nils-herrmann/clean_scirepeval huggingface dataset:
Robust named entity disambiguation with random walks

Named Entity Disambiguation is the task of assigning entities from a Knowledge Graph (KG) to mentions of such entities in a textual document. The state-of-the-art for this task balances two disparate sources of similarity: lexical, defined as the pairwise similarity between mentions in the text and names of entities in the KG; and semantic, defined through some graph-theoretic property of a subgraph of the KG induced by the choice of entities for each mention. Departing from previous work, our notion of semantic similarity is rooted in Information Theory and is defined as the mutual information between random walks on the disambiguation graph induced by choice of entities for each mention. We describe an iterative algorithm based on this idea, and show an extension that uses learning-to-rank, which yields further improvements. Our experimental evaluation demonstrates that this approach is robust and very competitive on well-known existing benchmarks. We also justify the need for new and more difficult benchmarks, and provide an extensive experimental comparison of our method and previous work on these new benchmarks.
Unethical Research: How to Create a Malevolent Artificial Intelligence

Cybersecurity research involves publishing papers about malicious exploits as much as publishing information on how to design tools to protect cyber-infrastructure. It is this information exchange between ethical hackers and security experts, which results in a well-balanced cyber-ecosystem. In the blooming domain of AI Safety Engineering, hundreds of papers have been published on different proposals geared at the creation of a safe machine, yet nothing, to our knowledge, has been published on how to design a malevolent machine. Availability of such information would be of great value particularly to computer scientists, mathematicians, and others who have an interest in AI safety, and who are attempting to avoid the spontaneous emergence or the deliberate creation of a dangerous AI, which can negatively affect human activities and in the worst case cause the complete obliteration of the human species. This paper provides some general guidelines for the creation of a Malevolent Artificial Intelligence (MAI).
A Novel Grain-Oriented Lamination Rotor Core Assembly for a Synchronous Reluctance Traction Motor With a Reduced Torque Ripple Algorithm

High torque density and low torque ripple are crucial for traction applications, which allow electrified powertrains to perform properly during start-up, acceleration, and cruising. High-quality anisotropic magnetic materials such as cold-rolled grain-oriented electrical steels can be used for achieving higher efficiency, torque density, and compactness in synchronous reluctance motors equipped with transverse laminated rotors. However, the rotor cylindrical geometry makes utilization of these materials with pole numbers higher than two more difficult. From a reduced torque ripple viewpoint, particular attention to the rotor slot pitch angle design can lead to improvements. This paper presents an innovative rotor lamination design and assembly using cold-rolled grain-oriented electrical steel to achieve higher torque density along with an algorithm for rotor slot pitch angle design for reduced torque ripple. The design methods and prototyping process are discussed, finite-element analyses and experimental examinations are carried out, and the results are compared to verify and validate the proposed methods.
Accelerated focused crawling through online relevance feedback

The organization of HTML into a tag tree structure, which is rendered by browsers as roughly rectangular regions with embedded text and HREF links, greatly helps surfers locate and click on links that best satisfy their information need. Can an automatic program emulate this human behavior and thereby learn to predict the relevance of an unseen HREF target page w.r.t. an information need, based on information limited to the HREF source page? Such a capability would be of great interest in focused crawling and resource discovery, because it can fine-tune the priority of unvisited URLs in the crawl frontier, and reduce the number of irrelevant pages which are fetched and discarded.
Efficient and scalable trie-based algorithms for computing set containment relations

Computing containment relations between massive collections of sets is a fundamental operation in data management, for example in graph analytics and data mining applications. Motivated by recent hardware trends, in this paper we present two novel solutions for computing set-containment joins over massive sets: the Patricia Trie-based Signature Join (PTSJ) and PRETTI+, a Patricia trie enhanced extension of the state-of-the-art PRETTI join. The compact trie structure not only enables efficient use of main-memory, but also significantly boosts the performance of both approaches. By carefully analyzing the algorithms and conducting extensive experiments with various synthetic and real-world datasets, we show that, in many practical cases, our algorithms are an order of magnitude faster than the state-of-the-art.
Biometric Recognition Using Three-Dimensional Ear Shape

Previous works have shown that the ear is a promising candida te for biometric identification. However, in prior work, the pre-processing of ear images has had manua l steps, and algorithms have not necessarily handled problems caused by hair and earrings. We pres ent a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimen tal study to date in ear biometrics, achieving a rank-one recognition rate of 97.8% for an identification sc enario, and equal error rate of 1.2% for a verification scenario on a database of 415 subjects and 1,386 total probes. Keyword: biometrics, ear biometrics, 3-D shape, skin detection, cur vat e estimation, active contour, iterative closest point.
Thinking about the self influences thinking in general : cognitive consequences of salient self-concept

