nils-herrmann/clean_scirepeval huggingface dataset:
An Enhanced Hypercube-Based Encoding for Evolving the Placement, Density, and Connectivity of Neurons

Intelligence in nature is the product of living brains, which are themselves the product of natural evolution. Although researchers in the field of neuroevolution (NE) attempt to recapitulate this process, artificial neural networks (ANNs) so far evolved through NE algorithms do not match the distinctive capabilities of biological brains. The recently introduced hypercube-based neuroevolution of augmenting topologies (HyperNEAT) approach narrowed this gap by demonstrating that the pattern of weights across the connectivity of an ANN can be generated as a function of its geometry, thereby allowing large ANNs to be evolved for high-dimensional problems. Yet the positions and number of the neurons connected through this approach must be decided a priori by the user and, unlike in living brains, cannot change during evolution. Evolvable-substrate HyperNEAT (ES-HyperNEAT), introduced in this article, addresses this limitation by automatically deducing the node geometry from implicit information in the pattern of weights encoded by HyperNEAT, thereby avoiding the need to evolve explicit placement. This approach not only can evolve the location of every neuron in the network, but also can represent regions of varying density, which means resolution can increase holistically over evolution. ES-HyperNEAT is demonstrated through multi-task, maze navigation, and modular retina domains, revealing that the ANNs generated by this new approach assume natural properties such as neural topography and geometric regularity. Also importantly, ES-HyperNEAT's compact indirect encoding can be seeded to begin with a bias toward a desired class of ANN topographies, which facilitates the evolutionary search. The main conclusion is that ES-HyperNEAT significantly expands the scope of neural structures that evolution can discover.
AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation

Supervised deep learning methods have shown promising results for the task of monocular depth estimation; but acquiring ground truth is costly, and prone to noise as well as inaccuracies. While synthetic datasets have been used to circumvent above problems, the resultant models do not generalize well to natural scenes due to the inherent domain shift. Recent adversarial approaches for domain adaption have performed well in mitigating the differences between the source and target domains. But these methods are mostly limited to a classification setup and do not scale well for fully-convolutional architectures. In this work, we propose AdaDepth - an unsupervised domain adaptation strategy for the pixel-wise regression task of monocular depth estimation. The proposed approach is devoid of above limitations through a) adversarial learning and b) explicit imposition of content consistency on the adapted target representation. Our unsupervised approach performs competitively with other established approaches on depth estimation tasks and achieves state-of-the-art results in a semi-supervised setting.
Do Conglomerate Firms Allocate Resources Inefficiently Across Industries ? Theory and Evidence

We develop a profit-maximizing neoclassical model of optimal firm size and growth across different industries based on differences in industry fundamentals and firm productivity. In the model, a conglomerate discount is consistent with profit maximization. The model predicts how conglomerate firms will allocate resources across divisions over the business cycle and how their responses to industry shocks will differ from those of single-segment firms. Using plant level data, we find that growth and investment of conglomerate and single-segment firms is related to fundamental industry factors and individual segment level productivity. The majority of conglomerate firms exhibit growth across industry segments that is consistent with optimal behavior.  These studies implicitly assume that the conglomerates and single-industry firms possess similar ability to compete, and that they differ mainly in that conglomerates * Robert H. Smith School of Business, The University of Maryland. A previous draft was circulated and presented with the title “Optimal Firm Size and the Growth of Conglomerate and Single-industry Firms.” This research was supported by NSF Grant #SBR-9709427. We wish to thank the editor, René Stulz, two anonymous referees, John Graham, Robert Hauswald, Sheridan Titman, Anjan Thakor, Haluk Unal, and seminar participants at Carnegie Mellon, Colorado, Harvard, Illinois, Indiana, Maryland, Michigan, Ohio State, the 1999 AFA meetings, the National Bureau of Economic Research and the 1998 Finance and Accounting conference at NYU for their comments. We also wish to thank researchers at the Center for Economic Studies for comments and their help with the data used in this study. The research was conducted at the Center for Economic Studies, U.S. Bureau of the Census, Department of Commerce. The authors alone are responsible for the work and any errors or omissions. 1 Lang and Stulz ~1994!, Berger and Ofek ~1995!, and Comment and Jarrell ~1995! document a conglomerate discount in the stock market and low returns to conglomerate firms. Rajan, Servaes, and Zingales ~2000! and Scharfstein ~1997! examine conglomerate investment across different business segments. Lamont ~1997! and Shin and Stulz ~1997! examine the relation of investment to cash f low for conglomerate industry segments. For an example from the business press see Deutsch ~1998!. THE JOURNAL OF FINANCE • VOL. LVII, NO. 2 • APRIL 2002
Shoulder Surfing Resistant Virtual Keyboard for Internet Banking

