nils-herrmann/clean_scirepeval huggingface dataset:
Supervised sentiment analysis in Czech social media

This article describes in-depth research on machine learning methods for sentiment analysis of Czech social media. Whereas in English, Chinese, or Spanish this field has a long history and evaluation datasets for various domains are widely available, in the case of the Czech language no systematic research has yet been conducted. We tackle this issue and establish a common ground for further research by providing a large human-annotated Czech social media corpus. Furthermore, we evaluate state-of-the-art supervised machine learning methods for sentiment analysis. We explore different pre-processing techniques and employ various features and classifiers. We also experiment with five different feature selection algorithms and investigate the influence of named entity recognition and preprocessing on sentiment classification performance. Moreover, in addition to our newly created social media dataset, we also report results for other popular domains, such as movie and product reviews. We believe that this article will not only extend the current sentiment analysis research to another family of languages, but will also encourage competition, potentially leading to the production of high-end commercial solutions.
SMART: A Web-Based, Ontology-Driven, Semantic Web Query Answering Application

SMART (Semantic web information Management with automated Reasoning Tool) is an open-source project, which aims to provide intuitive tools for life scientists for represent, integrate, manage and query heterogeneous and distributed biological knowledge. SMART was designed with interoperability and extensibility in mind and uses AJAX, SVG and JSF technologies, RDF, OWL, SPARQL semantic web languages, triple stores (i.e. Jena) and DL reasoners (i.e. Pellet) for the automated reasoning. Features include semantic query composition and validation using DL reasoners, a graphical representation of the query, a mapping of DL queries to SPARQL, and the retrieval of pre-computed inferences from an RDF triple store. With a use case scenario, we illustrate how a biological scientist can intuitively query the yeast knowledge base and navigate the results. Continued development of this web-based resource for the biological semantic web will enable new information retrieval opportunities for the life sciences.
Applying graph-based data mining concepts to the educational sphere

In this study, we discuss the possible application of the ubiquitous complex network approach for information extraction from educational data. Since a huge amount of data (which is detailed as well) is produced by the complex administration systems of educational institutes, instead of the classical statistical methods, new types of data processing techniques are required to handle it. We define several suitable network representations of students, teachers and subjects in public education and present some possible ways of how graph mining techniques can be used to get detailed information about them. Depending on the construction of the underlying graph, we examine several network models and discuss which are the most appropriate graph mining tools (like community detection and ranking and centrality measures) that can be applied on them. Lastly, we attempt to highlight the many advantages of using graph-based data mining in educational data against the classical evaluation techniques.
Current Multistage Drug Delivery Systems Based on the Tumor Microenvironment

The development of traditional tumor-targeted drug delivery systems based on EPR effect and receptor-mediated endocytosis is very challenging probably because of the biological complexity of tumors as well as the limitations in the design of the functional nano-sized delivery systems. Recently, multistage drug delivery systems (Ms-DDS) triggered by various specific tumor microenvironment stimuli have emerged for tumor therapy and imaging. In response to the differences in the physiological blood circulation, tumor microenvironment, and intracellular environment, Ms-DDS can change their physicochemical properties (such as size, hydrophobicity, or zeta potential) to achieve deeper tumor penetration, enhanced cellular uptake, timely drug release, as well as effective endosomal escape. Based on these mechanisms, Ms-DDS could deliver maximum quantity of drugs to the therapeutic targets including tumor tissues, cells, and subcellular organelles and eventually exhibit the highest therapeutic efficacy. In this review, we expatiate on various responsive modes triggered by the tumor microenvironment stimuli, introduce recent advances in multistage nanoparticle systems, especially the multi-stimuli responsive delivery systems, and discuss their functions, effects, and prospects.
"Hey Alexa, What's Up?": A Mixed-Methods Studies of In-Home Conversational Agent Usage

