nils-herrmann/clean_scirepeval huggingface dataset:
Linearization Through Dithering: A 50 MHz Bandwidth, 10-b ENOB, 8.2 mW VCO-Based ADC

Non-linear voltage-to-frequency characteristic of a voltage-controlled oscillator (VCO) severely curtails the dynamic range of analog-to-digital converters (ADCs) built with VCOs. Typical approaches to enhance the dynamic range include embedding the VCO-based ADC in a ΔΣ loop or to post-process the digital data for calibration, both of which impose significant power constraints. In contrast, in this work the VCO-based ADC is linearized through a filtered dithering technique, wherein the VCO-based ADC is used as a fine stage that processes the residue from a coarse stage in a 0-1 MASH structure. The proposed filtered dithering technique conditions the signal to the VCO input to appear as white noise thereby eliminating spurious signal content arising out of the VCO nonlinearity. The work resorts to multiple other signal processing techniques to build a high-resolution, wideband prototype, in 65 nm complementary metal-oxide semiconductor (CMOS), that achieves 10 effective number of bits (ENOB) in digitizing signals with 50 MHz bandwidth consuming 8.2 mW at a figure of merit (FoM) of 90 fJ/conv.step.
Histone H2B Monoubiquitination Functions Cooperatively with FACT to Regulate Elongation by RNA Polymerase II

Over the past years, a large number of histone posttranslational modifications have been described, some of which function to attain a repressed chromatin structure, while others facilitate activation by allowing access of regulators to DNA. Histone H2B monoubiquitination is a mark associated with transcriptional activity. Using a highly reconstituted chromatin-transcription system incorporating the inducible RARbeta2 promoter, we find that the establishment of H2B monoubiquitination by RNF20/40 and UbcH6 is dependent on the transcription elongation regulator complex PAF, the histone chaperone FACT, and transcription. H2B monoubiquitination facilitates FACT function, thereby stimulating transcript elongation and the generation of longer transcripts. These in vitro analyses and corroborating in vivo experiments demonstrate that elongation by RNA polymerase II through the nucleosomal barrier is minimally dependent upon (1) FACT and (2) the recruitment of PAF and the H2B monoubiquitination machinery.
Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model

The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different spatial property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different spatial property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-spatial adaptive total variation (TV) model, in which the spectral noise differences and spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-spatial adaptive mechanism in the denoising process, and superior denoising results are produced.
A Microservice Architecture for the Intranet of Things and Energy in Smart Buildings: Research Paper

This paper presents challenges and issues in smart buildings and the Internet of Things (IoT), which we identified in years of research in real buildings. To tackle these challenges, a decentralized service-oriented architecture based on a message-oriented middleware has been implemented for the domain of smart buildings. It uses a network-transparent IoT message bus and provides the means for composing applications from auxiliary services, which facilitate device abstraction, protocol adaption, modularity, and maintainability. We demonstrate the flexibility of our architecture by describing how three distinct applications---privacy-enhancing energy data visualization, automated building energy management, and a generic user interface---can be integrated and operated simultaneously in our real smart building laboratory. We compare the advantages of our architecture to conventional ones and provide a best-practice solution for the Intranet of Things and Energy in smart buildings.
Automatic Identification and Classification of Misogynistic Language on Twitter

Hate speech may take different forms in online social media. Most of the investigations in the literature are focused on detecting abusive language in discussions about ethnicity, religion, gender identity and sexual orientation. In this paper, we address the problem of automatic detection and categorization of misogynous language in online social media. The main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigations on NLP features and ML models for detecting and classifying misogynistic language.
Global Inference for Sentence Compression: An Integer Linear Programming Approach

Sentence compression holds promise for many applications ranging from summarization to subtitle generation. Our work views sentence compression as an optimization problem and uses integer linear programming (ILP) to infer globally optimal compressions in the presence of linguistically motivated constraints. We show how previous formulations of sentence compression can be recast as ILPs and extend these models with novel global constraints. Experimental results on written and spoken texts demonstrate improvements over state-of-the-art models.
System performance of cooperative massive MIMO downlink 5G cellular systems

