nils-herrmann/clean_scirepeval huggingface dataset:
Vision Based Front and Rear Vehicle Collision Warning System

In the current driving environment, the top priority is the safety of person. There are two methods proposed to solve safety problems. One is active sensors method and another is passive sensor method. Though with high accuracy, active sensors method has many disadvantages such as high cost, failure to adapt to complex change of environments, and problems relating to laws. Thus there is no way to popularize it. In contrast, passive sensor method is more suitable to current assist systems in virtue of low cost, ability to acquire lots of information. In this paper, the passive sensor method is applied to front and rear vision-based collision warning application. Meanwhile, time-to-contact is used to collision judgment analysis and dedicated short range communications is used to give alert information to near vehicle.
The next information architecture evolution: the data lake wave

Data warehouses and data marts have long been considered as the unique solution for providing end-users with decisional information. More recently, data lakes have been proposed in order to govern data swamps. However, no formal definition has been proposed in the literature. Existing works are not complete and miss important parts of the topic. In particular, they do not focus on the influence of the data gravity, the infrastructure role of those solutions and of course are proposing divergent definitions and positioning regarding the usage and the interaction with existing decision support system.
 In this paper, we propose a novel definition of data lakes, together with a comparison with other over several criteria as the way to populate them, how to use, what is the Data Lake end user profile. We claim that data lakes are complementary components in decisional information systems and we discuss their position and interactions regarding the other components by proposing an interaction model.
Ensuring Long-Term Data Integrity: ETCS Data Integrity Requirements Can Be Fulfilled Even under Unfavorable Conditions with the Proper LTE Mechanisms

The European Train Control System (ETCS) is the leading signaling system for train command and control. In the future, ETCS may be delivered over long-term evolution (LTE) networks. Thus, LTE performance offered to ETCS must be analyzed and confronted with the railway safety requirements. It is especially important to ensure the integrity of the ETCS data, i.e., to protect ETCS data against loss and corruption. In this article, various retransmission mechanisms are considered for providing end-to-end ETCS data integrity in LTE. These mechanisms are validated in simulations, which model worst-case conditions regarding train locations, traffic load, and base-station density. The simulation results show that ETCS data integrity requirements can be fulfilled even under these unfavorable conditions with the proper LTE mechanisms.
Data Mining and Gene Expression Analysis in Bioinformatics

Recent technological advances in computational biology and bioinformatics have generated a lot of biological information and voluminous biological data all over the world. This biological dat/information has necessitated the requirement of computerized databases to store, categorize and index the data as well as to view and analyze the data using specialized tools and algorithms. Feature selection, normalization and standardization of the data, visualization of the results and evaluation of the produced knowledge are equally important steps in the knowledge discovery process. Data mining aims to provide the analysts with novel and efficient computational tools to overcome the constraints posed by the traditional statistical methods. It is used to extract interesting, nontrivial, implicit, previously unknown and potentially useful information from data. Likewise, bioinformatics is the analysis of accurate and reliable biological information using computers and statistical techniques in order to gain new biological insights. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to gene-based drug discovery and development. This paper will focus on issues related to data mining and relevance of these data base sequences and gene expression in bioinformatics.
Exploring Peopleware in Continuous Delivery

Traditionally, releasing new software has been a fragile and painful procedure. This view has been challenged by a new approach to software deployment, where the goal is to always be able to deploy the system. The transition from manual releases to instant deployments requires a high degree of automation. Furthermore, the transition requires a new mindset, where both developers and operators act together to deliver value to end users. As this process involves humans and cooperation, developers and their attitude towards the new way of working is important. To this end, in this paper we study the developer perspective of applying continuous delivery in the light of interviewing practitioners working for projects where continuous delivery practices are applied. More precisely, we place the focus on how continuous delivery practices affect software developers and what are the prerequisites for achieving continuous delivery. Based on our research, higher quality and other benefits of continuous delivery can be achieved by giving developers more responsibility. Still, the added responsibilities do not necessarily increase stress, but can actually decrease it, and at the same time improve motivation and job satisfaction.
A Large-Scale 3D Object Recognition Dataset

