nils-herrmann/clean_scirepeval huggingface dataset:
Keyframe-based Video Summarization with Human in the Loop

In this work, we focus on the popular keyframe-based approach for video summarization. Keyframes represent important and diverse content of an input video and a summary is generated by temporally expanding the keyframes to key shots which are merged to a continuous dynamic video summary. In our approach, keyframes are selected from scenes that represent semantically similar content. For scene detection, we propose a simple yet effective dynamic extension of a video Bag-of-Words (BoW) method which provides over segmentation (high recall) for keyframe selection. For keyframe selection, we investigate two effective approaches: local region descriptors (visual content) and optical flow descriptors (motion content). We provide several interesting findings. 1) While scenes (visually similar content) can be effectively detected by region descriptors, optical flow (motion changes) provides better keyframes. 2) However, the suitable parameters of the motion descriptor based keyframe selection vary from one video to another and average performances remain low. To avoid more complex processing, we introduce a human-in-the-loop step where user selects keyframes produced by the three best methods. 3) Our human assisted and learning-free method achieves superior accuracy to learning-based methods and for many videos is on par with average human accuracy.
Safety helmet wearing detection based on image processing and machine learning

Safety helmet wearing detection is very essential in power substation. This paper proposed a innovative and practical safety helmet wearing detection method based on image processing and machine learning. At first, the ViBe background modelling algorithm is exploited to detect motion object under a view of fix surveillant camera in power substation. After obtaining the motion region of interest, the Histogram of Oriented Gradient (HOG) feature is extracted to describe inner human. And then, based on the result of HOG feature extraction, the Support Vector Machine (SVM) is trained to classify pedestrians. Finally, the safety helmet detection will be implemented by color feature recognition. Compelling experimental results demonstrated the correctness and effectiveness of our proposed method.
Running Head : CHLORHEXIDINE GLUCONATE BATHING DNP Practice Inquiry Project Report Evaluation of Chlorhexidine Bathing Practices in Trauma / Surgical Intensive Care Units

The complexity of the healthcare system is increasing, and the moral duty to provide quality patient care is threatened by the sky rocketing cost of healthcare. A major concern for both patients and the hospital’s economic bottom line are hospital-acquired infections (HAIs), including central line associated blood stream infections (CLABSIs). These often serious infections result in significantly increased patient morbidity, mortality, length of stay, and use of health care resources. Historically, most infection prevention and control measures have focused on aseptic technique of health care providers and in managing the environment. Emerging evidence for the role of host decontamination in preventing HAIs is shifting the paradigm and paving a new path for novel infection prevention interventions. Chlorhexidine gluconate has a long-standing track record of being a safe and effective product with broad antiseptic activity, and little evidence of emerging resistance. As the attention is directed toward control and prevention of HAIs, chlorhexidine-containing products may prove to be a vital tool in infection control. Increasing rates of multidrug-resistant organisms (MDROs), including methicillinresistant Staphylococcus aureus (MRSA), Acinetobacter baumanniic and vancomycin-resistant Enterococcus (VRE) demand that evidence-based research drive all interventions to prevent transmission of these organisms and the development of HAIs. This review of literature examines current evidence related to daily chlorhexidine gluconate bathing and its impact on CLABSI rates in the adult critically ill patient population.
Improved Intrusion Detection in DDoS Applying Feature Selection Using Rank &amp; Score of Attributes in KDD-99 Data Set

In today's networked environment, massive volume of data being generated, gathered and stored in databases across the world. This trend is growing very fast, year after year. Today it is normal to find databases with terabytes of data, in which vital information and knowledge is hidden. The unseen information in such databases is not feasible to mine without efficient mining techniques for extracting information. In past years many algorithms are created to extract knowledge from large sets of data. There are many different methodologies to approach data mining: classification, clustering, association rule, etc. Classification is the most conventional technique to analyse the large data sets. Classification can help identify intrusions, as well as for discovering new and unknown types of intrusions. For classification, feature selection provides an efficient mechanism to analyse the dataset. We are trying to analyse the NSL-KDD cup 99, dataset using various classification algorithms. Primary experiments are performed in WEKA environment. The accuracy of the various algorithms is also calculated. A feature selection method has been implemented to provide improved accuracy. The main objective of this analysis is to deliver the broad analysis feature selection methods for NSL-KDD intrusion detection dataset.
Intrusion Detection Systems - Analysis and Containment of False Positives Alerts

