nils-herrmann/clean_scirepeval huggingface dataset:
A Comparative Study on Feature Selection in Text Categorization

This paper is a comparative study of feature selection methods in statistical learning of text categorization The focus is on aggres sive dimensionality reduction Five meth ods were evaluated including term selection based on document frequency DF informa tion gain IG mutual information MI a test CHI and term strength TS We found IG and CHI most e ective in our ex periments Using IG thresholding with a k nearest neighbor classi er on the Reuters cor pus removal of up to removal of unique terms actually yielded an improved classi cation accuracy measured by average preci sion DF thresholding performed similarly Indeed we found strong correlations between the DF IG and CHI values of a term This suggests that DF thresholding the simplest method with the lowest cost in computation can be reliably used instead of IG or CHI when the computation of these measures are too expensive TS compares favorably with the other methods with up to vocabulary reduction but is not competitive at higher vo cabulary reduction levels In contrast MI had relatively poor performance due to its bias towards favoring rare terms and its sen sitivity to probability estimation errors
Transforming Web Tables to a Relational Database

HTML tables represent a significant fraction of web data. The often complex headers of such tables are determined accurately using their indexing property. Isolated headers are factored to extract category hierarchies. Web tables are then transformed into a canonical form and imported into a relational database. The proposed processing allows for the formulation of arbitrary SQL queries over the collection of induced relational tables. table segmentation; Wang categories; header paths; relational table SQL queries
Fundamentals for Future Mobile-Health (mHealth): A Systematic Review of Mobile Phone and Web-Based Text Messaging in Mental Health

Mobile phone text messages (short message service, SMS) are used pervasively as a form of communication. Almost 100% of the population uses text messaging worldwide and this technology is being suggested as a promising tool in psychiatry. Text messages can be sent either from a classic mobile phone or a web-based application. Reviews are needed to better understand how text messaging can be used in mental health care and other fields of medicine.



The objective of the study was to review the literature regarding the use of mobile phone text messaging in mental health care.



We conducted a thorough literature review of studies involving text messaging in health care management. Searches included PubMed, PsycINFO, Cochrane, Scopus, Embase and Web of Science databases on May 25, 2015. Studies reporting the use of text messaging as a tool in managing patients with mental health disorders were included. Given the heterogeneity of studies, this review was summarized using a descriptive approach.



From 677 initial citations, 36 studies were included in the review. Text messaging was used in a wide range of mental health situations, notably substance abuse (31%), schizophrenia (22%), and affective disorders (17%). We identified four ways in which text messages were used: reminders (14%), information (17%), supportive messages (42%), and self-monitoring procedures (42%). Applications were sometimes combined.



We report growing interest in text messaging since 2006. Text messages have been proposed as a health care tool in a wide spectrum of psychiatric disorders including substance abuse, schizophrenia, affective disorders, and suicide prevention. Most papers described pilot studies, while some randomized clinical trials (RCTs) were also reported. Overall, a positive attitude toward text messages was reported. RCTs reported improved treatment adherence and symptom surveillance. Other positive points included an increase in appointment attendance and in satisfaction with management and health care services. Insight into message content, preventative strategies, and innovative approaches derived from the mental health field may be applicable in other medical specialties.
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes

Bayesian learning methods have recently been shown to provi de an elegant solution to the explorationexploitation trade-off in reinforcement learning. Howeve r most investigations of Bayesian reinforcement learning to date focus on the standard Markov Deci sion Processes (MDPs). The primary focus of this paper is to extend these ideas to the case of part ially observable domains, by introducing the Bayes-Adaptive Partially Observable Markov Decisi on Processes. This new framework can be used to simultaneously (1) learn a model of the POMDP domai n through interaction with the environment, (2) track the state of the system under partial ob servability, and (3) plan (near-)optimal sequences of actions. An important contribution of this pap er is to provide theoretical results showing how the model can be finitely approximated while preservi ng good learning performance. We present approximate algorithms for belief tracking and pla nning in this model, as well as empirical results that illustrate how the model estimate and agent’s r eturn improve as a function of experience.
Effects of bacterial inoculants on the indigenous microbiome and secondary metabolites of chamomile plants

