nils-herrmann/clean_scirepeval huggingface dataset:
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys

Automated identification of diverse sentiment types can be beneficial for many NLP systems such as review summarization and public media analysis. In some of these systems there is an option of assigning a sentiment value to a single sentence or a very short text. In this paper we propose a supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service. By utilizing 50 Twitter tags and 15 smileys as sentiment labels, this framework avoids the need for labor intensive manual annotation, allowing identification and classification of diverse sentiment types of short texts. We evaluate the contribution of different feature types for sentiment classification and show that our framework successfully identifies sentiment types of untagged sentences. The quality of the sentiment identification was also confirmed by human judges. We also explore dependencies and overlap between different sentiment types represented by smileys and Twitter hashtags.
Graph-based forensic investigation of Bitcoin transactions

This thesis illustrates forensic research work on Bitcoin, an innovative Internet based global transaction system that attracts ascending popularity during the recent few years. As an open, public and scalable distributed payment system, Bitcoin brings forward significant economic and technological impact to our world. Meanwhile, a new notion of virtual currency, “Bitcoin” comes into existence such that Bitcoin currency can be “mined” from all over world complying with specific algorithms. Mined bit “coins” has practical monetary values that turn the Bitcoin system into a digital currency circulation system. Due to Bitcoin’s decentralized semantics, Bitcoin transaction and currency are not subject to control and censorship from any single authority. Therefore, Bitcoin brings out various security concerns about its application as a long-term reliable system. The research in the thesis focuses on forensic study on Bitcoin. It covers experimental study on the Bitcoin network as a peer-to-peer system and a graph-based forensic approach against Bitcoin’s transaction data. Major contributions include network data evaluation and transaction history analysis. In case of forensic investigation is needed against criminal incidents such as fraud, false transactions and money theft, which are commonly seen in commonly used digital payment systems, the research provides a guidance of efficient information collection and framework of evidence data processing and extraction.
Automatic Classification of Power Quality Events Using Balanced Neural Tree

This paper proposes an empirical-mode decomposition (EMD) and Hilbert transform (HT)-based method for the classification of power quality (PQ) events. Nonstationary power signal disturbance waveforms are considered as the superimposition of various undulating modes, and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMFs). The HT is applied on all the IMFs to extract instantaneous amplitude and frequency components. This time-frequency analysis results in the clear visual detection, localization, and classification of the different power signal disturbances. The required feature vectors are extracted from the time-frequency distribution to perform the classification. A balanced neural tree is constructed to classify the power signal patterns. Finally, the proposed method is compared with an S-transform-based classifier to show the efficacy of the proposed technique in classifying the PQ disturbances.
Automated News Generation for TV Program Ratings

Automated journalism, automatically generating stories based on algorithms, has received considerable critical attention in diverse fields. However, automated journalism has not addressed the TV industry in much detail. This research aims to create a system to automatically generate news about TV ratings. The framework will involve undergoing the processes of data gathering, identifying important events by predefined algorithms, generating a story in narrative format, and publishing the output. The algorithm that determines the structure of the stories is defined by analyzing existing news about TV ratings that reflects key variables. Although the output of the research is limited to one type of news template, further attempts could expand to various formats.
Optimizing distributions over molecular space . An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry ( ORGANIC )

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.
Hardware design experiences in ZebraNet

The enormous potential for wireless sensor networks to make a positive impact on our society has spawned a great deal of research on the topic, and this research is now producing environment-ready systems. Current technology limits coupled with widely-varying application requirements lead to a diversity of hardware platforms for different portions of the design space. In addition, the unique energy and reliability constraints of a system that must function for months at a time without human intervention mean that demands on sensor network hardware are different from the demands on standard integrated circuits. This paper describes our experiences designing sensor nodes and low level software to control them.
 In the ZebraNet system we use GPS technology to record fine-grained position data in order to track long term animal migrations . The ZebraNet hardware is composed of a 16-bit TI microcontroller, 4 Mbits of off-chip flash memory, a 900 MHz radio, and a low-power GPS chip. In this paper, we discuss our techniques for devising efficient power supplies for sensor networks, methods of managing the energy consumption of the nodes, and methods of managing the peripheral devices including the radio, flash, and sensors. We conclude by evaluating the design of the ZebraNet nodes and discussing how it can be improved. Our lessons learned in developing this hardware can be useful both in designing future sensor nodes and in using them in real systems.
A Study on Watermarking Schemes for Image Authentication