Two studies support our hypothesis that connected and interdependent self-focus evokes a generally context-dependent cognitive mode (focused on object–context relations) and provide some evidence that separate and independent self-focus evokes a generally context-independent cognitive mode (focused on objects, independent of contexts). Consistent with our predictions, experimental manipulation of interdependent self-focus influences cognitive speed/accuracy (Experiment 1) and memory (Experiment 2). When primed self-focus is congruent with the perceptual task at hand, perceptual speed increases (as shown by a significant task by prime interaction effect) and when primed, interdependent self-focus improves memory for incidentally encoded contextual information. Further research to link primed and chronic self-focus effects is called for.
A Realistic Power Consumption Model for Wireless Sensor Network Devices

A realistic power consumption model of wireless communication subsystems typically used in many sensor network node devices is presented. Simple power consumption models for major components are individually identified, and the effective transmission range of a sensor node is modeled by the output power of the transmitting power amplifier, sensitivity of the receiving low noise amplifier, and RF environment. Using this basic model, conditions for minimum sensor network power consumption are derived for communication of sensor data from a source device to a destination node. Power consumption model parameters are extracted for two types of wireless sensor nodes that are widely used and commercially available. For typical hardware configurations and RF environments, it is shown that whenever single hop routing is possible it is almost always more power efficient than multi-hop routing. Further consideration of communication protocol overhead also shows that single hop routing will be more power efficient compared to multi-hop routing under realistic circumstances. This power consumption model can be used to guide design choices at many different layers of the design space including, topology design, node placement, energy efficient routing schemes, power management and the hardware design of future wireless sensor network devices
Quantum Interference and Superposition in Cognition : Development of a Theory for the Disjunction of Concepts

We elaborate a theory for the modeling of concepts using the mathematical structure of quantum mechanics. Items and concepts are represented by vectors in the complex Hilbert space of quantum mechanics and membership weights of items are modeled by quantum weights calculated following the quantum rules. We apply this theory to model the disjunction of concepts and show that the predictions of our theory for the membership weights of items with respect to the disjunction of concepts match with great accuracy the results of an experiment conducted by Hampton (1988b). It is the quantum effects of interference and superposition that are at the origin of the effects of overextension and underextension observed by Hampton as deviations from a classical use of the disjunction. We show that the complex numbers of the Hilbert space are essential to obtaining the experimental predictions, i.e. vector space models over real numbers do not provide predictions matching the experimental data. We put forward an explanation of the effects of overextension and underextension by interpreting the quantum model applied to the modeling of the disjunction of concepts.
Building a Social Dimensional Threat Model from Current and Historic Events of Cyber Attacks

The volume of cyber attacks has increased tremendously in the recent years. Many of these cyber attacks can be linked to current and historic events in the social, political, economic, and cultural (SPEC) dimensions in the human world. In this paper, we analyze the expression of such cyber attacks in news media and publications based on past social events, explore the factors in social dimension that triggered the cyber attacks, and map these factors to concepts in a cyber attack domain model. The goal of this research is to gain a better understanding of the likelihood of cyber attacks upon social disturbances in the human world. Such insight would allow predictions to be made about an impending cyber attack, the means and methods with which such attacks could be carried out, the potential victims, and the time and duration of the attacks. In our future work, we aim to utilize our models to provide early warnings such that necessary defenses can be built up before cyber attacks occur in response to disturbances in the SPEC dimensions.
Linear subspace design for real-time shape deformation

We propose a method to design linear deformation subspaces, unifying linear blend skinning and generalized barycentric coordinates. Deformation subspaces cut down the time complexity of variational shape deformation methods and physics-based animation (reduced-order physics). Our subspaces feature many desirable properties: interpolation, smoothness, shape-awareness, locality, and both constant and linear precision. We achieve these by minimizing a quadratic deformation energy, built via a discrete Laplacian inducing linear precision on the domain boundary. Our main advantage is speed: subspace bases are solutions to a sparse linear system, computed interactively even for generously tessellated domains. Users may seamlessly switch between applying transformations at handles and editing the subspace by adding, removing or relocating control handles. The combination of fast computation and good properties means that designing the right subspace is now just as creative as manipulating handles. This paradigm shift in handle-based deformation opens new opportunities to explore the space of shape deformations.
Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization

Electroencephalography (EEG) headsets are the most commonly used sensing devices for brain-computer interface. In real-world applications, there are advantages to extrapolating data from one user session to another. However, these advantages are limited if the data arise from different hardware systems, which often vary between application spaces. Currently, this creates a need to recalibrate classifiers, which negatively affects people's interest in using such systems. In this paper, we employ active weighted adaptation regularization (AwAR), which integrates weighted adaptation regularization (wAR) and active learning, to expedite the calibration process. wAR makes use of labeled data from the previous headset and handles class-imbalance, and active learning selects the most informative samples from the new headset to label. Experiments on single-trial event-related potential classification show that AwAR can significantly increase the classification accuracy, given the same number of labeled samples from the new headset. In other words, AwAR can effectively reduce the number of labeled samples required from the new headset, given a desired classification accuracy, suggesting value in collating data for use in wide scale transfer-learning applications.
Memristance can explain Spike-Time-Dependent-Plasticity in Neural Synapses

Interdisciplinary research broadens the view of particular problems yielding fresh and possibly unexpected insights. This is the case of neuromorphic engineering where technology and neuroscience cross-fertilize each other. For example, consider on one side the recently discovered memristor, postulated in 1971, thanks to research in nanotechnology electronics. On the other side, consider the mechanism known as Spike-TimeDependent-Plasticity (STDP) which describes a neuronal synaptic learning mechanism that outperforms the traditional Hebbian synaptic plasticity proposed in 1949. STDP was originally postulated as a computer learning algorithm, and is being used by the machine intelligence and computational neuroscience community. At the same time its biological and physiological foundations have been reasonably well established during the past decade. If memristance and STDP can be related, then (a) recent discoveries in nanophysics and nanoelectronic principles may shed new lights into understanding the intricate molecular and physiological mechanisms behind STDP in neuroscience, and (b) new neuromorphic-like computers built out of nanotechnology memristive devices could incorporate the biological STDP mechanisms yielding a new generation of self-adaptive ultrahigh-dense intelligent machines. Here we show that by combining memristance models with the electrical wave signals of neural impulses (spikes) converging from preand post-synaptic neurons into a synaptic junction, STDP behavior emerges naturally. This result serves to understand how neural and memristance parameters modulate STDP, which might bring new insights to neurophysiologists in searching for the ultimate physiological mechanisms responsible for STDP in biological synapses. At the same time, this result also provides a direct mean to incorporate STDP learning mechanisms into a new generation of nanotechnology computers employing memristors. Memristance was postulated in 1971 by Chua based on circuit theoretical reasonings and has been recently demonstrated in nanoscale two-terminal devices, such as certain titanium-dioxide and amorphous Silicon cross-point switches. Memristance arises naturally in nanoscale devices because small voltages can yield enormous electric fields that produce the motion of charged atomic or molecular species changing structural properties of a device (such as its conductance) while it operates. By definition a memristor obeys equations of the form
A Magneto-Inductive Sensor Based Wireless Tongue-Computer Interface

We have developed a noninvasive, unobtrusive magnetic wireless tongue-computer interface, called ldquoTongue Drive,rdquo to provide people with severe disabilities with flexible and effective computer access and environment control. A small permanent magnet secured on the tongue by implantation, piercing, or tissue adhesives, is utilized as a tracer to track the tongue movements. The magnetic field variations inside and around the mouth due to the tongue movements are detected by a pair of three-axial linear magneto-inductive sensor modules mounted bilaterally on a headset near the user's cheeks. After being wirelessly transmitted to a portable computer, the sensor output signals are processed by a differential field cancellation algorithm to eliminate the external magnetic field interference, and translated into user control commands, which could then be used to access a desktop computer, maneuver a powered wheelchair, or control other devices in the user's environment. The system has been successfully tested on six able-bodied subjects for computer access by defining six individual commands to resemble mouse functions. Results show that the Tongue Drive system response time for 87% correctly completed commands is 0.8 s, which yields to an information transfer rate of ~ 130 b/min.
The Business Value of CRM Technology: From the Perspective of Organizational Ambidexterity

In this paper we report a case study on how firms realize the business value of CRM by developing CRM-enabled ambidexterity to simultaneously pursue stability and develop adaptability. Results show that as the focal firm used CRM as a platform for standardizing operations and building routines, the top management team (TMT) and the operation team worked together to reflexively monitor the status of CRM-enabled capabilities and the usage of CRM. Such reflexive monitoring led to proactive changes in CRM and CRM usage in response to market changes and market needs, ensuring adaptability. In this way CRM enabled organizational ambidexterity through mechanisms of capabilities building and reflexive monitoring. By examining the previously unexplored relationship between CRM use and organizational ambidexterity, this study contributes to literature in both ambidexterity and CRM business value.
High efficiency X-band class-E GaN MMIC high-power amplifiers