In-home, place-based, conversational agents have exploded in popularity over the past three years. In particular, Amazon's conversational agent, Alexa, now dominates the market and is in millions of homes. This paper presents two complementary studies investigating the experience of households living with a conversational agent over an extended period of time. First, we gathered the history logs of 75 Alexa participants and quantitatively analyzed over 278,000 commands. Second, we performed seven in-home, contextual interviews of Alexa owners focusing on how their household interacts with Alexa. Our findings give the first glimpse of how households integrate Alexa into their lives. We found interesting behaviors around purchasing and acclimating to Alexa, in the number and physical placement of devices, and in daily use patterns. Participants also uniformly described interactions between children and Alexa. We conclude with suggestions for future improvement for intelligent conversational agents.
A column-driver IC with improved DVO for TFT-LCDs

This paper presents an 8-bit column-driver IC with improved deviation of voltage output (DVO) for thin-film-transistor (TFT) liquid crystal displays (LCDs). The various DVO results contributed by the output buffer of a column driver are predicted by using Monte Carlo simulation under different variation conditions. Relying on this prediction, a better compromise can be achieved between DVO and chip size. This work was implemented using 0.35-μm CMOS technology and the measured maximum DVO is only 6.2 mV.
A Test Architecture for Machine Learning Product

As machine learning (ML) technology continues to spread by rapid evolution, the system or service using Machine Learning technology, called ML product, makes big impact on our life, society and economy. Meanwhile, Quality Assurance (QA) for ML product is quite more difficult than hardware, non-ML software and service because performance of ML technology is much better than non-ML technology in exchange for the characteristics of ML product, e.g. low explainability. We must keep rapid evolution and reduce quality risk of ML product simultaneously. In this paper, we show a Quality Assurance Framework for Machine Learning product. Scope of QA in this paper is limited to product evaluation. First, a policy of QA for ML Product is proposed. General principles of product evaluation is introduced and applied to ML product evaluation as a part of the policy. They are composed of A-ARAI: Allowability, Achievability, Robustness, Avoidability and Improvability. A strategy of ML Product Evaluation is constructed as another part of the policy. Quality Integrity Level for ML product is also modelled. Second, we propose a test architecture of ML product testing. It consists of test levels and fundamental test types of ML product testing, including snapshot testing, learning testing and confrontation testing. Finally, we defines QA activity levels for ML product.
Laser ranging at 1550 nm with 1-GHz sine-wave gated InGaAs/InP APD single-photon detector.

We demonstrated a laser ranging system with single photon detection at 1550 nm. The single-photon detector was a 1-GHz sine-wave gated InGaAs/InP avalanche photodiode. In daylight, 8-cm depth resolution was achieved directly by using a time-of-flight approach based on time-correlated single photon counting measurement. This system presented a potential for low energy level and eye-safe laser ranging system in long-range measurement.
Value-based interventional pain management: a review of medicare national and local coverage determination policies.

Major policies, regulations, and practice patterns related to interventional pain management are dependent on Medicare policies which include national coverage policies - national coverage determinations (NCDs), and local coverage policies - local coverage determinations (LCDs). The NCDs are Medicare coverage policies issued by the Centers for Medicare and Medicaid Services (CMS). The process used by the CMS in deciding what is and what is not medically necessary is lengthy, involving a review of evidence-based literature on the subject, expert opinion, and public comments. In contrast, LCDs are rules and Medicare coverage that are issued by regional contractors and fiscal intermediaries when an NCD has not addressed the policy at issue. The evidence utilized in preparing LCDs includes the highest level of evidence which is based on published authoritative evidence derived from definitive randomized clinical trials or other definitive studies, and general acceptance by the medical community (standard of practice), as supported by sound medical evidence. In addition, the intervention must be safe and effective and appropriate including duration and frequency that is considered appropriate for the item or service in terms of whether it is furnished in accordance with accepted standards of medical practice for the diagnosis or treatment of the patient's condition or to improve the function. In addition, the safe and effective provision includes that service must be furnished in a setting appropriate to the patient's medical needs and condition, ordered and furnished by qualified personnel, the service must meet, but does not exceed, the patient's medical need, and be at least as beneficial as an existing and available medically appropriate alternative. The LCDs are prepared with literature review, state medical societies, and carrier advisory committees (CACs) of which interventional pain management is a member. The LCDs may be appealed by beneficiaries. The NCDs are prepared by the CMS following a request for a national coverage decision after an appropriate national coverage request along with a draft decision memorandum, and public comments. After the request, the staff review, external technology assessment, Medicare Evidence Development and Coverage Advisory Committee (MedCAC) assessment, public comments, a draft decision memorandum may be posted which will be followed by a final decision and implementation instructions. This decision may be appealed to the department appeals board, but may be difficult to reverse. This manuscript describes NCDs and LCDs and the process of development, their development, issues related to the development, and finally their relation to interventional pain management.
An evaluation of methods for modeling contact in multibody simulation

