nils-herrmann/clean_scirepeval huggingface dataset:
Quantum machine learning: a classical perspective

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.
Multilingual Sequence-to-Sequence Speech Recognition: Architecture, Transfer Learning, and Language Modeling

Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new direction in speech research. The approach benefits by performing model training without using lexicon and alignments. However, this poses a new problem of requiring more data compared to conventional DNN-HMM systems. In this work, we attempt to use data from 10 BABEL languages to build a multilingual seq2seq model as a prior model, and then port them towards 4 other BABEL languages using transfer learning approach. We also explore different architectures for improving the prior multilingual seq2seq model. The paper also discusses the effect of integrating a recurrent neural network language model (RNNLM) with a seq2seq model during decoding. Experimental results show that the transfer learning approach from the multilingual model shows substantial gains over monolingual models across all 4 BABEL languages. Incorporating an RNNLM also brings significant improvements in terms of %WER, and achieves recognition performance comparable to the models trained with twice more training data.
Explaining Embodied Cognition Results

From the late 1950s until 1975, cognition was understood mainly as disembodied symbol manipulation in cognitive psychology, linguistics, artificial intelligence, and the nascent field of Cognitive Science. The idea of embodied cognition entered the field of Cognitive Linguistics at its beginning in 1975. Since then, cognitive linguists, working with neuroscientists, computer scientists, and experimental psychologists, have been developing a neural theory of thought and language (NTTL). Central to NTTL are the following ideas: (a) we think with our brains, that is, thought is physical and is carried out by functional neural circuitry; (b) what makes thought meaningful are the ways those neural circuits are connected to the body and characterize embodied experience; (c) so-called abstract ideas are embodied in this way as well, as is language. Experimental results in embodied cognition are seen not only as confirming NTTL but also explained via NTTL, mostly via the neural theory of conceptual metaphor. Left behind more than three decades ago is the old idea that cognition uses the abstract manipulation of disembodied symbols that are meaningless in themselves but that somehow constitute internal "representations of external reality" without serious mediation by the body and brain. This article uniquely explains the connections between embodied cognition results since that time and results from cognitive linguistics, experimental psychology, computational modeling, and neuroscience.
Multi-Task Learning of Keyphrase Boundary Classification

Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far underexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases.
77 GHz automotive anti-collision radar used for meteorological purposes

An always-growing number of cars are equipped with radars, mainly used for drivers and passengers' safety. In particular, according to European Telecommunications Standards Institute (ETSI) one specific frequency band is dedicated to automatic cruise control long-range radar operating around 77 GHz (W-band). After the discussion of the Mie scattering formulation applied to a weather radar working in the W-band, the proposal of a new Z-R equation to be used for correct rain estimation is given. Functional requirements to adapt an automatic cruise control long-range radar to a mini-weather radar are analyzed and the technical specifications are evaluated. Results provide the basis for the use of a 77 GHz automotive anti-collision radar for meteorological purposes.
Effect of atorvastatin and irbesartan, alone and in combination, on postprandial endothelial dysfunction, oxidative stress, and inflammation in type 2 diabetic patients.

Postprandial hypertriglyceridemia and hyperglycemia are considered risk factors for cardiovascular disease. Evidence suggests that postprandial hypertriglyceridemia and hyperglycemia induce endothelial dysfunction and inflammation through oxidative stress. Statins and angiotensin type 1 receptor blockers have been shown to reduce oxidative stress and inflammation, improving endothelial function.



Twenty type 2 diabetic patients ate 3 different test meals: a high-fat meal, 75 g glucose alone, and a high-fat meal plus glucose. Glycemia, triglyceridemia, endothelial function, nitrotyrosine, C-reactive protein, intercellular adhesion molecule-1, and interleukin-6 were assayed during the tests. Subsequently, diabetics took atorvastatin 40 mg/d, irbesartan 300 mg/d, both, or placebo for 1 week. The 3 tests were performed again between 5 and 7 days after the start of each treatment. High-fat load and glucose alone produced a decrease in endothelial function and increases in nitrotyrosine, C-reactive protein, intercellular adhesion molecule-1, and interleukin-6. These effects were more pronounced when high-fat load and glucose were combined. Short-term atorvastatin and irbesartan treatments significantly counterbalanced these phenomena, and their combination was more effective than either therapy alone.