This paper presents a new large scale dataset targeting evaluation of local shape descriptors and 3d object recognition algorithms. The dataset consists of point clouds and triangulated meshes from 292 physical scenes taken from 11 different views, a total of approximately 3204 views. Each of the physical scenes contain 10 occluded objects resulting in a dataset with 32040 unique object poses and 45 different object models. The 45 object models are full 360 degree models which are scanned with a high precision structured light scanner and a turntable. All the included objects belong to different geometric groups, concave, convex, cylindrical and flat 3D object models. The object models have varying amount of local geometric features to challenge existing local shape feature descriptors in terms of descriptiveness and robustness. The dataset is validated in a benchmark which evaluates the matching performance of 7 different state-of-the-art local shape descriptors. Further, we validate the dataset in a 3D object recognition pipeline. Our benchmark shows as expected that local shape feature descriptors without any global point relation across the surface have a poor matching performance with flat and cylindrical objects. It is our objective that this dataset contributes to the future development of next generation of 3D object recognition algorithms. The dataset is public available at http://roboimagedata.compute.dtu.dk/.
Vision-based approach towards lane line detection and vehicle localization

Localization of the vehicle with respect to road lanes plays a critical role in the advances of making the vehicle fully autonomous. Vision based road lane line detection provides a feasible and low cost solution as the vehicle pose can be derived from the detection. While good progress has been made, the road lane line detection has remained an open one, given challenging road appearances with shadows, varying lighting conditions, worn-out lane lines etc. In this paper, we propose a more robust vision-based approach with respect to these challenges. The approach incorporates four key steps. Lane line pixels are first pooled with a ridge detector. An effective noise filtering mechanism will next remove noise pixels to a large extent.  is then adopted in a model fitting procedure to ensure each lane line in the image is captured correctly. Finally, if lane lines on both sides of the road exist, a parallelism reinforcement technique is imposed to improve the model accuracy. The results obtained show that the proposed approach is able to detect the lane lines accurately and at a high success rate compared to current approaches. The model derived from the lane line detection is capable of generating precise and consistent vehicle localization information with respect to road lane lines, including road geometry, vehicle position and orientation.
Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for epileptic seizure detection. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Some conclusions concerning the impacts of features on the detection of epileptic seizures were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in epileptic seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.
A computer vision approach for weeds identification through Support Vector Machines

Support Vector Machines Machine vision Weed identification Image segmentation Decision making This paper outlines an automatic computer vision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the Support Vector Machines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the Support Vector Machines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies.
Investigating different types of research collaboration and citation impact: a case study of Harvard University’s publications

This study aims to investigate the influence of different patterns of collaboration on the citation impact of Harvard University’s publications. Those documents published by researchers affiliated with Harvard University in WoS from 2000–2009, constituted the population of the research which was counted for 124,937 records. Based on the results, only 12% of Harvard publications were single author publications. Different patterns of collaboration were investigated in different subject fields. In all 22 examined fields, the number of co-authored publications is much higher than single author publications. In fact, more than 60% of all publications in each field are multi-author publications. Also, the normalized citation per paper for co-authored publications is higher than that of single author publications in all fields. In addition, the largest number of publications in all 22 fields were also published through inter-institutional collaboration and were as a result of collaboration among domestic researchers and not international ones. In general, the results of the study showed that there was a significant positive correlation between the number of authors and the number of citations in Harvard publications. In addition, publications with more number of institutions have received more number of citations, whereas publications with more number of foreign collaborators were not much highly cited.
General AI Challenge - Round One: Gradual Learning

The General AI Challenge is an initiative to encourage the wider artificial intelligence community to focus on important problems in building intelligent machines with more general scope than is currently possible. The challenge comprises of multiple rounds, with the first round focusing on gradual learning, i.e. the ability to re-use already learned knowledge for efficiently learning to solve subsequent problems. In this article, we will present details of the first round of the challenge, its inspiration and aims. We also outline a more formal description of the challenge and present a preliminary analysis of its curriculum, based on ideas from computational mechanics. We believe, that such formalism will allow for a more principled approach towards investigating tasks in the challenge, building new curricula and for potentially improving consequent challenge rounds.
A Novel Method for Detecting Image Forgery Based on Convolutional Neural Network

