search strategy. We then use this strategy to measure the difficulty of breaking newly chosen passwords from old ones. We believe our study calls into question the merit of continuing the practice of password expiration.
The Reasonable Effectiveness of Synthetic Visual Data

The recent successes in many visual recognition tasks, such as image classification, object detection, and semantic segmentation can be attributed in large part to three factors: (i) advances in end-to-end trainable deep learning models (LeCun 2015), (ii) the progress of computing hardware, and (iii) the introduction of increasingly larger labeled datasets such as PASCAL VOC (Everingham et al. 2010), KITTI (Geiger et al. 2012), ImageNet (Russakovsky et al. 2015), MS-COCO (Lin et al. 2014), and Cityscapes (Cordts et al. 2016), among others. In fact, recent results (Sun et al. 2017; Hestness et al. 2017) indicate that the reliability of current visual models might not be limited by the algorithms themselves but by the type and amount of supervised data available. Therefore, to tackle more challenging tasks, such as video scene understanding, progress is needed not only on the algorithmic and hardware fronts but also on the data front, both for learning and quantitative evaluation. However, acquiring and densely labeling a large visual dataset with ground truth information (e.g. semantic labels, depth, optical flow) for each new problem is not a scalable alternative. Observing the parallel progress of the computer graphics community, computer vision (CV) researchers have recently revived the use of synthetic visual datasets (Butler et al. 2012; Handa et al. 2016; Mayer et al. 2016; Ros et al. 2016; Gaidon et al. 2016; Richter et al. 2017; de Souza et al. 2017; Li et al. 2017; Johnson-Roberson et al. 2017) and simulators (Chen et al. 2015; Shah et al. 2017; Dosovitskiy et al. 2017;
S-Match: an algorithm and an implementation of semantic matching

We think of Match as an operator which takes two graph-like structures (e.g., conceptual hierarchies or ontologies) and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing, and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall.
Visual firewall: real-time network security monitor

Networked systems still suffer from poor firewall configuration and monitoring. VisualFirewall seeks to aid in the configuration of firewalls and monitoring of networks by providing four simultaneous views that display varying levels of detail and time-scales as well as correctly visualizing firewall reactions to individual packets. The four implemented views, real-time traffic, visual signature, statistics, and IDS alarm, provide the levels of detail and temporality that system administrators need to properly monitor their systems in a passive or an active manner. We have visualized several attacks, and we feel that even individuals unfamiliar with networking concepts can quickly distinguish between benign and malignant traffic patterns with a minimal amount of introduction.
Independent components analysis for fetal electrocardiogram extraction: a case for the data efficient Mermaid algorithm

Fetal heart rate (FHR) monitoring is currently the primary methodology for antenatal determination of fetal well-being. Currently, the FHR can be detected with ultrasonography, but the additional information from fetal electrocardiogram (FECG) is only available via an invasive scalp electrode. A cost effective noninvasive monitoring through standard ECG electrodes could be used on nearly every patient in lieu of the ultrasound monitors. In this method, a number of electrodes are positioned on the abdomen of the mother to collect, simultaneously, various combinations of the signals including the heartbeats of the mother and the fetus. For accurate fetal heart-rate estimation, a clean FECG must be extracted from the collected mixtures. It is well known that this can be achieved using blind source separation (BSS) techniques. In this paper, the performance of the Mermaid algorithm, which is based on minimizing Renyi’s mutual information, is evaluated on this problem of great practical importance. The effectiveness and data efficiency of Mermaid and its superiority over alternative information theoretic BSS algorithms are illustrated using artificially mixed ECG signals as well as fetal heart rate estimates in real ECG mixtures.
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights

Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-based methods, such as BackPropagation (BP). Inference in probabilistic graphical models is often done using variational Bayes methods, such as Expectation Propagation (EP). We show how an EP based approach can also be used to train deterministic MNNs. Specifically, we approximate the posterior of the weights given the data using a “mean-field” factorized distribution, in an online setting. Using online EP and the central limit theorem we find an analytical approximation to the Bayes update of this posterior, as well as the resulting Bayes estimates of the weights and outputs. Despite a different origin, the resulting algorithm, Expectation BackPropagation (EBP), is very similar to BP in form and efficiency. However, it has several additional advantages: (1) Training is parameter-free, given initial conditions (prior) and the MNN architecture. This is useful for large-scale problems, where parameter tuning is a major challenge. (2) The weights can be restricted to have discrete values. This is especially useful for implementing trained MNNs in precision limited hardware chips, thus improving their speed and energy efficiency by several orders of magnitude. We test the EBP algorithm numerically in eight binary text classification tasks. In all tasks, EBP outperforms: (1) standard BP with the optimal constant learning rate (2) previously reported state of the art. Interestingly, EBP-trained MNNs with binary weights usually perform better than MNNs with continuous (real) weights if we average the MNN output using the inferred posterior.
Software metrics : usability and evaluation of software quality

It is difficult to understand, let alone improve, the quality of software without the knowledge of its software development process and software products. There must be some measurement process to predict the software development, and to evaluate the software products. This thesis provides a brief view on Software Quality, Software Metrics, and Software Metrics methods that will predict and measure the specified quality factors of software. It further discusses about the Quality as given by the standards such as ISO, principal elements required for the Software Quality and Software Metrics as the measurement technique to predict the Software Quality. This thesis was performed by evaluating a source code developed in Java, using Software Metrics, such as Size Metrics, Complexity Metrics, and Defect Metrics. Results show that, the quality of software can be analyzed, studied and improved by the usage of software metrics.
Image Processing on Compressed Data for Large Video Databases

This paper presents a novel approach to processing encoded video sequences prior to decoding. Scene changes may be easily detected using DCT coefficients in JPEG and MPEG encoded video sequences. In addition, by analyzing the DCT coefficients, regions of interest may be isolated prior to decompression, increasing efficiency of any subsequent image processing steps, such as edge detection. The results are currently used in a video browser, and are part of an ongoing research project in creating large video databases. The procedure is presented in detail and several examples are exhibited.