nils-herrmann/clean_scirepeval huggingface dataset:
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.
Sedimentology of the ~ 3 . 3 Ga upper Mendon Formation , Barberton Greenstone Belt , South Africa

The Mendon Formation is the uppermost unit of the 3.5-3.26 Ga Onverwacht Group in the Barberton Greenstone Belt, South Africa. It consists of a cyclic stack of komatiitic volcanic units separated by thin cherty sedimentary layers. In most areas, the uppermost Mendon Formation is a sedimentary interval characterized by black chert, banded black-and-white chert, and banded ferruginous chert, although the detailed patterns of lithofacies in different sections are more complex. Previously reported zircon U/Pb ages suggest that Mendon deposition could represent more than 70 Myr of time between ~3,334 Ma and ~3,260 Ma. This study presents sedimentological and petrographic observations of the upper Mendon Formation from across the central part of the Barberton Greenstone Belt in order to investigate sediment sources, depositional processes, and environments of sedimentation. The dominant mode of sedimentation was quiet settling of carbonaceous grains and, in the deepest sections below storm wave base, fine ferruginous material, resulting in finely laminated black and grey chert. In situ carbonaceous laminations are rare, suggesting that benthic microbial mat growth had little direct influence on deposition. The hemipelagic background deposition was punctuated by occasional inputs of fine pyroclastic debris, formation and deposition of silica granules, and reworking by infrequent storm
THE EFFECT OF AIR TRAFFIC INCREASE ON CONTROLLER WORKLOAD

The Federal Aviation Administration (FAA) has been increasing the National Airspace System (NAS) capacity to accommodate the predicted rapid growth of air traffic. One method to increase the capacity is reducing air traffic controller workload so that they can handle more air traffic. It is crucial to measure the impact of the increasing future air traffic on controller workload. Our experimental data show a linear relationship between the number of aircraft in the en route center sector and controllers’ perceived workload. Based on the extensive range of aircraft count from 14 to 38 in the experiment, we can predict en route center controllers working as a team of Radar and Data controllers with the automation tools available in the our experiment could handle up to about 28 aircraft. This is 33% more than the 21 aircraft that en route center controllers typically handle in a busy sector.
From natural language business requirements to executable models via SBVR

This paper presents a methodology for transforming business rules (BR) written in natural language (NL) such as English into a set of executable models as Unified Modeling Language (UML), Structured Query language (SQL), etc. As the direct automatic transformation of NL specifications to executable models is very difficult due to the inherent ambiguities of NL, this methodology aims at using the Semantics of Business Vocabulary and Business Rules (SBVR) as an intermediate model front-ended by Micro-Systemic Linguistic Analysis (MSLA) because of their mathematical underpinnings. SBVR is a Semantic Metamodel (SMM) introduced by the Object Management Group (OMG) for specifying semantic models of business using NL. SBVR is not only easy to process by machine since it is grounded in formal logic, but it is also easy to understand both by software developers and other stakeholders. Given that SBVR is fully integrated in OMG's Model Driven Architecture (MDA) and behaves as a Computational Independent Model (CIM), our approach advocates model transformation which is the key constituent of the MDA standard.
Characterisation of forward stutter in the AmpFlSTR SGM Plus PCR.

PCR amplification of tetrameric short tandem repeats (STRs) can lead to Taq enzyme slippage and artefact products typically one repeat unit less in size than the parent STR. These back stutter or n-4 amplification products are low-level relative to the amplification of the parent STR but are widely seen in the forensic community where tetrameric STRs are employed in the generation of DNA profiles. To aid the interpretation of DNA mixtures where minor contributor(s) might be present in comparable amounts to the back stutter products, the typical amounts of back stutter generated have been well characterised and guidelines for interpretation are in place. However, further artefacts thought to be Taq enzyme slippage leading to products with one repeat unit greater than the parent sequence (n+4 or forward stutter) or two repeats less (n-8 or double back stutter) also occur, but these have not been well characterised despite their potential influence in mixture interpretations. Here we present findings with respect to these additional artefacts from a study of 10,000 alleles and include guidelines for interpretation.
Learning To Use Formulas To Solve Simple Arithmetic Problems

