nils-herrmann/clean_scirepeval huggingface dataset:
Comprehensible Predictive Models for Business Processes

Table A1 summarizes the various characteristics of the synthetic models used in the experiments, including the number of event types, the size of the state space, whether a challenging construct is contained (loops, duplicates, nonlocal choice, and concurrency), and the entropy of the process defined by the model (estimated based on a sample of size 10,000). The original models may contain either duplicate tasks (two conceptually different transitions with the same label) or invisible tasks (transitions that have no label, as their firing is not recorded in the event log). We transformed all invisible transitions to duplicates such that, when there was an invisible task i in the original model, we added duplicates for all transitions t that, when fired, enable the invisible transition. These duplicates emulate the combined firing of t and i. Since we do not distinguish between duplicates and invisible tasks, we combined this category.
Superpixel segmentation using Linear Spectral Clustering

We present in this paper a superpixel segmentation algorithm called Linear Spectral Clustering (LSC), which produces compact and uniform superpixels with low computational costs. Basically, a normalized cuts formulation of the superpixel segmentation is adopted based on a similarity metric that measures the color similarity and space proximity between image pixels. However, instead of using the traditional eigen-based algorithm, we approximate the similarity metric using a kernel function leading to an explicitly mapping of pixel values and coordinates into a high dimensional feature space. We revisit the conclusion that by appropriately weighting each point in this feature space, the objective functions of weighted K-means and normalized cuts share the same optimum point. As such, it is possible to optimize the cost function of normalized cuts by iteratively applying simple K-means clustering in the proposed feature space. LSC is of linear computational complexity and high memory efficiency and is able to preserve global properties of images. Experimental results show that LSC performs equally well or better than state of the art superpixel segmentation algorithms in terms of several commonly used evaluation metrics in image segmentation.
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering

A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answerreranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strengthbased re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer. Our models have achieved state-of-the-art results on three public open-domain QA datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8 percentage points of improvement over the former two datasets.
Text Mining in Multimedia

A large amount of multimedia data (e.g., image and video) is now available on the Web. A multimedia entity does not appear in isolation, but is accompanied by various forms of metadata, such as surrounding text, user tags, ratings, and comments etc. Mining these textual metadata has been found to be effective in facilitating multimedia information processing and management. A wealth of research efforts has been dedicated to text mining in multimedia. This chapter provides a comprehensive survey of recent research efforts. Specifically, the survey focuses on four aspects: (a) surrounding text mining; (b) tag mining; (c) joint text and visual content mining; and (d) cross text and visual content mining. Furthermore, open research issues are identified based on the current research efforts.
A Face Detection Algorithm Based on Deep Learning

To achieve the problem of partial occlusion and multi-pose in the face detection, a face detection algorithm based on deep learning is proposed. Through establishing deep model, the probabilistic correlations of the visibilities are learned in different face local region. Firstly, face local regions are detected by part-based face detector. Secondly, the detection results are feed to deep model, which describes the correlations of different regions. Finally, the face detection is completed according to the correlations. The experimental results show that the proposed algorithm is effective in the case of occlusion and multi-pose.
Inductive Logic Programming: Theory and Methods

Inductive Logic Programming (ILP) is a new discipline which investigates the inductive construction of rst-order clausal theories from examples and background knowledge. We survey the most important theories and methods of this new eld. Firstly, various problem speciications of ILP are formalised in semantic settings for ILP, yielding a \model-theory" for ILP. Secondly, a generic ILP algorithm is presented. Thirdly, the inference rules and corresponding operators used in ILP are presented, resulting in a \proof-theory" for ILP. Fourthly, since inductive inference does not produce statements which are assured to follow from what is given, inductive inferences require an alternative form of justiication. This can take the form of either probabilistic support or logical constraints on the hypothesis language. Information compression techniques used within ILP are presented within a unifying Bayesian approach to connrmation and corroboration of hypotheses. Also, diierent ways to constrain the hypothesis language, or specify the declarative bias are presented. Fifthly, some advanced topics in ILP are addressed. These include aspects of computational learning theory as applied to ILP, and the issue of predicate invention. Finally, we survey some applications and implementations of ILP. ILP applications fall under two diierent categories: rstly scientiic discovery and knowledge acquisition, and secondly programming assistants.
Malware detection with quantitative data flow graphs

