nils-herrmann/clean_scirepeval huggingface dataset:
Building contextual visual vocabulary for large-scale image applications

Not withstanding its great success and wide adoption in Bag-of-visual Words representation, visual vocabulary created from single image local features is often shown to be ineffective largely due to three reasons. First, many detected local features are not stable enough, resulting in many noisy and non-descriptive visual words in images. Second, single visual word discards the rich spatial contextual information among the local features, which has been proven to be valuable for visual matching. Third, the distance metric commonly used for generating visual vocabulary does not take the semantic context into consideration, which renders them to be prone to noise. To address these three confrontations, we propose an effective visual vocabulary generation framework containing three novel contributions: 1) we propose an effective unsupervised local feature refinement strategy; 2) we consider local features in groups to model their spatial contexts; 3) we further learn a discriminant distance metric between local feature groups, which we call discriminant group distance. This group distance is further leveraged to induce visual vocabulary from groups of local features. We name it contextual visual vocabulary, which captures both the spatial and semantic contexts. We evaluate the proposed local feature refinement strategy and the contextual visual vocabulary in two large-scale image applications: large-scale near-duplicate image retrieval on a dataset containing 1.5 million images and image search re-ranking tasks. Our experimental results show that the contextual visual vocabulary shows significant improvement over the classic visual vocabulary. Moreover, it outperforms the state-of-the-art Bundled Feature in the terms of retrieval precision, memory consumption and efficiency.
Robust and unobtrusive marker tracking on mobile phones

Marker tracking has revolutionized augmented reality about a decade ago. However, this revolution came at the expense of visual clutter. In this paper, we propose several new marker techniques, which are less obtrusive than the usual black and white squares. Furthermore, we report methods that allow tracking beyond the visibility of these markers further improving robustness. All presented techniques are implemented in a single tracking library, are highly efficient in their memory and CPU usage and run at interactive frame rates on mobile phones.
Improving Named Entity Recognition for Morphologically Rich Languages Using Word Embeddings

In this paper, we addressed the Named Entity Recognition (NER) problem for morphologically rich languages by employing a semi-supervised learning approach based on neural networks. We adopted a fast unsupervised method for learning continuous vector representations of words, and used these representations along with language independent features to develop a NER system. We evaluated our system for the highly inflectional Turkish and Czech languages. We improved the state-of-the-art F-score obtained for Turkish without using gazetteers by 2.26% and for Czech by 1.53%. Unlike the previous state-of-the-art systems developed for these languages, our system does not make use of any language dependent features. Therefore, we believe it can easily be applied to other morphologically rich languages.
Frequency or probability? A qualitative study of risk communication formats used in health care.

The communication of probabilistic outcomes is an essential aspect of shared medical decision making.



The authors conducted a qualitative study using focus groups to evaluate the response of women to various formats used in the communication of breast cancer risk.



Graphic discrete frequency formats using highlighted human figures had greater salience than continuous probability formats using bar graphs. Potential biases in the estimation of risk magnitude were associated with the use of highlighted human figures versus bar graphs and the denominator size in graphics using highlighted human figures. The presentation of uncertainty associated with risk estimates caused some to loose trust in the information, whereas others were accepting of uncertainty in scientific data.



The qualitative studyidentified new constructs with regard to how patients process probabilistic information. Further research in the clinical setting is needed to provide a theoretical justification for the format used when presenting risk information to patients.
Horse-race model simulations of the stop-signal procedure.

In the stop-signal paradigm, subjects perform a standard two-choice reaction task in which, occasionally and unpredictably, a stop-signal is presented requiring the inhibition of the response to the choice signal. The stop-signal paradigm has been successfully applied to assess the ability to inhibit under a wide range of experimental conditions and in various populations. The current study presents a set of evidence-based guidelines for using the stop-signal paradigm. The evidence was derived from a series of simulations aimed at (a) examining the effects of experimental design features on inhibition indices, and (b) testing the assumptions of the horse-race model that underlies the stop-signal paradigm. The simulations indicate that, under most conditions, the latency, but not variability, of response inhibition can be reliably estimated.
Ontology semantic approach to extraction of knowledge from holy quran

