nils-herrmann/clean_scirepeval huggingface dataset:
Harmonic Networks: Deep Translation and Rotation Equivariance

Translating or rotating an input image should not affect the results of many computer vision tasks. Convolutional neural networks (CNNs) are already translation equivariant: input image translations produce proportionate feature map translations. This is not the case for rotations. Global rotation equivariance is typically sought through data augmentation, but patch-wise equivariance is more difficult. We present Harmonic Networks or H-Nets, a CNN exhibiting equivariance to patch-wise translation and 360-rotation. We achieve this by replacing regular CNN filters with circular harmonics, returning a maximal response and orientation for every receptive field patch. H-Nets use a rich, parameter-efficient and fixed computational complexity representation, and we show that deep feature maps within the network encode complicated rotational invariants. We demonstrate that our layers are general enough to be used in conjunction with the latest architectures and techniques, such as deep supervision and batch normalization. We also achieve state-of-the-art classification on rotated-MNIST, and competitive results on other benchmark challenges.
Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.
In the eye of the beholder: employing statistical analysis and eye tracking for analyzing abstract paintings

Most artworks are explicitly created to evoke a strong emotional response. During the centuries there were several art movements which employed different techniques to achieve emotional expressions conveyed by artworks. Yet people were always consistently able to read the emotional messages even from the most abstract paintings. Can a machine learn what makes an artwork emotional? In this work, we consider a set of 500 abstract paintings from Museum of Modern and Contemporary Art of Trento and Rovereto (MART), where each painting was scored as carrying a positive or negative response on a Likert scale of 1-7. We employ a state-of-the-art recognition system to learn which statistical patterns are associated with positive and negative emotions. Additionally, we dissect the classification machinery to determine which parts of an image evokes what emotions. This opens new opportunities to research why a specific painting is perceived as emotional. We also demonstrate how quantification of evidence for positive and negative emotions can be used to predict the way in which people observe paintings.
Genome-wide mapping of in vivo protein-DNA interactions.

In vivo protein-DNA interactions connect each transcription factor with its direct targets to form a gene network scaffold. To map these protein-DNA interactions comprehensively across entire mammalian genomes, we developed a large-scale chromatin immunoprecipitation assay (ChIPSeq) based on direct ultrahigh-throughput DNA sequencing. This sequence census method was then used to map in vivo binding of the neuron-restrictive silencer factor (NRSF; also known as REST, for repressor element-1 silencing transcription factor) to 1946 locations in the human genome. The data display sharp resolution of binding position [+/-50 base pairs (bp)], which facilitated our finding motifs and allowed us to identify noncanonical NRSF-binding motifs. These ChIPSeq data also have high sensitivity and specificity [ROC (receiver operator characteristic) area >/= 0.96] and statistical confidence (P <10(-4)), properties that were important for inferring new candidate interactions. These include key transcription factors in the gene network that regulates pancreatic islet cell development.
#bias: Measuring the Tweeting Behavior of Propagandists

Twitter is an efficient conduit of information for millions of users around the world. Its ability to quickly spread information to a large number of people makes it an efficient way to shape information and, hence, shape public opinion. We study the tweeting behavior of Twitter propagandists, users who consistently express the same opinion or ideology, focusing on two online communities: the 2010 Nevada senate race and the 2011 debt-ceiling debate. We identify several extreme tweeting patterns that could characterize users who spread propaganda: (1) sending high volumes of tweets over short periods of time, (2) retweeting while publishing little original content, (3) quickly retweeting, and (4) colluding with other, seemingly unrelated, users to send duplicate or near-duplicate messages on the same topic simultaneously. These four features appear to distinguish tweeters who spread propaganda from other more neutral users and could serve as starting point for developing behavioral-based propaganda detection techniques for Twitter.
A novel privacy preserving authentication and access control scheme for pervasive computing environments

