nils-herrmann/clean_scirepeval huggingface dataset:
Deep Texture and Structure Aware Filtering Network for Image Smoothing

Image smoothing is a fundamental task in computer vision, that attempts to retain salient structures and remove insignificant textures. In this paper, we aim to address the fundamental shortcomings of existing image smoothing methods, which cannot properly distinguish textures and structures with similar low-level appearance. While deep learning approaches have started to explore structure preservation through image smoothing, existing work does not yet properly address textures. To this end, we generate a large dataset by blending natural textures with clean structure-only images, and use this to build a texture prediction network (TPN) that predicts the location and magnitude of textures. We then combine the TPN with a semantic structure prediction network (SPN) so that the final texture and structure aware filtering network (TSAFN) is able to identify the textures to remove (“textureawareness”) and the structures to preserve (“structure-awareness”). The proposed model is easy to understand and implement, and shows good performance on real images in the wild as well as our generated dataset.
Clinical-radiologic features and treatment of hepatic lesions caused by inadvertent infusion of parenteral nutrition in liver parenchyma due to malposition of umbilical vein catheters

Umbilical venous catheterization is a common procedure performed in neonatal intensive care units. Hepatic collections due to inadvertent extravasation of parenteral nutrition into the liver have been described previously in literature. To recognize the clinicoradiologic features and treatment options of hepatic collections due to inadvertent extravasation of parenteral nutrition fluids caused by malpositioning of umbilical venous catheter (UVC) in the portal venous system. This is a case series describing five neonates during a 6-year period at a single tertiary care referral center, with extravasation of parenteral nutrition into the liver parenchyma causing hepatic collections. All five neonates receiving parenteral nutrition presented with abdominal distension in the second week of life. Two out of five (40%) had anemia requiring blood transfusion and 3/5 (60%) had hemodynamic instability at presentation. Ultrasound of the liver confirmed the diagnosis in all the cases. Three of the five (60%) cases underwent US-guided aspiration of the collections, one case underwent conservative management and one case required emergent laparotomy due to abdominal compartment syndrome. US used in follow-up of these cases revealed decrease in size of the lesions and/or development of calcifications. Early recognition of this complication, prompt diagnosis with US of liver and timely treatment can lead to better outcome in newborns with hepatic collections secondary to inadvertent parenteral nutrition infusion via malposition of UVC.
Effective Exploration for MAVs Based on the Expected Information Gain

Micro aerial vehicles (MAVs) are an excellent platform for autonomous exploration. Most MAVs rely mainly on cameras for buliding a map of the 3D environment. Therefore, vision-based MAVs require an efficient exploration algorithm to select viewpoints that provide informative measurements. In this paper, we propose an exploration approach that selects in real time the next-best-view that maximizes the expected information gain of new measurements. In addition, we take into account the cost of reaching a new viewpoint in terms of distance and predictability of the flight path for a human observer. Finally, our approach selects a path that reduces the risk of crashes when the expected battery life comes to an end, while still maximizing the information gain in the process. We implemented and thoroughly tested our approach and the experiments show that it offers an improved performance compared to other state-of-the-art algorithms in terms of precision of the reconstruction, execution time, and smoothness of the path.
EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery

Biclustering algorithms are commonly used for gene expression data analysis. However, accurate identification of meaningful structures is very challenging and state-of-the-art methods are incapable of discovering with high accuracy different patterns of high biological relevance.



In this paper, a novel biclustering algorithm based on evolutionary computation, a sub-field of artificial intelligence, is introduced. The method called EBIC aims to detect order-preserving patterns in complex data. EBIC is capable of discovering multiple complex patterns with unprecedented accuracy in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units. We demonstrate that EBIC greatly outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms.


Availability and implementation
EBIC source code is available on GitHub at https://github.com/EpistasisLab/ebic.



Supplementary data are available at Bioinformatics online.
Touchscreen based ordering system for restaurants

Automation has gained importance in every field of human life. But there are still some fields where more traditional methods are being employed. One such field is the ordering system in restaurants. Generally, in restaurants menu ordering system will be available in paper format from that the customer has to select the menu items and then the order is handed over to waiter who takes the corresponding order, which is a very time consuming process. In this paper we propose a fully automated ordering system in which the conventional paper based menu is replaced by a more user friendly touchscreen based menu card. The system consists of microcontroller which is interfaced with the input and output modules. The input module is the touchscreen sensor which is placed on GLCD to have graphical image display, which takes the input from the user and provides the same to the microcontroller. The output module is a RF module which is used for communication between system at table and system at ordering department. Microcontroller also displays the menu items on the GLCD. At the receiving end the selected items will be displayed on the LCD and the ordering department will note down the order received.
Follow-up CT findings of recurrent familial gigantiform cementoma of a female child

