nils-herrmann/clean_scirepeval huggingface dataset:
Learning Conditional Generative Models for Temporal Point Processes

Estimating the future event sequence conditioned on current observations is a long-standing and challenging task in temporal analysis. On one hand for many real-world problems the underlying dynamics can be very complex and often unknown. This renders the traditional parametric point process models often fail to fit the data for their limited capacity. On the other hand, long-term prediction suffers from the problem of bias exposure where the error accumulates and propagates to future prediction. Our new model builds upon the sequence to sequence (seq2seq) prediction network. Compared with parametric point process models, its modeling capacity is higher and has better flexibility for fitting real-world data. The main novelty of the paper is to mitigate the second challenge by introducing the likelihood-free loss based on Wasserstein distance between point processes, besides negative maximum likelihood loss used in the traditional seq2seq model. Wasserstein distance, unlike KL divergence i.e. MLE loss, is sensitive to the underlying geometry between samples and can robustly enforce close geometry structure between them. This technique is proven able to improve the vanilla seq2seq model by a notable margin on various tasks.
Discovering evolutionary theme patterns from text: an exploration of temporal text mining

Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has many applications in multiple domains, such as summarizing events in news articles and revealing research trends in scientific literature. In this paper, we study a particular TTM task -- discovering and summarizing the evolutionary patterns of themes in a text stream. We define this new text mining problem and present general probabilistic methods for solving this problem through (1) discovering latent themes from text; (2) constructing an evolution graph of themes; and (3) analyzing life cycles of themes. Evaluation of the proposed methods on two different domains (i.e., news articles and literature) shows that the proposed methods can discover interesting evolutionary theme patterns effectively.
GPS and Map matching based vehicle accident detection system

GPS (Global Positioning System) has become an integral part of a vehicle system which provides speed, time, direction etc besides the navigation data. Speed is one of the primary attributes of vehicle accident. Many lives could have been saved if emergency service could receive accident information timely. This paper proposes to detect an accident from the map matched position of a vehicle by utilizing the GPS speed data and map matching algorithm and send accident location to an Alert Service Center. The GPS provides speed and position in every 0.1 second. The position data will be used in the map matching algorithm to locate the vehicle on the road. The present speed will be compared with the previous speed in every 0.1 second through a Microcontroller Unit. Whenever the speed will be falling below the safe calculated threshold speed, the system will generate an accident situation. It will check the vehicle location from map matching module and generate an accident situation if the vehicle is found outside the road network. This will reduce the false accident detection drastically. The map matched accident location is then sent by utilizing the GSM network. The proposed system will save many accident victims with timely rescue.
MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation

Radiologist is ”doctor’s doctor”, biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) and U-Net, deep convolutional networks (DNNs) have made significant contributions in biomedical image segmentation applications. In this paper, based on U-Net, we propose MDUnet, a multiscale densely connected U-net for biomedical image segmentation. we propose three different multi-scale dense connections for U shaped architectures encoder, decoder and across them. The highlights of our architecture is directly fuses the neighboring different scale feature maps from both higher layers and lower layers to strengthen feature propagation in current layer. Which can largely improves the information flow encoder, decoder and across them. Multi-scale dense connections, which means containing shorter connections between layers close to the input and output, also makes much deeper U-net possible. We adopt the optimal model based on the experiment and propose a novel Multi-scale Dense U-Net (MDU-Net) architecture with quantization. Which reduce overfitting in MDU-Net for better accuracy. We evaluate our purpose model on the MICCAI 2015 Gland Segmentation dataset (GlaS). The three multi-scale dense connections improve Unet performance by up to 1.8% on test A and 3.5% on test B in the MICCAI Gland dataset. Meanwhile the MDU-net with quantization achieves the superiority over U-Net performance by up to 3% on test A and 4.1% on test B.
Stimulation of lignocellulosic biomass hydrolysis by proteins of glycoside hydrolase family 61: structure and function of a large, enigmatic family.

