nils-herrmann/clean_scirepeval huggingface dataset:
Detecting Tomato Flowers in Greenhouses Using Computer Vision

This paper presents an image analysis algorithm to detect and count yellow tomato flowers in a greenhouse with uneven illumination conditions, complex growth conditions and different flower sizes. The algorithm is designed to be employed on a drone that flies in greenhouses to accomplish several tasks such as pollination and yield estimation. Detecting the flowers can provide useful information for the farmer, such as the number of flowers in a row, and the number of flowers that were pollinated since the last visit to the row. The developed algorithm is designed to handle the real world difficulties in a greenhouse which include varying lighting conditions, shadowing, and occlusion, while considering the computational limitations of the simple processor in the drone. The algorithm identifies flowers using an adaptive global threshold, segmentation over the HSV color space, and morphological cues. The adaptive threshold divides the images into darker and lighter images. Then, segmentation on the hue, saturation and volume is performed accordingly, and classification is done according to size and location of the flowers. 1069 images of greenhouse tomato flowers were acquired in a commercial greenhouse in Israel, using two different RGB Cameras – an LG G4 smartphone and a Canon PowerShot A590. The images were acquired from multiple angles and distances and were sampled manually at various periods along the day to obtain varying lighting conditions. Ground truth was created by manually tagging approximately 25,000 individual flowers in the images. Sensitivity analyses on the acquisition angle of the images, periods throughout the day, different cameras and thresholding types were performed. Precision, recall and their derived F1 score were calculated. Results indicate better performance for the view angle facing the flowers than any other angle. Acquiring images in the afternoon resulted with the best precision and recall results. Applying a global adaptive threshold improved the median F1 score by 3%. Results showed no difference between the two cameras used. Using hue values of 0.12-0.18 in the segmentation process provided the best results in precision and recall, and the best F1 score. The precision and recall average for all the images when using these values was 74% and 75% respectively with an F1 score of 0.73. Further analysis showed a 5% increase in precision and recall when analyzing images acquired in the afternoon and from the front viewpoint. Agricultural engineering, computer vision, image processing, flower detection.
Branchial cleft cyst.

Branchial cleft cysts are benign lesions caused by anomalous development of the brachial cleft. This report describes a 20-year-old girl with swelling on the right lateral aspect of the neck, which expanded slowly but progressively. The clinical suspicion was that of a branchial cleft cyst. Sonography revealed a homogeneously hypo- to anechoic mass with well-defined margins and no intralesional septa. Color Doppler reviewed no internal vascularization. The ultrasound examination confirmed the clinical diagnosis of a second branchial cleft cyst, demonstrating the cystic nature of the mass and excluding the presence of complications. For superficial lesions like these, ultrasound is the first-level imaging study of choice because it is non-invasive, rapid, low-cost, and does not involve exposure to ionizing radiation.
Biologically inspired robots using Smart Composite Microstructures

Various biologically inspired small scale robots are built with endoskeleton structures. Generally, endoskeletons consist of rotational joints and rigid links. For millimeter scale robots, endoskeletons should be scalable and easy to manufacture. Layers of composite materials and polymer film can be fabricated into small scale joints and link structures as seen in fig. 1. The structures are called SCM (Smart Composite Microstructures) . These structures have many advantages in small scale robot structures in aspect of friction and manufacturing simplicity. We expand the structure with SMA spring actuator for actuation of small components. In this video, we show three robot prototypes using SCM. The first robot is a fully foldable robot using torsional spring type SMA actuator. As seen in fig. 2, three segments of plate structures can be folded into a compact cube shape. The Second robot is Omegabot: Inchworm inspired robot . It crawls with two anchors gripping the ground alternatively by making its body into an omega shape. This simple locomotion is a suitable moving mechanism for small scale robots. The Omegabot produces motions of crawling, climbing, and steering with tethered power and control. The third robot is a flea inspired robot. The flea can jump more than 130 times of its body length with its body length just below 1mm. The flea uses a torque reversal mechanism which enables storing large amount of energy and releasing it with a small amount of force. This mechanism is simple and efficient, which makes it a good mechanism for small jumping robots. The current prototype of the flea inspired robot jumps 64cm high, which is about 30 times of the robot body length. The SCM enabled these novel small scale robots, and has a potential to enable various small scale robots.
Applications of Linear Algebra in Information Retrieval and Hypertext Analysis