Familial gigantiform cementoma (FGC) is a rare autosomal dominant, benign fibro-cemento-osseous lesion of the jaws that can cause severe facial deformity. True FGC with familial history is extremely rare and there has been no literature regarding the radiological follow-up of FGC. We report a case of recurrent FGC in an Asian female child who has been under our observation for 6 years since she was 15 months old. After repeated recurrences and subsequent surgeries, the growth of the tumor had seemed to plateau on recent follow-up CT images. The transition from an enhancing soft tissue lesion to a homogeneous bony lesion on CT may indicate decreased growth potential of FGC.
Internet of Things: A survey related to various recent architectures and platforms available

IP-based Internet is the largest network in the world therefore; there are excessive steps towards connecting Wireless Sensor Networks (WSNs) to the Internet. It is popularly known as to IoT (Internet of Things). IETF has developed a suite of protocols and open standards for accessing applications and services for wireless resource constrained networks such as IoT. Development of application requires standardized architecture and platform for design and analysis of new ideas. This paper provides a brief awareness about recent IoT architectures and platforms. It is also discussed some of the gaps issues of the platforms related to usability of the user. This helps researcher to select a particular platform according to need.
Retrospective of electric machines for EV and HEV traction applications at general motors

This paper presents a retrospective of electric motor developments in General Motors (GM) for electric vehicle (EV), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), and fuel cell electric vehicle (FCEV) production programs. This paper includes i) the progression of electric motor stator and rotor design methodologies that gradually improved motor torque, power, and efficiency performance while mitigating for noise, ii) Heavy rare earth (HRE) mitigation in subsequent design to lower cost and supply uncertainty, iii) Design techniques to lower torque ripple and radial force to mitigate noise and vibration issues. These techniques are elaborated in details with design examples, simulation and test data.
Preceding vehicle detection using Histograms of Oriented Gradients

This paper presents a monocular vision-based preceding vehicle detection system using Histogram of Oriented Gradient (HOG) based method and linear SVM classification. Our detection algorithm consists of three main components: HOG feature extraction, linear SVM classifier training and vehicles detection. Integral Image method is adopted to improve the HOG computational efficiency, and hard examples are generated to reduce false positives in the training phase. In detection step, the multiple overlapping detections due to multi-scale window searching are very well fused by non-maximum suppression based on mean-shift. The monocular system is tested under different traffic scenarios (e.g., simply structured highway, complex urban environments, local occlusion conditions), illustrating good performance.
Hyperspectral Band Selection Using Improved Firefly Algorithm

An improved firefly algorithm (FA)-based band selection method is proposed for hyperspectral dimensionality reduction (DR). In this letter, DR is formulated as an optimization problem that searches a small number of bands from a hyperspectral data set, and a feature subset search algorithm using the FA is developed. To avoid employing an actual classifier within the band searching process to greatly reduce computational cost, criterion functions that can gauge class separability are preferred; specifically, the minimum estimated abundance covariance and Jeffreys-Matusita distances are employed. The proposed band selection technique is compared with an FA-based method that actually employs a classifier, the well-known sequential forward selection, and particle swarm optimization algorithms. Experimental results show that the proposed algorithm outperforms others, providing an effective option for DR.
Measuring service quality and its relationship to future consumer behavior.

The authors adapt the SERVQUAL scale for medical care services and examine it for reliability, dimensionality, and validity in a primary care clinic setting. In addition, they explore the possibility of a link between perceived service quality--and its various dimensions--and a patient's future intent to complain, compliment, repeat purchase, and switch providers. Findings from 159 matched-pair responses indicate that the SERVQUAL scale can be adapted reliably to a clinic setting and that the dimensions of reliability, dependability, and empathy are most predictive of a patient's intent to complain, compliment, repeat purchase, and switch providers.
Neuropathy after sodium tetradecyl sulfate sclerotherapy of venous malformations in children.

To estimate the risk of nerve injuries and assess outcomes after sodium tetradecyl sulfate (STS) sclerotherapy of venous malformations (VMs) in children.



Sclerotherapy is the treatment of choice for most VMs, but all sclerotherapy agents are associated with the risk of complications. Neuropathy is considered a rare but potentially serious complication of venous sclerotherapy. The institutional review board waived ethical approval for this retrospective review, in which 647 sclerotherapy procedures were performed in 204 patients (104 female and 100 male patients; mean age, 9 years 6 months [range, 6 months to 17 years 11 months]) as treatment for symptomatic VMs. Technical and clinical success of the treatment was evaluated. Complications were reviewed with a particular focus on nerve injury. Informed consent, specifying the risk of neuropathy, as well as pain, swelling, infection, risks of anesthesia, skin injury, nonresolution or worsening of symptoms, and possible need for further or multiple procedures, was obtained for all patients. Standard sclerotherapy techniques were used. Technical details of all procedures were recorded prospectively. Follow-up included immediate postprocedural assessment and outpatient clinic review. All nerve injuries were recorded. Patients were monitored and treated according to clinical need. Confidence intervals were calculated by using the Wilson method, without correction for continuity.