Massive MIMO (multiple-input multiple-output) antenna technology can provide significant performance improvement for cellular systems in terms of both throughput and energy efficiency. It is widely recognized that inter-user interference can be eliminated with a large number of antennas because of the asymptotical orthogonality among users when linear MF (Matched Filter) downlink precoding is used in the eNodeB. Due to the complexity and deployment consideration in practical scenarios at individual eNodeBs, cooperative massive MIMO [CM-MIMO] where multiple base stations cooperate together and form a distributed antenna array to serve multiple users simultaneously is an attractive alternative. Furthermore, cooperative massive MIMO can also help increase the system performance especially for cell edge users because of the cooperative transmission among neighboring cells. In this paper, system level simulation performance for the downlink, based upon current LTE systems, provides an indication of the achievable potential system performance improvement by employing CM-MIMO in future (5G) cellular networks. It is demonstrated that CM-MIMO can improve the system performance of cell edge users significantly even if the cell average performance is very slightly degraded or maintained caused by the power imbalance of received signal from different cooperative neighboring cells.
BP: Profiling Vulnerabilities on the Attack Surface

Security practitioners use the attack surface of software systems to prioritize areas of systems to test and analyze. To date, approaches for predicting which code artifacts are vulnerable have utilized a binary classification of code as vulnerable or not vulnerable. To better understand the strengths and weaknesses of vulnerability prediction approaches, vulnerability datasets with classification and severity data are needed. The goal of this paper is to help researchers and practitioners make security effort prioritization decisions by evaluating which classifications and severities of vulnerabilities are on an attack surface approximated using crash dump stack traces. In this work, we use crash dump stack traces to approximate the attack surface of Mozilla Firefox. We then generate a dataset of 271 vulnerable files in Firefox, classified using the Common Weakness Enumeration (CWE) system. We use these files as an oracle for the evaluation of the attack surface generated using crash data. In the Firefox vulnerability dataset, 14 different classifications of vulnerabilities appeared at least once. In our study, 85.3% of vulnerable files were on the attack surface generated using crash data. We found no difference between the severity of vulnerabilities found on the attack surface generated using crash data and vulnerabilities not occurring on the attack surface. Additionally, we discuss lessons learned during the development of this vulnerability dataset.
True self-configuration for the IoT

For the Internet of Things to finally become a reality, obstacles on different levels need to be overcome. This is especially true for the upcoming challenge of leaving the domain of technical experts and scientists. Devices need to connect to the Internet and be able to offer services. They have to announce and describe these services in machine-understandable ways so that user-facing systems are able to find and utilize them. They have to learn about their physical surroundings, so that they can serve sensing or acting purposes without explicit configuration or programming. Finally, it must be possible to include IoT devices in complex systems that combine local and remote data, from different sources, in novel and surprising ways. We show how all of that is possible today. Our solution uses open standards and state-of-the art protocols to achieve this. It is based on 6LowPAN and CoAP for the communications part, semantic web technologies for meaningful data exchange, autonomous sensor correlation to learn about the environment, and software built around the Linked Data principles to be open for novel and unforeseen applications.
Potsdam Commentary Corpus 2.0: Annotation for Discourse Research

We present a revised and extended version of the Potsdam Commentary Corpus, a collection of 175 German newspaper commentaries (op-ed pieces) that has been annotated with syntax trees and three layers of discourse-level information: nominal coreference, connectives and their arguments (similar to the PDTB, (Prasad et al., 2008)), and trees reflecting discourse structure according to Rhetorical Structure Theory (Mann and Thompson, 1988). Connectives have been annotated with the help of a semi-automatic tool (Conano, (Stede and Heintze, 2004)) that identifies most connectives and suggests arguments based on their syntactic category. The other layers have been created manually with dedicated annotation tools. The corpus is made available on the one hand as a set of original XML files produced with the annotation tools, based on identical tokenization. On the other hand, it will be distributed together with the open-source linguistic database ANNIS3 (Chiarcos et al., 2008; Zeldes et al., 2009), which provides multi-layer search functionality and layer-specific visualization modules. This allows for comfortable qualitative evaluation of the correlations between annotation layers.
Chip Level Techniques for EMI Reduction in LCD Panels

This paper presents chip level techniques to improve electro-magnetic interference (EMI) characteristics of LCD-TV panels. A timing controller (TCON) uses over-driving algorithms to improve the response time of liquid crystals (LC). Since this algorithm needs previous frame data, external memory such as double data rate synchronous DRAM (DDR SDRAM) is widely used as a frame buffer. A TTL interface between the TCON and memory is used for read and write operations, generating EMI noise. For reduction of this EMI, three methods are described. The first approach is to reduce the driving current of data I/O buffers. The second is to adopt spread spectrum clock generation (SSCG), and the third is to apply a proposed algorithm which minimizes data transitions. EMI measurement of a 32" LCD-TV panel shows that these approaches are very effective for reduction of EMI, achieving 20dB reduction at 175MHz.
Dual-Rotor Multiphase Permanent Magnet Machine With Harmonic Injection to Enhance Torque Density