Information retrieval is concerned with representing content in a form that can be easily accessed by users with information needs [61, 65]. A definition at this level of generality applies equally well to any index-based retrieval system or database application; so let us focus the topic a little more carefully. Information retrieval, as a field, works primarily with highly unstructured content, such as text documents written in natural language; it deals with information needs that are generally not formulated according to precise specifications; and its criteria for success are based in large part on the demands of a diverse set of human users. Our purpose in this short article is not to provide a survey of the field of information retrieval — for this we refer the reader to texts and surveys such as [25, 29, 51, 60, 61, 62, 63, 65, 70]. Rather, we wish to discuss some specific applications of techniques from linear algebra to information retrieval and hypertext analysis. In particular, we focus on spectral methods — the use of eigenvectors and singular vectors of matrices — and their role in these areas. After briefly introducing the use of vector-space models in information retrieval [52, 65], we describe the application of the singular value decomposition to dimensionreduction, through the Latent Semantic Indexing technique [14]. We contrast this with several other approaches to clustering and dimension-reduction based on vector-space models.
Single switch converter with high voltage gain and low voltage stress for photovoltaic applications

This paper presents a novel single-switch converter with high voltage gain and low voltage stress for photovoltaic applications. The proposed converter is composed of coupled-inductor and switched-capacitor techniques to achieve high step-up conversion ratio without adopting extremely high duty ratio or high turns ratio. The capacitors are charged in parallel and discharged in series by the coupled inductor to achieve high step-up voltage gain with an appropriate duty ratio. Besides, the voltage stress on the main switch is reduced with a passive clamp circuit, and the conduction losses are reduced. In addition, the reverse-recovery problem of the diode is alleviated by a coupled inductor. Thus, the efficiency can be further improved. The operating principle, steady state analysis and design of the proposed single switch converter with high step-up gain is carried out. A 24 V input voltage, 400 V output, and 300W maximum output power integrated converter is designed and analysed using MATLAB simulink. Simulation result proves the performance and functionality of the proposed single switch DC-DC converter for validation.
Parallel Metaheuristic Search

We present a general picture of the parallel meta-heuristic search for optimization. We recall the main concepts and strategies in designing parallel metaheuristics, pointing to a number of contributions that instantiated them for neighborhoodand populationbased meta-heuristics, and identify trends and promising research directions. We focus on cooperation-based strategies, which display remarkable performances, in particular on asynchronous cooperation and advanced cooperation strategies that create new information out of exchanged data to enhance the global guidance of the search.
Miniature Implantable Antennas for Biomedical Telemetry: From Simulation to Realization

We address numerical versus experimental design and testing of miniature implantable antennas for biomedical telemetry in the medical implant communications service band (402-405 MHz). A model of a novel miniature antenna is initially proposed for skin implantation, which includes varying parameters to deal with fabrication-specific details. An iterative design-and-testing methodology is further suggested to determine the parameter values that minimize deviations between numerical and experimental results. To assist in vitro testing, a low-cost technique is proposed for reliably measuring the electric properties of liquids without requiring commercial equipment. Validation is performed within a specific prototype fabrication/testing approach for miniature antennas. To speed up design while providing an antenna for generic skin implantation, investigations are performed inside a canonical skin-tissue model. Resonance, radiation, and safety performance of the proposed antenna is finally evaluated inside an anatomical head model. This study provides valuable insight into the design of implantable antennas, assessing the significance of fabrication-specific details in numerical simulations and uncertainties in experimental testing for miniature structures. The proposed methodology can be applied to optimize antennas for several fabrication/testing approaches and biotelemetry applications.
Total bacterial diversity in soil and sediment communities—A review

Advances in microbial methods have demonstrated that microorganisms globally are the dominating organisms both concerning biomass and diversity. Their functional and genetic potential may exceed that of higher organisms. Studies of bacterial diversity have been hampered by their dependence on phenotypic characterization of bacterial isolates. Molecular techniques have provided the tools for analyzing the entire bacterial community including those which we are not able to grow in the laboratory. Reassociation analysis of DNA isolated directly from the bacteria in pristine soil and marine sediment samples revealed that such environments contained in the order of 10 000 bacterial types. The diversity of the total bacterial community was approximately 170 times higher than the diversity of the collection of bacterial isolates from the same soil. The culturing conditions therefore select for a small and probably skewed fraction of the organisms present in the environment. Environmental stress and agricultural management reduce the bacterial diversity. With the reassociation technique it was demonstrated that in heavily polluted fish farm sediments the diversity was reduced by a factor of 200 as compared to pristine sediments. Here we discuss some molecular mechanisms and environmental factors controlling the bacterial diversity in soil and sediments.
A Chronological Perspective on the Acheulian and Its  Transition to the Middle Stone Age in Southern Africa:  The Question of the Fauresmith