Currently, the relatively high cost of enzymes such as glycoside hydrolases that catalyze cellulose hydrolysis represents a barrier to commercialization of a biorefinery capable of producing renewable transportable fuels such as ethanol from abundant lignocellulosic biomass. Among the many families of glycoside hydrolases that catalyze cellulose and hemicellulose hydrolysis, few are more enigmatic than family 61 (GH61), originally classified based on measurement of very weak endo-1,4-beta-d-glucanase activity in one family member. Here we show that certain GH61 proteins lack measurable hydrolytic activity by themselves but in the presence of various divalent metal ions can significantly reduce the total protein loading required to hydrolyze lignocellulosic biomass. We also solved the structure of one highly active GH61 protein and find that it is devoid of conserved, closely juxtaposed acidic side chains that could serve as general proton donor and nucleophile/base in a canonical hydrolytic reaction, and we conclude that the GH61 proteins are unlikely to be glycoside hydrolases. Structure-based mutagenesis shows the importance of several conserved residues for GH61 function. By incorporating the gene for one GH61 protein into a commercial Trichoderma reesei strain producing high levels of cellulolytic enzymes, we are able to reduce by 2-fold the total protein loading (and hence the cost) required to hydrolyze lignocellulosic biomass.
Application of CFD in building performance simulation for the outdoor environment : an overview

This paper provides an overview of the application of CFD in building performance simulation for the outdoor environment, focused on four topics: (1) pedestrian wind environment around buildings, (2) wind-driven rain on building facades, (3) convective heat transfer coefficients at exterior building surfaces, and (4) air pollutant dispersion around buildings. For each topic, its background, the need for CFD, an overview of some past CFD studies, a discussion about accuracy and some perspectives for practical application are provided. The paper indicates that for all four topics, CFD offers considerable advantages compared to wind tunnel modelling or (semi-)empirical formulae because it can provide detailed whole-flow field data under fully controlled conditions and without similarity constraints. The main limitations are the deficiencies of steady RANS modelling, the increased complexity and computational expense of LES and the requirement of systematic and time-consuming CFD solution verification and validation studies.
Corpora – the best possible solution for tracking rare phenomena in underresourced languages : clitics in Bosnian , Croatian and Serbian

Complex linguistic phenomena, such as Clitic Climbing in Bosnian, Croatian and Serbian, are often described intuitively, only from the perspective of the main tendency. In this paper, we argue that web corpora currently offer the best source of empirical material for studying Clitic Climbing in BCS. They thus allow the most accurate description of this phenomenon, as less frequent constructions can be tracked only in big, well-annotated data sources. We compare the properties of web corpora for BCS with traditional sources and give examples of studies on CC based on web corpora. Furthermore, we discuss problems related to web corpora and suggest some improvements for
A Digital Security System with Door Lock System Using RFID Technology

RFID, Radio Frequency Identification is an inexpensive technology, can be implemented for several applications such as security, asset tracking, people tracking, inventory detection, access control applications. The main objective of this paper is to design and implement a digital security system which can deploy in secured zone where only authentic person can be entered. We implemented a security system containing door locking system using passive type of RFID which can activate, authenticate, and validate the user and unlock the door in real time for secure access. The advantage of using passive RFID is that it functions without a battery and passive tags are lighter and are less expensive than the active tags. A centralized system manages the controlling, transaction and operation task. The door locking system functions in real time as the door open quickly when user put their tag in contact of reader. The system also creates a log containing check-in and check-out of each user along with basic information of user.
YAFIM: A Parallel Frequent Itemset Mining Algorithm with Spark

The frequent itemset mining (FIM) is one of the most important techniques to extract knowledge from data in many real-world applications. The Apriori algorithm is the widely-used algorithm for mining frequent itemsets from a transactional dataset. However, the FIM process is both data-intensive and computing-intensive. On one side, large scale data sets are usually adopted in data mining nowadays, on the other side, in order to generate valid information, the algorithm needs to scan the datasets iteratively for many times. These make the FIM algorithm very time-consuming over big data. The parallel and distributed computing is effective and mostly-used strategy for speeding up large scale dataset algorithms. However, the existing parallel Apriori algorithms implemented with the MapReduce model are not efficient enough for iterative computation. In this paper, we proposed YAFIM (Yet Another Frequent Itemset Mining), a parallel Apriori algorithm based on the Spark RDD framework -- a specially-designed in-memory parallel computing model to support iterative algorithms and interactive data mining. Experimental results show that, compared with the algorithms implemented with MapReduce, YAFIM achieved 18× speedup in average for various benchmarks. Especially, we apply YAFIM in a real-world medical application to explore the relationships in medicine. It outperforms the MapReduce method around 25 times.
Seeded watershed cut uncertainty estimators for guided interactive segmentation

