nils-herrmann/clean_scirepeval huggingface dataset:
A predictive maintenance system based on regularization methods for ion-implantation

Ion Implantation is one of the most sensitive processes in Semiconductor Manufacturing. It consists in impacting accelerated ions with a material substrate and is performed by an Implanter tool. The major maintenance issue of such tool concerns the breaking of the tungsten filament contained within the ion source of the tool. This kind of fault can happen on a weekly basis, and the associated maintenance operations can last up to 3 hours. It is important to optimize the maintenance activities by synchronizing the Filament change operations with other minor maintenance interventions. In this paper, a Predictive Maintenance (PdM) system is proposed to tackle such issue; the filament lifetime is estimated on a statistical basis exploiting the knowledge of physical variables acting on the process. Given the high-dimensionality of the data, the statistical modeling has been based on  Lasso, Ridge Regression and Elastic Nets. The predictive performances of the aforementioned regularization methods and of the proposed PdM module have been tested on actual productive semiconductor data.
Deep Learning for Acoustic Echo Cancellation in Noisy and Double-Talk Scenarios

Traditional acoustic echo cancellation (AEC) works by identifying an acoustic impulse response using adaptive algorithms. We formulate AEC as a supervised speech separation problem, which separates the loudspeaker signal and the nearend signal so that only the latter is transmitted to the far end. A recurrent neural network with bidirectional long short-term memory (BLSTM) is trained to estimate the ideal ratio mask from features extracted from the mixtures of near-end and farend signals. A BLSTM estimated mask is then applied to separate and suppress the far-end signal, hence removing the echo. Experimental results show the effectiveness of the proposed method for echo removal in double-talk, background noise, and nonlinear distortion scenarios. In addition, the proposed method can be generalized to untrained speakers.
Extensive Imitation is Irrational and Harmful ∗

Rationality leads people to imitate those with similar tastes but different information. But people who imitate common sources develop correlated beliefs, and rationality demands that later social learners take this redundancy into account. This implies severe limits to rational imitation. We show that (i) in most natural observation structures besides the canonical single-file case, full rationality dictates that people must “anti-imitate” some of those they observe; and (ii) in every observation structure full rationality dictates that people who do not anti-imitate can, in essence, imitate at most one person among predecessors who share common information. We also show that in a very broad class of settings, virtually any learning rule in which people regularly do imitate more than one person without anti-imitating others will lead to a positive (and, in some environments, arbitrarily high) probability of people converging to confident and wrong long-run beliefs. When testing either the rationality or the efficiency of social learning, researchers should not focus on whether people follow others’ behavior—but instead whether they follow it too much.
Collective Intelligence and its Implementation on the Web: Algorithms to Develop a Collective Mental Map

Collective intelligence is defined as the ability of a group to solve more problems than its individual members. It is argued that the obstacles created by individual cognitive limits and the difficulty of coordination can be overcome by using a collective mental map (CMM). A CMM is defined as an external memory with shared read/write access, that represents problem states, actions and preferences for actions. It can be formalized as a weighted, directed graph. The creation of a network of pheromone trails by ant colonies points us to some basic mechanisms of CMM development: averaging of individual preferences, amplification of weak links by positive feedback, and integration of specialised subnetworks through division of labor. Similar mechanisms can be used to transform the World-Wide Web into a CMM, by supplementing it with weighted links. Two types of algorithms are explored: 1) the co-occurrence of links in web pages or user selections can be used to compute a matrix of link strengths, thus generalizing the technique of “collaborative filtering”; 2) learning web rules extract information from a user’s sequential path through the web in order to change link strengths and create new links. The resulting weighted web can be used to facilitate problem-solving by suggesting related links to the user, or, more powerfully, by supporting a software agent that discovers relevant documents through spreading activation.
Modeling punctuation prediction as machine translation

Punctuation prediction is an important task in Spoken Language Translation. The output of speech recognition systems does not typically contain punctuation marks. In this paper we analyze different methods for punctuation prediction and show improvements in the quality of the final translation output. In our experiments we compare the different approaches and show improvements of up to 0.8 BLEU points on the IWSLT 2011 English French Speech Translation of Talks task using a translation system to translate from unpunctuated to punctuated text instead of a language model based punctuation prediction method. Furthermore, we do a system combination of the hypotheses of all our different approaches and get an additional improvement of 0.4 points in BLEU.
Scattering Parameters and Surface Normals from Homogeneous Translucent Materials Using Photometric Stereo