This study confirms an independent and cumulative effect of postprandial hypertriglyceridemia and hyperglycemia on endothelial function and inflammation, suggesting oxidative stress as a common mediator of such an effect. Short-term treatment with atorvastatin and irbesartan may counterbalance this phenomenon; the combination of the 2 compounds is most effective.
Vehicle-to-Grid Reactive Power Operation Using Plug-In Electric Vehicle Bidirectional Offboard Charger

The number of offboard fast charging stations is increasing as plug-in electric vehicles (PEVs) are more widespread in the world. Additional features on the operation of chargers will result in more benefits for investors, utility companies, and PEV owners. This paper investigates reactive power support operation using offboard PEV charging stations while charging a PEV battery. The topology consists of a three-phase ac-dc boost rectifier that is capable of operating in all four quadrants. The operation modes that are of interest are power-factor-corrected charging operation, and charging and capacitive/inductive reactive power operation. This paper also presents a control system for the PQ command following of a bidirectional offboard charger. The controller only receives the charging power command from a user and the reactive power command (when needed) from a utility, and it adjusts the line current and the battery charging current correspondingly. The vehicle's battery is not affected during the reactive power operation. A simulation study is developed utilizing PSIM, and the control system is experimentally tested using a 12.5-kVA charging station design.
Directions in Blockchain Data Management and Analytics

Blockchain technology has emerged as a primary enabler for verification-driven transactions between parties that do not have complete trust among themselves. Bitcoin uses this technology to provide a provenance-driven verifiable ledger that is based on consensus. Nevertheless, the use of blockchain as a transaction service in non-cryptocurrency applications, for example, business networks, is at a very nascent stage. While the blockchain supports transactional provenance, the datamanagement community and other scientific and industrial communities are assessing how blockchain can be used to enable certain key capabilities for business applications. We have reviewed a number of proof of concepts and early adoptions of blockchain solutions that we have been involved spanning diverse use cases to draw common data life cycle, persistence as well as analytics patterns used in real-world applications with the ultimate aim to identify new frontier of exciting research in blockchain data management and analytics. In this paper, we discuss several open topics that researchers could increase focus on: (1) leverage existing capabilities of mature data and information systems, (2) enhance data security and privacy assurances, (3) enable analytics services on blockchain as well as across off-chain data, and (4) make blockchain-based systems active-oriented and intelligent.
Digital Discrimination : The Case of Airbnb

Online marketplaces often contain information not only about products, but also about the people selling the products. In an effort to facilitate trust, many platforms encourage sellers to provide personal profiles and even to post pictures of themselves. However, these features may also facilitate discrimination based on sellers’ race, gender, age, or other aspects of appearance. In this paper, we test for racial discrimination against landlords in the online rental marketplace Airbnb.com. Using a new data set combining pictures of all New York City landlords on Airbnb with their rental prices and information about quality of the rentals, we show that non-black hosts charge approximately 12% more than black hosts for the equivalent rental. These effects are robust when controlling for all information visible in the Airbnb marketplace. These findings highlight the prevalence of discrimination in online marketplaces, suggesting an important unintended consequence of a seemingly-routine mechanism for building trust. 1 Harvard Business School, bedelman@hbs.edu 2 Harvard Business School,
On the Feasibility of Creating Double-Identity Fingerprints

A double-identity fingerprint is a fake fingerprint created by combining features from two different fingers, so that it has a high chance to be falsely matched with fingerprints from both fingers. This paper studies the feasibility of creating double-identity fingerprints by proposing two possible techniques and evaluating to what extent they may be used to fool the state-of-the-art fingerprint recognition systems. The results of systematic experiments suggest that existing algorithms are highly vulnerable to this specific attack (about 90% chance of success at FAR = 0.1%) and that the fingerprint patterns generated might be realistic enough to fool human examiners.
Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey

Software security vulnerabilities are one of the critical issues in the realm of computer security. Due to their potential high severity impacts, many different approaches have been proposed in the past decades to mitigate the damages of software vulnerabilities. Machine-learning and data-mining techniques are also among the many approaches to address this issue. In this article, we provide an extensive review of the many different works in the field of software vulnerability analysis and discovery that utilize machine-learning and data-mining techniques. We review different categories of works in this domain, discuss both advantages and shortcomings, and point out challenges and some uncharted territories in the field.
Long-Term Memory Networks for Question Answering

Question answering is an important and difficult task in the natural language processing domain, because many basic natural language processing tasks can be cast into a question answering task. Several deep neural network architectures have been developed recently, which employ memory and inference components to memorize and reason over text information, and generate answers to questions. However, a major drawback of many such models is that they are capable of only generating single-word answers. In addition, they require large amount of training data to generate accurate answers. In this paper, we introduce the LongTerm Memory Network (LTMN), which incorporates both an external memory module and a Long Short-Term Memory (LSTM) module to comprehend the input data and generate multi-word answers. The LTMN model can be trained end-to-end using back-propagation and requires minimal supervision. We test our model on two synthetic data sets (based on Facebook’s bAbI data set) and the real-world Stanford question answering data set, and show that it can achieve state-of-the-art performance.
Recent Approaches to Dialog Management for Spoken Dialog Systems

A field of spoken dialog systems is a rapidly growing research area because the performance improvement of speech technologies motivates the possibility of building systems that a human can easily operate in order to access useful information via spoken languages. Among the components in a spoken dialog system, the dialog management plays major roles such as discourse analysis, database access, error handling, and system action prediction. This survey covers design issues and recent approaches to the dialog management techniques for modeling the dialogs. We also explain the user simulation techniques for automatic evaluation of spoken dialog systems.
The Baryonic Tully-Fisher Relation.

We explore the Tully-Fisher relation over five decades in stellar mass in galaxies with circular velocities ranging over 30 less, similarVc less, similar300 km s-1. We find a clear break in the optical Tully-Fisher relation: field galaxies with Vc less, similar90 km s-1 fall below the relation defined by brighter galaxies. These faint galaxies, however, are very rich in gas; adding in the gas mass and plotting the baryonic disk mass Md=M*+Mgas in place of luminosity restores the single linear relation. The Tully-Fisher relation thus appears fundamentally to be a relation between rotation velocity and total baryonic mass of the form Md~V4c.
Horizontal Mismatch in the Labour Market of Graduates : The Role of Signalling

Horizontal Mismatch in the Labour Market of Graduates: The Role of Signalling We follow Brodaty et al. (2008) and develop a model within the signalling literature where an employer decides whether to hire a worker or not conditionally on the signals she sends – field and length of study and high education (HE) institution. The empirical design of our paper builds on evidence relative to first labour market entry of graduates to identify a signalling effect of individual and institutional quality of study on individual horizontal match quality. First, based on a matched unique employer-employee dataset we report the extent of horizontal mismatch for graduates of different fields of education for a post-transition economy (Slovenia). Second, we test the signal of HE institutions and above average study duration on the likelihood of a horizontal mismatch separately for each field of education. We find that graduates from specific HE institutions experience significantly higher likelihood to get a job that matches the field of study for social sciences, namely business and administration and to a smaller extent education. On the contrary, HE institutions do not signal skills or abilities in the most technical fields of education (engineering, computing, manufacturing). The above average study duration has mixed effects based on the field of education. It can either signal lower innate ability (i.e. for law graduates) or increased skills due to student work (i.e. computing graduates).
Generative OpenMax for Multi-Class Open Set Classification

