nils-herrmann/clean_scirepeval huggingface dataset:
Design of a Monitor for Detecting Money Laundering and Terrorist Financing

Money laundering is a global problem that affects all countries to various degrees. Although, many countries take benefits from money laundering, by accepting the money from laundering but keeping the crime abroad, at the long run, “money laundering attracts crime”. Criminals come to know a country, create networks and eventually also locate their criminal activities there. Most financial institutions have been implementing antimoney laundering solutions (AML) to fight investment fraud. The key pillar of a strong Anti-Money Laundering system for any financial institution depends mainly on a well-designed and effective monitoring system. The main purpose of the Anti-Money Laundering transactions monitoring system is to identify potential suspicious behaviors embedded in legitimate transactions. This paper presents a monitor framework that uses various techniques to enhance the monitoring capabilities. This framework is depending on rule base monitoring, behavior detection monitoring, cluster monitoring and link analysis based monitoring. The monitor detection processes are based on a money laundering deterministic finite automaton that has been obtained from their corresponding regular expressions. Index Terms – Anti Money Laundering system, Money laundering monitoring and detecting, Cycle detection monitoring, Suspected Link monitoring.
MACHINING WITH ROBOTS : A CRITICAL REVIEW

Conventional material removal techniques, like CNC milling, have been proven to be able to tackle nearly any machining challenge. On the other hand, the major drawback of using conventional CNC machines is the restricted working area and their produced shape limitation limitations. From a conceptual point of view, industrial robot technology could provide an excellent base for machining being both flexible and cost efficient. However, industrial machining robots lack absolute positioning accuracy, are unable to reject/absorb disturbances in terms of process forces and lack reliable programming and simulation tools to ensure right first time machining, at production startups. This paper reviews the penetration of industrial robots in the challenging field of machining.
COINVENT: Towards a Computational Concept Invention Theory

We aim to develop a computationally feasible, cognitivelyinspired, formal model of concept invention, drawing on Fauconnier and Turner’s theory of conceptual blending, and grounding it on a sound mathematical theory of concepts. Conceptual blending, although successfully applied to describing combinational creativity in a varied number of fields, has barely been used at all for implementing creative computational systems, mainly due to the lack of sufficiently precise mathematical characterisations thereof. The model we will define will be based on Goguen’s proposal of a Unified Concept Theory, and will draw from interdisciplinary research results from cognitive science, artificial intelligence, formal methods and computational creativity. To validate our model, we will implement a proof of concept of an autonomous computational creative system that will be evaluated in two testbed scenarios: mathematical reasoning and melodic harmonisation. We envisage that the results of this project will be significant for gaining a deeper scientific understanding of creativity, for fostering the synergy between understanding and enhancing human creativity, and for developing new technologies for autonomous creative systems.
Data Augmentation for Time Series Classification using Convolutional Neural Networks

Time series classification has been around for decades in the data-mining and machine learning communities. In this paper, we investigate the use of convolutional neural networks (CNN) for time series classification. Such networks have been widely used in many domains like computer vision and speech recognition, but only a little for time series classification. We design a convolutional neural network that consists of two convolutional layers. One drawback with CNN is that they need a lot of training data to be efficient. We propose two ways to circumvent this problem: designing data-augmentation techniques and learning the network in a semi-supervised way using training time series from different datasets. These techniques are experimentally evaluated on a benchmark of time series datasets.
Occupational therapy service use among people aging with multiple sclerosis.

We examined the use of occupational therapy services in a sample of people aging with multiple sclerosis (MS).



A total of 1,282 people with MS, ages 45 to 90, participated in telephone interviews to identify unmet health-related service needs. Occupational therapy was 1 of 22 services examined. Proportional odds models were used to examine factors associated with how recently services were used.



Four hundred eighty-four participants (38.2%) had used occupational therapy services at some point since their diagnosis; 211 had used these services in the year before the interview. Recent users identified occupational therapy services as important to health and well-being. Satisfaction with services was high. Greater activity limitations and living in an urban or suburban area were associated with more recent use of occupational therapy services.