Detection of image forgery is an important part of digital forensics and has attracted a lot of attention in the past few years. Previous research has examined residual pattern noise, wavelet transform and statistics, image pixel value histogram and other features of images to authenticate the primordial nature. With the development of neural network technologies, some effort has recently applied convolutional neural networks to detecting image forgery to achieve high-level image representation. This paper proposes to build a convolutional neural network different from the related work in which we try to understand extracted features from each convolutional layer and detect different types of image tampering through automatic feature learning. The proposed network involves five convolutional layers, two full-connected layers and a Softmax classifier. Our experiment has utilized CASIA v1.0, a public image set that contains authentic images and splicing images, and its further reformed versions containing retouching images and re-compressing images as the training data. Experimental results can clearly demonstrate the effectiveness and adaptability of the proposed network.
Addressing Organisational Pressures as Drivers towards Sustainability in Manufacturing Projects and Project Management Methodologies

The concept of sustainability continues to rapidly grow in interest from disparate academic and industrial fields. This research aims to elucidate further the implications of the sustainability drivers upon project management methodological approaches specifically in the manufacturing industry. This paper studies the three prevalent dialogues in the field of sustainability, relevant to the environmental and social aspects of the Triple Bottom Line, and utilises Institutional Theory to propose organisational pressures as affecting sustainability efforts in industrial manufacturing project management. Furthermore, the literature bodies of Lean and Life Cycle Analysis in manufacturing project management guided our reflection that the various drivers of sustainability put forward that do not consider the distinctive organisational pressures fail to address institutional and systemic project management issues holistically. The authors further conduct and draw on a systematic literature review on the constructs of sustainability in the manufacturing industry and their adopted methodologies, evaluating academic articles published from the year 2001 to 2017. The findings indicate that normative pressures prevail over coercive and mimetic pressures and are seen as the main drivers of sustainability in the manufacturing industry. In an incremental reductionist approach, project management knowledge areas are analysed, and the study posits that Stakeholder and Communications Management are two of the knowledge areas that need to integrate the above pressures to achieve cohesive sustainable industrial results. The principle contribution is to offer a new conceptual perspective on integrating project management knowledge areas with Institutional Theory pressures for more sustainable project management methodologies.
Implementation and evaluation of WAVE 1609.4/802.11p in ns-3

Since the IEEE 1609/WAVE standards were published, much research has continued on validation and optimization. However, precise simulation models of these standards are lacking recently, especially within the ns-3 network simulator. In this paper, we present the ns-3 implementation details of the IEEE 1609.4 and IEEE 802.11p standards which are key elements of the WAVE MAC layer. Moreover we discuss some implementation issues and describe our solutions. Lastly, we also analyze and evaluate the performance of the WAVE MAC layer with the implemented model. Our simulation results show that multiple channel operation specified in the WAVE standards could impact vehicular wireless communication differently, depending on the different scenarios, and the results should be considered carefully during the development of VANET applications.
Back to the Blocks World: Learning New Actions through Situated Human-Robot Dialogue

This paper describes an approach for a robotic arm to learn new actions through dialogue in a simplified blocks world. In particular, we have developed a threetier action knowledge representation that on one hand, supports the connection between symbolic representations of language and continuous sensorimotor representations of the robot; and on the other hand, supports the application of existing planning algorithms to address novel situations. Our empirical studies have shown that, based on this representation the robot was able to learn and execute basic actions in the blocks world. When a human is engaged in a dialogue to teach the robot new actions, step-by-step instructions lead to better learning performance compared to one-shot instructions.
The relationship of retinal vascular calibre to diabetes and retinopathy: the Australian Diabetes, Obesity and Lifestyle (AusDiab) study