Watershed cuts are among the fastest segmentation algorithms and therefore well suited for interactive segmentation of very large 3D data sets. To minimize the number of user interactions (“seeds”) required until the result is correct, we want the computer to actively query the human for input at the most critical locations, in analogy to active learning. These locations are found by means of suitable uncertainty measures. We propose various such measures for watershed cuts along with a theoretical analysis of some of their properties. Extensive evaluation on two types of 3D electron microscopic volumes of neural tissue shows that measures which estimate the non-local consequences of new user inputs achieve performance close to an oracle endowed with complete knowledge of the ground truth.
Embedded Bandits for Large-Scale Black-Box Optimization

Random embedding has been applied with empirical success to large-scale black-box optimization problems with low effective dimensions. This paper proposes the EMBEDDEDHUNTER algorithm, which incorporates the technique in a hierarchical stochastic bandit setting, following the optimism in the face of uncertainty principle and breaking away from the multiple-run framework in which random embedding has been conventionally applied similar to stochastic black-box optimization solvers. Our proposition is motivated by the bounded mean variation in the objective value for a low-dimensional point projected randomly into the decision space of Lipschitz-continuous problems. In essence, the EMBEDDEDHUNTER algorithm expands optimistically a partitioning tree over a low-dimensional—equal to the effective dimension of the problem—search space based on a bounded number of random embeddings of sampled points from the low-dimensional space. In contrast to the probabilistic theoretical guarantees of multiple-run randomembedding algorithms, the finite-time analysis of the proposed algorithm presents a theoretical upper bound on the regret as a function of the algorithm’s number of iterations. Furthermore, numerical experiments were conducted to validate its performance. The results show a clear performance gain over recently proposed random embedding methods for large-scale problems, provided the intrinsic dimensionality is low.
A Language and Program for Complex Bayesian Modelling

A Language and Program for Complex Bayesian Modelling Author(s): W. R. Gilks, A. Thomas and D. J. Spiegelhalter Source: Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 43, No. 1, Special Issue: Conference on Practical Bayesian Statistics, 1992 (3) (1994), pp. 169-177 Published by: for the Wiley Royal Statistical Society Stable URL: http://www.jstor.org/stable/2348941 Accessed: 19-08-2014 17:40 UTC
Crowd Geofencing

Geofencing mechanisms allow for timely message delivery to the visitors of predefined target areas. However, conventional geofencing approaches poorly support mobile data collection scenarios in which experts need in situ assistance. In this paper, we propose crowd geofencing environments, in which a large number of crowdworkers generate geofences to support mobile experts. As a first step to open up the possibilities of crowd geofencing, we have tested its feasibility by collecting more than one thousand geofences in an unfamiliar city prior to the visit to look into urban water and air quality issues. Our experience has revealed the strengths and weaknesses of crowd geofencing in terms of geofence quality and crowd-powered situated actions.
Data Provenance in the Internet of Things

There is a need to create a trusted and secure IoT environment to share information, create knowledge and perform digital transactions. Trustworthy data collection, mining and fusion is vital for the successful widespread acceptance of IoT applications. This requires not only accurate, secure, and precise data collection; but also provisioning of data provenance throughout the whole life-cycle of the IoT device. To this end, this paper introduces a provenance-based trust management solution which helps in establishing a trust relationship among communicating devices in the IoT. This work extends the Internet of Things Management Platform (IoT-MP) by assuring data provenance. Thus complementing the previous IoT-MP capabilities in preserving the privacy of users in the IoT.
Process Mining and Security: Visualization in Database Intrusion Detection

Nowadays, more and more organizations keep their valuable and sensitive data in Database Management Systems (DBMSs). The traditional database security mechanisms such as access control mechanisms, authentication, data encryption technologies do not offer a strong enough protection against the exploitation of vulnerabilities (e.g. intrusions) in DBMSs from insiders. Intrusion detection systems recently proposed in the literature focus on statistical approaches, which are not intuitive. Our research is the first ever effort to use process mining modeling low-level event logs for database intrusion detection. We have proposed a novel approach for visualizing database intrusion detection using process mining techniques. Our experiments showed that intrusion detection visualization will be able to help security officers who might not know deeply the complex system, identify the true positive detection and eliminate the false positive results.
Compact Personalized Models for Neural Machine Translation

We propose and compare methods for gradientbased domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture—combining a state-of-the-art self-attentive model with compact domain adaptation—provides high quality personalized machine translation that is both space and time efficient.
Situation and Perspective of Knowledge Engineering