Modeling contact in multibody simulation is a difficult problem frequently characterized by numerically brittle algorithms, long running times, and inaccurate (with respect to theory) models. We present a comprehensive evaluation of four methods for contact modeling on seven benchmark scenarios in order to quantify the performance of these methods with respect to robustness and speed. We also assess the accuracy of these methods where possible. We conclude the paper with a prescriptive description in order to guide the user of multibody simulation.
Reduced graphene oxide electrically contacted graphene sensor for highly sensitive nitric oxide detection.

We develop graphene-based devices fabricated by alternating current dielectrophoresis (ac-DEP) for highly sensitive nitric oxide (NO) gas detection. The novel device comprises the sensitive channels of palladium-decorated reduced graphene oxide (Pd-RGO) and the electrodes covered with chemical vapor deposition (CVD)-grown graphene. The highly sensitive, recoverable, and reliable detection of NO gas ranging from 2 to 420 ppb with response time of several hundred seconds has been achieved at room temperature. The facile and scalable route for high performance suggests a promising application of graphene devices toward the human exhaled NO and environmental pollutant detections.
Comparison of Bone Resorption Rates after Intraoral Block Bone and Guided Bone Regeneration Augmentation for the Reconstruction of Horizontally Deficient Maxillary Alveolar Ridges

Bone atrophy after tooth loss may leave insufficient bone for implant placement. We compared volumetric changes after autogenous ramus block bone grafting (RBG) or guided bone regeneration (GBR) in horizontally deficient maxilla before implant placement.  In this retrospective study, volumetric changes at RBG or GBR graft sites were evaluated using cone-beam computed tomography. The primary outcome variable was the volumetric resorption rate. Secondary outcomes were bone gain, graft success, and implant insertion torque.  Twenty-four patients (28 grafted sites) were included (GBR, 15; RBG, 13). One patient (RBG) suffered mucosal dehiscence at the recipient site 6 weeks after surgery, which healed spontaneously. Mean volume reduction in the GBR and RBG groups was 12.48 ± 2.67% and 7.20 ± 1.40%, respectively. GBR resulted in significantly more bone resorption than RBG (P < 0.001). Mean horizontal bone gain and width after healing were significantly greater in the GBR than in the RBG group (P = 0.002 and 0.005, resp.). Implant torque was similar between groups (P > 0.05).  Both RBG and GBR hard-tissue augmentation techniques provide adequate bone graft volume and stability for implant insertion. However, GBR causes greater resorption at maxillary augmented sites than RBG, which clinicians should consider during treatment planning.
Sustainable development of smart textiles : a review of ‘ self-functioning ’ abilities which makes textiles alive

Technologically-friendly and user-friendly textile products, which can make daily life more convenient and comfortable, are progressively popular in modern society. Going along with the advanced development of innovative technology, textile clothing is no longer only used as body shielding for protection, warmth keeping at cold weather and aesthetic enhancing for fashion design. Furthermore, it can provide different ‘self-functioning’ processes for interacting with and adapting to external changes like living organisms. Various applications can hence be initiated for propelling the textile industry forward.
Vertical excess of the premaxilla in bilateral cleft lip and palate patients: a protocol for treatment.