Solving simple arithmetic word problems is one of the challenges in Natural Language Understanding. This paper presents a novel method to learn to use formulas to solve simple arithmetic word problems. Our system, analyzes each of the sentences to identify the variables and their attributes; and automatically maps this information into a higher level representation. It then uses that representation to recognize the presence of a formula along with its associated variables. An equation is then generated from the formal description of the formula. In the training phase, it learns to score the <formula, variables> pair from the systematically generated higher level representation. It is able to solve 86.07% of the problems in a corpus of standard primary school test questions and beats the state-of-the-art by
Towards Crafting Text Adversarial Samples

Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a classifier at hand. An attacker introduces specially crafted adversarial samples to a deployed classifier, which are being mis-classified by the classifier. However, the samples are perceived to be drawn from entirely different classes and thus it becomes hard to detect the adversarial samples. Most of the prior works have been focused on synthesizing adversarial samples in the image domain. In this paper, we propose a new method of crafting adversarial text samples by modification of the original samples. Modifications of the original text samples are done by deleting or replacing the important or salient words in the text or by introducing new words in the text sample. Our algorithm works best for the datasets which have sub-categories within each of the classes of examples. While crafting adversarial samples, one of the key constraint is to generate meaningful sentences which can at pass off as legitimate from language (English) viewpoint. Experimental results on IMDB movie review dataset for sentiment analysis and Twitter dataset for gender detection show the efficiency of our proposed method.
Interpreting TF-IDF term weights as making relevance decisions

A novel probabilistic retrieval model is presented. It forms a basis to interpret the TF-IDF term weights as making relevance decisions. It simulates the local relevance decision-making for every location of a document, and combines all of these “local” relevance decisions as the “document-wide” relevance decision for the document. The significance of interpreting TF-IDF in this way is the potential to: (1) establish a unifying perspective about information retrieval as relevance decision-making; and (2) develop advanced TF-IDF-related term weights for future elaborate retrieval models. Our novel retrieval model is simplified to a basic ranking formula that directly corresponds to the TF-IDF term weights. In general, we show that the term-frequency factor of the ranking formula can be rendered into different term-frequency factors of existing retrieval systems. In the basic ranking formula, the remaining quantity - log p(&rmacr;|t ∈ d) is interpreted as the probability of randomly picking a nonrelevant usage (denoted by &rmacr;) of term t. Mathematically, we show that this quantity can be approximated by the inverse document-frequency (IDF). Empirically, we show that this quantity is related to IDF, using four reference TREC ad hoc retrieval data collections.
Ranking of fuzzy numbers based on alpha-distance

This paper presents a new approach to compare fuzzy numbers using α-distance. Initially, the metric distance on the interval numbers based on the convex hull of the endpoints is proposed and it is extended to fuzzy numbers. All the properties of the α-distance are proved in details. Finally, the ranking of fuzzy numbers by the α-distance is discussed. In addition, the proposed method is compared with some known ones, the validity of the new method is illustrated by applying its to several group of fuzzy numbers.
An analysis of psychedelic drug flashbacks.

Psychedelic drug flashbacks have been a puzzling clinical phenomenon observed by clinicians. Flashbacks are defined as transient, spontaneous recurrences of the psychedelic drug effect appearing after a period of normalcy following an intoxication of psychedelics. The paper traces the evolution of the concept of flashback and gives examples of the varieties encountered. Although many drugs have been advocated for the treatment of flashback, flashbacks generally decrease in intensity and frequency with abstinence from psychedelic drugs.
Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis

A DEA-based stochastic estimation framework is presented to evaluate contextual variables affecting productivity. Conditions are identified under which a two-stage procedure consisting of DEA followed by regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimation in the second stage yields consistent estimators of the impact of contextual variables. Monte Carlo simulations are carried out to compare the performance of our two-stage approach with one-stage and two-stage parametric approaches. Simulation results suggest that DEA-based procedures perform as well as the best parametric method in the estimation of the impact of contextual variables on productivity. Simulation results also indicate that DEA-based procedures perform better than parametric methods in the estimation of individual decision making unit (DMU) productivity.
Disease progression models : A review and comparison