This paper proposes a novel photometric stereo solution to jointly estimate surface normals and scattering parameters from a globally planar, homogeneous, translucent object. Similar to classic photometric stereo, our method only requires as few as three observations of the translucent object under directional lighting. Naively applying classic photometric stereo results in blurred photometric normals. We develop a novel blind deconvolution algorithm based on inverse rendering for recovering the sharp surface normals and the material properties. We demonstrate our method on a variety of translucent objects.
Azimuthal Scattering from Elliptical Hair Fibers

The appearance of hair follows from the small-scale geometry of hair fibers, with the cross-sectional shape determining the azimuthal distribution of scattered light. Although previous research has described some of the effects of non-circular cross sections, no accurate scattering models for non-circular fibers exist. This article presents a scattering model for elliptical fibers, which predicts that even small deviations from circularity produce important changes in the scattering distribution and which disagrees with previous approximations for the effects of eccentricity.
 To confirm the model’s predictions, new scattering measurements of fibers from a wide range of hair types were made, using a new measurement device that provides a more complete and detailed picture of the light scattered by fibers than was previously possible. The measurements show features that conclusively match the model’s predictions, but they also contain an ideal-specular forward-scattering behavior that is not predicted and has not been fully described before.
 The results of this article indicate that an accurate and efficient method for computing scattering in elliptical cylinders—something not provided in this article—is the correct model to use for realistic hair in the future and that the new specular behavior should be included as well.
Linking the Big Five personality constructs to organizational commitment

This study explored the linkages between the five-factor model of personality and Meyer and Allen’s (1991) model of organizational commitment using a field sample. Results indicated that Extraversion was significantly related to affective commitment, continuance commitment, and normative commitment. Neuroticism, Conscientiousness, and Openness to Experience were all significantly related to continuance commitment. Lastly, Agreeableness was significantly related to normative commitment. Theoretical and practical implications of the results are discussed.
ZooKeeper: Wait-free Coordination for Internet-scale Systems

In this paper, we describe ZooKeeper, a service for coordinating processes of distributed applications. Since ZooKeeper is part of critical infrastructure, ZooKeeper aims to provide a simple and high performance kernel for building more complex coordination primitives at the client. It incorporates elements from group messaging, shared registers, and distributed lock services in a replicated, centralized service. The interface exposed by ZooKeeper has the wait-free aspects of shared registers with an event-driven mechanism similar to cache invalidations of distributed file systems to provide a simple, yet powerful coordination service. The ZooKeeper interface enables a high-performance service implementation. In addition to the wait-free property, ZooKeeper provides a per client guarantee of FIFO execution of requests and linearizability for all requests that change the ZooKeeper state. These design decisions enable the implementation of a high performance processing pipeline with read requests being satisfied by local servers. We show for the target workloads, 2:1 to 100:1 read to write ratio, that ZooKeeper can handle tens to hundreds of thousands of transactions per second. This performance allows ZooKeeper to be used extensively by client applications.
Inference Attacks on Property-Preserving Encrypted Databases

Many encrypted database (EDB) systems have been proposed in the last few years as cloud computing has grown in popularity and data breaches have increased. The state-of-the-art EDB systems for relational databases can handle SQL queries over encrypted data and are competitive with commercial database systems. These systems, most of which are based on the design of CryptDB (SOSP 2011), achieve these properties by making use of property-preserving encryption schemes such as deterministic (DTE) and order- preserving encryption (OPE). In this paper, we study the concrete security provided by such systems. We present a series of attacks that recover the plaintext from DTE- and OPE-encrypted database columns using only the encrypted column and publicly-available auxiliary information. We consider well-known attacks, including frequency analysis and sorting, as well as new attacks based on combinatorial optimization.
 We evaluate these attacks empirically in an electronic medical records (EMR) scenario using real patient data from 200 U.S. hospitals. When the encrypted database is operating in a steady-state where enough encryption layers have been peeled to permit the application to run its queries, our experimental results show that an alarming amount of sensitive information can be recovered. In particular, our attacks correctly recovered certain OPE-encrypted attributes (e.g., age and disease severity) for more than 80% of the patient records from 95% of the hospitals; and certain DTE- encrypted attributes (e.g., sex, race, and mortality risk) for more than 60% of the patient records from more than 60% of the hospitals.
The daylight blocking optical stereo see-through HMD

In this paper we present an innovative daylight blocking optical stereo see-through HMD. Its outstanding capability is to pixelwise block incident daylight before super-imposing virtual content on the real scene. By doing so the device allows to seamlessly mix real and virtual content without the well-known effects of other optical see-through displays where real content will always shine through virtual content. To our knowledge related work on daylight blocking displays is very limited and we present the first commercial HMD available.
Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer?

