nils-herrmann/clean_scirepeval huggingface dataset:
An Introduction to Hyperspectral Imaging and Its Application for Security , Surveillance , and Target Acquisition

This paper introduces the concept and principles of hyperspectral imaging (HSI) and it briefly outlines how the defence and homeland security sectors can benefit from the application of this extremely versatile technology. This paper outlines the pros and cons of the various HSI system configurations, with particular emphasis on two of the most commonly deployed spectrograph techniques, namely, the dispersive system and the narrow-band tuning filter system. It describes how HSI can be utilized for target acquisition particularly when there is no a priori knowledge of the target, and then shows how it can be used for the recognition and tracking of targets with desired or known signature characteristics. The paper also briefly mentions the possibility of remote HSI being used for recognizing a human’s physiological state such as that induced by stress or anxiety. Real experimental data collected during the course of our research have been utilized throughout this paper to help understand the versatility and effectiveness of HSI technology.
Pre-emptive arc fault detection techniques in switchgear and controlgear

Earlier and continuous detection of potential failure points within electrical control equipment can facilitate a more proactive and complete arc fault prevention system. When these new sensor systems are interconnected to predictive protection systems communicating with SCADA or PLC systems, on-line predictive monitoring is now a very real option available to increase the safety and reliability of switchgear and controlgear (motor control centers). A number of different new sensor technologies, for pre-emptive continuous monitoring, are evaluated based on extensive studies and actual user experiences. The most significant new technologies are examined more thoroughly. In these tests, equipment has been subjected to some common causes of arc fault events. Analytical results are provided for associated pre-arc conditions to establish conclusions for applying any of these new sensor technologies.
Multi-label dataless text classification with topic modeling

Manually labeling documents is tedious and expensive, but it is essential for training a traditional text classifier. In recent years, a few dataless text classification techniques have been proposed to address this problem. However, existing works mainly center on single-label classification problems, that is, each document is restricted to belonging to a single category. In this paper, we propose a novel Seed-guided Multi-label Topic Model, named SMTM. With a few seed words relevant to each category, SMTM conducts multi-label classification for a collection of documents without any labeled document. In SMTM, each category is associated with a single category-topic which covers the meaning of the category. To accommodate with multi-label documents, we explicitly model the category sparsity in SMTM by using spike and slab prior and weak smoothing prior. That is, without using any threshold tuning, SMTM automatically selects the relevant categories for each document. To incorporate the supervision of the seed words, we propose a seed-guided biased GPU (i.e., generalized Pólya urn) sampling procedure to guide the topic inference of SMTM. Experiments on two public datasets show that SMTM achieves better classification accuracy than state-of-the-art alternatives and even outperforms supervised solutions in some scenarios.
Obesity, Diabetes, and Gut Microbiota

The connection between gut microbiota and energy homeostasis and inflammation and its role in the pathogenesis of obesity-related disorders are increasingly recognized. Animals models of obesity connect an altered microbiota composition to the development of obesity, insulin resistance, and diabetes in the host through several mechanisms: increased energy harvest from the diet, altered fatty acid metabolism and composition in adipose tissue and liver, modulation of gut peptide YY and glucagon-like peptide (GLP)-1 secretion, activation of the lipopolysaccharide toll-like receptor-4 axis, and modulation of intestinal barrier integrity by GLP-2. Instrumental for gut microbiota manipulation is the understanding of mechanisms regulating gut microbiota composition. Several factors shape the gut microflora during infancy: mode of delivery, type of infant feeding, hospitalization, and prematurity. Furthermore, the key importance of antibiotic use and dietary nutrient composition are increasingly recognized. The role of the Western diet in promoting an obesogenic gut microbiota is being confirmation in subjects. Following encouraging results in animals, several short-term randomized controlled trials showed the benefit of prebiotics and probiotics on insulin sensitivity, inflammatory markers, postprandial incretins, and glucose tolerance. Future research is needed to unravel the hormonal, immunomodulatory, and metabolic mechanisms underlying microbe-microbe and microbiota-host interactions and the specific genes that determine the health benefit derived from probiotics. While awaiting further randomized trials assessing long-term safety and benefits on clinical end points, a healthy lifestyle--including breast lactation, appropriate antibiotic use, and the avoidance of excessive dietary fat intake--may ensure a friendly gut microbiota and positively affect prevention and treatment of metabolic disorders.
A Novel Computer-Aided Lung Cancer Detection Method Based on Transfer Learning from GoogLeNet and Median Intensity Projections