Privacy and security are two important but seemingly contradictory objectives in a pervasive computing environment (PCE). On one hand, service providers want to authenticate legitimate users and make sure they are accessing their authorized services in a legal way. On the other hand, users want to maintain the necessary privacy without being tracked down for wherever they are and whatever they are doing. In this paper, a novel privacy preserving authentication and access control scheme to secure the interactions between mobile users and services in PCEs is proposed. The proposed scheme seamlessly integrates two underlying cryptographic primitives, namely blind signature and hash chain, into a highly flexible and lightweight authentication and key establishment protocol. The scheme provides explicit mutual authentication between a user and a service while allowing the user to anonymously interact with the service. Differentiated service access control is also enabled in the proposed scheme by classifying mobile users into different service groups. The correctness of the proposed authentication and key establishment protocol is formally verified based on Burrows-Abadi-Needham logic
Impulsive and self-control choices in opioid-dependent patients and non-drug-using control participants: drug and monetary rewards.

Delay discounting was investigated in opioid-dependent and non-drug-using control participants. The latter participants were matched to the former on age, gender, education, and IQ. Participants in both groups chose between hypothetical monetary rewards available either immediately or after a delay. Delayed rewards were $1,000, and the immediate-reward amount was adjusted until choices reflected indifference. This procedure was repeated at each of 7 delays (1 week to 25 years). Opioid-dependent participants were given a second series of choices between immediate and delayed heroin, using the same procedures (i.e., the amount of delayed heroin was that which could be purchased with $1,000). Opioid-dependent participants discounted delayed monetary rewards significantly more than did non-drug-using participants. Furthermore opioid-dependent participants discounted delayed heroin significantly more than delayed money.
A free-head, simple calibration, gaze tracking system that enables gaze-based interaction

Human eye gaze is a strong candidate to create a new application area based on human-computer interaction. To implement a really practical gaze-based interaction system, gaze detection must be realized without placing any restriction on the user's behavior or comfort. This paper describes a gaze tracking system that offers freehead, simple personal calibration. It does not require the user wear anything on her head, and she can move her head freely. Personal calibration takes only a very short time; the user is asked to look at two markers on the screen. An experiment shows that the accuracy of the implemented system is about 1.0 degrees (view angle).
How to Build Complete Creatures Rather than Isolated Cognitive Simulators

Artificial Intelligence as a discipline has gotten bogged down in subproblems of intelligence. These subproblems are the result of applying reductionist methods to the goal of creating a complete artificial thinking mind. In Brooks (1987) 1 have argued that these methods will lead us to solving irrelevant problems; interesting as intellectual puzzles, but useless in the long run for creating an artificial being.
Efficient algorithms for document retrieval problems

We are given a collection D of text documents d1,…,dk, with ∑i = n, which may be preprocessed. In the document listing problem, we are given an online query comprising of a pattern string p of length m and our goal is to return the set of all documents that contain one or more copies of p. In the closely related occurrence listing problem, we output the set of all positions within the documents where pattern p occurs. In 1973, Weiner [24] presented an algorithm with O(n) time and space preprocessing following which the occurrence listing problem can be solved in time O(m + output) where output is the number of positions where p occurs; this algorithm is clearly optimal. In contrast, no optimal algorithm is known for the closely related document listing problem, which is perhaps more natural and certainly well-motivated.We provide the first known optimal algorithm for the document listing problem. More generally, we initiate the study of pattern matching problems that require retrieving documents matched by the patterns; this contrasts with pattern matching problems that have been studied more frequently, namely, those that involve retrieving all occurrences of patterns. We consider document retrieval problems that are motivated by online query processing in databases, Information Retrieval systems and Computational Biology. We present very efficient (optimal) algorithms for our document retrieval problems. Our approach for solving such problems involve performing "local" encodings whereby they are reduced to range query problems on geometric objects --- points and lines --- that have color. We present improved algorithms for these colored range query problems that arise in our reductions using the structural properties of strings. This approach is quite general and yields simple, efficient, implementable algorithms for all the document retrieval problems in this paper.
User Association in 5G Networks: A Survey and an Outlook