Treatment was technically successful in 197 of 204 patients (96.6%), and clinical success was achieved in 174 of 204 (85.3%). Thirty-seven of the 647 procedures (5.7%) resulted in a complication, including 11 cases of excessive swelling, nine cases of skin injury, two patients with infection, and two with pain. Motor and/or sensory nerve injuries occurred after seven procedures (1.1%). Five of the seven children had undergone at least one previous sclerotherapy procedure. Neuropathy resolved spontaneously in four patients and partially recovered in three, of whom two underwent surgery. Surgery included debridement of necrotic tissue, carpal tunnel decompression, and external neurolysis.



Nerve injury is an unusual but not rare complication of STS sclerotherapy. A degree of recovery, which may be complete, can be expected in most patients.
Black-box Testing of First-Order Logic Ontologies Using WordNet

A long-standing dream of Artificial Intelligence (AI) has pursued to enrich computer programs with commonsense knowledge enabling machines to reason about our world. This paper offers a new practical insight towards the automation of commonsense reasoning with first-order logic (FOL) ontologies. We propose a new black-box testing methodology of FOL SUMO-based ontologies by exploiting WordNet and its mapping into SUMO. Our proposal includes a method for the (semi-)automatic creation of a very large set of tests and a procedure for its automated evaluation by using automated theorem provers (ATPs). Applying our testing proposal, we are able to successfully evaluate a) the competency of several translations of SUMO into FOL and b) the performance of various automated ATPs. In addition, we are also able to evaluate the resulting set of tests according to different quality criteria.
Gait algorithm of personal mobility vehicle for negotiating obstacles.

Personal mobility vehicles (PMVs) are under active development. Most PMVs are wheel-driven, a mode of transport notable for its efficiency. However, wheeled PMVs tend to have poor mobility against negotiating obstacles. The four-wheeled vehicle RT-Mover PType 3 has been developed featuring wheeled legs capable of leg motion. This allows the PMV to overcome uneven terrains, including a step approached at an angle, which ordinary wheelchairs cannot negotiate.



This article discusses a gait algorithm in which a leg executes the necessary leg motion when optionally presented with obstacles on a road. In order to lift a wheel off the ground and perform a leg motion, the support wheels must be moved to support points to ensure that the vehicle remains stable on three wheels. When moving towards the target support point, a wheel may encounter another obstacle, and a response method for this case is also described.



To assess the gait algorithm, several configurations of obstacles were used for performance tests with a passenger. The capabilities of the PMV were demonstrated through experiments.



We proposed a novel gait algorithm for our PMV and realised the proposed motion pattern for PMV-based negotiating obstacles.



Our single-seat personal mobility vehicle, RT-Mover PType 3 features wheels attached on legs capable of performing leg motion, which allows the vehicle to traverse rough terrains in urban areas. We proposed a gait algorithm for RT-Mover PType 3 consisting of a series of leg motions in response to rough terrain. With this algorithm, the vehicle can traverse not only randomly placed obstacles, but also a step approached at an oblique angle, which conventional powered wheelchairs cannot navigate. Experiments with a passenger demonstrated the effectiveness of the proposed gait algorithm, suggesting that RT-Mover PType 3 can expand the mobility and range of activities of wheelchair users.
Noise Level Estimation for Natural Images Based on Scale-Invariant Kurtosis and Piecewise Stationarity

Noise level estimation is crucial in many image processing applications, such as blind image denoising. In this paper, we propose a novel noise level estimation approach for natural images by jointly exploiting the piecewise stationarity and a regular property of the kurtosis in bandpass domains. We design a  $K$ -means-based algorithm to adaptively partition an image into a series of non-overlapping regions, each of whose clean versions is assumed to be associated with a constant, but unknown kurtosis throughout scales. The noise level estimation is then cast into a problem to optimally fit this new kurtosis model. In addition, we develop a rectification scheme to further reduce the estimation bias through noise injection mechanism. Extensive experimental results show that our method can reliably estimate the noise level for a variety of noise types, and outperforms some state-of-the-art techniques, especially for non-Gaussian noises.
The Performance Evaluation of Speech Recognition by Comparative Approach

For more than five decades, Automatic speech recognition has been the area of active research. This problem was thoroughly studied with the advent of digital computing and signal processing. Increased awareness of the advantages of conversational systems led to the development of this problem. Speech recognition has wide applications and includes voice-controlled appliances fully featured speech-to-text software, automation of operatorassisted services and voice recognition aids for the handicapped.
Development of large die fine pitch flip chip BGA using TCNCP technology