With the continued demand for Islamic knowledge, which is mainly based on the Quran as a source of knowledge and wisdom, systems that facilitate an easy search of the content of the Quran remain a considerable challenge. Although in recent years there have been tools for Quran search, most of these tools are based on keyword search, meaning that the user needs to know the correct keywords before being able to retrieve the content of al-Quran. In this paper, we propose a system that supports the end user in querying and exploring the Quran ontology. The system comprises user query reformulation against the Quran ontology stored and annotated in the knowledge base. The Quran ontology consists of noun concepts identified in al-Quran, and the relationship that exists between these concepts. The user writes a query in the natural language and the proposed system reformulates the query to match the content found in the knowledge base in order to retrieve the relevant answer. The answer is represented by the Quranic verse related to the user query.
A compact branch-line coupler using folded microstrip lines

This paper presents a compact 3 dB Quadrature branch-line coupler using folded microstrip line geometry. Commercially available IE3D software has been used to design and simulate the structure. The proposed structure is simple and takes lesser optimization time. It has been shown that the structure provides about 43.63% compactness compared to the conventional branch line coupler. The structure, therefore, can be incorporated in the microwave circuit design where medium level compactness and faster design are preferred.
Event-Triggered State Observers for Sparse Sensor Noise/Attacks

This paper describes two algorithms for state reconstruction from sensor measurements that are corrupted with sparse, but otherwise arbitrary, “noise.” These results are motivated by the need to secure cyber-physical systems against a malicious adversary that can arbitrarily corrupt sensor measurements. The first algorithm reconstructs the state from a batch of sensor measurements while the second algorithm is able to incorporate new measurements as they become available, in the spirit of a Luenberger observer. A distinguishing point of these algorithms is the use of event-triggered techniques to improve the computational performance of the proposed algorithms.
Facial Expression Analysis under Partial Occlusion: A Survey

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment, and human–computer interaction. The vast majority of completed FEA studies are based on nonoccluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better-informed and benchmarked future work.
Personal projects, happiness, and meaning: on doing well and being yourself.

Personal Projects Analysis (B. R. Little, 1983) was adapted to examine relations between participants' appraisals of their goal characteristics and orthogonal happiness and meaning factors that emerged from factor analyses of diverse well-being measures. In two studies with 146 and 179 university students, goal efficacy was associated with happiness and goal integrity was associated with meaning. A new technique for classifying participants according to emergent identity themes is introduced. In both studies, identity-compensatory predictors of happiness were apparent. Agentic participants were happiest if their goals were supported by others, communal participants were happiest if their goals were fun, and hedonistic participants were happiest if their goals were being accomplished. The distinction between happiness and meaning is emphasized, and the tension between efficacy and integrity is discussed. Developmental implications are discussed with reference to results from archival data from a sample of senior managers.
A Marx-based power converter topology as a current source for pulsed magnets in particle accelerators

A modified Marx-based solid-state topology for supplying inductive loads is presented. The basic principle of charging capacitors in parallel and discharging them in series is conserved. Due to the negative di/dt phase the inductive load imposes a reversible voltage power converter. Furthermore, in some applications such as eddy current septum magnets a negative current is required as well. The presented topology allows the production of controlled half or full sine waves, with or without short or long flat-tops. The solution is able to regenerate back to the capacitor banks the energy from the magnet. To shape the desired current it is possible to time-shift the discharge of different Marx cells. Numerical simulations are used to illustrate the functionalities of this solution. This flexible converter can be easily used for all pulsed current converters in accelerator facilities with a high degree of modularity and flexibility.
A Salience-based Quality Metric for Visualization

Salience detection is a principle mechanism to facilitate visual attention. A good visualization guides the observer’s attention to the relevant aspects of the representation. Hence, the distribution of salience over a visualization image is an essential measure of the quality of the visualization. We describe a method for computing such a metric for a visualization image in the context of a given dataset. We show how this technique can be used to analyze a visualization’s salience, improve an existing visualization, and choose the best representation from a set of alternatives. The usefulness of this proposed metric is illustrated using examples from information visualization, volume visualization and flow visualization.
ModelSeek: an effective 3D model retrieval system