The fifth generation (5G) mobile networks are envisioned to support the deluge of data traffic with reduced energy consumption and improved quality of service (QoS) provision. To this end, key enabling technologies, such as heterogeneous networks (HetNets), massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) techniques, have been identified to bring 5G to fruition. Regardless of the technology adopted, a user association mechanism is needed to determine whether a user is associated with a particular base station (BS) before data transmission commences. User association plays a pivotal role in enhancing the load balancing, the spectrum efficiency, and the energy efficiency of networks. The emerging 5G networks introduce numerous challenges and opportunities for the design of sophisticated user association mechanisms. Hence, substantial research efforts are dedicated to the issues of user association in HetNets, massive MIMO networks, mmWave networks, and energy harvesting networks. We introduce a taxonomy as a framework for systematically studying the existing user association algorithms. Based on the proposed taxonomy, we then proceed to present an extensive overview of the state-of-the-art in user association algorithms conceived for HetNets, massive MIMO, mmWave, and energy harvesting networks. Finally, we summarize the challenges as well as opportunities of user association in 5G and provide design guidelines and potential solutions for sophisticated user association mechanisms.
A Practical Guide to Applying Echo State Networks

Reservoir computing has emerged in the last decade as an alternative to gradient descent methods for training recurrent neural networks. Echo State Network (ESN) is one of the key reservoir computing “flavors”. While being practical, conceptually simple, and easy to implement, ESNs require some experience and insight to achieve the hailed good performance in many tasks. Here we present practical techniques and recommendations for successfully applying ESNs, as well as some more advanced application-specific modifications. To appear in Neural Networks: Tricks of the Trade, Reloaded. G. Montavon, G. B. Orr, and K.-R. Müller, editors, Springer, 2012.
A Survey of Signed Network Mining in Social Media

Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines. With the increasing prevalence of social media networks, signed network analysis has evolved from developing and measuring theories to mining tasks. In this article, we present a review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers. We begin by giving basic concepts and unique properties and principles of signed networks. Then we classify and review tasks of signed network mining with representative algorithms. We also delineate some tasks that have not been extensively studied with formal definitions and also propose research directions to expand the field of signed network mining.
Lines of malicious code: insights into the malicious software industry

Malicious software installed on infected computers is a fundamental component of online crime. Malware development thus plays an essential role in the underground economy of cyber-crime. Malware authors regularly update their software to defeat defenses or to support new or improved criminal business models. A large body of research has focused on detecting malware, defending against it and identifying its functionality. In addition to these goals, however, the analysis of malware can provide a glimpse into the software development industry that develops malicious code.
 In this work, we present techniques to observe the evolution of a malware family over time. First, we develop techniques to compare versions of malicious code and quantify their differences. Furthermore, we use behavior observed from dynamic analysis to assign semantics to binary code and to identify functional components within a malware binary. By combining these techniques, we are able to monitor the evolution of a malware's functional components. We implement these techniques in a system we call Beagle, and apply it to the observation of 16 malware strains over several months. The results of these experiments provide insight into the effort involved in updating malware code, and show that Beagle can identify changes to individual malware components.
Management of impending necrosis associated with soft tissue filler injections.

As the number of soft tissue filler injections increases, the number of adverse events associated with injection may rise. Impending necrosis represents a serious complication that, if not treated correctly and timely, may have grave consequences.



We describe a protocol utilizing hyaluronidase, nitroglycerin paste, aspirin, antacid and a topical oxygen therapy that may be used to treat impending necrosis subsequent to injection with soft tissue fillers.