The results raise questions about what constitutes appropriate levels of occupational therapy service use and how to ensure that these levels are achieved.
Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

With the development of Web 2.0, many studies have tried to analyze tourist behavior utilizing user-generated contents. The primary purpose of this study is to propose a topic-based sentiment analysis approach, including a polarity classification and an emotion classification. We use the Latent Dirichlet Allocation model to extract topics from online travel review data and analyze the sentiments and emotions for each topic with our proposed approach. The top frequent words are extracted for each topic from online reviews on Ctrip.com. By comparing the relative importance of each topic, we conclude that many tourists prefer to provide “suggestion” reviews. In particular, we propose a new approach to classify the emotions of online reviews at the topic level utilizing an emotion lexicon, focusing on specific emotions to analyze customer complaints. The results reveal that attraction “management” obtains most complaints. These findings may provide useful insights for the development of attractions and the measurement of online destination image. Our proposed method can be used to analyze reviews from many online platforms and domains.
Learning Selfie-Friendly Abstraction from Artistic Style Images

Artistic style transfer can be thought as a process to generate different versions of abstraction of the original image. However, most of the artistic style transfer operators are not optimized for human faces thus mainly suffers from two undesirable features when applying them to selfies. First, the edges of human faces may unpleasantly deviate from the ones in the original image. Second, the skin color is far from faithful to the original one which is usually problematic in producing quality selfies. In this paper, we take a different approach and formulate this abstraction process as a gradient domain learning problem. We aim to learn a type of abstraction which not only achieves the specified artistic style but also circumvents the two aforementioned drawbacks thus highly applicable to selfie photography. We also show that our method can be directly generalized to videos with high inter-frame consistency. Our method is also robust to non-selfie images, and the generalization to various kinds of real-life scenes is discussed. We will make our code publicly available.
CO 2 EMISSION AND CARBON CAPTURE FOR COAL FIRED POWER PLANTS IN MALAYSIA AND INDONESIA

Coal consumption in Malaysia and Indonesia is growing at the rate of 9.7 and 4.7% per year since 2002, respectively. The increase in coal utilization usually tallies fairly well with the increase in CO2 emission. The present study attempts at predicting the emissions of CO2 from coal fired power plants from 2005 until 2020. The paper also analyzes the potential of carbon capture (CC) program as a source of foreign direct investment in Malaysia and Indonesia. The perceived emission rate is based on the percentage of coal for energy mix and coal consumption for electricity generation. Results from the study shows that CO2 emission from coal fired power plants will grow at 4.1% per year to reach 98 million tons in Malaysia and 171 million tons in Indonesia by 2020. It is learnt that adsorption technology can be applied in coal fired power plants to reduce CO2 emissions in Malaysia and Indonesia. Integrated Gasification Combined Cycle (IGCC) power plants incooperating a pre-combustion capture with the adsorption technology is one of the available options for new plants in Malaysia and Indonesia.
MQTT-Topic Naming Criteria of Open Data for Smart Cities

Nowadays, the need of sharing of dynamic open data to improve the cities intelligence are increased significantly. To share these data, MQTT, the lightweight messaging publish/subscribe protocol is one of most popular protocols. The sharing of data via MQTT includes two components; topic and message where the topic element is used to define which the subscriber are received which data, meanwhile the data are presented in the message element. However, There are none of criteria or standard of topic naming to share the open data via MQTT. It makes the sharing of open data is too difficult with MQTT protocol. In this work, we propose MQTT-Topic Naming Criteria of Open Data for Smart Cities (MTNC) to be the criteria of topic naming for the open data of any city. The proposed criteria used in both processes; publishing and subscription of MQTT messages. Then, we evaluate the impact of MTNC on the data selecting and searching of the subscribers who not know the MTNC. The results show that topic naming with MTNC is more effective than the topic naming without MTNC in selecting and searching topics of the subscribers. The average numbers of the subscribers can found their needed data when they select the data from the list of topics are 7/10 and 3/10 time with and without MTNC respectively. Moreover, the experimental results show that the most keywords that the subscribers often use in their topic searching are the objective of data presented in MTNC.
How do people manage their digital photographs?

In this paper we present and discuss the findings of a study that investigated how people manage their collections of digital photographs. The six-month, 13-participant study included interviews, questionnaires, and analysis of usage statistics gathered from an instrumented digital photograph management tool called Shoebox. Alongside simple browsing features such as folders, thumbnails and timelines, Shoebox has some advanced multimedia features: content-based image retrieval and speech recognition applied to voice annotations. Our results suggest that participants found their digital photos much easier to manage than their non-digital ones, but that this advantage was almost entirely due to the simple browsing features. The advanced features were not used very often and their perceived utility was low. These results should help to inform the design of improved tools for managing personal digital photographs.
Relation of shame and guilt to constructive versus destructive responses to anger across the lifespan.