The aim of the study was to examine the relationship of retinal vascular calibre with glucose intolerance, diabetes and retinopathy in a population-based cohort. The Australian Diabetes, Obesity and Lifestyle study recruited adults aged ≥25 years old from across Australia. Participants were classified using an oral glucose tolerance test as having normal glucose tolerance (NGT), impaired glucose tolerance (IGT), impaired fasting glucose (IFG), known diabetes or newly diagnosed diabetes. Digital retinal photographs were taken of all participants with diabetes, IGT and IFG, and a sample of those with NGT, and graded for the presence of retinopathy. Retinal vascular calibre was measured from photographs by a computer-assisted method. Of the 1,998 participants with gradable retinal images, 16% had known diabetes, 17% newly diagnosed diabetes, 42% IGT, 6% IFG and 19% NGT. After multivariable adjustment, retinal arteriolar calibre was significantly larger in people with known diabetes (178.9 μm) compared with participants with NGT (174.6 μm, p = 0.02), IGT/IFG (175.5 μm, p = 0.02) or newly diagnosed diabetes (175.6 μm, p = 0.047). One SD increase in mean arteriolar calibre was associated with higher odds of diabetes compared with NGT (odds ratio [OR] = 1.28, 95%CI = 1.06–1.55). After multivariable adjustment, each SD increase in venular calibre was associated with higher odds of having retinopathy in persons with IGT/IFG (OR = 1.78, 95%CI = 1.36–2.34) or in persons with diabetes (OR = 1.68, 95%CI = 1.23–2.29). Diabetes is associated with larger retinal arteriolar calibre and retinopathy with larger retinal venular calibre. The contrasting associations may reflect different underlying pathophysiological processes in the natural history of diabetes.
Keratin Hydrogel Enhances In Vivo Skeletal Muscle Function in a Rat Model of Volumetric Muscle Loss.

Volumetric muscle loss (VML) injuries exceed the considerable intrinsic regenerative capacity of skeletal muscle, resulting in permanent functional and cosmetic deficits. VML and VML-like injuries occur in military and civilian populations, due to trauma and surgery as well as due to a host of congenital and acquired diseases/syndromes. Current therapeutic options are limited, and new approaches are needed for a more complete functional regeneration of muscle. A potential solution is human hair-derived keratin (KN) biomaterials that may have significant potential for regenerative therapy. The goal of these studies was to evaluate the utility of keratin hydrogel formulations as a cell and/or growth factor delivery vehicle for functional muscle regeneration in a surgically created VML injury in the rat tibialis anterior (TA) muscle. VML injuries were treated with KN hydrogels in the absence and presence of skeletal muscle progenitor cells (MPCs), and/or insulin-like growth factor 1 (IGF-1), and/or basic fibroblast growth factor (bFGF). Controls included VML injuries with no repair (NR), and implantation of bladder acellular matrix (BAM, without cells). Initial studies conducted 8 weeks post-VML injury indicated that application of keratin hydrogels with growth factors (KN, KN+IGF-1, KN+bFGF, and KN+IGF-1+bFGF, n = 8 each) enabled a significantly greater functional recovery than NR (n = 7), BAM (n = 8), or the addition of MPCs to the keratin hydrogel (KN+MPC, KN+MPC+IGF-1, KN+MPC+bFGF, and KN+MPC+IGF-1+bFGF, n = 8 each) (p < 0.05). A second series of studies examined functional recovery for as many as 12 weeks post-VML injury after application of keratin hydrogels in the absence of cells. A significant time-dependent increase in functional recovery of the KN, KN+bFGF, and KN+IGF+bFGF groups was observed, relative to NR and BAM implantation, achieving as much as 90% of the maximum possible functional recovery. Histological findings from harvested tissue at 12 weeks post-VML injury documented significant increases in neo-muscle tissue formation in all keratin treatment groups as well as diminished fibrosis, in comparison to both BAM and NR. In conclusion, keratin hydrogel implantation promoted statistically significant and physiologically relevant improvements in functional outcomes post-VML injury to the rodent TA muscle.
Subpectoral biceps tenodesis.

The long head of the biceps brachii (LHB) tendon has long been recognized as a source of shoulder pain. Surgeons have debated the merits of tenotomy versus tenodesis, open versus arthroscopic approaches, and various fixation methods. This article reviews the clinical findings associated with LHB pathology, describes the operative technique of subpectoral biceps tenodesis, and reviews the current literature related to treatment of the symptomatic LHB tendon. The miniopen subpectoral approach is technically less demanding than purely arthroscopic techniques, and offers the potential for improved pain relief without cosmetic deformity by removing most of the LHB and its associated tenosynovium. As the literature on the topic continues to grow, subpectoral biceps tenodesis has emerged as an effective treatment for pathology of the LHB.