Assume that a multi-user multiple-input multiple-output (MIMO) system is designed from scratch to uniformly cover a given area with maximal energy efficiency (EE). What are the optimal number of antennas, active users, and transmit power? The aim of this paper is to answer this fundamental question. We consider jointly the uplink and downlink with different processing schemes at the base station and propose a new realistic power consumption model that reveals how the above parameters affect the EE. Closed-form expressions for the EE-optimal value of each parameter, when the other two are fixed, are provided for zero-forcing (ZF) processing in single-cell scenarios. These expressions prove how the parameters interact. For example, in sharp contrast to common belief, the transmit power is found to increase (not to decrease) with the number of antennas. This implies that energy-efficient systems can operate in high signal-to-noise ratio regimes in which interference-suppressing signal processing is mandatory. Numerical and analytical results show that the maximal EE is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve a relatively large number of users using ZF processing. The numerical results show the same behavior under imperfect channel state information and in symmetric multi-cell scenarios.
From Classical To Hip-Hop : Can Machines Learn Genres ?

The ability to classify the genre of a song is an easy task for the human ear. After listening to a song for just several seconds, it is often not difficult to take note of that song’s characteristics and subsequently identify this genre. However, this task is one that computers have historically not been able to solve well, at least not until the advent of more sophisticated machine learning techniques. Our team explored several of these techniques over the course of this project and successfully built a system that achieves 61.873% accuracy in classifying music genres. This paper discusses the methods we used for exploratory data analysis, feature selection, hyperparameter optimization, and eventual implementation of several algorithms for classification.
Array Data Transformation for Source Code Obfuscation

Obfuscation is a low cost software protection methodology to avoid reverse engineering and re engineering of applications. Source code obfuscation aims in obscuring the source code to hide the functionality of the codes. This paper proposes an Array data transformation in order to obfuscate the source code which uses arrays. The applications using the proposed data structures force the programmer to obscure the logic manually. It makes the developed obscured codes hard to reverse engineer and also protects the functionality of the codes. Reverse Engineering, Source Code Obfuscation.
A 100-ps time-resolution CMOS time-to-digital converter for positron emission tomography imaging applications

An integrated CMOS subnanosecond time-to-digital converter (TDC) has been developed and evaluated for positron emission tomography (PET) front-end applications. The TDC architecture combines an accurate digital counter and an analog time interpolation circuit to make the time interval measurement. The dynamic range of the TDC is programmable and can be easily extended without any timing resolution degradation. The converter was designed to operate over a reference clock frequency range of 62.5 MHz up to 100 MHz and can have a bin size as small as 312.5 ps LSB with 100-ns conversion times. Measurements indicate the TDC achieves a DNL of under /spl plusmn/0.20 LSB and INL less than /spl plusmn/0.30 LSB with an rms timing resolution of 0.312 LSB (97.5 ps), very close to the theoretical limit of 0.289 LSB (90 ps). The design is believed to be the first fully integrated CMOS subnanosecond TDC used in PET medical imaging and the first realization of a CMOS TDC that achieves an rms timing resolution below 100 ps within a 100-ns conversion time.
Reaching out: involving Users in Innovation Tasks through Social Media

Integrating social media into the innovation process can open up the potential for organizations to utilize the collective creativity of consumers from all over the world. The research in this paper sets out to identify how social media can facilitate innovation. By taking a Design Science Research approach this research presents the Social Media Innovation Method for matching innovation tasks with social media characteristics. This supports the selection of best suitable social media and can help organizations to achieve their innovation goals. At the core of the method is the honeycomb model which describes seven social media characteristics on three dimensions: audience, content and time. The method has been evaluated by using an approach called scenario walkthrough that is applied in a real-life spatial planning project. This research concludes that there is no one-size-fits-all answer to the question how social media can be of value for the innovation process. However, organizations that want to know how it can benefit their own innovation process can use the Social Media Innovation Method presented in this research as a way to provide an answer to that question, uniquely tailored to each innovation task for which social media is to be used.
Transient potential measurement on stress grading under multi-level PWM

In this paper, the influence of lower-frequency voltage components in PWM voltage on the performance of stress grading system of motor insulation is investigated by observing the light emission of partial discharge and by measuring potential distribution under 2- and 3-level PWM and sinusoidal ac voltages. Computations by finite element method are conducted to determine electrical field on coil surface. According to the results, the potential gradient under PWM voltage is largest during the first pulse after polarity reversal, as space charge of reversed polarity accumulates at the interface between corona armor tape (CAT) and stress grading tape (SGT) due to a fundamental frequency component in PWM voltage. Therefore, the intensity of light emission during the first pulse is the largest. From the computed results, it has become cleared the maximum field at the interface between the CAT and the SGT becomes higher under PWM voltage than that under rectangular ac voltage whose jump voltage is the same due to such charge accumulation.
Glial and neuronal control of brain blood flow

