nils-herrmann/clean_scirepeval huggingface dataset:
Penile advancement and lengthening for the management of post-circumcision traumatic short penis in adolescents.

To report on the safety of combing suprapubic fat liposuction, penile suspensory ligament severing, and Z-plasty of penoscrotal webbing for penile lengthening in cases of post-circumcision traumatic short penis in adolescents.



This prospective study was conducted in 9 male patients with a mean age of 15.6 ± 1.4 years. All patients had a short penis because of traumatic amputation of the glans in 7 patients and both glanular and partial distal corporal loss in 2 patients during circumcision. All were initially treated in childhood by penile skin advancement for phallic coverage. Penile length and circumference were measured pre- and post-operatively, both in the flaccid state at maximal stretch and in the erect state. Operative technique included Z-plasty of penoscrotal web, release of suspensory ligament, and suprapubic fat liposuction.



Average functional penile length measured 3 months post-operatively had significantly increased by 18-30 mm (mean: 23 ± 4 mm) in the provoked, erect state. All patients had normally developed penile corpora cavernosa and wide circumference (mean: 11.7 ± 0.2 cm). No post-operative complications occurred in any of the patients. No patients had sexual partners or were sexually active.



The described technique of penile advancement and lengthening can be safely applied to patients with post-circumcision traumatic short penis.
A functional imaging study of translation and language switching.

The neural systems underlying translation and language switching were investigated using PET. Proficient German-English adult bilinguals were scanned whilst either translating or reading visually presented words in German (L1), English (L2) or alternating L1/L2. We refer to alternating L1/L2 as 'switching'. The results revealed contrasting patterns of activation for translation and switching, suggesting at least partially independent mechanisms. Translation, but not switching, increased activity in the anterior cingulate and subcortical structures whilst decreasing activation in several other temporal and parietal language areas associated with the meaning of words. Translation also increased activation in regions associated with articulation (the anterior insula, cerebellum and supplementary motor area) arguably because the reading response to the stimulus must be inhibited whilst a response in a different language is activated. In contrast, switching the input language resulted in activation of Broca's area and the supramarginal gyri, areas associated with phonological recoding. The results are discussed in terms of the cognitive control of language processes.
Evolving Multimodal Behavior Through Subtask and Switch Neural Networks

While neuroevolution has been used successfully to discover effective control policies for intelligent agents, it has been difficult to evolve behavior that is multimodal, i.e. consists of distinctly different behaviors in different situations. This article proposes a new method, Modular NeuroEvolution of Augmenting Topologies (ModNEAT), to meet this challenge. ModNEAT decomposes complex tasks into tractable subtasks and utilizes neuroevolution to learn each subtask. Switch networks are evolved with the subtask networks to arbitrate among them and thus combine separate subtask networks into a complete hierarchical policy. Further, the need for new subtask modules is detected automatically by monitoring fitness of the agent population. Experimental results in the machine learning game of OpenNERO showed that ModNEAT outperforms the non-modular rtNEAT in both agent fitness and training efficiency.
npn-soft sets theory and their applications

In this paper, we firstly defined neutrosophic parameterized neutrosophic soft sets(npn−soft sets) which is combination of a neutrosophic sets and a soft sets. Our npn−soft sets generalizes the concept of the other soft sets such as; fuzzy soft sets, intuitionistic fuzzy soft sets, neutrosophic soft sets, fuzzy parameterized soft sets, intuitionistic fuzzy parameterized soft sets, neutrosophic parameterized soft sets and so on. Then, we introduce some definitions and operations on npn−soft sets and some properties of the sets which are connected to operations have been established. Also, we have introduced the concept of npn−soft matrix and their operators which are more functional to make theoretical studies in the npn−soft set theory. Finally, we proposed the decision making method on the npn−soft set theory which can be applied to problems of many fields that contain uncertainty and provided an example that demonstrated that this method can be successfully worked.
Supporting visual quality assessment with machine learning

Objective metrics for visual quality assessment often base their reliability on the explicit modeling of the highly non-linear behavior of human perception; as a result, they may be complex and computationally expensive. Conversely, machine learning (ML) paradigms allow to tackle the quality assessment task from a different perspective, as the eventual goal is to mimic quality perception instead of designing an explicit model the human visual system. Several studies already proved the ability of ML-based approaches to address visual quality assessment; nevertheless, these paradigms are highly prone to overfitting, and their overall reliability may be questionable. In fact, a prerequisite for successfully using ML in modeling perceptual mechanisms is a profound understanding of the advantages and limitations that characterize learning machines. This paper illustrates and exemplifies the good practices to be followed.
High Performance Computer Acoustic Data Accelerator: A New System for Exploring Marine Mammal Acoustics for Big Data Applications