Recently, technologies related to Fine Pitch Flip Chip or FPFC have been great achievements for various next generation devices, allowing a significant increase in the number of signal I/O and achieving low form factor packages. Consequently, fine pitch Cu pillar flip chip Chip Scale Package (CSP) with small sized die, with package dimension of less than 16×16mm, is already under high volume production using the Thermal-Compression Bonding with Non-conductive Paste (TCNCP) technology [1-2]. In the case of Flip Chip Ball Grid Array (FCBGA), there is a growing need for FPFC technology with Cu pillar in supporting next generation silicon node. However, there will be a high possibility of yield drop issue in conventional mass-reflow process and potential reliability due to the highly concerned tensile stress between low k die and substrate by CTE mismatch especially at the edge of the die. This can be a critical quality issue for fine pitch devices compared to normal pitch (i.e., 150um) flip chip BGA. Therefore, TCNCP bonding as an alternative should be studied on fine pitch Cu pillar flip chip BGA. This paper will discuss fine pitch flip chip assembly technology for large sized flip chip BGA. Two kinds of assembly method, mass reflow bonding versus thermal compression bonding, for the flip chip bonding will be compared for the large FPFCBGA package. Meanwhile, the advantage of TC bonding with pre-applied underfill process will be described. For robust interconnection between die and substrate for large FPFCBGA, the result of the bonding test will be described with several surface finishes such as ENEPIG, Direct Immersion Gold (DIG), Immersion Tin (IT), and Solder Coating on substrate. Interestingly, one of selected surface finishes has shown excellent reliability test results. Finally, this paper will discuss an effective approach for fine pitch devices from an assembly perspective.
Phylogenetic analysis of the main neutralization and hemagglutination determinants of all human adenovirus prototypes as a basis for molecular classification and taxonomy.

Human adenoviruses (HAdV) are responsible for a wide spectrum of diseases. The neutralization epsilon determinant (loops 1 and 2) and the hemagglutination gamma determinant are relevant for the taxonomy of HAdV. Precise type identification of HAdV prototypes is crucial for detection of infection chains and epidemiology. epsilon and gamma determinant sequences of all 51 HAdV were generated to propose molecular classification criteria. Phylogenetic analysis of epsilon determinant sequences demonstrated sufficient genetic divergence for molecular classification, with the exception of HAdV-15 and HAdV-29, which also cannot be differentiated by classical cross-neutralization. Precise sequence divergence criteria for typing (<2.5% from loop 2 prototype sequence and <2.4% from loop 1 sequence) were deduced from phylogenetic analysis. These criteria may also facilitate identification of new HAdV prototypes. Fiber knob (gamma determinant) phylogeny indicated a two-step model of species evolution and multiple intraspecies recombination events in the origin of HAdV prototypes. HAdV-29 was identified as a recombination variant of HAdV-15 (epsilon determinant) and a speculative, not-yet-isolated HAdV prototype (gamma determinant). Subanalysis of molecular evolution in hypervariable regions 1 to 6 of the epsilon determinant indicated different selective pressures in subclusters of species HAdV-D. Additionally, gamma determinant phylogenetic analysis demonstrated that HAdV-8 did not cluster with -19 and -37 in spite of their having the same tissue tropism. The phylogeny of HAdV-E4 suggested origination by interspecies recombination between HAdV-B (hexon) and HAdV-C (fiber), as in simian adenovirus 25, indicating additional zoonotic transfer. In conclusion, molecular classification by systematic sequence analysis of immunogenic determinants yields new insights into HAdV phylogeny and evolution.
Topology Discovery in Software Defined Networks: Threats, Taxonomy, and State-of-the-Art

The fundamental role of the software defined networks (SDNs) is to decouple the data plane from the control plane, thus providing a logically centralized visibility of the entire network to the controller. This enables the applications to innovate through network programmability. To establish a centralized visibility, a controller is required to discover a network topology of the entire SDN infrastructure. However, discovering a network topology is challenging due to: 1) the frequent migration of the virtual machines in the data centers; 2) lack of authentication mechanisms; 3) scarcity of the SDN standards; and 4) integration of security mechanisms for the topology discovery. To this end, in this paper, we present a comprehensive survey of the topology discovery and the associated security implications in SDNs. This survey provides discussions related to the possible threats relevant to each layer of the SDN architecture, highlights the role of the topology discovery in the traditional network and SDN, presents a thematic taxonomy of topology discovery in SDN, and provides insights into the potential threats to the topology discovery along with its state-of-the-art solutions in SDN. Finally, this survey also presents future challenges and research directions in the field of SDN topology discovery.