We propose a novel behavioral malware detection approach based on a generic system-wide quantitative data flow model. We base our data flow analysis on the incremental construction of aggregated quantitative data flow graphs. These graphs represent communication between different system entities such as processes, sockets, files or system registries. We demonstrate the feasibility of our approach through a prototypical instantiation and implementation for the Windows operating system. Our experiments yield encouraging results: in our data set of samples from common malware families and popular non-malicious applications, our approach has a detection rate of 96% and a false positive rate of less than 1.6%. In comparison with closely related data flow based approaches, we achieve similar detection effectiveness with considerably better performance: an average full system analysis takes less than one second.
A Novel Framework for Robustness Analysis of Visual QA Models

Deep neural networks have been playing an essential role in many computer vision tasks including Visual Question Answering (VQA). Until recently, the study of their accuracy has been the main focus of research and now there is a huge trend toward assessing the robustness of these models against adversarial attacks by evaluating the accuracy of these models under increasing levels of noisiness. In VQA, the attack can target the image and/or the proposed main question and yet there is a lack of proper analysis of this aspect of VQA. In this work, we propose a new framework that uses semantically relevant questions, dubbed basic questions, acting as noise to evaluate the robustness of VQA models. We hypothesize that as the similarity of a basic question to the main question decreases, the level of noise increases. So, to generate a reasonable noise level for a given main question, we rank a pool of basic questions based on their similarity with this main question. We cast this ranking problem as a LASSO optimization problem. We also propose a novel robustness measure Rscore and two large-scale question datasets, General Basic Question Dataset and Yes/No Basic Question Dataset in order to standardize robustness analysis of VQA models. We analyze the robustness of several state-of-the-art VQA models and show that attention-based VQA models are more robust than other methods in general. The main goal of this framework is to serve as a benchmark to help the community in building more accurate and robust VQA models.
A system model and inversion for synthetic aperture radar imaging

A system model and its corresponding inversion for synthetic aperture radar (SAR) imaging are presented. The system model incorporates the spherical nature of a radar's radiation pattern at far field. The inverse method based on this model performs a spatial Fourier transform (Doppler processing) on the recorded signals with respect to the available coordinates of a translational radar (SAR) or target (inverse SAR). It is shown that the transformed data provide samples of the spatial Fourier transform of the target's reflectivity function. The inverse method can be modified to incorporate deviations of the radar's motion from its prescribed straight line path. The effects of finite aperture on resolution, reconstruction, and sampling constraints for the imaging problem are discussed.
Automatic discovery and transfer of MAXQ hierarchies

We present an algorithm, HI-MAT (Hierarchy Induction via Models And Trajectories), that discovers MAXQ task hierarchies by applying dynamic Bayesian network models to a successful trajectory from a source reinforcement learning task. HI-MAT discovers subtasks by analyzing the causal and temporal relationships among the actions in the trajectory. Under appropriate assumptions, HI-MAT induces hierarchies that are consistent with the observed trajectory and have compact value-function tables employing safe state abstractions. We demonstrate empirically that HI-MAT constructs compact hierarchies that are comparable to manually-engineered hierarchies and facilitate significant speedup in learning when transferred to a target task.
Integrating nature-inspired optimization algorithms to K-means clustering

Although K-means clustering algorithm is simple and popular, it has a fundamental drawback of falling into local optima that depend on the randomly generated initial centroid values. Optimization algorithms are well known for their ability to guide iterative computation in searching for global optima. They also speed up the clustering process by achieving early convergence. Contemporary optimization algorithms inspired by biology, including the Wolf, Firefly, Cuckoo, Bat and Ant algorithms, simulate swarm behavior in which peers are attracted while steering towards a global objective. It is found that these bio-inspired algorithms have their own virtues and could be logically integrated into K-means clustering to avoid local optima during iteration to convergence. In this paper, the constructs of the integration of bio-inspired optimization methods into K-means clustering are presented. The extended versions of clustering algorithms integrated with bio-inspired optimization methods produce improved results. Experiments are conducted to validate the benefits of the proposed approach.
Sequence and organization of the human mitochondrial genome

The complete sequence of the 16,569-base pair human mitochondrial genome is presented. The genes for the 12S and 16S rRNAs, 22 tRNAs, cytochrome c oxidase subunits I, II and III, ATPase subunit 6, cytochrome b and eight other predicted protein coding genes have been located. The sequence shows extreme economy in that the genes have none or only a few noncoding bases between them, and in many cases the termination codons are not coded in the DNA but are created post-transcriptionally by polyadenylation of the mRNAs.
Dissecting BitTorrent: Five Months in a Torrent's Lifetime