Bilateral cleft lip and palate patients may present a vertical excess of the premaxilla, which is a severe aesthetic and functional problem. Early surgical correction may lead to secondary growth impairment. We present a suggested protocol based on the severity of the vertical excess and on the age of the patient, which includes orthopedic, orthodontic, and surgical corrections. Patients are presented to elucidate each different approach.
Comparing Policy-Gradient Algorithms

We present a series of formal and empirical results comparing the efficiency of various policy-gradient methods—methods for reinforcement learning that directly update a parameterized policy according to an approximation of the gradient of performance with respect to the policy parameter. Such methods have recently become of interest as an alternative to value-function-based methods because of superior convergence guarantees, ability to find stochastic policies, and ability to handle large and continuous action spaces. Our results include: 1) formal and empirical demonstrations that a policy-gradient method suggested by Sutton et al. (2000) and Konda and Tsitsiklis (2000) is no better than REINFORCE, 2) derivation of the optimal baseline for policy-gradient methods, which differs from the widely used V (s) previously thought to be optimal, 3) introduction of a new all-action policy-gradient algorithm that is unbiased and requires no baseline, and demonstrating empirically and semi-formally that it is more efficient than the methods mentioned above, and 4) an overall comparison of methods on the mountain-car problem including value-function-based methods and bootstrapping actor-critic methods. One general conclusion we draw is that the bias of conventional value functions is a feature, not a bug; it seems required is order for the value function to significantly accelerate learning.
Venlafaxine extended release vs placebo and paroxetine in social anxiety disorder.

Evidence indicates that venlafaxine hydrochloride extended release (ER) effectively ameliorates anxiety symptoms.



To evaluate the efficacy, safety, and tolerability of flexible-dose venlafaxine ER compared with placebo in the short-term treatment of generalized social anxiety disorder and, secondarily, to compare paroxetine with venlafaxine ER and paroxetine with placebo.



Adult outpatients with DSM-IV generalized social anxiety disorder for 6 months or longer were randomly assigned to receive venlafaxine hydrochloride ER (75-225 mg/d), paroxetine (20-50 mg/d), or placebo for 12 weeks or less at 26 centers in the United States. The primary outcome measure was the total Liebowitz Social Anxiety Scale score. Secondary measures included response (Clinical Global Impression-Improvement score, 1 or 2) rates and Clinical Global Impression-Severity of Illness and Social Phobia Inventory scores.



Of 440 patients treated, 413 (93.9%) were included in the last-observation-carried-forward efficacy analysis; of the 429 patients in the safety population, 318 (74.1%) completed the study. Mean daily doses were 201.7 mg (SD, 38.1 mg) of venlafaxine hydrochloride ER and 46.0 mg (SD, 7.9 mg) of paroxetine. Venlafaxine ER treatment was significantly superior to placebo at weeks 1 through 12 on the Liebowitz Social Anxiety Scale and Social Phobia Inventory and at week 2 and weeks 6 through 12 for Clinical Global Impression-Severity of Illness and responder status, and was significantly superior to paroxetine treatment at weeks 1 and 2 for the Social Phobia Inventory (P < .05 for all). Paroxetine treatment was significantly superior to placebo at weeks 3 through 12 on the Liebowitz Social Anxiety Scale, the Clinical Global Impression-Severity of Illness scale, and the Social Phobia Inventory, and at weeks 4 through 12 for response (P < .05 for all). Week 12 response rates were significantly greater for the venlafaxine ER and paroxetine groups (58.6% and 62.5%, respectively) vs the placebo group (36.1%) (P < .001 for both).