Torque density of a novel dual-rotor, radial-flux multiphase permanent magnet (PM) machine is enhanced by injecting stator current harmonics without increasing stator yoke thickness. In order to enhance a regular PM machine torque capability by injecting harmonic currents, the no-load air-gap flux density should be designed to be trapezoidal, which increases the flux per pole, and results in a thicker stator yoke. However, this drawback can be avoided in a dual-rotor, radial-flux PM machine. In such a dual-rotor PM machine, the main flux can be designed to go directly from the inner rotor poles, to stator inner and outer teeth, then to the outer rotor poles, or reverse, without through the stator yoke. Therefore, the stator yoke thickness is not affected by air-gap flux at all and can actually be reduced to minimum for mechanical reasons. This paper applies stator current harmonic injection technique to a dual-rotor, radial-flux PM machine. Sizing equations of the multiphase PM machines are deduced and used for initial design and theoretical comparison. The torque performance of the proposed dual-rotor, radial-flux multiphase PM machine with stator harmonic current injection is evaluated using finite element simulation, and compared to a traditional three-phase dual-rotor, radial-flux PM machine. Simulation results validate the feasibility of torque density enhancement. The same technique is proven that it is also suitable in a dual-rotor axial-flux PM machine topology.
The FOUR score predicts outcome in patients after traumatic brain injury.

The most widely used and most studied coma score to date is the Glasgow Coma Scale (GCS), which is used worldwide to assess level of consciousness and predict outcome after traumatic brain injury (TBI). Our aim was to determine whether the Full Outline of UnResponsiveness (FOUR) score is an accurate predictor of outcome in TBI patients and to compare its performance to GCS.



We prospectively identified TBI patients admitted to our Neuro-ICU between July 2010 and February 2011. We enrolled 51 patients. The FOUR score and GCS were determined by one of the investigators. Outcomes were in-hospital mortality, and poor neurologic outcome (Glasgow Outcome Scale (GOS) 1-3 and Modified Rankin Scale (mRS) score 3-6) at 3-6 months.



There was a high degree of internal consistency for both the FOUR score (Cronbach's alpha = 0.89) and GCS (Cronbach's alpha = 0.85). In terms of predictive power for in-hospital mortality, the area under the receiver operating characteristic (ROC) curve was 0.93 for FOUR score and 0.89 for GCS. In terms of predictive power of poor neurologic outcome at 3-6 months, the area under the ROC curve was 0.85 for FOUR score and 0.83 for GCS as evidenced by GOS 1-3, and 0.80 for FOUR score and 0.78 for GCS as evidenced by mRS 3-6. The odds ratio (OR) for in-hospital mortality was 0.64 (0.46-0.88) from FOUR score and 0.63 (0.45-0.89) from GCS, for poor neurologic outcome was 0.67 (0.53-0.85) from FOUR score and 0.65 (0.51-0.83) from GCS for GOS, and was 0.71 (0.57-0.87) from FOUR score and 0.71 (0.57-0.87) from GCS for mRS.



The FOUR score is an accurate predictor of outcome in TBI patients. It has some advantages over GCS, such as all components of FOUR score but not GCS can be rated in intubated patients.
Gender bias in hospital leadership: a qualitative study on the experiences of women CEOs.

The purpose of this paper is to examine the experiences of gender bias among women hospital CEOs and explore to what these female leaders attribute their success within a male-dominated hospital executive leadership milieu.  This qualitative study involved 12 women hospital CEOs from across Ontario, Canada. Purposeful sampling techniques and in-depth qualitative interview methods were used to facilitate discussion around experiences of gender and leadership.  Responses fell into two groups: the first group represented the statement "Gender inequality is alive and well". The second group reflected the statement "Gender inequity is not significant, did not happen to me, and things are better now". This group contained a sub-group with no consciousness of systemic discrimination and that claimed having no gendered experiences in their leadership journey. The first group described gender issues in various contexts, from the individual to the systemic. The second group was ambivalent about gender as a factor impacting leadership trajectories.  Representations of women's leadership have become detached from feminism, with major consequences for women. This study reveals how difficult it is for some women CEOs to identify gender bias. The subtle everyday norms and practices within the workplace make it difficult to name and explain gender bias explicitly and may explain the challenges in understanding how it might affect a woman's career path.
Reinforcement Learning in Continuous State and Action Spaces