Popular content such as software updates is requested by a large number of users. Traditionally, to satisfy a large number of requests, lager server farms or mirroring are used, both of which are expensive. An inexpensive alternative are peer-to-peer based replication systems, where users who retrieve the file, act simultaneously as clients and servers. In this paper, we study BitTorrent, a new and already very popular peerto-peer application that allows distribution of very large contents to a large set of hosts. Our analysis of BitTorrent is based on measurements collected on a five months long period that involved thousands of peers. We assess the performance of the algorithms used in BitTorrent through several metrics. Our conclusions indicate that BitTorrent is a realistic and inexpensive alternative to the classical server-based content distribution.
Multilabel dimensionality reduction via dependence maximization

Multilabel learning deals with data associated with multiple labels simultaneously. Like other data mining and machine learning tasks, multilabel learning also suffers from the curse of dimensionality. Dimensionality reduction has been studied for many years, however, multilabel dimensionality reduction remains almost untouched. In this article, we propose a multilabel dimensionality reduction method, MDDM, with two kinds of projection strategies, attempting to project the original data into a lower-dimensional feature space maximizing the dependence between the original feature description and the associated class labels. Based on the Hilbert-Schmidt Independence Criterion, we derive a eigen-decomposition problem which enables the dimensionality reduction process to be efficient. Experiments validate the performance of MDDM.
Low-Cost CPW Meander Inductors Utilizing Ink-Jet Printing on Flexible Substrate for High-Frequency Applications

This paper describes the design and fabrication of low-cost coplanar waveguide (CPW) miniature meander inductors. Inductors are fabricated on a flexible plastic polyimide foil in ink-jet printed technology with silver nanoparticle ink in a single layer. For the first time, the detailed characterization and simulation of CPW inductors in this technology is reported. The inductors are developed with impressive measured self-resonance frequency up to 18.6 GHz. The 2.107-nH inductor measures only 1 mm × 1.7 mm × 0.075 mm and demonstrates a high level of miniaturization in ink-jet printing technology. The measured response characteristics are in excellent agreement with the predicted simulation response.
Coherent Spatiotemporal Filtering, Upsampling and Rendering of RGBZ Videos

Sophisticated video processing effects require both image and geometry information. We explore the possibility to augment a video camera with a recent infrared time-of-flight depth camera, to capture high-resolution RGB and low-resolution, noisy depth at video frame rates. To turn such a setup into a practical RGBZ video camera, we develop efficient data filtering techniques that are tailored to the noise characteristics of IR depth cameras. We first remove typical artefacts in the RGBZ data and then apply an efficient spatiotemporal denoising and upsampling scheme. This allows us to record temporally coherent RGBZ videos at interactive frame rates and to use them to render a variety of effects in unprecedented quality. We show effects such as video relighting, geometry-based abstraction and stylisation, background segmentation and rendering in stereoscopic 3D.
Mobilizing the Micro-Ops: Exploiting Context Sensitive Decoding for Security and Energy Efficiency

Modern instruction set decoders feature translation of native instructions into internal micro-ops to simplify CPU design and improve instruction-level parallelism. However, this translation is static in most known instances. This work proposes context-sensitive decoding, a technique that enables customization of the micro-op translation at the microsecond or faster granularity, based on the current execution context and/or preset hardware events. While there are many potential applications, this work demonstrates its effectiveness with two use cases: 1) as a novel security defense to thwart instruction/data cache-based side-channel attacks, as demonstrated on commercial implementations of RSA and AES and 2) as a power management technique that performs selective devectorization to enable efficient unit-level power gating. This architecture, first by allowing execution to transition between different translation modes rapidly, defends against a variety of attacks, completely obfuscating code-dependent cache access, only sacrificing 5% in steady-state performance – orders of magnitude less than prior art. By selectively disabling the vector units without disabling vector arithmetic, context-sensitive decoding reduces energy by 12.9% with minimal loss in performance. Both optimizations work with no significant changes to the pipeline or the external ISA.
GRID codes: Strip-based erasure codes with high fault tolerance for storage systems