The digital revolution in digital image processing has made it possible to create, manipulate and transmit digital images in a simple and fast manner. The adverse affect of this is that the same image processing techniques can be used by hackers to tamper with any image and use it illegally. This has made digital image safety and integrity the top prioritized issue in today’s information explosion. Watermarking is a popular technique that is used for copyright protection and authentication. This paper presents an overview of the various concepts and research works in the field of image watermark authentication. In particular, the concept of content-based image watermarking is reviewed in details.
Andromeda: Performance, Isolation, and Velocity at Scale in Cloud Network Virtualization

This paper presents our design and experience with Andromeda, Google Cloud Platform’s network virtualization stack. Our production deployment poses several challenging requirements, including performance isolation among customer virtual networks, scalability, rapid provisioning of large numbers of virtual hosts, bandwidth and latency largely indistinguishable from the underlying hardware, and high feature velocity combined with high availability. Andromeda is designed around a flexible hierarchy of flow processing paths. Flows are mapped to a programming path dynamically based on feature and performance requirements. We introduce the Hoverboard programming model, which uses gateways for the long tail of low bandwidth flows, and enables the control plane to program network connectivity for tens of thousands of VMs in seconds. The on-host dataplane is based around a highperformance OS bypass software packet processing path. CPU-intensive per packet operations with higher latency targets are executed on coprocessor threads. This architecture allows Andromeda to decouple feature growth from fast path performance, as many features can be implemented solely on the coprocessor path. We demonstrate that the Andromeda datapath achieves performance that is competitive with hardware while maintaining the flexibility and velocity of a software-based architecture.
Occlusion horizons for driving through urban scenery

Wepresentarapidocclusioncullingalgorithmspecificallydesigned for urbanenvironments. For eachframe,an occlusionhorizonis beingbuilt on-the-flyduring a hierarchicalfront-to-backtraversal of the scene.All conservatively hiddenobjectsareculled, while all theoccludingimpostorsof all conservatively visibleobjectsare addedto the  D occlusionhorizon.Our framework alsosupports levels-of-detail(LOD) renderingby estimatingthe visible areaof the projectionof an objectin orderto selecttheappropriateLOD for eachobject.This algorithmrequiresno substantial preprocessing andnoexcessi ve storage. In a test sceneof 10,000buildings, the cull phasetook 11 ms on a PentiumII333 MHz and45 ms on an SGI Octaneper frame on average. In typical views, the occlusionhorizonculled away 80-90%of theobjectsthatwerewithin theview frustum,giving a 10 timesspeedupover view frustumculling alone.Combiningthe occlusionhorizonwith LOD renderinggavea17 timesspeedupon anSGIOctane,and23 timeson aPII. CR Categoriesand SubjectDescriptors: I.3.7[ComputerGraphics]: I.3.7 Three-Dimensional GraphicsandRealism.
The neural basis of rationalization: cognitive dissonance reduction during decision-making.

People rationalize the choices they make when confronted with difficult decisions by claiming they never wanted the option they did not choose. Behavioral studies on cognitive dissonance provide evidence for decision-induced attitude change, but these studies cannot fully uncover the mechanisms driving the attitude change because only pre- and post-decision attitudes are measured, rather than the process of change itself. In the first fMRI study to examine the decision phase in a decision-based cognitive dissonance paradigm, we observed that increased activity in right-inferior frontal gyrus, medial fronto-parietal regions and ventral striatum, and decreased activity in anterior insula were associated with subsequent decision-related attitude change. These findings suggest the characteristic rationalization processes that are associated with decision-making may be engaged very quickly at the moment of the decision, without extended deliberation and may involve reappraisal-like emotion regulation processes.
A generalized multilevel inverter topology with self voltage balancing

Multilevel power converters that provide more than two levels of voltage to achieve smoother and less distorted ac-to-dc, dc-to-ac, and dc-to-dc power conversion, have attracted many contributors. This paper presents a generalized multilevel inverter (converter) topology with self voltage balancing. The existing multilevel inverters such as diode-clamped and capacitor-clamped multilevel inverters can be derived from the generalized inverter topology. Moreover, the generalized multilevel inverter topology provides a true multilevel structure that can balance each dc voltage level automatically without any assistance from other circuits, thus, in principle, providing a complete and true multilevel topology that embraces the existing multilevel inverters. From this generalized multilevel inverter topology, several new multilevel inverter structures can be derived. Some application examples of the generalized multilevel converter will be given.
Natural history and manifestations of the hypermobility type Ehlers-Danlos syndrome: a pilot study on 21 patients.