In this research, a fast, accurate, and stable system of lung cancer detection based on novel deep learning techniques is proposed. A convolutional neural network (CNN) structure akin to that of GoogLeNet was built using a transfer learning approach. In contrast to previous studies, Median Intensity Projection (MIP) was employed to include multi-view features of three-dimensional computed tomography (CT) scans. The system was evaluated on the LIDC-IDRI public dataset of lung nodule images and 100-fold data augmentation was performed to ensure training efficiency. The trained system produced 81% accuracy, 84% sensitivity, and 78% specificity after 300 epochs, better than other available programs. In addition, a t-based confidence interval for the population mean of the validation accuracies verified that the proposed system would produce consistent results for multiple trials. Subsequently, a controlled variable experiment was performed to elucidate the net effects of two core factors of the system - fine-tuned GoogLeNet and MIPs - on its detection accuracy. Four treatment groups were set by training and testing fine-tuned GoogLeNet and Alexnet on MIPs and common 2D CT scans, respectively. It was noteworthy that MIPs improved the network's accuracy by 12.3%, and GoogLeNet outperformed Alexnet by 2%. Lastly, remote access to the GPU-based system was enabled through a web server, which allows long-distance management of the system and its future transition into a practical tool.
Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation

The explicitly observed social relations from online social platforms have been widely incorporated into recommender systems to mitigate the data sparsity issue. However, the direct usage of explicit social relations may lead to an inferior performance due to the unreliability (e.g., noises) of observed links. To this end, the discovery of reliable relations among users plays a central role in advancing social recommendation. In this paper, we propose a novel approach to adaptively identify implicit friends toward discovering more credible user relations. Particularly, implicit friends are those who share similar tastes but could be distant from each other on the network topology of social relations. Methodologically, to find the implicit friends for each user, we first model the whole system as a heterogeneous information network, and then capture the similarity of users through the meta-path based embedding representation learning. Finally, based on the intuition that social relations have varying degrees of impact on different users, our approach adaptively incorporates different numbers of similar users as implicit friends for each user to alleviate the adverse impact of unreliable social relations for a more effective recommendation. Experimental analysis on three real-world datasets demonstrates the superiority of our method and explain why implicit friends are helpful in improving social recommendation.
Architecture and implementation for 3 D Neural Network based image compression

Image compression is one of the key image processing techniques in signal processing and communication systems. Compression of images leads to reduction of storage space and reduces transmission bandwidth and hence also the cost. Advances in VLSI technology are rapidly changing the technological needs of common man. One of the major technological domains that are directly related to mankind is image compression. Neural networks can be used for image compression. Neural network architectures have proven to be more reliable, robust, and programmable and offer better performance when compared with classical techniques. In this work the main focus is on development of new architectures for hardware implementation of 3-D neural network based image compression optimizing area, power and speed as specific to ASIC implementation, and comparison with FPGA.
Discussion about functionalities of the Virtual Fauteuil simulator for wheelchair training environment

A physical disability can highly harm the capability of a person to move. There are vehicles, like powered or manual wheelchair that can give to disabled people a part of their mobility back but this will not give them their entire mobility and autonomy. Virtual Fauteuil is a platform of simulation for wheelchair that has been developed in order to analyze issues linked to the use of a wheelchair in the daily life. Indeed, Virtual Fauteuil is a platform that offers immersive simulations of travel with a wheelchair in a virtual environment. This immersion is guaranteed by the projection of the user of the platform in a virtual environment, and through integrated haptic feedback devices. This article presents a discussion about the platform: its capacities and its challenges.
Vehicular roof antenna cavity for coverage at low elevation angles

Chassis cavities have recently been proposed as a new mounting position for vehicular antennas. Cavities can be concealed and potentially offer more space for antennas than shark-fin modules mounted on top of the roof. An antenna cavity for the front or rear edge of the vehicle roof is designed, manufactured and measured for 5.9 GHz. The cavity offers increased radiation in the horizontal plane and to angles below horizon, compared to cavities located in the roof center.
Innovations in Fashion Industry Fourier Analysis for Demand Forecasting in a Fashion Company Regular Paper

In the fashion industry, demand forecasting is particularly complex: companies operate with a large variety of short lifecycle products, deeply influenced by seasonal sales, promotional events, weather conditions, advertising and marketing campaigns, on top of festivities and socio-economic factors. At the same time, shelf-out-of-stock phenomena must be avoided at all costs. Given the strong seasonal nature of the products that characterize the fashion sector, this paper aims to highlight how the Fourier method can represent an easy and more effective forecasting method compared to other widespread heuristics normally used. For this purpose, a comparison between the fast Fourier transform algorithm and another two techniques based on moving average and exponential smoothing was carried out on a set of 4year historical sales data of a €60+ million turnover mediumto large-sized Italian fashion company, which operates in the women’s textiles apparel and clothing sectors. The entire analysis was performed on a common spreadsheet, in order to demonstrate that accurate results exploiting advanced numerical computation techniques can be carried out without necessarily using expensive software.
Finite element mesh generation and adaptive meshing