Plant-associated bacteria fulfill important functions for plant growth and health. However, our knowledge about the impact of bacterial treatments on the host's microbiome and physiology is limited. The present study was conducted to assess the impact of bacterial inoculants on the microbiome of chamomile plants Chamomilla recutita (L.) Rauschert grown in a field under organic management in Egypt. Chamomile seedlings were inoculated with three indigenous Gram-positive strains (Streptomyces subrutilus Wbn2-11, Bacillus subtilis Co1-6, Paenibacillus polymyxa Mc5Re-14) from Egypt and three European Gram-negative strains (Pseudomonas fluorescens L13-6-12, Stenotrophomonas rhizophila P69, Serratia plymuthica 3Re4-18) already known for their beneficial plant-microbe interaction. Molecular fingerprints of 16S rRNA gene as well as real-time PCR analyses did not show statistically significant differences for all applied bacterial antagonists compared to the control. In contrast, a pyrosequencing analysis of the 16S rRNA gene libraries revealed significant differences in the community structure of bacteria between the treatments. These differences could be clearly shown by a shift within the community structure and corresponding beta-diversity indices. Moreover, B. subtilis Co1-6 and P. polymyxa Mc5Re-14 showed an enhancement of the bioactive secondary metabolite apigenin-7-O-glucoside. This indicates a possible new function of bacterial inoculants: to interact with the plant microbiome as well as to influence the plant metabolome.
Static Specification Mining Using Automata-Based Abstractions

We present a novel approach to client-side mining of temporal API specifications based on static analysis. Specifically, we present an interprocedural analysis over a combined domain that abstracts both aliasing and event sequences for individual objects. The analysis uses a new family of automata-based abstractions to represent unbounded event sequences, designed to disambiguate distinct usage patterns and merge similar usage patterns. Additionally, our approach includes an algorithm that summarizes abstract traces based on automata clusters, and effectively rules out spurious behaviors.
 We show experimental results mining specifications from a number of Java clients and APIs. The results indicate that effective static analysis for client-side mining requires fairly precise treatment of aliasing and abstract event sequences. Based on the results, we conclude that static client-side specification mining shows promise as a complement or alternative to dynamic approaches.
Online sketching hashing

Recently, hashing based approximate nearest neighbor (ANN) search has attracted much attention. Extensive new algorithms have been developed and successfully applied to different applications. However, two critical problems are rarely mentioned. First, in real-world applications, the data often comes in a streaming fashion but most of existing hashing methods are batch based models. Second, when the dataset becomes huge, it is almost impossible to load all the data into memory to train hashing models. In this paper, we propose a novel approach to handle these two problems simultaneously based on the idea of data sketching. A sketch of one dataset preserves its major characters but with significantly smaller size. With a small size sketch, our method can learn hash functions in an online fashion, while needs rather low computational complexity and storage space. Extensive experiments on two large scale benchmarks and one synthetic dataset demonstrate the efficacy of the proposed method.
The 8 requirements of real-time stream processing

Applications that require real-time processing of high-volume data steams are pushing the limits of traditional data processing infrastructures. These stream-based applications include market feed processing and electronic trading on Wall Street, network and infrastructure monitoring, fraud detection, and command and control in military environments. Furthermore, as the "sea change" caused by cheap micro-sensor technology takes hold, we expect to see everything of material significance on the planet get "sensor-tagged" and report its state or location in real time. This sensorization of the real world will lead to a "green field" of novel monitoring and control applications with high-volume and low-latency processing requirements.Recently, several technologies have emerged---including off-the-shelf stream processing engines---specifically to address the challenges of processing high-volume, real-time data without requiring the use of custom code. At the same time, some existing software technologies, such as main memory DBMSs and rule engines, are also being "repurposed" by marketing departments to address these applications.In this paper, we outline eight requirements that a system software should meet to excel at a variety of real-time stream processing applications. Our goal is to provide high-level guidance to information technologists so that they will know what to look for when evaluation alternative stream processing solutions. As such, this paper serves a purpose comparable to the requirements papers in relational DBMSs and on-line analytical processing. We also briefly review alternative system software technologies in the context of our requirements.The paper attempts to be vendor neutral, so no specific commercial products are mentioned.
Monolithically Integrated Si Photonics Transmitters in 0.25 um BiCMOS Platform for High-Speed Optical Communications