As storage systems grow in size and complexity, they are increasingly confronted with concurrent disk failures together with multiple unrecoverable sector errors. To ensure high data reliability and availability, erasure codes with high fault tolerance are required. In this article, we present a new family of erasure codes with high fault tolerance, named GRID codes. They are called such because they are a family of strip-based codes whose strips are arranged into multi-dimensional grids. In the construction of GRID codes, we first introduce a concept of matched codes and then discuss how to use matched codes to construct GRID codes. In addition, we propose an iterative reconstruction algorithm for GRID codes. We also discuss some important features of GRID codes. Finally, we compare GRID codes with several categories of existing codes. Our comparisons show that for large-scale storage systems, our GRID codes have attractive advantages over many existing erasure codes: (a) They are completely XOR-based and have very regular structures, ensuring easy implementation; (b) they can provide up to 15 and even higher fault tolerance; and (c) their storage efficiency can reach up to 80% and even higher. All the advantages make GRID codes more suitable for large-scale storage systems.
Asynchronous BCI control using high-frequency SSVEP

Steady-State Visual Evoked Potential (SSVEP) is a visual cortical response evoked by repetitive stimuli with a light source flickering at frequencies above 4 Hz and could be classified into three ranges: low (up to 12 Hz), medium (12-30) and high frequency (> 30 Hz). SSVEP-based Brain-Computer Interfaces (BCI) are principally focused on the low and medium range of frequencies whereas there are only a few projects in the high-frequency range. However, they only evaluate the performance of different methods to extract SSVEP. This research proposed a high-frequency SSVEP-based asynchronous BCI in order to control the navigation of a mobile object on the screen through a scenario and to reach its final destination. This could help impaired people to navigate a robotic wheelchair. There were three different scenarios with different difficulty levels (easy, medium and difficult). The signal processing method is based on Fourier transform and three EEG measurement channels. The research obtained accuracies ranging in classification from 65% to 100% with Information Transfer Rate varying from 9.4 to 45 bits/min. Our proposed method allows all subjects participating in the study to control the mobile object and to reach a final target without prior training.
Structured learning with constrained conditional models

Making complex decisions in real world problems often involves assigning values to sets of interdependent variables where an expressive dependency structure among these can influence, or even dictate, what assignments are possible. Commonly used models typically ignore expressive dependencies since the traditional way of incorporating non-local dependencies is inefficient and hence leads to expensive training and inference. The contribution of this paper is two-fold. First, this paper presents Constrained Conditional Models (CCMs), a framework that augments linear models with declarative constraints as a way to support decisions in an expressive output space while maintaining modularity and tractability of training. The paper develops, analyzes and compares novel algorithms for CCMs based on Hidden Markov Models and Structured Perceptron. The proposed CCM framework is also compared to task-tailored models, such as semi-CRFs. Second, we propose CoDL, a constraint-driven learning algorithm, which makes use of constraints to guide semi-supervised learning. We provide theoretical justification for CoDL along with empirical results which show the advantage of using declarative constraints in the context of semi-supervised training of probabilistic models.
Semi Supervised Preposition-Sense Disambiguation using Multilingual Data

Prepositions are very common and very ambiguous, and understanding their sense is critical for understanding the meaning of the sentence. Supervised corpora for the preposition-sense disambiguation task are small, suggesting a semi-supervised approach to the task. We show that signals from unannotated multilingual data can be used to improve supervised prepositionsense disambiguation. Our approach pre-trains an LSTM encoder for predicting the translation of a preposition, and then incorporates the pre-trained encoder as a component in a supervised classification system, and fine-tunes it for the task. The multilingual signals consistently improve results on two preposition-sense datasets.
The effect of a persuasive social impact game on affective learning and attitude

To investigate whether a persuasive social impact game may serve as a way to increase affective learning and attitude towards the homeless, this study examined the effects of persuasive mechanics in a video game designed to put the player in the shoes of an almost-homeless person. Data were collected from 5139 students in 200 middle/high school classes across four states. Classes were assigned to treatment groups based on matching. Two treatment conditions and a control group were employed in the study. All three groups affective learning and attitude scores decreased from the immediate posttest but the game group was significantly different from the control group in a positive direction. Students who played the persuasive social impact game sustained a significantly higher score on the Affective Learning Scale (ALS) and the Attitude Towards Homelessness Inventory (ATHI) after three weeks. Overall, findings suggest that when students play a video game that is designed using persuasive mechanics an affective and attitude change can be measured empirically.