This study explored the relation of shame proneness and guilt proneness to constructive versus destructive responses to anger among 302 children (Grades 4-6), adolescents (Grades 7-11), 176 college students, and 194 adults. Across all ages, shame proneness was clearly related to maladaptive response to anger, including malevolent intentions; direct, indirect, and displaced aggression; self-directed hostility; and negative long-term consequences. In contrast, guilt proneness was associated with constructive means of handling anger, including constructive intentions, corrective action and non-hostile discussion with the target of the anger, cognitive reappraisals of the target's role, and positive long-term consequences. Escapist-diffusing responses showed some interesting developmental trends. Among children, these dimensions were positively correlated with guilt and largely unrelated to shame; among older participants, the results were mixed.
Neural Importance Sampling

We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent components estimation (NICE), which we extend in numerous ways to improve performance and enable its application to integration problems. First, we introduce piecewise-polynomial coupling transforms that greatly increase the modeling power of individual coupling layers. Second, we propose to preprocess the inputs of neural networks using one-blob encoding, which stimulates localization of computation and improves inference. Third, we derive a gradient-descent-based optimization for the KL and the χ 2 divergence for the specific application of Monte Carlo integration with unnormalized stochastic estimates of the target distribution. Our approach enables fast and accurate inference and efficient sample generation independently of the dimensionality of the integration domain. We show its benefits on generating natural images and in two applications to light-transport simulation: first, we demonstrate learning of joint path-sampling densities in the primary sample space and importance sampling of multi-dimensional path prefixes thereof. Second, we use our technique to extract conditional directional densities driven by the product of incident illumination and the BSDF in the rendering equation, and we leverage the densities for path guiding. In all applications, our approach yields on-par or higher performance than competing techniques at equal sample count.
Cyber–Physical System Security for the Electric Power Grid

The development of a trustworthy smart grid requires a deeper understanding of potential impacts resulting from successful cyber attacks. Estimating feasible attack impact requires an evaluation of the grid's dependency on its cyber infrastructure and its ability to tolerate potential failures. A further exploration of the cyber-physical relationships within the smart grid and a specific review of possible attack vectors is necessary to determine the adequacy of cybersecurity efforts. This paper highlights the significance of cyber infrastructure security in conjunction with power application security to prevent, mitigate, and tolerate cyber attacks. A layered approach is introduced to evaluating risk based on the security of both the physical power applications and the supporting cyber infrastructure. A classification is presented to highlight dependencies between the cyber-physical controls required to support the smart grid and the communication and computations that must be protected from cyber attack. The paper then presents current research efforts aimed at enhancing the smart grid's application and infrastructure security. Finally, current challenges are identified to facilitate future research efforts.
Transformation knowledge in pattern analysis with kernel methods: distance and integration kernels

Modern techniques for data analysis and machine learning are so called kernel methods. The most famous and successful one is represented by the support vector machine (SVM) for classification or regression tasks. Further examples are kernel principal component analysis for feature extraction or other linear classifiers like the kernel perceptron. The fundamental ingredient in these methods is the choice of a kernel function, which computes a similarity measure between two input objects. For good generalization abilities of a learning algorithm it is indispensable to incorporate problem-specific a-priori knowledge into the learning process. The kernel function is an important element for this. This thesis focusses on a certain kind of a-priori knowledge namely transformation knowledge. This comprises explicit knowledge of pattern variations that do not or only slightly change the pattern’s inherent meaning e.g. rigid movements of 2D/3D objects or transformations like slight stretching, shifting, rotation of characters in optical character recognition etc. Several methods for incorporating such knowledge in kernel functions are presented and investigated. 1. Invariant distance substitution kernels (IDS-kernels): In many practical questions the transformations are implicitly captured by sophisticated distance measures between objects. Examples are nonlinear deformation models between images. Here an explicit parameterization would require an arbitrary number of parameters. Such distances can be incorporated in distanceand inner-product-based kernels. 2. Tangent distance kernels (TD-kernels): Specific instances of IDS-kernels are investigated in more detail as these can be efficiently computed. We assume differentiable transformations of the patterns. Given such knowledge, one can construct linear approximations of the transformation manifolds and use these efficiently for kernel construction by suitable distance functions. 3. Transformation integration kernels (TI-kernels): The technique of integration over transformation groups for feature extraction can be extended to kernel functions and more general group, non-group, discrete or continuous transformations in a suitable way. Theoretically, these approaches differ in the way the transformations are represented and in the adjustability of the transformation extent. More fundamentally, kernels from category 3 turn out to be positive definite, kernels of types 1 and 2 are not positive definite, which is generally required for being usable in kernel methods.
A Comparative Study of PI , IP , Fuzzy and Neuro-Fuzzy Controllers for Speed Control of DC Motor Drive