The dependence on information technology became critical and IT infrastructure, critical data, intangible intellectual property are vulnerable to threats and attacks. Organizations install Intrusion Detection Systems (IDS) to alert suspicious traffic or activity. IDS generate a large number of alerts and most of them are false positive as the behavior construe for partial attack pattern or lack of environment knowledge. Monitoring and identifying risky alerts is a major concern to security administrator. The present work is to design an operational model for minimization of false positive alarms, including recurring alarms by security administrator. The architecture, design and performance of model in minimization of false positives in IDS are explored and the experimental results are presented with reference to lab environment.
Emerging Robotic Platforms for Minimally Invasive Surgery

Recent technological advances in surgery have resulted in the development of a range of new techniques that have reduced patient trauma, shortened hospitalization, and improved diagnostic accuracy and therapeutic outcome. Despite the many appreciated benefits of minimally invasive surgery (MIS) compared to traditional approaches, there are still significant drawbacks associated with conventional MIS including poor instrument control and ergonomics caused by rigid instrumentation and its associated fulcrum effect. The use of robot assistance has helped to realize the full potential of MIS with improved consistency, safety and accuracy. The development of articulated, precision tools to enhance the surgeon's dexterity has evolved in parallel with advances in imaging and human-robot interaction. This has improved hand-eye coordination and manual precision down to micron scales, with the capability of navigating through complex anatomical pathways. In this review paper, clinical requirements and technical challenges related to the design of robotic platforms for flexible access surgery are discussed. Allied technical approaches and engineering challenges related to instrument design, intraoperative guidance, and intelligent human-robot interaction are reviewed. We also highlight emerging designs and research opportunities in the field by assessing the current limitations and open technical challenges for the wider clinical uptake of robotic platforms in MIS.
Optical Characteristics of Solution Processed MoO2/ZnO Quantum Dots Based Thin Film Transitor

The present work reports the ultra-violet (UV) and electrical characteristics of solution processed MoO2/ZnO Quantum Dot (QD) based thin film transistor, ZnO QDs thin films are deposited using low-cost solution processing over the Al2O3/p-Si substrates. A very thick layer of Al2O3 (250 nm) is deposited using e-beam evaporation (10-6 mbar). MoO2 is deposited using the thermal evaporation over the ZnO thin film and the achieved thickness is of 25nm. Top and bottom Al contacts were deposited using thermal evaporation and of 50nm and 80nm respectively. Device is characterized in the form of back gate ZnO Thin film transistor (TFT), illuminated from the front side. The UV detection property of the device is performed under the illumination of UV lamp (Puv = 650µW) with wavelength of 365nm. The results obtained with the Vds versus Ids plot shows the significant improvement in the drain current with UV exposure.
A case of seizures induced by abstract reasoning

We describe a case of reflex seizures induced by abstract reasoning but not other cognitive processes. The patient, a 46-year-old man, experienced myoclonic seizures whenever he played shogi (Japanese chess). To identify the critical thought processes responsible for inducing his seizures, we monitored his clinical seizures and epileptiform discharges while he performed comprehensive neuropsychological tests, including the Wechsler Adult Intelligence Scale-Revised (WAIS-R), spatial working memory, mental rotation, and Wisconsin Card Sorting Test (WCST) tasks. A myoclonic seizure occurred only during the WCST. Generalized 3- to 5-Hz spike-and-slow-wave bursts occurred repeatedly during the Block Design subtest of the WAIS-R and the WCST, whereas no discharges occurred during other subtests of the WAIS-R including the calculation, spatial working memory, and mental rotation tasks. These results indicate that abstract reasoning, independent of other cognitive processes, could induce the patient's epileptiform discharges, suggesting that his reflex seizures might be a distinct subtype of nonverbal thinking-induced seizures.
Evolutionary ensembles with negative correlation learning

Based on negative correlation learning and evolutionary learning, this paper presents evolutionary ensembles with negative correlation learning (EENCL) to address the issues of automatic determination of the number of individual neural networks (NNs) in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of EENCL is to encourage di erent individual NNs in the ensemble to learn di erent parts or aspects of the training data so that the ensemble can better learn the entire training data. The cooperation and specialization among di erent individual NNs are considered during the individual NN design. This provides an opportunity for di erent NNs to interact with each other and to specialize. Experiments on two real-world problems demonstrate that EENCL can produce NN ensembles with good generalization ability.
Agitation after traumatic brain injury: considerations and treatment options.