A better understanding of disease progression is beneficial for early diagnosis and appropriate individual therapy. There are many different approaches for statistical modelling of disease progression proposed in the literature, including simple path models up to complex restricted Bayesian networks. Important fields of application are diseases like cancer and HIV. Tumour progression is measured by means of chromosome aberrations, whereas people infected with HIV develop drug resistances because of genetic changes of the HI-virus. These two very different diseases have typical courses of disease progression, which can be modelled partly by consecutive and partly by independent steps. This paper gives an overview of the different progression models and points out their advantages and drawbacks. Different models are compared via simulations to analyse how they work if some of their assumptions are violated. So far, such a comparison has not been done and there are no established methods to compare different progression models. This paper is a step into both directions.
Bi-Directional Vectors from Apex in CNN for Micro-Expression Recognition

The impressive performance of utilizing deep learning or neural network has attracted much attention in both the industry and research communities, especially towards computer vision aspect related applications. Despite its superior capability of learning, generalization and interpretation on various form of input, micro-expression analysis field is yet remains new in applying this kind of computing system in automated expression recognition system. A new feature extractor, BiVACNN is presented in this paper, where it first estimates the optical flow fields from the apex frame, then encode the flow fields features using CNN. Concretely, the proposed method consists of three stages: apex frame acquisition, multivariate features formation and feature learning using CNN. In the multivariate features formation stage, we attempt to derive six distinct features from the apex details, which include: the apex itself, difference between the apex and onset frames, horizontal optical flow, vertical optical flow, magnitude and orientation. It is demonstrated that utilizing the horizontal and vertical optical flow capable to achieve 80% recognition accuracy in CASME II and SMIC-HS databases.
Social Identity Link Across Incomplete Social Information Sources Using Anchor Link Expansion

Social link identification SIL, that is to identify accounts across different online social networks that belong to the same user, is an important task in social network applications. Most existing methods to solve this problem directly applied machine-learning classifiers on features extracted from user’s rich information. In practice, however, only some limited user information can be obtained because of privacy concerns. In addition, we observe the existing methods cannot handle huge amount of potential account pairs from different OSNs. In this paper, we propose an effective SIL method to address the above two challenges by expanding known anchor links (seed account pairs belonging to the same person). In particular, we leverage potentially useful information possessed by the existing anchor link, and then develop a local expansion model to identify new social links, which are taken as a generated anchor link to be used for iteratively identifying additional new social link. We evaluate our method on two most popular Chinese social networks. Experimental results show our proposed method achieves much better performance in terms of both the number of correct account pairs and efficiency.
Business process modelling : Review and framework

A business process is the combination of a set of activities within an enterprise with a structure describing their logical order and dependence whose objective is to produce a desired result. Business process modelling enables a common understanding and analysis of a business process. A process model can provide a comprehensive understanding of a process. An enterprise can be analysed and integrated through its business processes. Hence the importance of correctly modelling its business processes. Using the right model involves taking into account the purpose of the analysis and, knowledge of the available process modelling techniques and tools. The number of references on business modelling is huge, thus making it very time consuming to get an overview and understand many of the concepts and vocabulary involved. The primary concern of this paper is to make that job easier, i.e. review business process modelling literature and describe the main process modelling techniques. Also a framework for classifying business process-modelling techniques according to their purpose is proposed and discussed. r 2003 Elsevier B.V. All rights reserved.
A current and future state of art development of hybrid energy system using wind and PV-solar : A review

The wind and solar energy are omnipresent, freely available, and environmental friendly. The wind energy systems may not be technically viable at all sites because of low wind speeds and being more unpredictable than solar energy. The combined utilization of these renewable energy sources are therefore becoming increasingly attractive and are being widely used as alternative of oil-produced energy. Economic aspects of these renewable energy technologies are sufficiently promising to include them for rising power generation capability in developing countries. A renewable hybrid energy system consists of two or more energy sources, a power conditioning equipment, a controller and an optional energy storage system. These hybrid energy systems are becoming popular in remote area power generation applications due to advancements in renewable energy technologies and substantial rise in prices of petroleum products. Research and development efforts in solar, wind, and other renewable energy technologies are required to continue for, improving their performance, establishing techniques for accurately predicting their output and reliably integrating them with other conventional generating sources. The aim of this paper is to review the current state of the design, operation and control requirement of the stand-alone PV solar–wind hybrid energy systems with conventional backup source i.e. diesel or grid. This Paper also highlights the future developments, which have the potential to increase the economic attractiveness of such systems and their acceptance by the user.
A comparative analysis of 5G mmWave antenna arrays on different substrate technolgies

In this work, we perform a comparative analysis of the impact of substrate technologies on the performance of 28 GHz antennas for 5G applications. For this purpose, we model, simulate, analyze and compare 2×2 patch antenna arrays on five substrate technologies typically used for manufacturing integrated antennas. The impact of these substrates on the impedance bandwidth, efficiency and gain of the antennas is quantified. Finally, the antennas are fabricated and measured. Excellent correlation is obtained between measurement and simulation results.
Is This a Bug or an Obsolete Test?