Since object similarity is a subjective matter, the gap between low-level feature representations and high-level semantic concepts is a major problem in the field of content-based 3D model retrieval. This paper presents a novel composite model descriptor, which takes into account both visual and geometric characteristics of 3D models. It also proposes an original mapping mechanism from low-level model features to high-level semantic concepts based on the user’s retrieval history, and so this method belongs to long-term relevance feedback algorithms for 3D model retrieval. Finally, an effective 3D model retrieval system “ModelSeek” has been built, and implemented with the introduced model descriptor and mapping mechanism. The experimental results show that the approaches above not only have significantly improved the retrieval performance, but have also achieved better retrieval effectiveness than the state-of-the-art techniques on the publicly available 3D model criterion that Princeton Shape Benchmark (PSB) and several standard evaluation measures.
A Categorization of Discovery Technologies for the Internet of Things

Discovery of things as well as their resources, metadata, properties, and capabilities is a fundamental requirement in any Internet of Things (IoT) ecosystem. This paper analyzes the state of the art of communication technologies for the IoT with respect to discovery functionalities. Therefore, a comprehensive study of the technology landscape on IoT discovery mechanisms is provided. As a key contribution, we introduce a novel categorization of the available discovery technologies. Further, we identify and analyze the generic interaction pattern of each category. Finally, the technologies of each category are evaluated. With this evaluation at hand, IoT system designers are given decision making support. In the future, this analysis will serve as a basis for a generic discovery framework for the IoT. This work has been elaborated as part of the W3C Web of Things interest group.
Automated Variational Inference for Gaussian Process Models

We develop an automated variational method for approximate inference in Gaussian process (GP) models whose posteriors are often intractable. Using a mixture of Gaussians as the variational distribution, we show that (i) the variational objective and its gradients can be approximated efficiently via sampling from univariate Gaussian distributions and (ii) the gradients wrt the GP hyperparameters can be obtained analytically regardless of the model likelihood. We further propose two instances of the variational distribution whose covariance matrices can be parametrized linearly in the number of observations. These results allow gradientbased optimization to be done efficiently in a black-box manner. Our approach is thoroughly verified on five models using six benchmark datasets, performing as well as the exact or hard-coded implementations while running orders of magnitude faster than the alternative MCMC sampling approaches. Our method can be a valuable tool for practitioners and researchers to investigate new models with minimal effort in deriving model-specific inference algorithms.
SPIDER: stealthy binary program instrumentation and debugging via hardware virtualization

The ability to trap the execution of a binary program at desired instructions is essential in many security scenarios such as malware analysis and attack provenance. However, an increasing percent of both malicious and legitimate programs are equipped with anti-debugging and anti-instrumentation techniques, which render existing debuggers and instrumentation tools inadequate. In this paper, we present Spider, a stealthy program instrumentation framework which enables transparent, efficient and flexible instruction-level trapping based on hardware virtualization. Spider uses invisible breakpoint, a novel primitive we develop that inherits the efficiency and flexibility of software breakpoint, and utilizes hardware virtualization to hide its side-effects from the guest. We have implemented a prototype of Spider on KVM. Our evaluation shows that Spider succeeds in remaining transparent against state-of-the-art anti-debugging and anti-instrumentation techniques; the overhead of invisible breakpoint is comparable with traditional hardware breakpoint. We also demonstrate Spider's usage in various security applications.
Performance bias: Why judgments of learning are not affected by learning.