Knowledge Engineering was in the past primary concerned with building and developing knowledge-based systems, an objective which puts Knowledge Engineering in a niche of the world-wide research efforts at best. This has changed dramatically: Knowledge Engineering is now a key technology in the upcoming knowledge society. Companies are recognizing knowledge as their key assets, which have to be exploited and protected in a fast changing, global and competitive economy. This situation has led to the application of Knowledge Engineering techniques in Knowledge Management. The demand for more efficient (business to) business processes requires the interconnection and interoperation of different information systems. But information integration is not an algorithmic task that is easy to solve: much knowledge is required to resolve the semantic differences of data residing in two information systems. Thus Knowledge Engineering has become a major technqiue for information integration. And, last but not least the fast growing World Wide Web generates an ever increasing demand for more efficient knowledge exploitation and creation techniques. Here again Knowledge Engineering technologies may become the key technology for solving the problem. In this paper we discuss these recent developments and describe our view of the future.
Mixing augmented reality and EEG technology to create an unique learning tool for construction process

The Augmented and Virtual Reality environments are proved to create excellent interactive tools for education. The development of relatively low-cost AR and VR visualization devices permitted to these technologies to form a part of educational process. By combining these immersive technologies with the EEG lecture in real-time it is possible to increase the efficiency of the digital learning tools. In the proposed study, we create an interactive immersive educational tool named “AR Constructor”, which fuse the Augmented Reality and EEG technology for the purpose of further instruction in the field of building constructions.
AN APPROACH TO THE PSYCHOLOGY OF INSTRUCTION

The relationship between a theory of learning and a theory of instruction is discussed. Examples are presented that illustrate how to proceed from a theoretical description of the learning process to the specification of an optimal strategy for carrying out instruction. The examples deal with fairly simple learning tasks and are admittedly of limited generality. Nevertheless, they clearly define the steps necessary for deriving and testing instructional strategies, thereby providing a set of procedures for analyzing more complex problems. The parameter-dependent optimization strategies are of particular importance because they take into account individual differences among learners as well as differences in difficulty among curriculum units. Experimental evaluations indicate that the parameter-dependent strategies lead to major gains in learning, when compared with strategies that do not take individual differences into account.
A Flooding Warning System based on RFID Tag Array for Energy Facility

Passive radio-frequency identification (RFID) tags are widely used due to its economic cost and satisfactory performance. So far, passive RFID tags are mostly applied to identify certain objects, such as underground pipe identification under buried conditions. However, there is lack of study on the application of buried tags for further application. In this paper, the performances of buried RFID tags are studied to develop a flooding warning system based on RFID tag array for energy facility such as power stations. In this study, the corresponding signal strength received by the RFID reader is evaluated when the RFID tags are buried by seven materials respectively. The results show that flood warning detector can be constructed using passive RFID tag array and reader.
On the unification of line processes, outlier rejection, and robust statistics with applications in early vision

The modeling of spatial discontinuities for problems such as surface recovery, segmentation, image reconstruction, and optical flow has been intensely studied in computer vision. While “line-process” models of discontinuities have received a great deal of attention, there has been recent interest in the use of robust statistical techniques to account for discontinuities. This paper unifies the two approaches. To achieve this we generalize the notion of a “line process” to that of an analog “outlier process” and show how a problem formulated in terms of outlier processes can be viewed in terms of robust statistics. We also characterize a class of robust statistical problems for which an equivalent outlier-process formulation exists and give a straightforward method for converting a robust estimation problem into an outlier-process formulation. We show how prior assumptions about the spatial structure of outliers can be expressed as constraints on the recovered analog outlier processes and how traditional continuation methods can be extended to the explicit outlier-process formulation. These results indicate that the outlier-process approach provides a general framework which subsumes the traditional line-process approaches as well as a wide class of robust estimation problems. Examples in surface reconstruction, image segmentation, and optical flow are presented to illustrate the use of outlier processes and to show how the relationship between outlier processes and robust statistics can be exploited. An appendix provides a catalog of common robust error norms and their equivalent outlier-process formulations.
Test setup for multi-finger gripper control based on robot operating system (ROS)

This paper presents the concept for a test setup to prototype control algorithms for a multi-finger gripper. The human-robot interface has to provide enough degrees of freedom (DOF) to intuitively control advanced gripper and to accomplish this task a simple sensor glove equipped with flex and force sensors has been prepared for this project. The software architecture has to support both real hardware and simulation, as well as flexible communication standards, therefore, ROS architecture was employed in this matter. Paper presents some preliminary results for using sensor glove and simulated model of the three finger gripper.