An understanding of the age of the Acheulian and the transition to the Middle Stone Age in southern Africa has been hampered by a lack of reliable dates for key sequences in the region. A number of researchers have hypothesised that the Acheulian first occurred simultaneously in southern and eastern Africa at around 1.7-1.6 Ma. A chronological evaluation of the southern African sites suggests that there is currently little firm evidence for the Acheulian occurring before 1.4 Ma in southern Africa. Many researchers have also suggested the occurrence of a transitional industry, the Fauresmith, covering the transition from the Early to Middle Stone Age, but again, the Fauresmith has been poorly defined, documented, and dated. Despite the occurrence of large cutting tools in these Fauresmith assemblages, they appear to include all the technological components characteristic of the MSA. New data from stratified Fauresmith bearing sites in southern Africa suggest this transitional industry maybe as old as 511-435 ka and should represent the beginning of the MSA as a broad entity rather than the terminal phase of the Acheulian. The MSA in this form is a technology associated with archaic H. sapiens and early modern humans in Africa with a trend of greater complexity through time.
Functional silk: colored and luminescent.

Silkworm silk is among the most widely used natural fibers for textile and biomedical applications due to its extraordinary mechanical properties and superior biocompatibility. A number of physical and chemical processes have also been developed to reconstruct silk into various forms or to artificially produce silk-like materials. In addition to the direct use and the delicate replication of silk's natural structure and properties, there is a growing interest to introduce more new functionalities into silk while maintaining its advantageous intrinsic properties. In this review we assess various methods and their merits to produce functional silk, specifically those with color and luminescence, through post-processing steps as well as biological approaches. There is a highlight on intrinsically colored and luminescent silk produced directly from silkworms for a wide range of applications, and a discussion on the suitable molecular properties for being incorporated effectively into silk while it is being produced in the silk gland. With these understanding, a new generation of silk containing various functional materials (e.g., drugs, antibiotics and stimuli-sensitive dyes) would be produced for novel applications such as cancer therapy with controlled release feature, wound dressing with monitoring/sensing feature, tissue engineering scaffolds with antibacterial, anticoagulant or anti-inflammatory feature, and many others.
Semantic smart grid services: Enabling a standards-compliant Internet of energy platform with IEC 61850 and OPC UA

Large scale dissemination of power grid entities such as distributed energy resources (DERs), electric vehicles (EVs), and smart meters has provided diverse challenges for Smart Grid automation. Novel control models such as virtual power plants (VPPs), microgrids, and smart houses introduce a new set of automation and integration demands that surpass capabilities of currently deployed solutions. Therefore, there is a strong need for finding an alternative technical approach, which can resolve identified issues and fulfill automation prerequisites implied by the Smart Grid vision. This paper presents a novel standards-compliant solution for accelerated Smart Grid integration and automation based on semantic services. Accordingly, two most influential industrial automation standards, IEC 61850 and OPC Unified Architecture (OPC UA) have been extensively analyzed in order to provide a value-added service-oriented integration framework for the Smart Grid.
A CMOS low power, wide dynamic range RSSI with integrated AGC loop

A low voltage low power CMOS limiter and received signal strength indicator (RSSI) with integrated automatic gain control (AGC) loop are designed using TSMC 0.13um CMOS technology. The limiter uses six-stage amplifier architecture for minimum power consideration achieves 56dB gain and 17MHz bandwidth. The RSSI has a dynamic range more than 60dB, and the RSSI linearity error is within ±0.5dB for an input power from −65dBm to −8dBm. The RSSI output voltage is from 0.2V to 1V and the slope of the curve is 14.28mV/dB. The RSSI with integrated AGC loop draws 1.5 mA (I and Q paths) from a 1.2V single supply, including limiters, RSSI and comparators.
An Akaike information criterion for multiple event mixture cure models

We derive the proper form of the Akaike information criterion for variable selection for mixture cure models, which are often fit via the expectation-maximization algorithm. Separate covariate sets may be used in the mixture components. The selection criteria are applicable to survival models for right-censored data with multiple competing risks and allow for the presence of an insusceptible group. The method is illustrated on credit loan data, with pre-payment and default as events and maturity as the insusceptible case and is used in a simulation study.
Breaking the Chains: On Declarative Data Analysis and Data Independence in the Big Data Era