This review paper gives a detailed account of the development of mesh generation techniques on planar regions, over curved surfaces and within volumes for the past years. Emphasis will be on the generation of the unstructured meshes for purpose of complex industrial applications and adaptive refinement finite element analysis. Over planar domains and on curved surfaces, triangular and quadrilateral elements will be used, whereas for three-dimensional structures, tetrahedral and hexahedral elements have to be generated. Recent advances indicate that mesh generation on curved surfaces is quite mature now that elements following closely to surface curvatures could be generated more or less in an automatic manner. As the boundary recovery procedure are getting more and more robust and efficient, discretization of complex solid objects into tetrahedra by means of Delaunay triangulation and other techniques becomes routine work in industrial applications. However, the decomposition of a general object into hexahedral elements in a robust and efficient manner remains as a challenge for researchers in the mesh generation community. Algorithms for the generation of anisotropic meshes on 2D and 3D domains have also been proposed for problems where elongated elements along certain directions are required. A web-site for the latest development in meshing techniques is included for the interested readers.
Unique bioactive polyphenolic profile of guava (Psidium guajava) budding leaf tea is related to plant biochemistry of budding leaves in early dawn.

Guava leaf tea (GLT), exhibiting a diversity of medicinal bioactivities, has become a popularly consumed daily beverage. To improve the product quality, a new process was recommended to the Ser-Tou Farmers' Association (SFA), who began field production in 2005. The new process comprised simplified steps: one bud-two leaves were plucked at 3:00-6:00 am, in the early dawn period, followed by withering at ambient temperature (25-28 °C), rolling at 50 °C for 50-70 min, with or without fermentation, then drying at 45-50 °C for 70-90 min, and finally sorted.



The product manufactured by this new process (named herein GLTSF) exhibited higher contents (in mg g(-1), based on dry ethyl acetate fraction/methanolic extract) of polyphenolics (417.9 ± 12.3) and flavonoids (452.5 ± 32.3) containing a compositional profile much simpler than previously found: total quercetins (190.3 ± 9.1), total myricetin (3.3 ± 0.9), total catechins (36.4 ± 5.3), gallic acid (8.8 ± 0.6), ellagic acid (39.1 ± 6.4) and tannins (2.5 ± 9.1).



We have successfully developed a new process for manufacturing GLTSF with a unique polyphenolic profile. Such characteristic compositional distribution can be ascribed to the right harvesting hour in the early dawn and appropriate treatment process at low temperature, avoiding direct sunlight.
Fully Automatic Segmentation of Sphenoid Sinus in CT Images with 3 D Convolutional Neural Networks

Today, Deep learning algorithms have quickly become essential in the field of medical image analysis. Compared to the traditional methods, these Deep learning techniques are more efficient in extracting compact information leading towards significant improvement performance of medical image analysis system. We present in this paper a new technique for sphenoid sinus automatic segmentation using a 3D Convolutional Neural Networks (CNN). Due to the scarcity of medical data, we chose to used a 3D CNN model learned on a small training set. Mathematical morphology operations are then used to automatically detect and segment the region of interest. Our proposed method is tested and compared with a semi-automatic method and manual delineations made by a specialist. The preliminary results from the Computed Tomography (CT) volumes seem to be very promising.
Security in Mobile Edge Caching with Reinforcement Learning

Mobile edge computing usually uses caching to support multimedia contents in 5G mobile Internet to reduce the computing overhead and latency. Mobile edge caching (MEC) systems are vulnerable to various attacks such as denial of service attacks and rogue edge attacks. This article investigates the attack models in MEC systems, focusing on both the mobile offloading and the caching procedures. In this article, we propose security solutions that apply reinforcement learning (RL) techniques to provide secure offloading to the edge nodes against jamming attacks. We also present lightweight authentication and secure collaborative caching schemes to protect data privacy. We evaluate the performance of the RL-based security solution for mobile edge caching and discuss the challenges that need to be addressed in the future.
Predicting Academic Performance from Behavioural and Learning Data