Past research has shown a performance bias: People expect their future performance level on a task to match their current performance level, even when there are good reasons to expect future performance to differ from current performance. One explanation of this bias is that judgments are controlled by what learners can observe, and while current performance is usually observable, changes in performance (i.e., learning or forgetting) are not. This explanation makes a prediction that we tested here: If learning becomes observable, it should begin to affect judgments. In three experiments, after practicing a skill, participants estimated how they performed in the past and how they expected to perform in the future. In Experiments 1 and 2, participants knew they had been improving, as shown by their responses, yet they did not predict that they would improve in the future. This finding was particularly striking because (a) they did improve in the future and (b) as Experiment 3 showed, they did hold the conscious belief that past improvement predicted future improvement. In short, when learning and performance are both observable, judgments of learning seem to be guided by performance and not learning.
A novel algorithm for detecting bacterial leaf scorch (BLS) of shade trees using image processing

Bacterial leaf scorch(BLS) is a common infection seen in different shade tree species such as American elm, red maple, sweet gum, sycamore and London plane, and a number of species of oak. The disease is caused by the bacterial pathogen Xylella fastidiosa. Spread of the disease may lead to early fall and determinate growth of the trees. The symptoms of the disease may first appear on branches and leaves and it spreads to other parts of the plant on a later stage. So detection of the infected areas of the plant leads to early analysis and elimination of disease spread and saves the plant from disease spread. Proposed scheme uses a novel approach to detect the infected areas of the plant by segmenting the plant leaves by clustering. K-means clustering is performed for different cluster centres to obtain different cluster groups of the region of interest (ROI).
Feature selection based on rough sets and particle swarm optimization

We propose a new feature selection strategy based on rough sets and Particle Swarm Optimization (PSO). Rough sets has been used as a feature selection method with much success, but current hill-climbing rough set approaches to feature selection are inadequate at finding optimal reductions as no perfect heuristic can guarantee optimality. On the other hand, complete searches are not feasible for even medium-sized datasets. So, stochastic approaches provide a promising feature selection mechanism. Like Genetic Algorithms, PSO is a new evolutionary computation technique, in which each potential solution is seen as a particle with a certain velocity flying through the problem space. The Particle Swarms find optimal regions of the complex search space through the interaction of individuals in the population. PSO is attractive for feature selection in that particle swarms will discover best feature combinations as they fly within the subset space. Compared with GAs, PSO does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. Experimentation is carried out, using UCI data, which compares the proposed algorithm with a GA-based approach and other deterministic rough set reduction algorithms. The results show that PSO is efficient for rough set-based feature selection.
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving

With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered and unseen environments with high uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors, that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize Driveability in some quantified form. To this end, this paper aims to create a framework for investigating different factors that can impact driveability. Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios. The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning. Therefore, we also perform a comparative overview of 45 public driving datasets that enable learning and publish this dataset index at https://sites.google.com/view/driveability-surveydatasets. Specifically, we categorize the datasets according to use cases, and highlight the datasets that capture complicated and hazardous driving conditions which can be better used for training robust driving models. Furthermore, by discussions of what driving scenarios are not covered by existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments.
Task Intelligence of Robots: Neural Model-Based Mechanism of Thought and Online Motion Planning

The crux of the realization of task intelligence for robots is to design the memory module for storing temporal event sequences of tasks, the mechanism of thought for reasoning, and motion planning methodology for execution, among others. In this paper, task intelligence is realized using episodic memory, neural model-based mechanism of thought, and an online motion planning algorithm. Robots are taught either by demonstration or symbolic description. A behavior appropriate to the current situation is selected by the developmental episodic memory-based mechanism of thought, while a proper task is retrieved from Deep adaptive resonance theory (ART). The behaviors are executed safely and quickly with the proposed motion planning algorithm. The effectiveness and applicability of task intelligence are demonstrated through experiments with the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST.
Fast Quicksort Implementation Using AVX Instructions

This article describes a technique for implementing the quicksort sorting algorithm. Our method ‘vectorizes’ the computations and leverages the capabilities of the advanced vector extensions (AVX) instructions, available on Intel Core processors, and of the AVX2 instructions that were introduced with Intel’s recent architecture codename Haswell. Our solution offers several advantages when compared with other high-performance sorting implementations, such as the radix sort, as implemented in Intel IPP library, or the introsort, as implemented in the C++ STL. In addition to sorting numeric arrays, our method can also be used to sort complex structures with numeric keys and even pointers to such structures.