Today the prevalence of online banking is enormous. People prefer to accomplish their financial transactions through the online banking services offered by their banks. This method of accessing is more convenient, quicker and secured. Banks are also encouraging their customers to opt for this mode of e-banking facilities since that result in cost savings for the banks and there is better customer satisfaction. An important aspect of online banking is the precise authentication of users before allowing them to access their accounts. Typically this is done by asking the customers to enter their unique login id and password combination. The success of this authentication relies on the ability of customers to maintain the secrecy of their passwords. Since the customer login to the banking portals normally occur in public environments, the passwords are prone to key logging attacks. To avoid this, virtual keyboards are provided. But virtual keyboards are vulnerable to shoulder surfing based attacks. In this paper, a secured virtual keyboard scheme that withstands such attacks is proposed. Elaborate user studies carried out on the proposed scheme have testified the security and the usability of the proposed approach.
Bridging the Qualitative-Quantitative Divide: Guidelines for Conducting Mixed Methods Research in Information Systems

Mixed methods research is an approach that combines quantitative and qualitative research methods in the same research inquiry. Such work can help develop rich insights into various phenomena of interest that cannot be fully understood using only a quantitative or a qualitative method. Notwithstanding the benefits and repeated calls for such work, there is a dearth of mixed methods research in information systems. Building on the literature on recent methodological advances in mixed methods research, we develop a set of guidelines for conducting mixed methods research in IS. We particularly elaborate on three important aspects of conducting mixed methods research: (1) appropriateness of a mixed methods approach; (2) development of meta-inferences (i.e., substantive theory) from mixed methods research; and (3) assessment of the quality of meta-inferences (i.e., validation of mixed methods research). The applicability of these guidelines is illustrated using two published IS papers that used mixed methods.
Conformal antennas based on holographic artificial impedance surfaces

Advances in artificial impedance surface conformal antennas are presented. A detailed conical impedance modulation is proposed for the first time. By coating an artificial impedance surface on a cone, we can control the conical surface wave radiating at the desired direction. The surface impedance is constructed by printing a dense texture of sub wavelength metal patches on a grounded dielectric slab. The effective surface impedance depends on the size of the patches, and can be varied as a function of position. The final devices are conical conformal antennas with simple layout and feeding. Simulated results are presented, and better aperture efficiency and lower side lobe level are obtained than our predecessors .
Threshold-optimal DSA/ECDSA signatures and an application to Bitcoin wallet security

While threshold signature schemes have been presented before, there has never been an optimal threshold signature algorithm for DSA. Due to the properties of DSA, it is far more difficult to create a threshold scheme for it than for other signature algorithms. In this paper, we present a breakthrough scheme that provides a threshold DSA algorithm that is efficient and optimal. We also present a compelling application to use our scheme: securing Bitcoin wallets. Bitcoin thefts are on the rise, and threshold DSA is necessary to secure Bitcoin wallets. Our scheme is the first general threshold DSA scheme that does not require an honest majority and is useful for securing Bitcoin wallets.
Antidepressant-associated mania and psychosis resulting in psychiatric admissions.

The safety and tolerability of the selective serotonin reuptake inhibitors and the newer atypical agents have led to a significant increase in antidepressant use. These changes raise concern as to the likelihood of a corresponding increase in adverse behavioral reactions attributable to these drugs.



All admissions to a university-based general hospital psychiatric unit during a 14-month period were reviewed.



Forty-three (8.1%) of 533 patients were found to have been admitted owing to antidepressant-associated mania or psychosis.



Despite the positive changes in the side effect profile of antidepressant drugs, the rate of admissions due to antidepressant-associated adverse behavioral effects remains significant.
Regional perfusion during venoarterial extracorporeal membrane oxygenation: a case report and educational modules on the concept of dual circulations.

A challenging aspect of managing patients on venoarterial extracorporeal membrane oxygenation (V-A ECMO) is a thorough understanding of the relationship between oxygenated blood from the ECMO circuit and blood being pumped from the patient's native heart. We present an adult V-A ECMO case report, which illustrates a unique encounter with the concept of "dual circulations." Despite blood gases from the ECMO arterial line showing respiratory acidosis, this patient with cardiogenic shock demonstrated regional respiratory alkalosis when blood was sampled from the right radial arterial line. In response, a sample was obtained from the left radial arterial line, which mimicked the ECMO arterial blood but was dramatically different from the blood sampled from the right radial arterial line. A retrospective analysis of patient data revealed that the mismatch of blood gas values in this patient corresponded to an increased pulse pressure. Having three arterial blood sampling sites and data on the patient's pulse pressure provided a dynamic view of blood mixing and guided proper management, which contributed to a successful patient outcome that otherwise may not have occurred. As a result of this unique encounter, we created and distributed graphics representing the concept of "dual circulations" to facilitate the education of ECMO specialists at our institution.