Hypermobility type Ehlers-Danlos syndrome (HT-EDS) is a relatively frequent, although commonly misdiagnosed variant of Ehlers-Danlos syndrome, mainly characterized by marked joint instability and mild cutaneous involvement. Chronic pain, asthenia, and gastrointestinal and pelvic dysfunction are characteristic additional manifestations. We report on 21 HT-EDS patients selected from a group of 40 subjects with suspected mild hereditary connective tissue disorder. General, mucocutaneous, musculoskeletal, cardiovascular, neurologic, gastrointestinal, urogynecological, and ear-nose-throat abnormalities are investigated systematically and tabulated. Six distinct clinical presentations of HT-EDS are outlined, whose tabulation is a mnemonic for the practicing clinical geneticist in an attempt to diagnose this condition accurately. With detailed clinical records and phenotype comparison among patients of different ages, the natural history of the disorder is defined. Three phases (namely, hypermobility, pain, and stiffness) are delineated based on distinguishing manifestations. A constellation of additional, apparently uncommon abnormalities is also identified, including dolichocolon, dysphonia, and Arnold-Chiari type I malformation. Their further investigation may contribute to an understanding of the pathogenesis of the protean manifestations of HT-EDS, and a more effective approach to the evaluation and management of affected individuals.
Study of Parallel Image Processing with the Implementation of vHGW Algorithm using CUDA on NVIDIA ’ S GPU Framework

This paper provides an effective study of the implementation of parallel image processing techniques using CUDA on NVIDIA GPU framework. It also discusses about the major requirements of parallelism in medical image processing techniques. Additional important aspect of this paper is to develop vHGW(van Herk/Gill-Werman morphology) algorithm intended for erosion and dilation proposed for diverse types of structuring elements of random length and along with random angle parallely on NVidia’s GPU GeForce GTX 860M. The main motive behind implementing image morphological operations is its importance for extracting components of an image. That can be beneficial in the demonstration and explanation of shape of the region. These experiments have been implemented on CUDA 5.0 architecture with NVidia’s GPU GeForce GTX 860M and got significant results in terms of time.
Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability.

Epidemiological studies indicate that experimentation with addictive drugs and onset of addictive disorders is primarily concentrated in adolescence and young adulthood. The authors describe basic and clinical data supporting adolescent neurodevelopment as a biologically critical period of greater vulnerability for experimentation with substances and acquisition of substance use disorders.



The authors reviewed recent literature regarding neurocircuitry underlying motivation, impulsivity, and addiction, with a focus on studies investigating adolescent neurodevelopment.



Adolescent neurodevelopment occurs in brain regions associated with motivation, impulsivity, and addiction. Adolescent impulsivity and/or novelty seeking as a transitional trait behavior can be explained in part by maturational changes in frontal cortical and subcortical monoaminergic systems. These developmental processes may advantageously promote learning drives for adaptation to adult roles but may also confer greater vulnerability to the addictive actions of drugs.



An exploration of developmental changes in neurocircuitry involved in impulse control has significant implications for understanding adolescent behavior, addiction vulnerability, and the prevention of addiction in adolescence and adulthood.
Investigating Language Universal and Specific Properties in Word Embeddings

Recently, many NLP tasks have benefited from distributed word representation. However, it remains unknown whether embedding models are really immune to the typological diversity of languages, despite the language-independent architecture. Here we investigate three representative models on a large set of language samples by mapping dense embedding to sparse linguistic property space. Experiment results reveal the language universal and specific properties encoded in various word representation. Additionally, strong evidence supports the utility of word form, especially for inflectional languages.
Making Neural Programming Architectures Generalize via Recursion

Empirically, neural networks that attempt to learn programs from data have exhibited poor generalizability. Moreover, it has traditionally been difficult to reason about the behavior of these models beyond a certain level of input complexity. In order to address these issues, we propose augmenting neural architectures with a key abstraction: recursion. As an application, we implement recursion in the Neural Programmer-Interpreter framework on four tasks: grade-school addition, bubble sort, topological sort, and quicksort. We demonstrate superior generalizability and interpretability with small amounts of training data. Recursion divides the problem into smaller pieces and drastically reduces the domain of each neural network component, making it tractable to prove guarantees about the overall system’s behavior. Our experience suggests that in order for neural architectures to robustly learn program semantics, it is necessary to incorporate a concept like recursion.
Credibilist occupancy grids for vehicle perception in dynamic environments

