nils-herrmann/clean_scirepeval huggingface dataset:
Accelerating the XGBoost algorithm using GPU computing

We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and shows high performance with a variety of datasets and settings, including sparse input matrices. Individual boosting iterations are parallelised, combining two approaches. An interleaved approach is used for shallow trees, switching to a more conventional radix sort-based approach for larger depths. We show speedups of between 3 and 6 using a Titan X compared to a 4 core i7 CPU, and 1.2 using a Titan X compared to 2 Xeon CPUs (24 cores). We show that it is possible to process the Higgs dataset (10 million instances, 28 features) entirely within GPU memory. The algorithm is made available as a plug-in within the XGBoost library and fully supports all XGBoost features including classification, regression and ranking tasks. Subjects Artificial Intelligence, Data Mining and Machine Learning,
JSON: Data model, Query languages and Schema specification

Despite the fact that JSON is currently one of the most popular formats for exchanging data on the Web, there are very few studies on this topic and there is no agreement upon a theoretical framework for dealing with JSON. Therefore in this paper we propose a formal data model for JSON documents and, based on the common features present in available systems using JSON, we define a lightweight query language allowing us to navigate through JSON documents. We also introduce a logic capturing the schema proposal for JSON and study the complexity of basic computational tasks associated with these two formalisms.
Challenges to learning and schooling in the digital networked world of the 21st century

This article elaborates on the competencies, often referred to as 21st century competencies, that are needed to be able to live in and contribute to our current (and future) society. We begin by describing, analysing and reflecting on international frameworks describing 21st century competencies, giving special attention to digital literacy as one of the core competencies for the 21st century. This is followed by an analysis of the learning approaches that are considered appropriate for acquiring 21st century competencies, and the specific role of technology in these learning processes. Despite some consensus about what 21st century competencies are and how they can be acquired, results from international studies indicate that teaching strategies for 21st century competencies are often not well implemented in actual educational practice. The reasons for this include a lack of integration of 21st century competencies in curriculum and assessment, insufficient preparation of teachers and the absence of any systematic attention for strategies to adopt at scale innovative teaching and learning practices. The article concludes with a range of specific recommendations for the implementation of 21st century competencies.
Distributed and Decentralized Voltage Control of Smart Distribution Networks: Models, Methods, and Future Research

The future grid is evolving into a smart distribution network that integrates multiple distributed energy resources ensuring at the same time reliable operation and increased power quality. In recent years, many research papers have addressed the voltage violation problems that arise from the high penetration of distributed generation. In view of the transition to active network management and the increase in the quantity of collected data, distributed control schemes have been proposed that use pervasive communications to deal with the complexity of smart grid. This paper reviews the recent publications on distributed and decentralized voltage control of smart distribution networks, summarizes their control models, and classifies the solution methodologies. Moreover, it comments on issues that should be addressed in the future and the perspectives of industry applications.
Poverty , development , and environment

This paper examines the complex relationship that exists between poverty and natural resource degradation in developing countries. The rural poor are often concentrated in fragile, or less favorable, environmental areas. Consequently, their livelihoods can be intimately dependent on natural resource use and ecosystem services. The relationship between poverty and natural resource degradation may depend on a complex range of choices and tradeoffs available to the poor, which in the absence of capital, labor, and land markets, is affected by their access to outside employment and any natural resource endowments. The paper develops a poverty–environment model to characterize some of these linkages, and concludes by discussing policy implications and avenues for further research.
A Community Detection Approach to Cleaning Extremely Large Face Database

Though it has been easier to build large face datasets by collecting images from the Internet in this Big Data era, the time-consuming manual annotation process prevents researchers from constructing larger ones, which makes the automatic cleaning of noisy labels highly desirable. However, identifying mislabeled faces by machine is quite challenging because the diversity of a person's face images that are captured wildly at all ages is extraordinarily rich. In view of this, we propose a graph-based cleaning method that mainly employs the community detection algorithm and deep CNN models to delete mislabeled images. As the diversity of faces is preserved in multiple large communities, our cleaning results have both high cleanness and rich data diversity. With our method, we clean the extremely large MS-Celeb-1M face dataset (approximately 10 million images with noisy labels) and obtain a clean version of it called C-MS-Celeb (6,464,018 images of 94,682 celebrities). By training a single-net model using our C-MS-Celeb dataset, without fine-tuning, we achieve 99.67% at Equal Error Rate on the LFW face recognition benchmark, which is comparable to other state-of-the-art results. This demonstrates the data cleaning positive effects on the model training. To the best of our knowledge, our C-MS-Celeb is the largest clean face dataset that is publicly available so far, which will benefit face recognition researchers.
‘Cycle Thieves, We Are Watching You’: Impact of a Simple Signage Intervention against Bicycle Theft

