nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive video-based algorithm for accident detection on highways

For the past few decades, automatic accident detection, especially using video analysis, has become a very important subject. It is important not only for traffic management but also, for Intelligent Transportation Systems (ITS) through its contribution to avoid the escalation of accidents especially on highways. In this paper a novel vision-based road accident detection algorithm on highways and expressways is proposed. This algorithm is based on an adaptive traffic motion flow modeling technique, using Farneback Optical Flow for motions detection and a statistic heuristic method for accident detection. The algorithm was applied on a set of collected videos of traffic and accidents on highways. The results prove the efficiency and practicability of the proposed algorithm using only 240 frames for traffic motion modeling. This method avoids to utilization of a large database while adequate and common accidents videos benchmarks do not exist.
A Correspondence Between Random Neural Networks and Statistical Field Theory

A number of recent papers have provided evidence that practical design questions about neural networks may be tackled theoretically by studying the behavior of random networks. However, until now the tools available for analyzing random neural networks have been relatively ad hoc. In this work, we show that the distribution of pre-activations in random neural networks can be exactly mapped onto lattice models in statistical physics. We argue that several previous investigations of stochastic networks actually studied a particular factorial approximation to the full lattice model. For random linear networks and random rectified linear networks we show that the corresponding lattice models in the wide network limit may be systematically approximated by a Gaussian distribution with covariance between the layers of the network. In each case, the approximate distribution can be diagonalized by Fourier transformation. We show that this approximation accurately describes the results of numerical simulations of wide random neural networks. Finally, we demonstrate that in each case the large scale behavior of the random networks can be approximated by an effective field theory.
Challenges of System-Level Simulations and Performance Evaluation for 5G Wireless Networks

With the evaluation and simulation of long-term evolution/4G cellular network and hot discussion about new technologies or network architecture for 5G, the appearance of simulation and evaluation guidelines for 5G is in urgent need. This paper analyzes the challenges of building a simulation platform for 5G considering the emerging new technologies and network architectures. Based on the overview of evaluation methodologies issued for 4G candidates, challenges in 5G evaluation are formulated. Additionally, a cloud-based two-level framework of system-level simulator is proposed to validate the candidate technologies and fulfill the promising technology performance identified for 5G.
An efficient framework for learning sentence representations

In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and recent work on learning sentence representations, we reformulate the problem of predicting the context in which a sentence appears as a classification problem. Given a sentence and the context in which it appears, a classifier distinguishes context sentences from other contrastive sentences based on their vector representations. This allows us to efficiently learn different types of encoding functions, and we show that the model learns high-quality sentence representations. We demonstrate that our sentence representations outperform stateof-the-art unsupervised and supervised representation learning methods on several downstream NLP tasks that involve understanding sentence semantics while achieving an order of magnitude speedup in training time.
A memory based approach to word sense disambiguation in Bengali using k-NN method

Word Sense Disambiguation (WSD) is an important and challenging task in the area of Natural Language Processing (NLP) where the task is to find the correct sense of an ambiguous word given its context. There have been very few attempts on WSD in Bengali or in Indian languages. The k-Nearest-Neighbor (k-NN) algorithm is a very well known and popular method for text classification. The k-NN algorithm determines the classification of a new sample from its k nearest neighbors. In this paper, we present how k-NN algorithm can be effectively applied to the task of WSD in Bengali. The k-NN algorithm achieved an accuracy of over 71% in a WSD task in Bengali reported in this paper.
Effective Strip Noise Removal for Low-Textured Infrared Images Based on 1-D Guided Filtering

Infrared images typically contain obvious strip noise. It is a challenging task to eliminate such noise without blurring fine image details in low-textured infrared images. In this paper, we introduce an effective single-image-based algorithm to accurately remove strip-type noise present in infrared images without causing blurring effects. First, a 1-D row guided filter is applied to perform edge-preserving image smoothing in the horizontal direction. The extracted high-frequency image part contains both strip noise and a significant amount of image details. Through a thermal calibration experiment, we discover that a local linear relationship exists between infrared data and strip noise of pixels within a column. Based on the derived strip noise behavioral model, strip noise components are accurately decomposed from the extracted high-frequency signals by applying a 1-D column guided filter. Finally, the estimated noise terms are subtracted from the raw infrared images to remove strips without blurring image details. The performance of the proposed technique is thoroughly investigated and is compared with the state-of-the-art 1-D and 2-D denoising algorithms using captured infrared images.
Release Practices for Mobile Apps -- What do Users and Developers Think?