Venlafaxine ER is effective in the short-term treatment of generalized social anxiety disorder, with efficacy and tolerability comparable to paroxetine.
Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning

Medication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance. Existing observational studies have mainly relied on structured EHR data to obtain ADE information; however, ADEs are often buried in the EHR narratives and not recorded in structured data.



To unlock ADE-related information from EHR narratives, there is a need to extract relevant entities and identify relations among them. In this study, we focus on relation identification. This study aimed to evaluate natural language processing and machine learning approaches using the expert-annotated medical entities and relations in the context of drug safety surveillance, and investigate how different learning approaches perform under different configurations.



We have manually annotated 791 EHR notes with 9 named entities (eg, medication, indication, severity, and ADEs) and 7 different types of relations (eg, medication-dosage, medication-ADE, and severity-ADE). Then, we explored 3 supervised machine learning systems for relation identification: (1) a support vector machines (SVM) system, (2) an end-to-end deep neural network system, and (3) a supervised descriptive rule induction baseline system. For the neural network system, we exploited the state-of-the-art recurrent neural network (RNN) and attention models. We report the performance by macro-averaged precision, recall, and F1-score across the relation types.



Our results show that the SVM model achieved the best average F1-score of 89.1% on test data, outperforming the long short-term memory (LSTM) model with attention (F1-score of 65.72%) as well as the rule induction baseline system (F1-score of 7.47%) by a large margin. The bidirectional LSTM model with attention achieved the best performance among different RNN models. With the inclusion of additional features in the LSTM model, its performance can be boosted to an average F1-score of 77.35%.



It shows that classical learning models (SVM) remains advantageous over deep learning models (RNN variants) for clinical relation identification, especially for long-distance intersentential relations. However, RNNs demonstrate a great potential of significant improvement if more training data become available. Our work is an important step toward mining EHRs to improve the efficacy of drug safety surveillance. Most importantly, the annotated data used in this study will be made publicly available, which will further promote drug safety research in the community.
DSRD-Based High-Power Repetitive Short-Pulse Generator Containing GDT: Theory and Experiment

A high-power short-pulse generator based on the diode step recovery phenomenon and high repetition rate discharges in a two-electrode gas discharge tube is presented. The proposed circuit is simple and low cost and driven by a low-power source. A full analysis of this generator is presented which, considering the nonlinear behavior of the gas tube, predicts the waveform of the output pulse. The proposed method has been shown to work properly by implementation of a kW-range prototype. Experimental measurements of the output pulse characteristics showed a rise time of 3.5 ns, with pulse repetition rate of 2.3 kHz for a 47- $\Omega $  load. The input peak power was 2.4 W, which translated to about 0.65-kW output, showing more than 270 times increase in the pulse peak power. The efficiency of the prototype was 57%. The overall price of the employed components in the prototype was less than U.S. $2.0. An excellent agreement between the analytical and experimental test results was established. The analysis predicts that the proposed circuit can generate nanosecond pulses with more than 100-kW peak powers by using a subkW power supply.
DiffAni: Visualizing Dynamic Graphs with a Hybrid of Difference Maps and Animation

Visualization of dynamically changing networks (graphs) is a significant challenge for researchers. Previous work has experimentally compared animation, small multiples, and other techniques, and found trade-offs between these. One potential way to avoid such trade-offs is to combine previous techniques in a hybrid visualization. We present two taxonomies of visualizations of dynamic graphs: one of non-hybrid techniques, and one of hybrid techniques. We also describe a prototype, called DiffAni, that allows a graph to be visualized as a sequence of three kinds of tiles: diff tiles that show difference maps over some time interval, animation tiles that show the evolution of the graph over some time interval, and small multiple tiles that show the graph state at an individual time slice. This sequence of tiles is ordered by time and covers all time slices in the data. An experimental evaluation of DiffAni shows that our hybrid approach has advantages over non-hybrid techniques in certain cases.