nils-herrmann/clean_scirepeval huggingface dataset:
Embedding-based News Recommendation for Millions of Users

It is necessary to understand the content of articles and user preferences to make effective news recommendations. While ID-based methods, such as collaborative filtering and low-rank factorization, are well known for making recommendations, they are not suitable for news recommendations because candidate articles expire quickly and are replaced with new ones within short spans of time. Word-based methods, which are often used in information retrieval settings, are good candidates in terms of system performance but have issues such as their ability to cope with synonyms and orthographical variants and define "queries" from users' historical activities. This paper proposes an embedding-based method to use distributed representations in a three step end-to-end manner: (i) start with distributed representations of articles based on a variant of a denoising autoencoder, (ii) generate user representations by using a recurrent neural network (RNN) with browsing histories as input sequences, and (iii) match and list articles for users based on inner-product operations by taking system performance into consideration. The proposed method performed well in an experimental offline evaluation using past access data on Yahoo! JAPAN's homepage. We implemented it on our actual news distribution system based on these experimental results and compared its online performance with a method that was conventionally incorporated into the system. As a result, the click-through rate (CTR) improved by 23% and the total duration improved by 10%, compared with the conventionally incorporated method. Services that incorporated the method we propose are already open to all users and provide recommendations to over ten million individual users per day who make billions of accesses per month.
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning

Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network can be increased by widening and adding layers. However, usually the former introduces additional parameters, while the latter increases the runtime. As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution. By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence. Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model.
Low-rank and sparse soft targets to learn better DNN acoustic models

Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training. Subword classes in speech recognition systems correspond to context-dependent tied states or senones. The present work addresses some limitations of GMM-HMM senone alignments for DNN training. We hypothesize that the senone probabilities obtained from a DNN trained with binary labels can provide more accurate targets to learn better acoustic models. However, DNN outputs bear inaccuracies which are exhibited as high dimensional unstructured noise, whereas the informative components are structured and low-dimensional. We exploit principal component analysis (PCA) and sparse coding to characterize the senone subspaces. Enhanced probabilities obtained from low-rank and sparse reconstructions are used as soft-targets for DNN acoustic modeling, that also enables training with untranscribed data. Experiments conducted on AMI corpus shows 4.6% relative reduction in word error rate.
Towards Deep Symbolic Reinforcement Learning

Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques. For example, they require very large datasets to work effectively, entailing that they are slow to learn even when such datasets are available. Moreover, they lack the ability to reason on an abstract level, which makes it difficult to implement high-level cognitive functions such as transfer learning, analogical reasoning, and hypothesis-based reasoning. Finally, their operation is largely opaque to humans, rendering them unsuitable for domains in which verifiability is important. In this paper, we propose an end-to-end reinforcement learning architecture comprising a neural back end and a symbolic front end with the potential to overcome each of these shortcomings. As proof-ofconcept, we present a preliminary implementation of the architecture and apply it to several variants of a simple video game. We show that the resulting system – though just a prototype – learns effectively, and, by acquiring a set of symbolic rules that are easily comprehensible to humans, dramatically outperforms a conventional, fully neural DRL system on a stochastic variant of the game.
Temperament and characteristics related to nomophobia

Nomophobia is defined as the fear of being out of mobile phone contact and is considered to be a phobia of the modern age. The current study set out to establish the relationship between temperament and personality and the development of nomophobia. The sample was composed of 968 participants selected from the Andalusian population, of which there were 182 males and 785 females aged from 23.19 years. The instruments used were the Questionnaire to Assess Nomophobia (QANIP; Olivencia-Carrión et al., 2018) and the Temperament and Character Inventory Revised (TCI-R; Cloninger et al., 1993). We found that cooperation is a characteristic that significantly reduces nomophobic levels, particularly for the two factors of Mobile Phone Addiction and Negative Consequences. Furthermore, Reward Dependence appears to be positively related to two of the factors involved in nomophobia, namely Mobile Phone Addiction and Loss of Control, suggesting a relationship between Nomophobia and personality. These findings are discussed in terms of their usefulness for identifying the personality predictors of nomophobia in order to develop preventive and intervention strategies.
Deep4MalDroid: A Deep Learning Framework for Android Malware Detection Based on Linux Kernel System Call Graphs