This paper presents a new software model designed for distributed sonic signal detection runtime using machine learning algorithms called DeLMA. A new algorithm--Acoustic Data-mining Accelerator (ADA)--is also presented. ADA is a robust yet scalable solution for efficiently processing big sound archives using distributing computing technologies. Together, DeLMA and the ADA algorithm provide a powerful tool currently being used by the Bioacoustics Research Program (BRP) at the Cornell Lab of Ornithology, Cornell University. This paper provides a high level technical overview of the system, and discusses various aspects of the design. Basic runtime performance and project summary are presented. The DeLMA-ADA baseline performance comparing desktop serial configuration to a 64 core distributed HPC system shows as much as a 44 times faster increase in runtime execution. Performance tests using 48 cores on the HPC shows a 9x to 12x efficiency over a 4 core desktop solution. Project summary results for 19 east coast deployments show that the DeLMA-ADA solution has processed over three million channel hours of sound to date.
Towards an ICF core set for ADHD: a worldwide expert survey on ability and disability

This is the second in a series of four empirical studies designed to develop International Classification of Functioning, Disability and Health (ICF and Children and Youth version, ICF-CY) core sets for attention deficit hyperactivity disorder (ADHD). The objective of this stage was to gather the opinions from international experts on which ability and disability concepts were considered relevant to functioning in ADHD. An email-based survey was carried out amongst international experts in ADHD. Relevant functional ability and disability concepts were extracted from their responses and linked to the ICF/-CY categories by two independent researchers using a standardised linking procedure. 174 experts from 11 different disciplines and 45 different countries completed the survey. Meaningful concepts identified in their responses were linked to 185 ICF/-CY categories. Of these, 83 categories were identified by at least 5 % of the experts and considered the most relevant to ADHD: 30 of these were related to Body functions (most identified: attention functions, 85 %), 30 to Activities and Participation (most identified: school education, 52 %), 20 to Environmental factors (most identified: support from immediate family, 61 %), and 3 to Body structures (most identified: structure of brain, 83 %). Experts also provided their views on particular abilities related to ADHD, naming characteristics such as high-energy levels, flexibility and resiliency. Gender differences in the expression of ADHD identified by experts pertained mainly to females showing more internalising (e.g. anxiety, low self-esteem) and less externalising behaviours (e.g. hyperactivity), leading to a risk of late- and under-diagnosis in females. Results indicate that the impact of ADHD extends beyond the core symptom domains, into all areas of life and across the lifespan. The current study in combination with three additional preparatory studies (comprehensive scoping review, focus groups, clinical study) will provide the scientific basis to define the ADHD ICF/-CY core sets for multi-purpose use in basic and applied research and every day clinical practice.
A Comparative Study of Bug Algorithms for Robot Navigation

This paper presents a literature survey and a comparative study of Bug Algorithms, with the goal of investigating their potential for robotic navigation. At first sight, these methods seem to provide an efficient navigation paradigm, ideal for implementations on tiny robots with limited resources. Closer inspection, however, shows that many of these Bug Algorithms assume perfect global position estimate of the robot which in GPS-denied environments implies considerable expenses of computation and memory – relying on accurate Simultaneous Localization And Mapping (SLAM) or Visual Odometry (VO) methods. We compare a selection of Bug Algorithms in a simulated robot and environment where they endure different types noise and failure-cases of their on-board sensors. From the simulation results, we conclude that the implemented Bug Algorithms’ performances are sensitive to many types of sensor-noise, which was most noticeable for odometry-drift. This raises the question if Bug Algorithms are suitable for real-world, on-board, robotic navigation as is. Variations that use multiple sensors to keep track of their progress towards the goal, were more adept in completing their task in the presence of sensor-failures. This shows that Bug Algorithms must spread their risk, by relying on the readings of multiple sensors, to be suitable for real-world deployment.
Sensor networks: evolution, opportunities, and challenges

Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. This paper traces the history of research in sensor networks over the past three decades, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor Information Technology (SensIT) programs. Technology trends that impact the development of sensor networks are reviewed, and new applications such as infrastructure security, habitat monitoring, and traffic control are presented. Technical challenges in sensor network development include network discovery, control and routing, collaborative signal and information processing, tasking and querying, and security. The paper concludes by presenting some recent research results in sensor network algorithms, including localized algorithms and directed diffusion, distributed tracking in wireless ad hoc networks, and distributed classification using local agents.
Facade: Auto-generating Tactile Interfaces to Appliances