Many traditional reinforcement-learning algorithms have been designed for problems with small finite state and action spaces. Learn ing in such discrete problems can been difficult, due to noise and delayed reinfor cements. However, many real-world problems have continuous state or action sp aces, which can make learning a good decision policy even more involved. In this c apter we discuss how to automatically find good decision policies in continuous d omains. Because analytically computing a good policy from a continuous model c an be infeasible, in this chapter we mainly focus on methods that explicitly up date a representation of a value function, a policy or both. We discuss conside rations in choosing an appropriate representation for these functions and disc uss gradient-based and gradient-free ways to update the parameters. We show how to a pply these methods to reinforcement-learning problems and discuss many speci fic algorithms. Amongst others, we cover gradient-based temporal-difference lear ning, evolutionary strategies, policy-gradient algorithms and (natural) actor-cri ti methods. We discuss the advantages of different approaches and compare the perform ance of a state-of-theart actor-critic method and a state-of-the-art evolutiona ry strategy empirically.
Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web

Over the past couple of years, anecdotal evidence has emerged linking coordinated campaigns by state-sponsored actors with efforts to manipulate public opinion on the Web, often around major political events, through dedicated accounts, or “trolls.” Although they are often involved in spreading disinformation on social media, there is little understanding of how these trolls operate, what type of content they disseminate, and most importantly their influence on the information ecosystem. In this paper, we shed light on these questions by analyzing 27K tweets posted by 1K Twitter users identified as having ties with Russia’s Internet Research Agency and thus likely state-sponsored trolls. We compare their behavior to a random set of Twitter users, finding interesting differences in terms of the content they disseminate, the evolution of their account, as well as their general behavior and use of the Twitter platform. Then, using a statistical model known as Hawkes Processes, we quantify the influence that these accounts had on the dissemination of news on social platforms such as Twitter, Reddit, and 4chan. Overall, our findings indicate that Russian troll accounts managed to stay active for long periods of time and to reach a substantial number of Twitter users with their messages. When looking at their ability of spreading news content and making it viral, however, we find that their effect on social platforms was minor, with the significant exception of news published by the Russian state-sponsored news outlet RT (Russia Today).
Easy over hard: a case study on deep learning

While deep learning is an exciting new technique, the benefits of this method need to be assessed with respect to its computational cost. This is particularly important for deep learning since these learners need hours (to weeks) to train the model. Such long training time limits the ability of (a)~a researcher to test the stability of their conclusion via repeated runs with different random seeds; and (b)~other researchers to repeat, improve, or even refute that original work. 
 For example, recently, deep learning was used to find which questions in the Stack Overflow programmer discussion forum can be linked together. That deep learning system took 14 hours to execute. We show here that applying a very simple optimizer called DE to fine tune SVM, it can achieve similar (and sometimes better) results. The DE approach terminated in 10 minutes; i.e. 84 times faster hours than deep learning method. 
 We offer these results as a cautionary tale to the software analytics community and suggest that not every new innovation should be applied without critical analysis. If researchers deploy some new and expensive process, that work should be baselined against some simpler and faster alternatives.
A dynamic instruction set computer

One of the key contributions of this paper is the idea of treating the two-dimensional FPGA area as a one-dimensional array of custom instructions, which the paper refers to as linear hardware space. Each custom instruction occupies the full width of the array but can occupy different heights to allow for instructions of varying complexity. Instructions also have a common interface that includes pass-thrus which connect to the previous and next instructions in the array. This communication interface greatly simplifies the problem of relocating a custom instruction to a free area, allowing other instructions to potentially remain in-place. The communication paths for an instruction are always at the same relative positions, and run-time CAD operations (and their corresponding overheads) are completely avoided. Thus, the linear hardware space reduces the overheads and simplifies the process of run-time reconfiguration of custom processor instructions.
Full virtualization based ARINC 653 partitioning

As the number of electronic components of avionics systems is significantly increasing, it is desirable to run several avionics software on a single computing device. In such system, providing a seamless way to integrate separate applications on a computing device is a very critical issue as the Integrated Modular Avionics (IMA) concept addresses. In this context, the ARINC 653 standard defines resource partitioning of avionics application software. The virtualization technology has very high potential of providing an optimal implementation of the partition concept. In this paper, we study supports for full virtualization based ARINC 653 partitioning. The supports include extension of XML-based configuration file format and hierarchical scheduler for temporal partitioning. We show that our implementation can support well-known VMMs, such as VirtualBox and VMware and present basic performance numbers.