Large software organizations such as Facebook or Netflix, who otherwise make daily or even hourly releases of their web applications using continuous delivery, have had to invest heavily into a customized release strategy for their mobile apps, because the vetting process of app stores introduces lag and uncertainty into the release process. Amidst these large, resourceful organizations, it is unknown how the average mobile app developer organizes her app's releases, even though an incorrect strategy might bring a premature app update to the market that drives away customers towards the heavy market competition. To understand the common release strategies used for mobile apps, the rationale behind them and their perceived impact on users, we performed two surveys with users and developers. We found that half of the developers have a clear strategy for their mobile app releases, since especially the more experienced developers believe that it affects user feedback. We also found that users are aware of new app updates, yet only half of the surveyed users enables automatic updating of apps. While the release date and frequency is not a decisive factor to install an app, users prefer to install apps that were updated more recently and less frequently. Our study suggests that an app's release strategy is a factor that affects the ongoing success of mobile apps.
Dolphin: Ultrasonic-Based Gesture Recognition on Smartphone Platform

User experience of smart mobile devices can be improved in numerous scenarios with the assist of in-air gesture recognition. Most existing methods proposed by industry and academia are based on special sensors. On the contrary, a special sensor-independent in-air gesture recognition method named Dolphin is proposed in this paper which can be applied to off-the-shelf smart devices directly. The only sensors Dolphin needs are the loudspeaker and microphone embedded in the device. Dolphin emits a continuous 21 KHz tone by the loudspeaker and receive the gesture-reflecting ultrasonic wave by the microphone. The gesture performed is encoded into the reflected ultrasonic in the form of Doppler shift. By combining manual recognition and machine leaning methods, Dolphin extracts features from Doppler shift and recognizes a rich set of pre-defined gestures with high accuracy in real time. Parameter selection strategy and gesture recognition under several scenarios are discussed and evaluated in detail. Dolphin can be adapted to multiple devices and users by training using machine learning methods.
Deep Model Based Domain Adaptation for Fault Diagnosis

In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis. Two main contributions are concluded by comparing to the previous works: first, the proposed model can utilize domain adaptation meanwhile strengthening the representative information of the original data, so that a high classification accuracy in the target domain can be achieved, and second, we proposed several strategies to explore the optimal hyperparameters of the model. Experimental results, on several real-world datasets, demonstrate the effectiveness and the reliability of both the proposed model and the exploring strategies for the parameters.
Stop-words in keyphrase extraction problem

Keyword extraction problem is one of the most significant tasks in information retrieval. High-quality keyword extraction sufficiently influences the progress in the following subtasks of information retrieval: classification and clustering, data mining, knowledge extraction and representation, etc. The research environment has specified a layout for keyphrase extraction. However, some of the possible decisions remain uninvolved in the paradigm. In the paper the authors observe the scope of interdisciplinary methods applicable to automatic stop list feeding. The chosen method belongs to the class of experiential models. The research procedure based on this method allows to improve the quality of keyphrase extraction on the stage of candidate keyphrase building. Several ways to automatic feeding of the stop lists are proposed in the paper as well. One of them is based on provisions of lexical statistics and the results of its application to the discussed task point out the non-gaussian nature of text corpora. The second way based on usage of the Inspec train collection to the feeding of stop lists improves the quality considerably.
Wireless sensor network: A complete solution for poultry farming

Poultry is a largest source of human food, rapid increasing trend in human population and badly effect of newly seen poultry diseases (N1H1 bird-flue, highly pathogenic avian influenza HPAI) making it difficult to meet the daily increasing human poultry requirements. To improve the poultry growth by using most modern technology we are proposing a complete wireless sensor network solution for poultry farming. Poultry farming is mostly divided into two categories (1) Egg production poultry farms and (2) Meat production poultry farm. In this study we will propose Complete Wireless network solution for poultry farming (CWNS-PF) to establish an ideal poultry farm with maximum productivity and economy. This proposed CWNS-PF is equally useful for both types of poultry farms. Our proposed system mainly consists of 7 components, if these are followed and managed well, quality and quantity of chickens can be improved which will ultimately lead to improve the human health. The proposed solution indicates a possibility that the wearable wireless sensor node would be a useful tool for early detection and outbreaks of infected chickens. Furthermore, system (including wearable sensors nodes, fix sensor nodes in the shed and in the soil) will improve the overall farm production, quality and economy.
Fast and Effective Text Mining Using Linear-Time Document Clustering