Bicycle theft is a serious problem in many countries, and there is a lack of evidence concerning effective prevention strategies. Displaying images of 'watching eyes' has been shown to make people behave in more socially desirable ways in a number of settings, but it is not yet clear if this effect can be exploited for purposes of crime prevention. We report the results of a simple intervention on a university campus where signs featuring watching eyes and a related verbal message were displayed above bicycle racks.



We installed durable signs at three locations which had experienced high levels of bicycle theft, and used the rest of the university campus as a control location. Reported thefts were monitored for 12 months before and after the intervention. Bicycle thefts decreased by 62% at the experimental locations, but increased by 65% in the control locations, suggesting that the signs were effective, but displaced offending to locations with no signs. The Odds Ratio for the effect of the intervention was 4.28 (95% confidence interval 2.04-8.98), a large effect compared to other place-based crime prevention interventions.



The effectiveness of this extremely cheap and simple intervention suggests that there can be considerable crime-reduction benefits to engaging the psychology of surveillance, even in the absence of surveillance itself. Simple interventions for high-crime locations based on this principle should be considered as an adjunct to other measures, although a possible negative consequence is displacement of offending.
Integration of Relational and NoSQL Databases

The chapter proposes three ways of integration of the two different worlds of relational and NoSQL databases: native, hybrid, and reducing to one option, either relational or NoSQL. The native solution includes using vendors’ standard APIs and integration on the business layer. In a relational environment, APIs are based on SQL standards, while the NoSQL world has its own, unstandardized solutions. The native solution means using the APIs of the individual systems that need to be connected, leaving to the businesslayer coding the task of linking and separating data in extraction and storage operations. A hybrid solution introduces an additional layer that provides SQL communication between the business layer and the data layer. The third integration solution includes vendors’ effort to foresee functionalities of “opposite” side, thus convincing developers’ community that their solution is sufficient.
Performance measurement for brain-computer or brain-machine interfaces: a tutorial.

Brain-computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research.



A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This paper contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop.



Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their use to encourage uniform application between laboratories.



Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field.
Augmented Reality tools for teaching and learning

Smartphones and tablets are becoming less expensive and many students already bring them to classes. The increased availability of smartphones and tablets with Internet connectivity and increasing power computing makes possible the use of augmented reality (AR) applications in these mobile devices. This makes it possible for a teacher to develop educational activities that can take advantage of the augmented reality technologies for improving learning activities. The use of information technology made many changes in the way of teaching and learning. We believe that the use of augmented reality will change significantly the teaching activities by enabling the addition of supplementary information that is seen on a mobile device. In this paper, we present several educational activities created using free augmented reality tools that do not require programming knowledge to be used by any teacher. We cover the marker and marker less based augmented reality technologies to show how we can create learning activities to visualize augmented information like animations and 3D objects that help students understand the educational content. There are currently many augmented reality applications. We looked to the most popular augmented-reality eco-systems. Our purpose was to find AR systems that can be used in daily learning activities. For this reason, they must be user friendly, since they are going to be used by teachers that in general do not have programming knowledge. Additionally, we were interested in using augmented reality applications that are open source or free.
MDL-CW: A Multimodal Deep Learning Framework with CrossWeights