In urban environments, moving obstacles detection and free space determination are key issues for driving assistance systems and autonomous vehicles. When using lidar sensors scanning in front of the vehicle, uncertainty arises from ignorance and errors. Ignorance is due to the perception of new areas and errors come from imprecise pose estimation and noisy measurements. Complexity is also increased when the lidar provides multi-echo and multi-layer information. This paper presents an occupancy grid framework that has been designed to manage these different sources of uncertainty. A way to address this problem is to use grids projected onto the road surface in global and local frames. The global one generates the mapping and the local one is used to deal with moving objects. A credibilist approach is used to model the sensor information and to do a global fusion with the world-fixed map. Outdoor experimental results carried out with a precise positioning system show that such a perception strategy increases significantly the performance compared to a standard approach.
New Canonical Representations by Augmenting OBDDs with Conjunctive Decomposition (Extended Abstract)

We identify two families of canonical representations called ROBDD[∧î]C and ROBDD[∧T̂ ,i]T by augmenting ROBDD with two types of conjunctive decompositions. These representations cover the three existing languages ROBDD, ROBDD with as many implied literals as possible (ROBDDL∞), and AND/OR BDD. We introduce a new time efficiency criterion called rapidity which reflects the idea that exponential operations may be preferable if the language can be exponentially more succinct. Then we demonstrate that the expressivity, succinctness and operation rapidity do not decrease from ROBDD[∧T̂ ,i]T to ROBDD[∧î]C , and then to ROBDD[∧ î+1 ]C . We also demonstrate that ROBDD[∧î]C (i > 1) and ROBDD[∧T̂ ,i]T are not less tractable than ROBDD-L∞ and ROBDD, respectively. Finally, we develop a compiler for ROBDD[∧∞̂]C which significantly advances the compiling efficiency of canonical representations.
A signal detection method for temporal variation of adverse effect with vaccine adverse event reporting system data

To identify safety signals by manual review of individual report in large surveillance databases is time consuming; such an approach is very unlikely to reveal complex relationships between medications and adverse events. Since the late 1990s, efforts have been made to develop data mining tools to systematically and automatically search for safety signals in surveillance databases. Influenza vaccines present special challenges to safety surveillance because the vaccine changes every year in response to the influenza strains predicted to be prevalent that year. Therefore, it may be expected that reporting rates of adverse events following flu vaccines (number of reports for a specific vaccine-event combination/number of reports for all vaccine-event combinations) may vary substantially across reporting years. Current surveillance methods seldom consider these variations in signal detection, and reports from different years are typically collapsed together to conduct safety analyses. However, merging reports from different years ignores the potential heterogeneity of reporting rates across years and may miss important safety signals.



Reports of adverse events between years 1990 to 2013 were extracted from the Vaccine Adverse Event Reporting System (VAERS) database and formatted into a three-dimensional data array with types of vaccine, groups of adverse events and reporting time as the three dimensions. We propose a random effects model to test the heterogeneity of reporting rates for a given vaccine-event combination across reporting years. The proposed method provides a rigorous statistical procedure to detect differences of reporting rates among years. We also introduce a new visualization tool to summarize the result of the proposed method when applied to multiple vaccine-adverse event combinations.



We applied the proposed method to detect safety signals of FLU3, an influenza vaccine containing three flu strains, in the VAERS database. We showed that it had high statistical power to detect the variation in reporting rates across years. The identified vaccine-event combinations with significant different reporting rates over years suggested potential safety issues due to changes in vaccines which require further investigation.



We developed a statistical model to detect safety signals arising from heterogeneity of reporting rates of a given vaccine-event combinations across reporting years. This method detects variation in reporting rates over years with high power. The temporal trend of reporting rate across years may reveal the impact of vaccine update on occurrence of adverse events and provide evidence for further investigations.
Can characters reveal your native language? A language-independent approach to native language identification

A common approach in text mining tasks such as text categorization, authorship identification or plagiarism detection is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. In this work, an approach that uses character n-grams as features is proposed for the task of native language identification. Instead of doing standard feature selection, the proposed approach combines several string kernels using multiple kernel learning. Kernel Ridge Regression and Kernel Discriminant Analysis are independently used in the learning stage. The empirical results obtained in all the experiments conducted in this work indicate that the proposed approach achieves state of the art performance in native language identification, reaching an accuracy that is 1.7% above the top scoring system of the 2013 NLI Shared Task. Furthermore, the proposed approach has an important advantage in that it is language independent and linguistic theory neutral. In the cross-corpus experiment, the proposed approach shows that it can also be topic independent, improving the state of the art system by 32.3%.