nils-herrmann/clean_scirepeval huggingface dataset:
Can we still avoid automatic face detection?

After decades of study, automatic face detection and recognition systems are now accurate and widespread. Naturally, this means users who wish to avoid automatic recognition are becoming less able to do so. Where do we stand in this cat-and-mouse race? We currently live in a society where everyone carries a camera in their pocket. Many people willfully upload most or all of the pictures they take to social networks which invest heavily in automatic face recognition systems. In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition? If so, how? Must evasion techniques be obvious to be effective, or are there still simple measures that users can use to protect themselves? In this work, we find ways to evade face detection on Facebook, a representative example of a popular social network that uses automatic face detection to enhance their service. We challenge widely-held beliefs about evading face detection: do our old techniques such as blurring the face region or wearing "privacy glasses" still work? We show that in general, state-of-the-art detectors can often find faces even if the subject wears occluding clothing or even if the uploader damages the photo to prevent faces from being detected.
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. The framework consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. This framework is realized in three instants in this work with a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. All three instances of the framework monopolize the top three positions in all experiments, outdistancing other compared trading algorithms. Although with a high commission rate of 0.25% in the backtests, the framework is able to achieve at least 4-fold returns in 50 days.
Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding

Spoken dialogue systems (SDS) typically require a predefined semantic ontology to train a spoken language understanding (SLU) module. In addition to the annotation cost, a key challenge for designing such an ontology is to define a coherent slot set while considering their complex relations. This paper introduces a novel matrix factorization (MF) approach to learn latent feature vectors for utterances and semantic elements without the need of corpus annotations. Specifically, our model learns the semantic slots for a domain-specific SDS in an unsupervised fashion, and carries out semantic parsing using latent MF techniques. To further consider the global semantic structure, such as inter-word and inter-slot relations, we augment the latent MF-based model with a knowledge graph propagation model based on a slot-based semantic graph and a word-based lexical graph. Our experiments show that the proposed MF approaches produce better SLU models that are able to predict semantic slots and word patterns taking into account their relations and domain-specificity in a joint manner.
Corporate social responsibility : Myth or reality ?

III Editorial V Corporate social responsibility: Challenges and opportunities for trade unionists, by Dwight W. Justice 1 Sustainable bargaining: Labour agreements go global, by Ian Graham 15 The social responsibilities of business and workers' rights, by Guy Ryder 21 OECD Guidelines – one tool for corporate social accountability, by John Evans 25 Corporate social responsibility – new morals for business?, by Philip Jennings 31 Corporate social responsibility in Europe: A chance for social dialogue?, by Anne Renaut 35 Strengths and weaknesses of Belgium's social label, by Bruno Melckmans 41 Social auditing, freedom of association and the right to collective bargaining, by Philip Hunter and Michael Urminsky 47 Corporate public reporting on labour and employment issues, by Michael Urminsky 55 The ILO Conventions: A " major reference " , by Nicole Notat 63 Workers' capital and corporate social responsibility, by Jon Robinson 67 The social responsibility of business, by Reg Green 75
Musical neurofeedback for treating depression in elderly people

We introduce a new neurofeedback approach, which allows users to manipulate expressive parameters in music performances using their emotional state, and we present the results of a pilot clinical experiment applying the approach to alleviate depression in elderly people. Ten adults (9 female and 1 male, mean = 84, SD = 5.8) with normal hearing participated in the neurofeedback study consisting of 10 sessions (2 sessions per week) of 15 min each. EEG data was acquired using the Emotiv EPOC EEG device. In all sessions, subjects were asked to sit in a comfortable chair facing two loudspeakers, to close their eyes, and to avoid moving during the experiment. Participants listened to music pieces preselected according to their music preferences, and were encouraged to increase the loudness and tempo of the pieces, based on their arousal and valence levels. The neurofeedback system was tuned so that increased arousal, computed as beta to alpha activity ratio in the frontal cortex corresponded to increased loudness, and increased valence, computed as relative frontal alpha activity in the right lobe compared to the left lobe, corresponded to increased tempo. Pre and post evaluation of six participants was performed using the BDI depression test, showing an average improvement of 17.2% (1.3) in their BDI scores at the end of the study. In addition, an analysis of the collected EEG data of the participants showed a significant decrease of relative alpha activity in their left frontal lobe (p = 0.00008), which may be interpreted as an improvement of their depression condition.
Vehicular WiFi offloading: Challenges and solutions

WiFi offloading is envisioned as a promising solution to the mobile data explosion problem in cellular networks. WiFi offloading for moving vehicles, however, poses unique characteristics and challenges, due to high mobility, fluctuating mobile channels, etc. In this paper, we focus on the problem of WiFi offloading in vehicular communication environments. Specifically, we discuss the challenges and identify the research issues related to drive-thru Internet access and effectiveness of vehicular WiFi offloading. Moreover, we review the state-of-the-art offloading solutions, in which advanced vehicular communications can be employed. We also shed some lights on the path for future research on this topic.
Knowledge transfer between and within alliance partners : Private versus collective benefits of social capital ☆