We present a conceptually new and flexible method for multi-class open set classification. Unlike previous methods where unknown classes are inferred with respect to the feature or decision distance to the known classes, our approach is able to provide explicit modelling and decision score for unknown classes. The proposed method, called Generative OpenMax (G-OpenMax), extends OpenMax by employing generative adversarial networks (GANs) for novel category image synthesis. We validate the proposed method on two datasets of handwritten digits and characters, resulting in superior results over previous deep learning based method OpenMax Moreover, G-OpenMax provides a way to visualize samples representing the unknown classes from open space. Our simple and effective approach could serve as a new direction to tackle the challenging multi-class open set classification problem.
Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-toapples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [30], R-FCN [6] and SSD  systems, which we view as meta-architectures and trace out the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision. Unlike many existing works, we do not require manual annotation of matching point clusters. Instead, we leverage on alignment and attention mechanisms to learn feature correspondences from GPS/INS tagged 3D point clouds without explicitly specifying them. We create training and benchmark outdoor Lidar datasets, and experiments show that 3DFeat-Net obtains state-of-the-art performance on these gravityaligned datasets.
Conceptual Privacy Framework for Health Information on Wearable Device

Wearable health tech provides doctors with the ability to remotely supervise their patients' wellness. It also makes it much easier to authorize someone else to take appropriate actions to ensure the person's wellness than ever before. Information Technology may soon change the way medicine is practiced, improving the performance, while reducing the price of healthcare. We analyzed the secrecy demands of wearable devices, including Smartphone, smart watch and their computing techniques, that can soon change the way healthcare is provided. However, before this is adopted in practice, all devices must be equipped with sufficient privacy capabilities related to healthcare service. In this paper, we formulated a new improved conceptual framework for wearable healthcare systems. This framework consists of ten principles and nine checklists, capable of providing complete privacy protection package to wearable device owners. We constructed this framework based on the analysis of existing mobile technology, the results of which are combined with the existing security standards. The approach also incorporates the market share percentage level of every app and its respective OS. This framework is evaluated based on the stringent CIA and HIPAA principles for information security. This evaluation is followed by testing the capability to revoke rights of subjects to access objects and ability to determine the set of available permissions for a particular subject for all models Finally, as the last step, we examine the complexity of the required initial setup.
Hypertext Transfer Protocol - HTTP/1.0

The Hypertext Transfer Protocol (HTTP) is an application-level protocol with the lightness and speed necessary for distributed, collaborative, hypermedia information systems. It is a generic, stateless, objectoriented protocol which can be used for many tasks, such as name servers and distributed object management systems, through extension of its request methods (commands). A feature of HTTP is the typing of data representation, allowing systems to be built independently of the data being transferred. HTTP has been in use by the World-Wide Web global information initiative since 1990. This specification reflects common usage of the protocol referred to as "HTTP/1.0". RFC 1945 HTTP/1.0 May 1996
Development of monitoring system for smart farming using Progressive Web App

Indonesia is one of countries well-known as the biggest palm oil producers in the world. In 2015, this country succeeded to produce 32.5 million tons of palm oil, and used 26.4 million of it to export to other countries. The quality of Indonesia's palm oil production has become the reason why Indonesia becomes the famous exporter in a global market. For this reason, many Indonesian palm oil companies are trying to improve their quality through smart farming. One of the ways to improve is by using technology such as Internet of Things (IoT). In order to have the actual and real-time condition of the land, using the IoT concept by connecting some sensors. A previous research has accomplished to create some Application Programming Interfaces (API), which can be used to support the use of technology. However, these APIs have not been integrated to a User Interface (UI), as it can only be used by developers or programmers. These APIs have not been able to be used as a monitoring information system for palm oil plantation, which can be understood by the employees. Based on those problems, this research attempts to develop a monitoring information system, which will be integrated with the APIs from the previous research by using the Progressive Web App (PWA) approach. So, this monitoring information system can be accessed by the employees, either by using smartphone or by using desktop. Even, it can work similar with a native application.