The volume and quality of data, but also their relevance, are crucial when performing data analysis. In this paper, a study of the influence of different types of data is presented, particularly in the context of educational data obtained from Learning Management Systems (LMSs). These systems provide a large amount of data from the student activity but they usually do not describe the results of the learning process, i.e., they describe the behaviour but not the learning results. The starting hypothesis states that complementing behavioural data with other more relevant data (regarding learning outcomes) can lead to a better analysis of the learning process, that is, in particular it is possible to early predict the student final performance. A learning platform has been specially developed to collect data not just from the usage but also related to the way students learn and progress in training activities. Data of both types are used to build a progressive predictive system for helping in the learning process. This model is based on a classifier that uses the Support Vector Machine technique. As a result, the system obtains a weekly classification of each student as the probability of belonging to one of three classes: high, medium and low performance. The results show that, supplementing behavioural data with learning data allows us to obtain better predictions about the results of the students in a learning system. Moreover, it can be deduced that the use of heterogeneous data enriches the final performance of the prediction algorithms.
A General Multi-Context Embedding Model for Mining Human Trajectory Data

The proliferation of location-based social networks, such as Foursquare and Facebook Places, offers a variety of ways to record human mobility, including user generated geo-tagged contents, check-in services, and mobile apps. Although trajectory data is of great value to many applications, it is challenging to analyze and mine trajectory data due to the complex characteristics reflected in human mobility, which is affected by multiple contextual information. In this paper, we propose a Multi-Context Trajectory Embedding Model, called MC-TEM, to explore contexts in a systematic way. MC-TEM is developed in the distributed representation learning framework, and it is flexible to characterize various kinds of useful contexts for different applications. To the best of our knowledge, it is the first time that the distributed representation learning methods apply to trajectory data. We formally incorporate multiple context information of trajectory data into the proposed model, including user-level, trajectory-level, location-level, and temporal contexts. All the context information is represented in the same embedding space. We apply MC-TEM to two challenging tasks, namely location recommendation and social link prediction. We conduct extensive experiments on three real-world datasets. Extensive experiment results have demonstrated the superiority of our MC-TEM model over several state-of-the-art methods.
Visible threads: a smart VR interface to digital libraries

The importance of information as a resource for economic growth and education is steadily increasing. Due to technological advances in computer industry and the explosive growth of the Internet much valuable information will be available in digital libraries. This paper introduces a system that aims to support a user's browsing activities in document sets retrieved from a digital library. Latent Semantic Analysis is applied to extract salient semantic structures and citation patterns of documents stored in a digital library in a computationally expensive batch job. At retrieval time, cluster techniques are used to organize retrieved documents into clusters according to the previously extracted semantic similarities. A modified Boltzman algorithm  is employed to spatially organize the resulting clusters and their documents in the form of a three-dimensional information landscape or "i-scape". The i-scape is then displayed for interactive exploration via a multi-modal, virtual reality CAVE interface . Users' browsing activities are recorded and user models are extracted to give newcomers online help based on previous navigation activity as well as to enable experienced users to recognize and exploit past user traces. In this way, the system provides interactive services to assist users in the spatial navigation, interpretation, and detailed exploration of potentially large document sets matching a query.
SWaT: a water treatment testbed for research and training on ICS security

This paper presents the SWaT testbed, a modern industrial control system (ICS) for security research and training. SWaT is currently in use to (a) understand the impact of cyber and physical attacks on a water treatment system, (b) assess the effectiveness of attack detection algorithms, (c) assess the effectiveness of defense mechanisms when the system is under attack, and (d) understand the cascading effects of failures in one ICS on another dependent ICS. SWaT consists of a 6-stage water treatment process, each stage is autonomously controlled by a local PLC. The local fieldbus communications between sensors, actuators, and PLCs is realized through alternative wired and wireless channels. While the experience with the testbed indicates its value in conducting research in an active and realistic environment, it also points to design limitations that make it difficult for system identification and attack detection in some experiments.
Vector Control Drive of Permanent Magnet Synchronous Motor Using Resolver Sensor

Now a days, Permanent Magnet Synchronous Motor (PMSM) is designed not only to be more powerful but also with lower mass and lower moment of inertia. Due to its high power density and smaller size, PMSM has in recent years evolved as the preferred solution for speed and position control drives on machine tools and robots. One of the efficient control strategies of PMSM is VectorControl (or Field oriented control).The rotor position is necessary to achieve the vector control drive system of Permanent Magnet Synchronous Motor. In this project, the resolver sensor detecting the rotor position of PMSM is focused. The outstanding features of this sensor are its robust structure and noise insensitivity. The resolver algorithm is proposed and implemented in the vector control drive system of PMSM. The proposed scheme has to be verified by simulation and using MATLAB/SIMULINK.
Inference about predictive ability

In this paper we provide a brief review of how out-of-sample methods can be used to construct tests that evaluate a time-series model's ability to predict. We focus on the role that parameter estimation plays in constructing asymptotically valid tests of predictive ability. We illustrate why forecasts and forecast errors that depend upon estimated parameters may have statistical properties that differ from those of their population counterparts. We explain how to conduct asymptotic inference, taking due account of dependence on estimated parameters.