We have successfully treated nine post-filler injection adverse events involving impending necrosis or necrosis following both hyaluronic acid and calcium hydroxylapatite injections using our protocol.
DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment, the output of DENFIS is calculated through a fuzzy inference system based on -most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: 1) dynamic creation of a first-order Takagi–Sugeno-type fuzzy rule set for a DENFIS online model; and 2) creation of a first-order Takagi–Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.
Learning to Answer Biomedical Factoid & List Questions: OAQA at BioASQ 3B

This paper describes the CMU OAQA system evaluated in the BioASQ 3B Question Answering track. We first present a three-layered architecture, and then describe the components integrated for exact answer generation and retrieval. Using over 400 factoid and list questions from past BioASQ 1B and 2B tasks as background knowledge, we focus on how to learn to answer questions using a gold standard dataset of question-answer pairs, using supervised models for answer type prediction and candidate answer scoring. On the three test sets where the system was evaluated (3, 4, and 5), the official evaluation results have shown that the system achieves an MRR of .1615, .5155, .2727 for factoid questions, and an F-measure of .0969, .3168, .1875 for list questions, respectively; five of these scores were the highest reported among all participating systems.
Context, learning, and extinction.

A. Redish et al. (2007) proposed a reinforcement learning model of context-dependent learning and extinction in conditioning experiments, using the idea of "state classification" to categorize new observations into states. In the current article, the authors propose an interpretation of this idea in terms of normative statistical inference. They focus on renewal and latent inhibition, 2 conditioning paradigms in which contextual manipulations have been studied extensively, and show that online Bayesian inference within a model that assumes an unbounded number of latent causes can characterize a diverse set of behavioral results from such manipulations, some of which pose problems for the model of Redish et al. Moreover, in both paradigms, context dependence is absent in younger animals, or if hippocampal lesions are made prior to training. The authors suggest an explanation in terms of a restricted capacity to infer new causes.
Bayesian learning theory applied to human cognition.

Probabilistic models based on Bayes' rule are an increasingly popular approach to understanding human cognition. Bayesian models allow immense representational latitude and complexity. Because they use normative Bayesian mathematics to process those representations, they define optimal performance on a given task. This article focuses on key mechanisms of Bayesian information processing, and provides numerous examples illustrating Bayesian approaches to the study of human cognition. We start by providing an overview of Bayesian modeling and Bayesian networks. We then describe three types of information processing operations-inference, parameter learning, and structure learning-in both Bayesian networks and human cognition. This is followed by a discussion of the important roles of prior knowledge and of active learning. We conclude by outlining some challenges for Bayesian models of human cognition that will need to be addressed by future research.
Anatomy of the Third-Party Web Tracking Ecosystem

The presence of third-party tracking on websites has become customary. However, our understanding of the thirdparty ecosystem is still very rudimentary. We examine thirdparty trackers from a geographical perspective, observing the third-party tracking ecosystem from 29 countries across the globe. When examining the data by region (North America, South America, Europe, East Asia, Middle East, and Oceania), we observe significant geographical variation between regions and countries within regions. We find trackers that focus on specific regions and countries, and some that are hosted in countries outside their expected target tracking domain. Given the differences in regulatory regimes between jurisdictions, we believe this analysis sheds light on the geographical properties of this ecosystem and on the problems that these may pose to our ability to track and manage the different data silos that now store personal data about us all.
Empirical analysis: stock market prediction via extreme learning machine

How to predict stock price movements based on quantitative market data modeling is an attractive topic. In front of the market news and stock prices that are commonly believed as two important market data sources, how to extract and exploit the hidden information within the raw data and make both accurate and fast predictions simultaneously becomes a challenging problem. In this paper, we present the design and architecture of our trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently. Comprehensive experimental comparisons between ELM and the state-of-the-art learning algorithms, including support vector machine (SVM) and back-propagation neural network (BP-NN), have been undertaken on the intra-day tick-by-tick data of the H-share market and contemporaneous news archives. The results have shown that (1) both RBF ELM and RBF SVM achieve higher prediction accuracy and faster prediction speed than BP-NN; (2) the RBF ELM achieves similar accuracy with the RBF SVM and (3) the RBF ELM has faster prediction speed than the RBF SVM. Simulations of a preliminary trading strategy with the signals are conducted. Results show that strategy with more accurate signals will make more profits with less risk.