We have demonstrated 8.5-11.5 GHz class-E MMIC high-power amplifiers (HPAs) with a peak power-added-efficiency (PAE) of 61% and drain efficiency (DE) of 70% with an output power of 3.7 W in a continuous-mode operation. At 5 W output power, PAE and DE of 58% and 67% are measured, respectively, which implies MMIC power density of 5 W/mm at Vds = 30 V. The peak gain is 11 dB, with an associated gain of 9 dB at the peak PAE. At an output power of 9 W, DE and PAE of 59% and 51 % were measured, respectively. In order to improve the linearity, we have designed and simulated X-band class-E MMIC PAs similar to a Doherty configuration. The Doherty-based class-E amplifiers show an excellent cancellation of a third-order intermodulation product (IM3), which improved the simulated two-tone linearity C/IM3 to >; 50 dBc.
An efficient implementation of the second order extended Kalman filter

The second order extended Kalman filter (EKF2) is based on a second order Taylor expansion of a nonlinear system, in contrast to the more common (first order) extended Kalman filter (EKF1). Despite a solid theoretical ground for its approximation, it is seldom used in applications, where the EKF and the unscented Kalman filter (UKF) are the standard algorithms. One reason for this might be the requirement for analytical Jacobian and Hessian of the system equations, and the high complexity that scales with the state order nx as n5x. We propose a numerical algorithm which is based on an extended set of sigma points (compared to the UKF) that needs neither Jacobian nor Hessian (or numerical approximations of these). Further, it scales as n4x, which is an order of magnitude better than the EKF2 algorithm presented in literature.
Online Algorithms for Mining Semi-structured Data Stream

In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semistructured data in the document order through a data stream, and can return the current set of frequent patterns immediately on request at any time. A crucial part of our algorithm is the incremental maintenance of the occurrences of possibly frequent patterns using a tree sweeping technique. We give modifications of the algorithm to other online mining model. We present theoretical and empirical analyses to evaluate the performance of the algorithm.
DroidWard: An Effective Dynamic Analysis Method for Vetting Android Applications

As the number of Android malicious applications has explosively increased, effectively vetting Android applications (apps) has become an emerging issue. Traditional static analysis is ineffective for vetting apps whose code have been obfuscated or encrypted. Dynamic analysis is suitable to deal with the obfuscation and encryption of codes. However, existing dynamic analysis methods cannot effectively vet the applications, as a limited number of dynamic features have been explored from apps that have become increasingly sophisticated. In this work, we propose an effective dynamic analysis method called DroidWard in the aim to extract most relevant and effective features to characterize malicious behavior and to improve the detection accuracy of malicious apps. In addition to using the existing 9 features, DroidWard extracts 6 novel types of effective features from apps through dynamic analysis. DroidWard runs apps, extracts features and identifies benign and malicious apps with Support Vector Machine (SVM), Decision Tree (DTree) and Random Forest. 666 Android apps are used in the experiments and the evaluation results show that DroidWard correctly classifies 98.54% of malicious apps with 1.55% of false positives. Compared to existing work, DroidWard improves the TPR with 16.07% and suppresses the FPR with 1.31% with SVM, indicating that it is more effective than existing methods.
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search

Model predictive control (MPC) is an effective method for controlling robotic systems, particularly autonomous aerial vehicles such as quadcopters. However, application of MPC can be computationally demanding, and typically requires estimating the state of the system, which can be challenging in complex, unstructured environments. Reinforcement learning can in principle forego the need for explicit state estimation and acquire a policy that directly maps sensor readings to actions, but is difficult to apply to unstable systems that are liable to fail catastrophically during training before an effective policy has been found. We propose to combine MPC with reinforcement learning in the framework of guided policy search, where MPC is used to generate data at training time, under full state observations provided by an instrumented training environment. This data is used to train a deep neural network policy, which is allowed to access only the raw observations from the vehicle's onboard sensors. After training, the neural network policy can successfully control the robot without knowledge of the full state, and at a fraction of the computational cost of MPC. We evaluate our method by learning obstacle avoidance policies for a simulated quadrotor, using simulated onboard sensors and no explicit state estimation at test time.