Blood flow in the brain is regulated by neurons and astrocytes. Knowledge of how these cells control blood flow is crucial for understanding how neural computation is powered, for interpreting functional imaging scans of brains, and for developing treatments for neurological disorders. It is now recognized that neurotransmitter-mediated signalling has a key role in regulating cerebral blood flow, that much of this control is mediated by astrocytes, that oxygen modulates blood flow regulation, and that blood flow may be controlled by capillaries as well as by arterioles. These conceptual shifts in our understanding of cerebral blood flow control have important implications for the development of new therapeutic approaches.
ASSISTON-SE: A self-aligning shoulder-elbow exoskeleton

We present AssistOn-SE:, a novel powered exoskeleton for robot-assisted rehabilitation that allows for movements of the shoulder girdle as well as shoulder rotations. Automatically adjusting its joint axes, AssistOn-SE: can enable a perfect match between human joint axes and the device axes, not only guaranteeing ergonomy and comfort throughout the therapy, but also extending the usable range of motion for the shoulder joint. Moreover, the adjustability feature significantly shortens the setup time required to attach the patient to the exoskeleton, allowing more effective time be spend on exercises instead of wasting this valuable resource for adjustments. Back-driveable design of AssistOn-SE: supports both passive translational movements of the center of glenohumeral joint and independent active control of these degrees of freedom. Thanks to this property, glenohumeral mobilization and scapular stabilization exercises can also be delivered with AssistOn-SE:, extending the type of therapies that can be administered using upper-arm exoskeletons. We introduce the design of the exoskeleton and present the kinematic analysis of its self-aligning joint. We also provide implementation details for an early prototype as well as some experimental results detailing range of motion of the device and its ability to track movements of the shoulder girdle.
EPOCHS: a platform for agent-based electric power and communication simulation built from commercial off-the-shelf components

This paper reports on the development and subsequent use of the electric power and communication synchronizing simulator (EPOCHS), a distributed simulation environment. Existing electric power simulation tools accurately model power systems of the past, which were controlled as large regional power pools without significant communication elements. However, as power systems increasingly turn to protection and control systems that make use of computer networks, these simulators are less and less capable of predicting the likely behavior of the resulting power grids. Similarly, the tools used to evaluate new communication protocols and systems have been developed without attention to the roles they might play in power scenarios. EPOCHS integrates multiple research and commercial off-the-shelf systems to bridge the gap.
Interactive Volumetric Textures

This paper presents a method for interactively rendering co mplex repetitive scenes such as landscapes, fur, organic tissues, etc. It is a n adaptation to Z-buffer of volumetric textures , a ray-traced method, in order to use the power of existing graphics hardware. Our approach consists in slicing a pi ece of 3D geometry (one repetitive detail of the complex data) into a series of t hin layers. A layer is a rectangle containing the shaded geometry that falls in that slice. These layers are used as transparent textures, that are mapped onto the under lyi g surface (e.g. a hill or an animal skin) with an extrusion offset. We show some results obtained with our first implementation, such as a scene of 13 millions o f virtual polygons animated at 2.5 frames per second on a SGI O 2.
Analysis of the wrench-closure workspace of planar parallel cable-driven mechanisms

The mobile platform of a parallel cable-driven mechanism is connected in parallel to a base by lightweight links, such as cables. Since the cables can only work in tension, the set of poses of the mobile platform for which the cables can balance any external wrench, i.e., for which the platform of the mechanism is fully constrained, is often limited or even nonexistent. Thus, the study and determination of this set of poses, called the wrench-closure workspace (WCW), is an important issue for parallel cable-driven mechanisms. In this paper, the case of planar parallel cable-driven mechanisms is addressed. Theorems that characterize the poses of the WCW are proposed. Then, these theorems are used to disclose the parts of the reachable workspace which belong to the WCW. Finally, an efficient algorithm that determines the constant-orientation cross-sections of these parts is introduced.
A Video Representation Using Temporal Superpixels

We develop a generative probabilistic model for temporally consistent super pixels in video sequences. In contrast to supermodel methods, object parts in different frames are tracked by the same temporal super pixel. We explicitly model flow between frames with a bilateral Gaussian process and use this information to propagate super pixels in an online fashion. We consider four novel metrics to quantify performance of a temporal super pixel representation and demonstrate superior performance when compared to supermodel methods.