Posttraumatic agitation is a challenging problem for acute and rehabilitation staff, persons with traumatic brain injury, and their families. Specific variables for evaluation and care remain elusive. Clinical trials have not yielded a strong foundation for evidence-based practice in this arena. This review seeks to evaluate the present literature (with a focus on the decade 1995-2005) and employ previous clinical experience to deliver a review of the topic. We will discuss definitions, pathophysiology, evaluation techniques, and treatment regimens. A recommended approach to the evaluation and treatment of the person with posttraumatic agitation will be presented. The authors hope that this review will spur discussion and assist in facilitating clinical care paradigms and research programs.
Use of Social Media for Disaster Management: A Prescriptive Framework

Social media is emerging as an important information-based communication tool for disaster management. Yet there are many relief organizations that are not able to develop strategies and allocate resources to effectively use social media for disaster management. The reason behind this inability may be a lack of understanding regarding the different functionalities of social media. In this paper, we examine the literature using content analysis to understand the current usage of social media in disaster management. We draw on the honeycomb framework and the results of our content analysis to suggest a new framework that can help in utilizing social media more effectively during the different phases of disaster management. We also discuss the implications of our study.  Management, Disaster Phases, Honeycomb Framework, Social Media Functionality, Social Media
CLOUD-BASED DESIGN AND MANUFACTURING SYSTEMS : A SOCIAL NETWORK ANALYSIS

A Cloud-Based Design and Manufacturing (CBDM) System refers to an information and communication technology (ICT) system that facilitates design and manufacturing knowledge sharing between actors (e.g., CBDM service providers and consumers) in the distributed and collaborative socio-technical network. The aim of this study is to address the challenge of information sharing and technical communication during the CBDM product development process. Specifically, we model a CBDM system as a socio-technical network. The research questions are: (1) What measures can be used to analyze the socio-technical network generated by CBDM? (2) How to detect communities/clusters and key actors in the socio-technical network? To answer these questions, a social network analysis (SNA) approach is formulated to analyze the socio-technical network generated by CBDM systems. The results indicate that SNA allows for visualizing collaborative relationship patterns of actors as well as detecting the community structure of CBDM systems.
Depth Estimation with Occlusion Modeling Using Light-Field Cameras

Light-field cameras have become widely available in both consumer and industrial applications. However, most previous approaches do not model occlusions explicitly, and therefore fail to capture sharp object boundaries. A common assumption is that for a Lambertian scene, a pixel will exhibit photo-consistency, which means all viewpoints converge to a single point when focused to its depth. However, in the presence of occlusions this assumption fails to hold, making most current approaches unreliable precisely where accurate depth information is most important - at depth discontinuities. In this paper, an occlusion-aware depth estimation algorithm is developed; the method also enables identification of occlusion edges, which may be useful in other applications. It can be shown that although photo-consistency is not preserved for pixels at occlusions, it still holds in approximately half the viewpoints. Moreover, the line separating the two view regions (occluded object versus occluder) has the same orientation as that of the occlusion edge in the spatial domain. By ensuring photo-consistency in only the occluded view region, depth estimation can be improved. Occlusion predictions can also be computed and used for regularization. Experimental results show that our method outperforms current state-of-the-art light-field depth estimation algorithms, especially near occlusion boundaries.
Weed Recognition Based on Erosion and Dilation Segmentation Algorithm

Farmer needs alternatives for weed control due to the desire to reduce chemicals used in farming. However, conventional mechanical cultivation cannot selectively remove weeds and there are no selective herbicides for some weed situation. Since hand labor is costly, an automated weed control system could be feasible. A robotic weed control system can also reduce or eliminate the need for chemicals. Many attempts have been made to develop efficient algorithms for recognition and classification. Currently research is going on for developing new machine vision algorithms for automatic recognition and classification of many divers object groups. In this paper an algorithm is developed for automatic spray control system. The algorithm is based on erosion followed by dilation segmentation algorithm. This algorithm can detect weeds and also classify it. Currently the algorithm is tested on two types of weeds i.e. broad and narrow. The developed algorithm has been tested on these two types of weeds in the lab, which gives a very reliable performance. The algorithm is applied on 240 images stored in a database in the lab, of which 100 images were taken from broad leaf weeds and 100 were taken from narrow leaf weeds, and the remaining 40 were taken from no or little weeds. The result showed over 89% results
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks

In this paper1, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of the alphaGo Zero model. Unlike previous studies that visualized image appearances corresponding to the network output or a neural activation only from a global perspective, our research aims to clarify how a certain input unit (dimension) collaborates with other units (dimensions) to constitute inference patterns of the neural network and thus contribute to the network output. The analysis of local contextual effects w.r.t. certain input units is of special values in real applications. Explaining the logic of the alphaGo Zero model is a typical application. In experiments, our method successfully disentangled the rationale of each move during the Go game.
Analysis and detection of speech under sleep deprivation