Clustering is a powerful technique for large-scale topic discovery from text. It involves two phases: first, feature extraction maps each document or record to a point in high-dimensional space, then clustering algorithms automatically group the points into a hierarchy of clusters. We describe an unsupervised, near-linear time text clustering system that offers a number of algorithm choices for each phase. We introduce a methodology for measuring the quality of a cluster hierarchy in terms of FMeasure, and present the results of experiments comparing different algorithms. The evaluation considers some feature selection parameters (tfidfand feature vector length) but focuses on the clustering algorithms, namely techniques from Scatter/Gather (buckshot, fractionation, and split/join) and kmeans. Our experiments suggest that continuous center adjustment contributes more to cluster quality than seed selection does. It follows that using a simpler seed selection algorithm gives a better time/quality tradeoff. We describe a refinement to center adjustment, “vector average damping,” that further improves cluster quality. We also compare the near-linear time algorithms to a group average greedy agglomerative clustering algorithm to demonstrate the time/quality tradeoff quantitatively.
Anomaly detection in wireless sensor networks: A survey

Since security threats to WSNs are increasingly being diversified and deliberate, prevention-based techniques alone can no longer provide WSNs with adequate security. However, detection-based techniques might be effective in collaboration with prevention-based techniques for securing WSNs. As a significant branch of detection-based techniques, the research of anomaly detection in wired networks and wireless ad hoc networks is already quite mature, but such solutions can be rarely applied to WSNs without any change, because WSNs are characterized by constrained resources, such as limited energy, weak computation capability, poor memory, short communication range, etc. The development of anomaly detection techniques suitable for WSNs is therefore regarded as an essential research area, which will enable WSNs to be much more secure and reliable. In this survey paper, a few of the key design principles relating to the development of anomaly detection techniques in WSNs are discussed in particular. Then, the state-of-the-art techniques of anomaly detection in WSNs are systematically introduced, according to WSNs’ architectures (Hierarchical/Flat) and detection technique categories (statistical techniques, rule based, data mining, computational intelligence, game theory, graph based, and hybrid, etc.). The analyses and comparisons of the approaches that belong to a similar technique category are represented technically, followed by a brief discussion towards the potential research areas in the near future and conclusion.
Learning Certifiably Optimal Rule Lists for Categorical Data

We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm produces rule lists with optimal training performance, according to the regularized empirical risk, with a certificate of optimality. By leveraging algorithmic bounds, efficient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in seconds. Our results indicate that it is possible to construct optimal sparse rule lists that are approximately as accurate as the COMPAS proprietary risk prediction tool on data from Broward County, Florida, but that are completely interpretable. This framework is a novel alternative to CART and other decision tree methods for interpretable modeling.
A Survey of Smartphone-based Sensing in Vehicles for ITS Applications

Road crashes are a growing concern of governments and is rising to become one of the leading preventable causes of death, especially in developing countries. The ubiquitous presence of smartphones provides a new platform on which to implement sensor networks and driver assistance systems, as well as other ITS applications. In this paper, existing approaches of using smartphones for ITS applications are analyzed and compared. Particular focus is placed on vehicle-based monitoring systems, such as driving behavior and style recognition, accident detection and road condition monitoring systems. Further opportunities for use of smartphones in ITS systems are highlighted, and remaining challenges in this emerging field of research are identified.
A Discourse Commitment-Based Framework for Recognizing Textual Entailment

In this paper, we introduce a new framework for recognizing textual entailment which depends on extraction of the set of publiclyheld beliefs – known as discourse commitments – that can be ascribed to the author of a text or a hypothesis. Once a set of commitments have been extracted from a t-h pair, the task of recognizing textual entailment is reduced to the identification of the commitments from a t which support the inference of the h. Promising results were achieved: our system correctly identified more than 80% of examples from the RTE-3 Test Set correctly, without the need for additional sources of training data or other web-based resources.
Multivariate time series classification with parametric derivative dynamic time warping