With explosive growth of Android malware and due to its damage to smart phone users (e.g., stealing user credentials, resource abuse), Android malware detection is one of the cyber security topics that are of great interests. Currently, the most significant line of defense against Android malware is anti-malware software products, such as Norton, Lookout, and Comodo Mobile Security, which mainly use the signature-based method to recognize threats. However, malware attackers increasingly employ techniques such as repackaging and obfuscation to bypass signatures and defeat attempts to analyze their inner mechanisms. The increasing sophistication of Android malware calls for new defensive techniques that are harder to evade, and are capable of protecting users against novel threats. In this paper, we propose a novel dynamic analysis method named Component Traversal that can automatically execute the code routines of each given Android application (app) as completely as possible. Based on the extracted Linux kernel system calls, we further construct the weighted directed graphs and then apply a deep learning framework resting on the graph based features for newly unknown Android malware detection. A comprehensive experimental study on a real sample collection from Comodo Cloud Security Center is performed to compare various malware detection approaches. Promising experimental results demonstrate that our proposed method outperforms other alternative Android malware detection techniques. Our developed system Deep4MalDroid has also been integrated into a commercial Android anti-malware software.
Basic Human Activity Recognition based on sensor fusion in smartphones

Smartphones are ubiquitous devices that offer endless possibilities for health-related applications such as Ambient Assisted Living (AAL). They are rich in sensors that can be used for Human Activity Recognition (HAR) and monitoring. The emerging problem now is the selection of optimal combinations of these sensors and existing methods to accurately and efficiently perform activity recognition in a resource and computationally constrained environment. To accomplish efficient activity recognition on mobile devices, the most discriminative features and classification algorithms must be chosen carefully. In this study, sensor fusion is employed to improve the classification results of a lightweight classifier. Furthermore, the recognition performance of accelerometer, gyroscope and magnetometer when used separately and simultaneously on a feature-level sensor fusion is examined to gain valuable knowledge that can be used in dynamic sensing and data collection. Six ambulatory activities, namely, walking, running, sitting, standing, walking upstairs and walking downstairs, are inferred from low-sensor data collected from the right trousers pocket of the subjects and feature selection is performed to further optimize resource use.
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solutions for Nonconvex Distributed Optimization

In this work, we study two first-order primal-dual based algorithms, the Gradient Primal-Dual Algorithm (GPDA) and the Gradient Alternating Direction Method of Multipliers (GADMM), for solving a class of linearly constrained non-convex optimization problems. We show that with random initialization of the primal and dual variables, both algorithms are able to compute second-order stationary solutions (ss2) with probability one. This is the first result showing that primal-dual algorithm is capable of finding ss2 when only using first-order information; it also extends the existing results for first-order, but primal-only algorithms. An important implication of our result is that it also gives rise to the first global convergence result to the ss2, for two classes of unconstrained distributed non-convex learning problems over multi-agent networks.
Deep Learning for Hate Speech Detection in

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ∼18 F1 points.
Fundamentals of Business Intelligence