Data management research, systems, and technologies have drastically improved the availability of data analysis capabilities, particularly for non-experts, due in part to low-entry barriers and reduced ownership costs (e.g., for data management infrastructures and applications). Major reasons for the widespread success of database systems and today’s multi-billion dollar data management market include data independence, separating physical representation and storage from the actual information, and declarative languages, separating the program specification from its intended execution environment. In contrast, today’s big data solutions do not offer data independence and declarative specification. As a result, big data technologies are mostly employed in newly-established companies with IT-savvy employees or in large well-established companies with big IT departments. We argue that current big data solutions will continue to fall short of widespread adoption, due to usability problems, despite the fact that in-situ data analytics technologies achieve a good degree of schema independence. In particular, we consider the lack of a declarative specification to be a major roadblock, contributing to the scarcity in available data scientists available and limiting the application of big data to the IT-savvy industries. In particular, data scientists currently have to spend a lot of time on tuning their data analysis programs for specific data characteristics and a specific execution environment. We believe that the research community needs to bring the powerful concepts of declarative specification to current data analysis systems, in order to achieve the broad big data technology adoption and effectively deliver the promise that novel big data technologies offer.
Photo Aesthetics Analysis via DCNN Feature Encoding

We propose an automatic framework for quality assessment of a photograph as well as analysis of its aesthetic attributes. In contrast to the previous methods that rely on manually designed features to account for photo aesthetics, our method automatically extracts such features using a pretrained deep convolutional neural network (DCNN). To make the DCNN-extracted features more suited to our target tasks of photo quality assessment and aesthetic attribute analysis, we propose a novel feature encoding scheme, which supports vector machines-driven sparse restricted Boltzmann machines, which enhances sparseness of features and discrimination between target classes. Experimental results show that our method outperforms the current state-of-the-art methods in automatic photo quality assessment, and gives aesthetic attribute ratings that can be used for photo editing. We demonstrate that our feature encoding scheme can also be applied to general object classification task to achieve performance gains.
Local region partition for person re-identification

Due to the different posture and view angle, the image will appear some objects that do not exist in another image of the same person captured by another camera. The region covered by new items adversely improved the difficulty of person re-identification. Therefore, we named these regions as Damaged Region (DR). To overcome the influence of DR, we propose a new way to extract feature based on the local region that divides both in the horizontal and vertical directions. Before splitting the image, we enlarge it with direction to increase the useful information, potentially reducing the impact of different viewing angles. Then each divided region is a separated part, and the results of the adjacent regions will be compared. As a result the region that gets a higher score is selected as the valid one, and which gets the lower score caused by pose variation and items occlusion will be invalid. Extensive experiments carried out on three person re-identification benchmarks, including VIPeR, PRID2011, CUHK01, clearly show the significant and consistent improvements over the state-of-the-art methods.
Rule-based composition of intelligent mechatronic components in manufacturing systems using prolog

This paper proposes a rule-based approach for automatic configuration of mechatronic components in a novel agent-based manufacturing automation architecture known as MIRA, implemented using Prolog. Through this method, MIRAs are enriched with semantic knowledge representation and, based on that, perform some reasoning and decision making (both at the design stage and even during the operation) to achieve the desired goals. This approach is illustrated in a simple case study in which composition of a reconfigurable pick-and-place robot with various linear cylinders is achieved through rule-based reasoning.
Leveraging Contextual Sentence Relations for Extractive Summarization Using a Neural Attention Model

As a framework for extractive summarization, sentence regression has achieved state-of-the-art performance in several widely-used practical systems. The most challenging task within the sentence regression framework is to identify discriminative features to encode a sentence into a feature vector. So far, sentence regression approaches have neglected to use features that capture contextual relations among sentences.
 We propose a neural network model, Contextual Relation-based Summarization (CRSum), to take advantage of contextual relations among sentences so as to improve the performance of sentence regression. Specifically, we first use sentence relations with a word-level attentive pooling convolutional neural network to construct sentence representations. Then, we use contextual relations with a sentence-level attentive pooling recurrent neural network to construct context representations. Finally, CRSum automatically learns useful contextual features by jointly learning representations of sentences and similarity scores between a sentence and sentences in its context. Using a two-level attention mechanism, CRSum is able to pay attention to important content, i.e., words and sentences, in the surrounding context of a given sentence.
 We carry out extensive experiments on six benchmark datasets. CRSum alone can achieve comparable performance with state-of-the-art approaches; when combined with a few basic surface features, it significantly outperforms the state-of-the-art in terms of multiple ROUGE metrics.
A maximum power point tracker (MPPT) using the incremental conductance (INC) technique

In the incremental conductance (INC) technique, the terminal voltage of the solar array is always adjustable with a power converter system and MPPT controller actions to harvest maximum energy as cost effective and sizeable solutions of power crisis during the load demand. A hard switching buck converter is chosen to achieve the objective and performance improvements of the systems. The efficiency of solar photo voltaic (SPV) systems is smart to capture the maximum available power from different surrounding situations such as solar irradiance and temperature.