Multivariate time series (MTS) data are widely used in a very broad range of fields, including medicine, finance, multimedia and engineering. In this paper a new approach for MTS classification, using a parametric derivative dynamic time warping distance, is proposed. Our approach combines two distances: the DTW distance between MTS and the DTW distance between derivatives of MTS. The new distance is used in classification with the nearest neighbor rule. Experimental results performed on 18 data sets demonstrate the effectiveness of the proposed approach for MTS classification.
Fast SIFT Design for Real-Time Visual Feature Extraction

Visual feature extraction with scale invariant feature transform (SIFT) is widely used for object recognition. However, its real-time implementation suffers from long latency, heavy computation, and high memory storage because of its frame level computation with iterated Gaussian blur operations. Thus, this paper proposes a layer parallel SIFT (LPSIFT) with integral image, and its parallel hardware design with an on-the-fly feature extraction flow for real-time application needs. Compared with the original SIFT algorithm, the proposed approach reduces the computational amount by 90% and memory usage by 95%. The final implementation uses 580-K gate count with 90-nm CMOS technology, and offers 6000 feature points/frame for VGA images at 30 frames/s and ~ 2000 feature points/frame for 1920 × 1080 images at 30 frames/s at the clock rate of 100 MHz.
A FRAMEWORK OF A MECHANICAL TRANSLATION BETWEEN JAPANESE AND ENGLISH BY ANALOGY PRINCIPLE

Problems inherent in current machine translation systems have been reviewed and have been shown to be inherently inconsistent. The present paper defines a model based on a series of human language processing and in particular the use of analogical thinking. Machine translation systems developed so far have a kind of inherent contradiction in themselves. The more detailed a system has become by the additional improvements, the clearer the limitation and the boundary will be for the translation ability. To break through this difficulty we have to think about the mechanism of human translation, and have to build a model based on the fundamental function of language processing in the human brain. The following is an attempt to do this based on the ability of analogy finding in human beings.
Competition-based user expertise score estimation

In this paper, we consider the problem of estimating the relative expertise score of users in community question and answering services (CQA). Previous approaches typically only utilize the explicit question answering relationship between askers and an-swerers and apply link analysis to address this problem. The im-plicit pairwise comparison between two users that is implied in the best answer selection is ignored. Given a question and answering thread, it's likely that the expertise score of the best answerer is higher than the asker's and all other non-best answerers'. The goal of this paper is to explore such pairwise comparisons inferred from best answer selections to estimate the relative expertise scores of users. Formally, we treat each pairwise comparison between two users as a two-player competition with one winner and one loser. Two competition models are proposed to estimate user expertise from pairwise comparisons. Using the NTCIR-8 CQA task data with 3 million questions and introducing answer quality prediction based evaluation metrics, the experimental results show that the pairwise comparison based competition model significantly outperforms link analysis based approaches (PageRank and HITS) and pointwise approaches (number of best answers and best answer ratio) for estimating the expertise of active users. Furthermore, it's shown that pairwise comparison based competi-tion models have better discriminative power than other methods. It's also found that answer quality (best answer) is an important factor to estimate user expertise.
Web-based quiz-game-like formative assessment: Development and evaluation

This research aims to develop a multiple-choice Web-based quiz-game-like formative assessment system, named GAM-WATA. The unique design of ‘Ask-Hint Strategy’ turns the Web-based formative assessment into an online quiz game. ‘Ask-Hint Strategy’ is composed of ‘Prune Strategy’ and ‘Call-in Strategy’. ‘Prune Strategy’ removes one incorrect option and turns the original 4-option item into a 3-option one. ‘Call-in Strategy’ provides the rate at which other test takers choose each option when answering a question. This research also compares the effectiveness of three different types of formative assessment in an e-Learning environment: paper-and-pencil test (PPT), normal Web-based test (NWBT) and GAM-WATA. In total, 165 fifth grade elementary students (from six classes) in central Taiwan participated in this research. The six classes of students were then divided into three groups and each group was randomly assigned one type of formative assessment. Overall results indicate that different types of formative assessment have significant impacts on e-Learning effectiveness and that the e-Learning effectiveness of the students in the GAM-WATA group appears to be better. Students in the GAM-WATA group more actively participate in Web-based formative assessment to do self-assessment than students in the N-WBT group. The effectiveness of formative assessment will not be significantly improved only by replacing the paper-and-pencil test with Web-based test. The strategies included in GAMWATA are recommended to be taken into consideration when researchers design Web-based formative assessment systems in the future.