Digital keypads have proliferated on common appliances, from microwaves and refrigerators to printers and remote controls. For blind people, such interfaces are inaccessible. We conducted a formative study with 6 blind people which demonstrated a need for custom designs for tactile labels without dependence on sighted assistance. To address this need, we introduce Facade - a crowdsourced fabrication pipeline to make physical interfaces accessible by adding a 3D printed augmentation of tactile buttons overlaying the original panel. Blind users capture a photo of an inaccessible interface with a standard marker for absolute measurements using perspective transformation. Then this image is sent to multiple crowd workers, who work in parallel to quickly label and describe elements of the interface. These labels are then used to generate 3D models for a layer of tactile and pressable buttons that fits over the original controls. Users can customize the shape and labels of the buttons using a web interface. Finally, a consumer-grade 3D printer fabricates the layer, which is then attached to the interface using adhesives. Such fabricated overlay is an inexpensive ($10) and more general solution to making physical interfaces accessible.
Implementation of page rank algorithm in Hadoop MapReduce framework

One of the most popular algorithm in processing internet data i.e. webpages is page rank algorithm which is intended to decide the importance of a webpage by assigning a weighting value based on any incoming link to those webpage. However, the large amount of internet data may lead into computational burden in processing those page rank algorithm. To take into account those burden, in this paper we present a page rank processing algorithm over distributed system using Hadoop MapReduce framework called MR PageRank. Our algorithm can be decomposed into three processes, each of which is implemented in one Map and Reduce job. We first parse the raw webpage input to produce title of page and its outgoing links as key and value pair, respectively, as well as total dangling node's weight and total amount of pages. We next calculate the probability of each page and distribute this probability to each of outgoing link evenly. Each of the outgoing weight is shuffled and aggregated based on similarity of page title to update a new weighting value of each page. Notice that in the calculation we consider the dangling node and jumping factor. In the end, all of the page are descendingly sorted based on their weighting values. From the experimental result, we show that our implementation has output with reasonable ordering result. This electronic document is a “live” template and already defines the components of your paper.
Agricultural sustainability: concepts, principles and evidence.

Concerns about sustainability in agricultural systems centre on the need to develop technologies and practices that do not have adverse effects on environmental goods and services, are accessible to and effective for farmers, and lead to improvements in food productivity. Despite great progress in agricultural productivity in the past half-century, with crop and livestock productivity strongly driven by increased use of fertilizers, irrigation water, agricultural machinery, pesticides and land, it would be over-optimistic to assume that these relationships will remain linear in the future. New approaches are needed that will integrate biological and ecological processes into food production, minimize the use of those non-renewable inputs that cause harm to the environment or to the health of farmers and consumers, make productive use of the knowledge and skills of farmers, so substituting human capital for costly external inputs, and make productive use of people's collective capacities to work together to solve common agricultural and natural resource problems, such as for pest, watershed, irrigation, forest and credit management. These principles help to build important capital assets for agricultural systems: natural; social; human; physical; and financial capital. Improving natural capital is a central aim, and dividends can come from making the best use of the genotypes of crops and animals and the ecological conditions under which they are grown or raised. Agricultural sustainability suggests a focus on both genotype improvements through the full range of modern biological approaches and improved understanding of the benefits of ecological and agronomic management, manipulation and redesign. The ecological management of agroecosystems that addresses energy flows, nutrient cycling, population-regulating mechanisms and system resilience can lead to the redesign of agriculture at a landscape scale. Sustainable agriculture outcomes can be positive for food productivity, reduced pesticide use and carbon balances. Significant challenges, however, remain to develop national and international policies to support the wider emergence of more sustainable forms of agricultural production across both industrialized and developing countries.
Document clustering algorithms, representations and evaluation for information retrieval