Monolithically integrated electro-optical transmitters fabricated in a 0.25 μm SiGe:C BiCMOS electronic-photonic integrated circuit (EPIC) technology with fT =190 GHz are presented. The modules are based on the co-design and integration of a segmented depletion-type Si Mach-Zehnder modulator and multichannel driver amplifiers. Two driving approaches and their performance trade-offs are discussed: a linear one, with direct interface to an external digital-to-analog converter (DAC), as well as a more power efficient implementation featuring integrated 4-bit DAC functionality. High extinction ratios at high speed are demonstrated, enabled by the high breakdown voltages of the HBT transistors. The modules support several modulation formats among which pulse-amplitude-modulation (PAM)−4 eye diagrams up to 37 GBd are shown, for the first time.
The Fake News Spreading Plague: Was it Preventable?

In 2010, a paper entitled "From Obscurity to Prominence in Minutes: Political Speech and Real-time search" won the Best Paper Prize of the WebSci'10 conference. Among its findings were the discovery and documentation of what was labeled a "Twitter bomb", an organized effort to spread misinformation about the democratic candidate Martha Coakley through anonymous Twitter accounts. In this paper, after summarizing the details of that event, we outline the recipe of how social networks are used to spread misinformation. One of the most important steps in such a recipe is the "infiltration" of a community of users who are already engaged in conversations about a topic, to use them as organic spreaders of misinformation in their extended subnetworks. Then, we take this misinformation spreading recipe and indicate how it was successfully used to spread fake news during the 2016 U.S. Presidential Election. The main differences between the scenarios are the use of Facebook instead of Twitter, and the respective motivations (in 2010: political influence; in 2016: financial benefit through online advertising). After situating these events in the broader context of exploiting the Web, we seize this opportunity to address limitations of the reach of research findings and to start a conversation about how communities of researchers can in- crease their impact on real-world societal issues.
Presence and significance of microvesicular steatosis in nonalcoholic fatty liver disease.

Liver biopsies from patients with nonalcoholic fatty liver disease (NAFLD) sometimes exhibit non-zonal aggregates of hepatocytes with microvesicular steatosis, but its prevalence and significance are unclear. In this study, we have evaluated the frequency of microvesicular steatosis and assessed its association with histological markers of disease severity in a large sample of NAFLD liver biopsies.



Liver biopsies from a large cohort of adults who participated in two studies conducted by the NASH Clinical Research Network (NASH CRN) were included in this cross-sectional study. Liver histology was assessed centrally and various histological features scored in a systematic fashion. The relationship between microvesicular steatosis and various histological features that characterize NAFLD was tested by multiple logistic regression, after controlling for age, gender, race, body mass index, and diabetes.



Among 1022 liver biopsies included, 102 (10%) had microvesicular steatosis. No demographic differences were noted between patients with or without microvesicular steatosis. The presence of microvesicular steatosis was associated with higher grades of steatosis (p<0.001), ballooning cell injury (p<0.001), presence of Mallory-Denk bodies (p<0.007), presence of megamitochondria (p<0.0001), higher NAS scores (p<0.0001), more advanced fibrosis (p<0.0001), and diagnosis of borderline or definite NASH (p<0.0001).



Microvesicular steatosis correlates with more advanced histology of NAFLD. Longitudinal studies are needed to address the role of microvesicular steatosis in mediating cellular injury and disease progression in NAFLD.
Sentiment Classification through Semantic Orientation Using SentiWordNet

Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease in making choices regards to online shopping, choosing events, products, entities. In this paper, a rule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual sentence structure. The results show the effectiveness of the proposed method and it outperforms the machine learning methods. The proposed method achieves an accuracy of 87% at the feedback level and 83% at the sentence level for comments.
Thermal Management in High-Density Power Converters

This paper gives an overview of basic principles of the thermal management in high density power converters. Methods for heat removal on a device and circuit board level are discussed. New packaging technologies for discrete power semiconductors as well as multifunctional board integration techniques are included. A view is given on future 3D integration techniques that take into account the poor thermal conductivity especially of the passive component materials. With these techniques much higher power densities are possible. Some examples of prototype systems are presented, together with the achieved technical data.
Linear Scan Register Allocation in the Context of SSA Form and Register Constraints