ECMO, education, cardiopulmonary bypass, cannulation.
Automatically Learning Semantic Features for Defect Prediction

Software defect prediction, which predicts defective code regions, can help developers find bugs and prioritize their testing efforts. To build accurate prediction models, previous studies focus on manually designing features that encode the characteristics of programs and exploring different machine learning algorithms. Existing traditional features often fail to capture the semantic differences of programs, and such a capability is needed for building accurate prediction models.
 To bridge the gap between programs' semantics and defect prediction features, this paper proposes to leverage a powerful representation-learning algorithm, deep learning, to learn semantic representation of programs automatically from source code. Specifically, we leverage Deep Belief Network (DBN) to automatically learn semantic features from token vectors extracted from programs' Abstract Syntax Trees (ASTs).
 Our evaluation on ten open source projects shows that our automatically learned semantic features significantly improve both within-project defect prediction (WPDP) and cross-project defect prediction (CPDP) compared to traditional features. Our semantic features improve WPDP on average by 14.7% in precision, 11.5% in recall, and 14.2% in F1. For CPDP, our semantic features based approach outperforms the state-of-the-art technique TCA+ with traditional features by 8.9% in F1.
Code and Data Integrity of Modern Operating Systems

Our society is more and more based on digital information. People’s lives depend on the availability of interconnected networks and the integrity of the information shared between the connected devices. Communication and sharing of information through this network is a cornerstone of our current way of living. Both criminals as well as nation states aim to extend their power and influence by controlling the devices and the information flowing through the network. For this, actors spend huge amount of energy into compromising the connected systems and thereby violate their integrity. For this, it is very important to be able to make assumptions about the integrity of a given device, most importantly the device’s hardware and the operating system powering the device. This thesis aims to tackle this problem by proposing mechanisms to validate the integrity of modern operating system kernels during runtime. In the first part of our work, we investigate, how runtime code integrity of a modern operating system can be achieved. While various mechanisms exist to ensure the load-time integrity of an operating system, runtime code integrity is still an open issue. Existing mechanisms agree on the assumption, that code, once loaded into memory is static. Thus, systems calculate and verify hashes of code pages in memory, or use dedicated hardware mechanisms to prohibit
Dynamic Routing for Flying Ad Hoc Networks

This paper reports experimental results on self-organizing wireless networks carried by small flying robots. Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy. This makes them a very attractive technology for many civilian and military applications. Due to the high mobility of the nodes, maintaining a communication link between the UAVs is a challenging task. The topology of these networks is more dynamic than that of typical mobile ad hoc networks (MANETs) and of typical vehicle ad hoc networks. As a consequence, the existing routing protocols designed for MANETs partly fail in tracking network topology changes. In this paper, we compare two different routing algorithms for ad hoc networks: optimized link-state routing (OLSR) and predictive OLSR (P-OLSR). The latter is an OLSR extension that we designed for FANETs; it takes advantage of the Global Positioning System (GPS) information available on board. To the best of our knowledge, P-OLSR is currently the only FANET-specific routing technique that has an available Linux implementation. We present results obtained by both media-access-control (MAC) layer emulations and real-world experiments. In the experiments, we used a testbed composed of two autonomous fixed-wing UAVs and a node on the ground. Our experiments evaluate the link performance and the communication range, as well as the routing performance. Our emulation and experimental results show that P-OLSR significantly outperforms OLSR in routing in the presence of frequent network topology changes.
Deep Learning with Minimal Training Data: TurkuNLP Entry in the BioNLP Shared Task 2016

We present the TurkuNLP entry to the BioNLP Shared Task 2016 Bacteria Biotopes event extraction (BB3-event) subtask. We propose a deep learningbased approach to event extraction using a combination of several Long Short-Term Memory (LSTM) networks over syntactic dependency graphs. Features for the proposed neural network are generated based on the shortest path connecting the two candidate entities in the dependency graph. We further detail how this network can be efficiently trained to have good generalization performance even when only a very limited number of training examples are available and part-of-speech (POS) and dependency type feature representations must be learned from scratch. Our method ranked second among the entries to the shared task, achieving an F-score of 52.1% with 62.3% precision and 44.8% recall.
Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction

We propose a novel geolocation prediction model using a complex neural network. Our model unifies text, metadata, and user network representations with an attention mechanism to overcome previous ensemble approaches. In an evaluation using two open datasets, the proposed model exhibited a maximum 3.8% increase in accuracy and a maximum of 6.6% increase in accuracy@161 against previous models. We further analyzed several intermediate layers of our model, which revealed that their states capture some statistical characteristics of the datasets.
Near-Synonymy and Lexical Choice

We develop a new computational model for representing the fine-grained meanings of near-synonyms and the differences between them. We also develop a lexical-choice process that can decide which of several near-synonyms is most appropriate in a particular situation. This research has direct applications in machine translation and text generation. We first identify the problems of representing near-synonyms in a computational lexicon and show that no previous model adequately accounts for near-synonymy. We then propose a preliminary theory to account for near-synonymy, relying crucially on the notion of granularity of representation, in which the meaning of a word arises out of a context-dependent combination of a context-independent core meaning and a set of explicit differences to its near-synonyms. That is, near-synonyms cluster together. We then develop a clustered model of lexical knowledge, derived from the conventional ontological model. The model cuts off the ontology at a coarse grain, thus avoiding an awkward proliferation of language-dependent concepts in the ontology, yet maintaining the advantages of efficient computation and reasoning. The model groups near-synonyms into subconceptual clusters that are linked to the ontology. A cluster differentiates near-synonyms in terms of fine-grained aspects of denotation, implication, expressed attitude, and style. The model is general enough to account for other types of variation, for instance, in collocational behavior. An efficient, robust, and flexible fine-grained lexical-choice process is a consequence of a clustered model of lexical knowledge. To make it work, we formalize criteria for lexical choice as preferences to express certain concepts with varying indirectness, to express attitudes, and to establish certain styles. The lexical-choice process itself works on two tiers: between clusters and between near-synonyns of clusters. We describe our prototype implementation of the system, called I-Saurus.
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection

Machine learning has become an important component for many systems and applications including computer vision, spam filtering, malware and network intrusion detection, among others. Despite the capabilities of machine learning algorithms to extract valuable information from data and produce accurate predictions, it has been shown that these algorithms are vulnerable to attacks. Data poisoning is one of the most relevant security threats against machine learning systems, where attackers can subvert the learning process by injecting malicious samples in the training data. Recent work in adversarial machine learning has shown that the so-called optimal attack strategies can successfully poison linear classifiers, degrading the performance of the system dramatically after compromising a small fraction of the training dataset. In this paper we propose a defence mechanism to mitigate the effect of these optimal poisoning attacks based on outlier detection. We show empirically that the adversarial examples generated by these attack strategies are quite different from genuine points, as no detectability constrains are considered to craft the attack. Hence, they can be detected with an appropriate pre-filtering of the training dataset.
Top-down induction of decision trees classifiers - a survey

Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of growing a decision tree from available data. This paper presents an updated survey of current methods for constructing decision tree classifiers in a top-down manner. The paper suggests a unified algorithmic framework for presenting these algorithms and describes the various splitting criteria and pruning methodologies.
SPARQL query processing with Apache Spark

The number of linked data sources and the size of the linked open data graph keep growing every day. As a consequence, semantic RDF services are more and more confronted to various “big data” problems. Query processing is one of them and needs to be efficiently addressed with executions over scalable, highly available and fault tolerant frameworks. Data management systems requiring these properties are rarely built from scratch but are rather designed on top of an existing cluster computing engine. In this work, we consider the processing of SPARQL queries with Apache Spark. We propose and compare five different query processing approaches based on different join execution models and Spark components. A detailed experimentation, on real-world and synthetic data sets, emphasizes that two approaches tailored for the RDF data model outperform the other ones on all major query shapes, i.e., star, snowflake, chain and hybrid.
Dual Ask-Answer Network for Machine Reading Comprehension

There are three modalities in the reading comprehension setting: question, answer and context. The task of question answering or question generation aims to infer an answer or a question when given the counterpart based on context. We present a novel two-way neural sequence transduction model that connects three modalities, allowing it to learn two tasks simultaneously and mutually benefit one another. During training, the model receives question-context-answer triplets as input and captures the cross-modal interaction via a hierarchical attention process. Unlike previous joint learning paradigms that leverage the duality of question generation and question answering at data level, we solve such dual tasks at the architecture level by mirroring the network structure and partially sharing components at different layers. This enables the knowledge to be transferred from one task to another, helping the model to find a general representation for each modality. The evaluation on four public datasets shows that our dual-learning model outperforms the mono-learning counterpart as well as the state-of-the-art joint models on both question answering and question generation tasks.