In this chapter, we provide definitions of Business Intelligence (BI) and outline the development of BI over time, particularly carving out current questions of BI. Different scenarios of BI applications are considered and business perspectives and views of BI on the business process are identified. Further, the goals and tasks of BI are discussed from a management and analysis point of view and a method format for BI applications is proposed. This format also gives an outline of the book’s contents. Finally, examples from different domain areas are introduced which are used for demonstration in later chapters of the book. 1.1 Definition of Business Intelligence If one looks for a definition of the term Business Intelligence (BI) one will find the first reference already in 1958 in a paper of H.P. Luhn (cf. ). Starting from the definition of the terms “Intelligence” as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal” and “Business” as “a collection of activities carried on for whatever purpose, be it science, technology, commerce, industry, law, government, defense, et cetera”, he specifies a business intelligence system as “[an] automatic system [that] is being developed to disseminate information to the various sections of any industrial, scientific or government organization.” The main task of Luhn’s system was automatic abstracting of documents and delivering this information to appropriate so-called action points. This definition did not come into effect for 30 years, and in 1989Howard Dresner coined the term Business Intelligence (BI) again. He introduced it as an umbrella term for a set of concepts and methods to improve business decision making, using systems based on facts. Many similar definitions have been given since. In Negash [18], important aspects of BI are emphasized by stating that “. . . business intelligence systems provide actionable information delivered at the right time, at the right location, and in the right form to assist decision makers.” Today one can find many different definitions which show that at the top level the intention of BI has not changed so much. For example, in [20] BI is defined as “an integrated, company-specific, IT-based total approach for managerial decision ©  W. Grossmann, S. Rinderle-Ma, Fundamentals of Business Intelligence, Data-Centric Systems and Applications, DOI 10.1007/978-3-662-46531-8_1 1
Implicit social cognition: attitudes, self-esteem, and stereotypes.

Social behavior is ordinarily treated as being under conscious (if not always thoughtful) control. However, considerable evidence now supports the view that social behavior often operates in an implicit or unconscious fashion. The identifying feature of implicit cognition is that past experience influences judgment in a fashion not introspectively known by the actor. The present conclusion--that attitudes, self-esteem, and stereotypes have important implicit modes of operation--extends both the construct validity and predictive usefulness of these major theoretical constructs of social psychology. Methodologically, this review calls for increased use of indirect measures--which are imperative in studies of implicit cognition. The theorized ordinariness of implicit stereotyping is consistent with recent findings of discrimination by people who explicitly disavow prejudice. The finding that implicit cognitive effects are often reduced by focusing judges' attention on their judgment task provides a basis for evaluating applications (such as affirmative action) aimed at reducing such unintended discrimination.
VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem

In this paper we present an on-manifold sequence-tosequence learning approach to motion estimation using visual and inertial sensors. It is to the best of our knowledge the first end-to-end trainable method for visual-inertial odometry which performs fusion of the data at an intermediate feature-representation level. Our method has numerous advantages over traditional approaches. Specifically, it eliminates the need for tedious manual synchronization of the camera and IMU as well as eliminating the need for manual calibration between the IMU and camera. A further advantage is that our model naturally and elegantly incorporates domain specific information which significantly mitigates drift. We show that our approach is competitive with state-of-theart traditional methods when accurate calibration data is available and can be trained to outperform them in the presence of calibration and synchronization errors.
Comparing Automatic and Human Evaluation of Local Explanations for Text Classification

Text classification models are becoming increasingly complex and opaque, however for many applications it is essential that the models are interpretable. Recently, a variety of approaches have been proposed for generating local explanations. While robust evaluations are needed to drive further progress, so far it is unclear which evaluation approaches are suitable. This paper is a first step towards more robust evaluations of local explanations. We evaluate a variety of local explanation approaches using automatic measures based on word deletion. Furthermore, we show that an evaluation using a crowdsourcing experiment correlates moderately with these automatic measures and that a variety of other factors also impact the human judgements.
Structural Equation Modeling and Regression: Guidelines for Research Practice

The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research suggests the need to compare and contrast different types of SEM techniques so that research designs can be selected appropriately. After assessing the extent to which these techniques are currently being used in IS research, the article presents a running example which analyzes the same dataset via three very different statistical techniques. It then compares two classes of SEM: covariance-based SEM and partial-least-squaresbased SEM. Finally, the article discusses linear regression models and offers guidelines as to when SEM techniques and when regression techniques should be used. The article concludes with heuristics and rule of thumb thresholds to guide practice, and a discussion of the extent to which practice is in accord with these guidelines.
Tests of Cognitive Ability The Sustained Attention to Response Task ( SART )