This article examines the process through which multilevel network structures translate into knowledge acquisition from alliance partners. The degree of knowledge transfer a multidivisional company achieves from its network of alliance partners is determined not only by the organization's external network structure, but also by the structure of relationships among its business units. By distinguishing two perspectives on the distribution of social capital's benefits – private versus collective – this article's approach reconciles the competing views on what types of network structures create social capital, that is, the brokerage and closure views of the social network literature. Private benefits of brokerage and centrality are more beneficial in interfirm networks, whereas collective benefits provided by network closure and low levels of centralization are more beneficial in intrafirm networks.
Inductive Reasoning about Ontologies Using Conceptual Spaces

Structured knowledge about concepts plays an increasingly important role in areas such as information retrieval. The available ontologies and knowledge graphs that encode such conceptual knowledge, however, are inevitably incomplete. This observation has led to a number of methods that aim to automatically complete existing knowledge bases. Unfortunately, most existing approaches rely on black box models, e.g. formulated as global optimization problems, which makes it difficult to support the underlying reasoning process with intuitive explanations. In this paper, we propose a new method for knowledge base completion, which uses interpretable conceptual space representations and an explicit model for inductive inference that is closer to human forms of commonsense reasoning. Moreover, by separating the task of representation learning from inductive reasoning, our method is easier to apply in a wider variety of contexts. Finally, unlike optimization based approaches, our method can naturally be applied in settings where various logical constraints between the extensions of concepts need to be taken into account.
Automatic differentiation in machine learning: a survey

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD) is a technique for calculating derivatives of numeric functions expressed as computer programs efficiently and accurately, used in fields such as computational fluid dynamics, nuclear engineering, and atmospheric sciences. Despite its advantages and use in other fields, machine learning practitioners have been little influenced by AD and make scant use of available tools. We survey the intersection of AD and machine learning, cover applications where AD has the potential to make a big impact, and report on some recent developments in the adoption of this technique. We aim to dispel some misconceptions that we contend have impeded the use of AD within the machine learning community.
A Tutorial on Deep Latent Variable Models of Natural Language

Latent variable models, and probabilistic graphical models more generally, provide a declarative language for specifying prior knowledge and structural relationships in complex datasets. They have a long and rich history in natural language processing, having contributed to fundamental advances such as statistical alignment for translation (Brown et al., 1993), topic modeling (Blei et al., 2003), unsupervised part-of-speech tagging (Brown et al., 1992), and grammar induction (Klein and Manning, 2004), among others. Deep learning, broadly construed, is a toolbox for learning rich representations (i.e., features) of data through numerical optimization. Deep learning is the current dominant paradigm in natural language processing, and some of the major successes include language modeling (Bengio et al., 2003; Mikolov et al., 2010; Zaremba et al., 2014), machine translation (Sutskever et al., 2014; Cho et al., 2014; Bahdanau et al., 2015; Vaswani et al., 2017), and natural language understanding tasks such as question answering and natural language inference.
Context Aware Middleware Architectures: Survey and Challenges

Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security &amp; privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work.
BEYOND COMPARE : METAPHOR IN ORGANIZATION THEORY

Despite the increased salience of metaphor in organization theory, current perspectives are flawed and misguided in assuming that metaphor can be explained with the so-called comparison model. I therefore outline an alternative model of metaphor understanding—the domains-interaction model—which suggests that metaphor involves the conjunction of whole semantic domains in which a correspondence between terms or concepts is constructed rather than deciphered and where the resulting image and meaning is creative. I also discuss implications of this model for organizational theorizing and research.
The Málaga urban dataset: High-rate stereo and LiDAR in a realistic urban scenario

This paper introduces a dataset gathered entirely in urban scenarios with a car equipped with one stereo camera and five laser scanners, among other sensors. One distinctive feature of the present dataset is the existence of high-resolution stereo images grabbed at high rate (20 fps) during a 36.8 km trajectory, which allows the benchmarking of a variety of computer vision techniques. We describe the employed sensors and highlight some applications which could be benchmarked with the presented work. Both plain text and binary files are provided, as well as open source tools for working with the binary versions. The dataset is available for download in http://www.mrpt.org/MalagaUrbanDataset.
Pros vs . Joes : Agent Pricing Behavior in the Sharing Economy

One of the major differences between markets that follow a " sharing economy " paradigm and traditional two-sided markets is that the supply side in the sharing economy often includes individual nonprofessional decision makers, in addition to firms and professional agents. Using a data set of prices and availability of listings on Airbnb, we find that there exist substantial differences in the operational and financial performance of professional and nonprofessional hosts. In particular, properties managed by professional hosts earn 16.9% more in daily revenue, have 15.5% higher occupancy rates, and are 13.6% less likely to exit the market compared with properties owned by nonprofessional hosts, while controlling for property and market characteristics. We demonstrate that these performance differences between professionals and nonprofessionals can be partly explained by pricing inefficiencies. Specifically, we provide empirical evidence that nonprofes-sional hosts are less likely to offer different rates across stay dates based on the underlying demand patterns, such as those created by major holidays and conventions. We develop a parsimonious model to analyze the implications of having two such different host groups for a profit-maximizing platform operator and for a social planner. While a profit-maximizing platform operator should charge lower prices to nonprofessional hosts, a social planner would charge the same prices to professionals and nonprofessionals.
MCapsNet: Capsule Network for Text with Multi-Task Learning