Digital collections of data continue to grow exponentially as the information age continues to infiltrate every aspect of society. These sources of data take many different forms such as unstructured text on the world wide web, sensor data, images, video, sound, results of scientific experiments and customer profiles for marketing. Clustering is an unsupervised learning approach that groups similar examples together without any human labeling of the data. Due to the very broad nature of this definition, there have been many different approaches to clustering explored in the scientific literature. This thesis addresses the computational efficiency of document clustering in an information retrieval setting. This includes compressed representations, efficient and scalable algorithms, and evaluation with a specific use case for increasing efficiency of a distributed and parallel search engine. Furthermore, it addresses the evaluation of document cluster quality for large scale collections containing millions to billions of documents where complete labeling of a collection is impractical. The cluster hypothesis from information retrieval is also tested using several different approaches throughout the thesis. This research introduces novel approaches to clustering algorithms, document representations, and clustering evaluation. The combination of document clustering algorithms and document representations outlined in this thesis are able to process large scale web search data sets. This has not previously been reported in the literature without resorting to sampling and producing a small number of clusters. Furthermore, the algorithms are able to do this on a single machine. However, they can also function in a distributed and parallel setting to further increase their scalability. This thesis introduces new clustering algorithms that can also be applied to problems outside the information retrieval domain. There are many large data sets being produced from advertising, social networks, videos, images, sound, satellites, sensor data, and a myriad of other sources. Therefore, I anticipate these approaches will become applicable to more domains, as larger data sets become available.
A deadlock prevention method for railway networks using monitors for colored Petri nets

The real-time traffic control of railway networks authorizes movements of the trains and imposes safety constraints. The paper deals with the real time traffic control focusing on deadlock prevention problem. Colored Petri nets are used to model the dynamics of the railway network system: places represent tracks and stations, tokens are trains. The prevention policy is expressed by a set of linear inequality constraints, called colored Generalized Mutual Exclusion Constraints that are enforced by adding appropriate monitor places. Using digraph tools, deadlock situations are characterized and a strategy is established to define off-line a set of Generalized Mutual Exclusion Constraints that prevent deadlock. An example shows in detail the design of the proposed control logic.
Recurrent Transformer Networks for Semantic Correspondence

We present recurrent transformer networks (RTNs) for obtaining dense correspondences between semantically similar images. Our networks accomplish this through an iterative process of estimating spatial transformations between the input images and using these transformations to generate aligned convolutional activations. By directly estimating the transformations between an image pair, rather than employing spatial transformer networks to independently normalize each individual image, we show that greater accuracy can be achieved. This process is conducted in a recursive manner to refine both the transformation estimates and the feature representations. In addition, a technique is presented for weakly-supervised training of RTNs that is based on a proposed classification loss. With RTNs, state-of-the-art performance is attained on several benchmarks for semantic correspondence.
PERT – Perfect Random Tree Ensembles

Ensemble classifiers originated in the machine learning community. They work by fitting many individual classifiers and combining them by weighted or unweighted voting. The ensemble classifier is often much more accurate than the individual classifiers from which it is built. In fact, ensemble classifiers are among the most accurate general-purpose classifiers available. We introduce a new ensemble method, PERT, in which each individual classifier is a perfectly-fit classification tree with random selection of splits. Compared to other ensemble methods, PERT is very fast to fit. Given the randomness of the split selection, PERT is surprisingly accurate. Calculations suggest that one reason why PERT works so well is that although the individual tree classifiers are extremely weak, they are almost uncorrelated. The simple probabilistic nature of the classifier lends itself to theoretical analysis. We show that PERT is fitting a continuous posterior probability surface for each class. As such, it can be viewed as a classification-via-regression procedure that fits a continuous interpolating surface. In theory, this surface could be found using a one-shot procedure.
Adaptive fuzzy PID temperature control system based on OPC and modbus/TCP protocol

In this paper, an adaptive fuzzy PID controller is designed to improve the control performance of the resistance furnace, and then is applied to a remote temperature control system based on modbus/TCP industrial Ethernet via OPC communication. The adaptive PID controller is developed by the fuzzy toolbox in Matlab. The data acquisition and devices driving are realized by PCAuto, a SCADA configuration software which is widely used in industry. The data exchange between Matlab and PCAuto is realized by OPC technology. The control process and results verify that the performance of resistance furnace temperature control system is satisfactory and the performance of OPC communication is very efficient and reliable.
Pest Detection and Extraction Using Image Processing Techniques

—Detection of pests in the paddy fields is a major challenge in the field of agriculture, therefore effective measures should be developed to fight the infestation while minimizing the use of pesticides. The techniques of image analysis are extensively applied to agricultural science, and it provides maximum protection to crops, which can ultimately lead to better crop management and production. Monitoring of pests infestation relies on manpower, however automatic monitoring has been advancing in order to minimize human efforts and errors. This study extends the implementation of different image processing techniques to detect and extract insect pests by establishing an automated detection and extraction system for estimating pest densities in paddy fields. Experiment results shows that the proposed system provides a simple, efficient and fast solution in detecting pests in the rice fields.