Substantial evidence suggests that mind-wandering typically occurs at a significant cost to performance. Mind-wandering–related deficits in performance have been observed in many contexts, most notably reading, tests of sustained attention, and tests of aptitude. Mind-wandering has been shown to negatively impact reading comprehension and model building, impair the ability to withhold automatized responses, and disrupt performance on tests of working memory and intelligence. These empirically identified costs of mind-wandering have led to the suggestion that mind-wandering may represent a pure failure of cognitive control and thus pose little benefit. However, emerging evidence suggests that the role of mind-wandering is not entirely pernicious. Recent studies have shown that mind-wandering may play a crucial role in both autobiographical planning and creative problem solving, thus providing at least two possible adaptive functions of the phenomenon. This article reviews these observed costs and possible functions of mind-wandering and identifies important avenues of future inquiry.
Concept and practical considerations of non-orthogonal multiple access (NOMA) for future radio access

As a promising downlink multiple access scheme for future radio access (FRA), this paper discusses the concept and practical considerations of non-orthogonal multiple access (NOMA) with a successive interference canceller (SIC) at the receiver side. The goal is to clarify the benefits of NOMA over orthogonal multiple access (OMA) such as OFDMA adopted by Long-Term Evolution (LTE). Practical considerations of NOMA, such as multi-user power allocation, signalling overhead, SIC error propagation, performance in high mobility scenarios, and combination with multiple input multiple output (MIMO) are discussed. Using computer simulations, we provide system-level performance of NOMA taking into account practical aspects of the cellular system and some of the key parameters and functionalities of the LTE radio interface such as adaptive modulation and coding (AMC) and frequency-domain scheduling. We show under multiple configurations that the system-level performance achieved by NOMA is higher by more than 30% compared to OMA.
Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting

Extraction of bone contours from radiographs plays an important role in disease diagnosis, preoperative planning, and treatment analysis. We present a fully automatic method to accurately segment the proximal femur in anteroposterior pelvic radiographs. A number of candidate positions are produced by a global search with a detector. Each is then refined using a statistical shape model together with local detectors for each model point. Both global and local models use Random Forest regression to vote for the optimal positions, leading to robust and accurate results. The performance of the system is evaluated using a set of 839 images of mixed quality. We show that the local search significantly outperforms a range of alternative matching techniques, and that the fully automated system is able to achieve a mean point-to-curve error of less than 0.9 mm for 99% of all 839 images. To the best of our knowledge, this is the most accurate automatic method for segmenting the proximal femur in radiographs yet reported.
Photosynthetic light harvesting: excitons and coherence.

Photosynthesis begins with light harvesting, where specialized pigment-protein complexes transform sunlight into electronic excitations delivered to reaction centres to initiate charge separation. There is evidence that quantum coherence between electronic excited states plays a role in energy transfer. In this review, we discuss how quantum coherence manifests in photosynthetic light harvesting and its implications. We begin by examining the concept of an exciton, an excited electronic state delocalized over several spatially separated molecules, which is the most widely available signature of quantum coherence in light harvesting. We then discuss recent results concerning the possibility that quantum coherence between electronically excited states of donors and acceptors may give rise to a quantum coherent evolution of excitations, modifying the traditional incoherent picture of energy transfer. Key to this (partially) coherent energy transfer appears to be the structure of the environment, in particular the participation of non-equilibrium vibrational modes. We discuss the open questions and controversies regarding quantum coherent energy transfer and how these can be addressed using new experimental techniques.
Mapping Ontologies into Cyc

The advent of Web services, and the Semantic Web described by domain ontologies, highlight the bottleneck to their growth: ontology mapping, merging, and integration. In this paper we present the process by which over the last 15 years several ontologies of varying complexity have been mapped or integrated with Cyc, a large commonsense knowledge base. These include SENSUS, FIPS 10-4, several large (300k-term) pharmaceutical thesauri, large portions of WordNet, MeSH/Snomed/UMLS, and the CIA World Factbook. This has to date required trained ontologists talking with subject matter experts. To break that bottleneck – to enable subject matter experts to directly map/merge/integrate their ontologies – we have been developing interactive clarification-dialog-based tools.