Deep learning has received much attention as of the most powerful approaches for multimodal representation learning in recent years. An ideal model for multimodal data can reason about missing modalities using the available ones, and usually provides more information when multiple modalities are being considered. All the previous deep models contain separate modality-specific networks and find a shared representation on top of those networks. Therefore, they only consider high level interactions between modalities to find a joint representation for them. In this paper, we propose a multimodal deep learning framework (MDLCW) that exploits the cross weights between representation of modalities, and try to gradually learn interactions of the modalities in a deep network manner (from low to high level interactions). Moreover, we theoretically show that considering these interactions provide more intra-modality information, and introduce a multi-stage pre-training method that is based on the properties of multi-modal data. In the proposed framework, as opposed to the existing deep methods for multi-modal data, we try to reconstruct the representation of each modality at a given level, with representation of other modalities in the previous layer. Extensive experimental results show that the proposed model outperforms state-of-the-art information retrieval methods for both image and text queries on the PASCAL-sentence and SUN-Attribute databases.
An error correcting technique for performance improvement of OFDM based secondary link

In this paper we consider the cognitive radio system based on spectrum sensing, and propose an error correction technique for its performance improvement. We analyze secondary user link based on Orthogonal Frequency-Division Multiplexing (OFDM), realized by using Universal Software Radio Peripheral N210 platforms. Parameters of low density parity check codes and interleaver that provide significant performance improvement for the acceptable decoding latency are identified. The experimental results will be compared with the Monte Carlo simulation results obtained by using the simplified channel models.
Reverse Brazil nut problem: competition between percolation and condensation.

In the Brazil nut problem (BNP), hard spheres with larger diameters rise to the top. There are various explanations (percolation, reorganization, convection), but a broad understanding or control of this effect is by no means achieved. A theory is presented for the crossover from BNP to the reverse Brazil nut problem based on a competition between the percolation effect and the condensation of hard spheres. The crossover condition is determined, and theoretical predictions are compared to molecular dynamics simulations in two and three dimensions.
Business Intelligence Maturity: Development and Evaluation of a Theoretical Model

In order to identify and explore the strengths and weaknesses of business intelligence (BI) initiatives, managers in charge need to assess the maturity of their BI efforts. For this, a wide range of maturity models has been developed, but these models often focus on technical details and do not address the potential value proposition of BI. Based on an extensive literature review and an empirical study, we develop and evaluate a theoretical model of impact-oriented BI maturity. Building on established IS theories, the model integrates BI deployment, BI usage, individual impact, and organizational performance. This conceptualization helps to refocus the topic of BI maturity to business needs and can be used as a theoretical foundation for future research.
Emerging therapies for acute myeloid leukemia

Acute myeloid leukemia (AML) is characterized by clinical and biological heterogeneity. Despite the advances in our understanding of its pathobiology, the chemotherapy-directed management has remained largely unchanged in the past 40 years. However, various novel agents have demonstrated clinical activity, either as single agents (e.g., isocitrate dehydrogenase (IDH) inhibitors, vadastuximab) or in combination with standard induction/consolidation at diagnosis and with salvage regimens at relapse. The classes of agents described in this review include novel cytotoxic chemotherapies (CPX-351 and vosaroxin), epigenetic modifiers (guadecitabine, IDH inhibitors, histone deacetylase (HDAC) inhibitors, bromodomain and extraterminal (BET) inhibitors), FMS-like tyrosine kinase receptor 3 (FLT3) inhibitors, and antibody-drug conjugates (vadastuximab), as well as cell cycle inhibitors (volasertib), B-cell lymphoma 2 (BCL-2) inhibitors, and aminopeptidase inhibitors. These agents are actively undergoing clinical investigation alone or in combination with available chemotherapy.
Evidence and a computational explanation of cultural differences in facial expression recognition.

Facial expressions are crucial to human social communication, but the extent to which they are innate and universal versus learned and culture dependent is a subject of debate. Two studies explored the effect of culture and learning on facial expression understanding. In Experiment 1, Japanese and U.S. participants interpreted facial expressions of emotion. Each group was better than the other at classifying facial expressions posed by members of the same culture. In Experiment 2, this reciprocal in-group advantage was reproduced by a neurocomputational model trained in either a Japanese cultural context or an American cultural context. The model demonstrates how each of us, interacting with others in a particular cultural context, learns to recognize a culture-specific facial expression dialect.
Graph-Based Semi-supervised Learning for Fault Detection and Classification in Solar Photovoltaic Arrays