This paper presents a comparative study of various controllers for the speed control of DC motor. The most commonly used controller for the speed control of dc motor is ProportionalIntegral (P-I) controller. However, the P-I controller has some disadvantages such as: the high starting overshoot, sensitivity to controller gains and sluggish response due to sudden disturbance. So, the relatively new Integral-Proportional (I-P) controller is proposed to overcome the disadvantages of the P-I controller. Further, two Fuzzy logic based controllers namely; Fuzzy control and Neuro-fuzzy control are proposed and the performance these controllers are compared with both P-I and I-P controllers. Simulation results are presented and analyzed for all the controllers. It is observed that fuzzy logic based controllers give better responses than the traditional P-I as well as I-P controller for the speed control of dc motor drives. Proportional-Integral (P-I) controller, IntegralProportional (I-P) controller, Fuzzy logic control, Neuro-fuzzy control, Speed control, DC Motor drive.
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model

We propose Efficient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods. In ENAS, a controller learns to discover neural network architectures by searching for an optimal path within a larger model. The controller is trained with policy gradient to select a path that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected path is trained to minimize the cross entropy loss. On the Penn Treebank dataset, ENAS can discover a novel architecture thats achieves a test perplexity of 57.8, which is state-of-the-art among automatic model design methods on Penn Treebank. On the CIFAR-10 dataset, ENAS can design novel architectures that achieve a test error of 2.89%, close to the 2.65% achieved by standard NAS (Zoph et al., 2017). Most importantly, our experiments show that ENAS is more than 10x faster and 100x less resource-demanding than NAS.
Text summarization from legal documents: a survey

Enormous amount of online information, available in legal domain, has made legal text processing an important area of research. In this paper, we attempt to survey different text summarization techniques that have taken place in the recent past. We put special emphasis on the issue of legal text summarization, as it is one of the most important areas in legal domain. We start with general introduction to text summarization, briefly touch the recent advances in single and multi-document summarization, and then delve into extraction based legal text summarization. We discuss different datasets and metrics used in summarization and compare performances of different approaches, first in general and then focused to legal text. we also mention highlights of different summarization techniques. We briefly cover a few software tools used in legal text summarization. We finally conclude with some future research directions.
Potential of remote sensing to predict species invasions A modelling perspective

Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results.
The Testing Effect Is Alive and Well with Complex Materials

Van Gog and Sweller (2015) claim that there is no testing effect—no benefit of practicing retrieval—for complex materials. We show that this claim is incorrect on several grounds. First, Van Gog and Sweller’s idea of “element interactivity” is not defined in a quantitative, measurable way. As a consequence, the idea is applied inconsistently in their literature review. Second, none of the experiments on retrieval practice with worked-example materials manipulated element interactivity. Third, Van Gog and Sweller’s literature review omitted several studies that have shown retrieval practice effects with complex materials, including studies that directly manipulated the complexity of the materials. Fourth, the experiments that did not show retrieval practice effects, which were emphasized by Van Gog and Sweller, either involved retrieval of isolated words in individual sentences or required immediate, massed retrieval practice. The experiments failed to observe retrieval practice effects because of the retrieval tasks, not because of the complexity of the materials. Finally, even though the worked-example experiments emphasized by Van Gog and Sweller have methodological problems, they do not show strong evidence favoring the null. Instead, the data provide evidence that there is indeed a small positive effect of retrieval practice with worked examples. Retrieval practice remains an effective way to improve meaningful learning of complex materials.