Stress has effect on speech characteristics and can influence the quality of speech. In this paper, we study the effect of SleepDeprivation (SD) on speech characteristics and classify Normal Speech (NS) and Sleep Deprived Speech (SDS). One of the indicators of sleep deprivation is flattened voice. We examine pitch and harmonic locations to analyse flatness of voice. To investigate, we compute the spectral coefficients that can capture the variations of pitch and harmonic patterns. These are derived using Two-Layer Cascaded-Subband Filter spread according to the pitch and harmonic frequency scale. Hidden Markov Model (HMM) is employed for statistical modeling. We use DCIEM map task corpus to conduct experiments. The analysis results show that SDS has less variation of pitch and harmonic pattern than NS. In addition, we achieve the relatively high accuracy for classification of Normal Speech (NS) and Sleep Deprived Speech (SDS) using proposed spectral coefficients.
Lean collaboration through video gestures: co-ordinating the production of live televised sport

This paper examines the work and interactions between camera operators and a vision mixer during an ice hockey match, and presents an interaction analysis using video data. We analyze video-mediated indexical gestures in the collaborative production of live sport on television between distributed team members. The findings demonstrate how video forms the topic, resource and product of collabora-tion: whilst it shapes the nature of the work (editing), it is simultaneously also the primary resource for supporting mutual orientation and negotiating shot transitions between remote participants (co-ordination), as well as its end prod-uct (broadcast). Our analysis of current professional activi-ties is used to develop implications for the design of future services for live collaborative video production.
An Isomorphism Between Substructural Proofs and Deterministic Finite Automata

Proof theory is a powerful tool for understanding computational phenomena, as most famously exemplified by the Curry–Howard isomorphism between intuitionistic logic and the simply-typed λ-calculus. In this paper, we identify a fragment of intuitionistic linear logic with least fixed points and establish a Curry–Howard isomorphism between a class of proofs in this fragment and deterministic finite automata. Proof-theoretically, closure of regular languages under complementation, union, and intersection can then be understood in terms of cut elimination. We also establish an isomorphism between a different class of proofs and subsequential string transducers. Because prior work has shown that linear proofs can be seen as session-typed processes, a concurrent semantics of transducer composition is obtained for free.
Ultrastructural characterization of male and female Physaloptera rara (Spirurida: Physalopteridae): feline stomach worms

Physaloptera rara (Spirurida: Physalopteridae) has been found in dogs, coyotes, raccoons, wolves, foxes, cats, and bobcats in North America. The parasites’ developmental cycles involve insects, including beetles, cockroaches, and crickets, as intermediate hosts. The nematodes firmly attach to the wall of the stomach and duodenum, where they feed on the mucosa and suck blood. Frequent movement of these nematodes results in erosions and ulcers in the gastrointestinal tract. The present study reports the morphological features of adult P. rara using scanning electron microscopy. Adult worms were removed from the stomach of an infected domestic cat. Male and female worms measured 25–29 and 27–41 mm, respectively. The worms were stout and the cuticle was reflected over the lips to form a large cephalic collarette with fine transverse striations. The worms possessed two large, simple triangular lateral pseudolabia, each armed with one external tooth, three internal teeth, two submedian cephalic papillae, an amphid, and three porous-like circumscribed regions. The internal margins of the lips had a pair of cuticular folds. At the anterior end of both male and female worms, an excretory pore was located on the ventral side and a pair of lateral ciliated cervical papillae was seen. The vulva was anterior to the middle of the body of female worms. The tail ends of the female worms were stumpy, with two large phasmids near their extremities. The males’ tails bore large lateral alae. Ventral ornamentation, in male worms, was composed of three different cuticular patterns; coblestone-like formations, longitudinal cuticular ridges, and rows of bead-like structures. The spicules were unequal and dissimilar; the right spicule had a thick end and the left spicule had a sharp tip. At the posterior end of the males, four pairs of stalked precloacal papillae, three pairs of postcloacal papillae, and two phasmids were present. Three and four sessile papillae were seen directly anterior and posterior to the cloaca, respectively. The middle papilla of the three sessile papillae, directly anterior to cloaca was different in shape and size.
Handwritten digit recognition via active belief decision trees

Considering uncertainty within the learning methods is attracting more and more attentions in recent years, yet not much work has been done when it comes to the area of handwritten digit recognition. In this paper, the active belief decision tree is used to handle the epistemic uncertainty in the practical application. The detailed feature extraction method for belief decision trees is proposed and discussed. Under the conditions such as lack of knowledge about true labels, few instances to train the model and low-dimensional image features, we still achieve a good classification performance as shown in experiments with data from the commonly used MNIST handwritten digits benchmark.