Fault detection in solar photovoltaic (PV) arrays is an essential task for increasing reliability and safety in PV systems. Because of PV's nonlinear characteristics, a variety of faults may be difficult to detect by conventional protection devices, leading to safety issues and fire hazards in PV fields. To fill this protection gap, machine learning techniques have been proposed for fault detection based on measurements, such as PV array voltage, current, irradiance, and temperature. However, existing solutions usually use supervised learning models, which are trained by numerous labeled data (known as fault types) and therefore, have drawbacks: 1) the labeled PV data are difficult or expensive to obtain, 2) the trained model is not easy to update, and 3) the model is difficult to visualize. To solve these issues, this paper proposes a graph-based semi-supervised learning model only using a few labeled training data that are normalized for better visualization. The proposed model not only detects the fault, but also further identifies the possible fault type in order to expedite system recovery. Once the model is built, it can learn PV systems autonomously over time as weather changes. Both simulation and experimental results show the effective fault detection and classification of the proposed method.
JANOSSY POOLING: LEARNING DEEP PERMUTATION-

We consider a simple and overarching representation for permutation-invariant functions of sequences (or set functions). Our approach, which we call Janossy pooling, expresses a permutation-invariant function as the average of a permutation-sensitive function applied to all reorderings of the input sequence. This allows us to leverage the rich and mature literature on permutation-sensitive functions to construct novel and flexible permutation-invariant functions. If carried out naively, Janossy pooling can be computationally prohibitive. To allow computational tractability, we consider three kinds of approximations: canonical orderings of sequences, functions with k-order interactions, and stochastic optimization algorithms with random permutations. Our framework unifies a variety of existing work in the literature, and suggests possible modeling and algorithmic extensions. We explore a few in our experiments, which demonstrate improved performance over current state-of-the-art methods.
THE CRITICAL PERIOD FOR LANGUAGE ACQUISITION AND THE DEAF CHILD ' S LANGUAGE COMPREHENSION : A PSYCHOLINGUISTIC APPROACH

The critical period hypothesis for language acquisition (CP) proposes that the outcome of language acquisition is not uniform over the lifespan but rather is best during early childhood. The CP hypothesis was originally proposed for spoken language but recent research has shown that it applies equally to sign language. This paper summarizes a series of experiments designed to investigate whether and how the CP affects the outcome of sign language acquisition. The results show that the CP has robust effects on the development of sign language comprehension. Effects are found at all levels of linguistic structure (phonology, morphology and syntax, the lexicon and semantics) and are greater for first as compared to second language acquisition. In addition, CP effects have been found on all measures of language comprehension examined to date, namely, working memory, narrative comprehension, sentence memory and interpretation, and on-line grammatical processing. The nature of these effects with respect to a model of language comprehension is discussed.
A Data-Driven Architecture for Personalized QoE Management in 5G Wireless Networks

With the emergence of a variety of new wireless network types, business types, and QoS in a more autonomic, diverse, and interactive manner, it is envisioned that a new era of personalized services has arrived, which emphasizes users' requirements and service experiences. As a result, users' QoE will become one of the key features in 5G/future networks. In this article, we first review the state of the art of QoE research from several perspectives, including definition, influencing factors, assessment methods, QoE models, and control methods. Then a data-driven architecture for enhancing personalized QoE is proposed for 5G networks. Under this architecture, we specifically propose a two-step QoE modeling approach to capture the strength of the relationship between users and services. Thereafter, the preferences of a user is introduced to model the user's subjectivity toward a specific service. With the comprehension of users' preferences, radio resources can be distributed more precisely. Simulation results show that overall QoE can be enhanced by 20 percent, while 96 percent of users have an improved QoE, which validates the efficiency of the proposed architecture.
Universal Intelligence: A Definition of Machine Intelligence

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.
A Multiplicative Coordinated Stealthy Attack and its Detection for Cyber Physical Systems

In this paper, we investigate a new stealthy attack simultaneously compromising actuators and sensors. This attack is referred to as coordinated attack. We show that the coordinated attack is capable of deriving the system states far away from the desired without being detected. Furthermore, designing such an attack practically does not require knowledge on target systems, which makes the attack much more dangerous compared to the other known attacks. Also, we present a method to detect the coordinated attack. To validate the effect of the proposed attack, we carry out experiments using a quadrotor.