In software evolution, developers typically need to identify whether the failure of a test is due to a bug in the source code under test or the obsoleteness of the test code when they execute a test suite. Only after finding the cause of a failure can developers determine whether to fix the bug or repair the obsolete test. Researchers have proposed several techniques to automate test repair. However, test-repair techniques typically assume that test failures are always due to obsolete tests. Thus, such techniques may not be applicable in real world software evolution when developers do not know whether the failure is due to a bug or an obsolete test. To know whether the cause of a test failure lies in the source code under test or in the test code, we view this problem as a classification problem and propose an automatic approach based on machine learning. Specifically, we target Java software using the JUnit testing framework and collect a set of features that may be related to failures of tests. Using this set of features, we adopt the Best-first Decision Tree Learning algorithm to train a classifier with some existing regression test failures as training instances. Then, we use the classifier to classify future failed tests. Furthermore, we evaluated our approach using two Java programs in three scenarios (within the same version, within different versions of a program, and between different programs), and found that our approach can effectively classify the causes of failed tests.
Cross-lingual information retrieval systems

In this work, we will explore different approaches used in Cross-Lingual Information Retrieval (CLIR) systems. Mainly, CLIR systems which use statistical machine translation (SMT) systems to translate queries into collection language. This will include using SMT systems as a black box or as a white box, also the SMT systems that are tuned towards better CLIR performance. After that, we will present our approach to rerank the alternative translations using machine learning regression model. This includes also introducing our set of features which we used to train the model. After that, we adapt this reranker for new languages. We also present our query expansion approach using word-embeddings model that is trained on medical data. Finally we reinvestigate translating the document collection into query language, then we present our future work.
How Gen Y and Boomers will reshape your agenda.

When it comes to workplace preferences, Generation Y workers closely resemble Baby Boomers. Because these two huge cohorts now coexist in the workforce, their shared values will hold sway in the companies that hire them. The authors, from the Center for Work-Life Policy, conducted two large-scale surveys that reveal those values. Gen Ys and Boomers are eager to contribute to positive social change, and they seek out workplaces where they can do that. They expect flexibility and the option to work remotely, but they also want to connect deeply with colleagues. They believe in employer loyalty but desire to embark on learning odysseys. Innovative firms are responding by crafting reward packages that benefit both generations of workers--and their employers.
Fraud Detection Using an Adaptive Neuro-Fuzzy Inference System in Mobile Telecommunication Networks

GSM (Global Services of Mobile Communications) 1800 licenses were granted in the beginning of the 2000’s in Turkey. Especially in the installation phase of the wireless telecom services, fraud usage can be an important source of revenue loss. Fraud can be defined as a dishonest or illegal use of services, with the intention to avoid service charges. Fraud detection is the name of the activities to identify unauthorized usage and prevent losses for the mobile network operators’. Mobile phone user’s intentions may be predicted by the call detail records (CDRs) by using data mining (DM) techniques. This study compares various data mining techniques to obtain the best practical solution for the telecom fraud detection and offers the Adaptive Neuro Fuzzy Inference (ANFIS) method as a means to efficient fraud detection. In the test run, shown that ANFIS has provided sensitivity of 97% and specificity of 99%, where it classified 98.33% of the instances correctly.
PKUSUMSUM : A Java Platform for Multilingual Document Summarization

PKUSUMSUM is a Java platform for multilingual document summarization, and it supports multiple languages, integrates 10 automatic summarization methods, and tackles three typical summarization tasks. The summarization platform has been released and users can easily use and update it. In this paper, we make a brief description of the characteristics, the summarization methods, and the evaluation results of the platform, and also compare PKUSUMSUM with other summarization toolkits.