Linear scan register allocation is an efficient alternative to the widely used graph coloring approach. We show how this algorithm can be applied to register-constrained architectures like the Intel x86. Our allocator relies on static single assignment form, which simplifies data flow analysis and tends to produce short live intervals. It makes use of lifetime holes and instruction weights to improve the quality of the allocation. Our measurements confirm that linear scan is several times faster than graph coloring for medium-sized to large programs.
Prioritized Experience Replay

Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their significance. In this paper we develop a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently. We use prioritized experience replay in the Deep Q-Network (DQN) algorithm, which achieved human-level performance in Atari games. DQN with prioritized experience replay achieves a new state-of-the-art, outperforming DQN with uniform replay on 42 out of 57 games.
Vacuum Electronic High Power Terahertz Sources

Recent research and development has been incredibly successful at advancing the capabilities for vacuum electronic device (VED) sources of powerful terahertz (THz) and near-THz coherent radiation, both CW or average and pulsed. Currently, the VED source portfolio covers over 12 orders of magnitude in power (mW-to-GW) and two orders of magnitude in frequency (from <; 0.1 to >; 10 THz). Further advances are still possible and anticipated. They will be enabled by improved understanding of fundamental beam-wave interactions, electromagnetic mode competition and mode control, along with research and development of new materials, fabrication methods, cathodes, electron beam alignment and focusing, magnet technologies, THz metrology and advanced, broadband output radiation coupling techniques.
A Comparative Analysis of Classification Algorithms for Students College Enrollment Approval Using Data Mining

In big data universities, there may be several problems related to college admission and enrollment due to the increasing volume of students' data applying for higher education. So that there is a need to apply efficient data mining algorithms for better decision making for students' data classification. In this paper, nine classification algorithms are comparatively tested to find the optimum algorithm for students' dataset classification. The KAUODUS+ data base for the preparatory year students are used as an approval dataset for the experimental purposes. The Weka-knowledge analysis tool which is open source data mining workbench software is used for simulation of practical measurements. The classification technique that has the potential to significantly improve the performance is suggested for use in colleges' admission and enrollment applications. Impact of students GPA and their grades qualified materials with respect to their colleges' admission desire are visually analyzed. Results show that C4.5, PART and Random Forest algorithms give the highest performance and accuracy with lowest errors while IBK-E and IBK-M algorithms give high errors and low accuracy.
Indian Language Speech Database: A Review

Speech is the most prominent and natural form of communication between humans. Human beings have long been motivated to create computer that can understand and talk like human. When the research tries to develop certain recognition system they require certain previously stored data i. e. database for respective recognition system. There are various speech databases available for European Language but very less for Indian Language. In this paper we discuss the various Speech Database developed in different Indian Languages for speech recognition system & Text to Speech System.
Automated Evaluation of Out-of-Context Errors

We present a new approach to evaluate computational models for the task of text understanding by the means of out-of-context error detection. Through the novel design of our automated modification process, existing large-scale data sources can be adopted for a vast number of text understanding tasks. The data is thereby altered on a semantic level, allowing models to be tested against a challenging set of modified text passages that require to comprise a broader narrative discourse. Our newly introduced task targets actual real-world problems of transcription and translation systems by inserting authentic out-of-context errors. The automated modification process is applied to the 2016 TEDTalk corpus. Entirely automating the process allows the adoption of complete datasets at low cost, facilitating supervised learning procedures and deeper networks to be trained and tested. To evaluate the quality of the modification algorithm a language model and a supervised binary classification model are trained and tested on the altered dataset. A human baseline evaluation is examined to compare the results with human performance. The outcome of the evaluation task indicates the difficulty to detect semantic errors for machine-learning algorithms and humans, showing that the errors cannot be identified when limited to a single sentence.
Table 1 : A Comparison between Lean Construction and Agile Project

This paper briefly summarises the evolution of Agile Project Management (APM) and differentiates it from lean and agile production and ‘leagile’ construction. The significant benefits being realized through employment of APM within the information systems industry are stated. The characteristics of APM are explored, including: philosophy, organizational attitudes and practices, planning, execution and control and learning. Finally, APM is subjectively assessed as to its potential contribution to the pre-design, design and construction phases. In conclusion, it is assessed that APM offers considerable potential for application in predesign and design but that there are significant hurdles to its adoption in the actual construction phase. Should these be overcome, APM offers benefits well beyond any individual project.