Multi-task learning has an ability to share the knowledge among related tasks and implicitly increase the training data. However, it has long been frustrated by the interference among tasks. This paper investigates the performance of capsule network for text, and proposes a capsule-based multi-task learning architecture, which is unified, simple and effective. With the advantages of capsules for feature clustering, proposed task routing algorithm can cluster the features for each task in the network, which helps reduce the interference among tasks. Experiments on six text classification datasets demonstrate the effectiveness of our models and their characteristics for feature clustering.
Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges

Voluminous amounts of data have been produced, since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This paper investigates the state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this paper adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Finally, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions.
Efficient and Effective Clustering Methods for Spatial Data Mining

Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLAHANS which is based on randomized search. We also develop two spatial data mining algorithms that use CLAHANS. Our analysis and experiments show that with the assistance of CLAHANS, these two algorithms are very effective and can lead to discoveries that are difficult to find with current spatial data mining algorithms. Furthermore, experiments conducted to compare the performance of CLAHANS with that of existing clustering methods show that CLAHANS is the most efficient.
Optical Character Recognition Techniques: A survey

This paper presents a literature review on English OCR techniques. English OCR system is compulsory to convert numerous published books of English into editable computer text files. Latest research in this area has been able to grown some new methodologies to overcome the complexity of English writing style. Still these algorithms have not been tested for complete characters of English Alphabet. Hence, a system is required which can handle all classes of English text and identify characters among these classes.
Using learning analytics to assess students' behavior in open-ended programming tasks

There is great interest in assessing student learning in unscripted, open-ended environments, but students' work can evolve in ways that are too subtle or too complex to be detected by the human eye. In this paper, I describe an automated technique to assess, analyze and visualize students learning computer programming. I logged hundreds of snapshots of students' code during a programming assignment, and I employ different quantitative techniques to extract students' behaviors and categorize them in terms of programming experience. First I review the literature on educational data mining, learning analytics, computer vision applied to assessment, and emotion detection, discuss the relevance of the work, and describe one case study with a group undergraduate engineering students
Evolutionary background for stress-coping styles: Relationships between physiological, behavioral, and cognitive traits in non-mammalian vertebrates

Reactions to stress vary between individuals, and physiological and behavioral responses tend to be associated in distinct suites of correlated traits, often termed stress-coping styles. In mammals, individuals exhibiting divergent stress-coping styles also appear to exhibit intrinsic differences in cognitive processing. A connection between physiology, behavior, and cognition was also recently demonstrated in strains of rainbow trout (Oncorhynchus mykiss) selected for consistently high or low cortisol responses to stress. The low-responsive (LR) strain display longer retention of a conditioned response, and tend to show proactive behaviors such as enhanced aggression, social dominance, and rapid resumption of feed intake after stress. Differences in brain monoamine neurochemistry have also been reported in these lines. In comparative studies, experiments with the lizard Anolis carolinensis reveal connections between monoaminergic activity in limbic structures, proactive behavior in novel environments, and the establishment of social status via agonistic behavior. Together these observations suggest that within-species diversity of physiological, behavioral and cognitive correlates of stress responsiveness is maintained by natural selection throughout the vertebrate sub-phylum.
An Ultra-High-Speed, 500000 rpm, 1 kW Electrical Drive System

New emerging micro gas turbine generator sets and turbocompressor systems push the speed limits of rotating machinery. To directly connect to these applications, ultra-high-speed electrical drive systems are needed. Therefore a 1 kW, 500000 rpm machine and the according power and control electronics are designed and built. This paper includes design considerations for the mechanical and electromagnetic machine design. Furthermore, a voltage source inverter with an additional dc-dc converter is described, and sensorless rotor position detection and digital control is used to drive the machine. Finally, the hardware and experimental results are presented.
Neural Cryptanalysis of Classical Ciphers

Cryptanalysis identifies weaknesses of ciphers and investigates methods to exploit them in order to compute the plaintext and/or the secret cipher key. Exploitation is nontrivial and, in many cases, weaknesses have been shown to be effective only on reduced versions of the ciphers. In this paper we apply artificial neural networks to automatically “assist” cryptanalysts into exploiting cipher weaknesses. The networks are trained by providing data in a form that points out the weakness together with the encryption key, until the network is able to generalize and predict the key (or evaluate its likelihood) for any possible ciphertext. We illustrate the effectiveness of the approach through simple classical ciphers, by providing the first ciphertext-only attack on substitution ciphers based on neural networks.