nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Integrity Auditing for Shared Data in the Cloud with Secure User Revocation

With the cloud storage services, users can easily form a group and share data with each other. Given the fact that the cloud is not trustable, users need to compute signatures for blocks of the shared data to allow public integrity auditing. Once a user is revoked from the group, the blocks that were previously signed by this revoked user must be re-signed by an existing user, which may result in heavy communication and computation cost for the user. Proxy re-signatures can be used here to allow the cloud to do the re-signing work on behalf of the group. However, a malicious cloud is able to use the re-signing keys to arbitrarily convert signatures from one user to another deliberately. Moreover, collusions between revoked users and a malicious cloud will disclose the secret values of the existing users. In this paper, we propose a novel public auditing scheme for the integrity of shared data with efficient and collusion-resistant user revocation utilizing the concept of Shamir secret sharing. Besides, our scheme also supports secure and efficient public auditing due to our improved polynomial-based authentication tags. The numerical analysis and experimental results demonstrate that our proposed scheme is provably secure and highly efficient.
Endogenous DNA Damage as a Source of Genomic Instability in Cancer

Genome instability, defined as higher than normal rates of mutation, is a double-edged sword. As a source of genetic diversity and natural selection, mutations are beneficial for evolution. On the other hand, genomic instability can have catastrophic consequences for age-related diseases such as cancer. Mutations arise either from inactivation of DNA repair pathways or in a repair-competent background due to genotoxic stress from celluar processes such as transcription and replication that overwhelm high-fidelity DNA repair. Here, we review recent studies that shed light on endogenous sources of mutation and epigenomic features that promote genomic instability during cancer evolution.
Automatic system for determination of blood types using image processing techniques

Determine blood type is essential before administering a blood transfusion, including in emergency situation. Currently, these tests are performed manually by technicians, which can lead to human errors. Various systems have been developed to automate these tests, but none is able to perform the analysis in time for emergency situations. This work aims to develop an automatic system to perform these tests in a short period of time, adapting to emergency situations. To do so, it uses the slide test and image processing techniques using the IMAQ Vision from National Instruments. The image captured after the slide test is processed and detects the occurrence of agglutination. Next the classification algorithm determines the blood type in analysis. Finally, all the information is stored in a database. Thus, the system allows determining the blood type in an emergency, eliminating transfusions based on the principle of universal donor and reducing transfusion reactions risks.
Osteochondritis dissecans of the glenoid cavity: a case report

Osteochondrosis dissecans (OCD) is a form of osteochondrosis limited to the articular epiphysis. The most commonly affected areas include, in decreasing order of frequency, the femoral condyles, talar dome and capitellum of the humerus. OCD rarely occurs in the shoulder joint, where it involves either the humeral head or the glenoid. The purpose of this report is to present a case with glenoid cavity osteochondritis dissecans and clinical and radiological outcome after arthroscopic debridement. The patient underwent arthroscopy to remove the loose body and to microfracture the cavity. The patient was followed-up for 4 years and she is pain-free with full range of motion and a stable shoulder joint.
Modern aspects of mushroom culture technology

The production and culture of new species of mushrooms is increasing. The breeding of new strains has significantly improved, allowing the use of strains with high yield and resistance to diseases, increasing productivity and diminishing the use of chemicals for pest control. The improvement and development of modern technologies, such as computerized control, automated mushroom harvesting, preparation of compost, production of mushrooms in a non-composted substrate, and new methods of substrate sterilization and spawn preparation, will increase the productivity of mushroom culture. All these aspects are crucial for the production of mushrooms with better flavor, appearance, texture, nutritional qualities, and medicinal properties at low cost. Mushroom culture is a biotechnological process that recycles ligninocellulosic wastes, since mushrooms are food for human consumption and the spent substrate can be used in different ways.
Allometry, sexual dimorphism, and phylogeny: a cladistic analysis of extant African papionins using craniodental data.

This study conducts a phylogenetic analysis of extant African papionin craniodental morphology, including both quantitative and qualitative characters. We use two different methods to control for allometry: the previously described narrow allometric coding method, and the general allometric coding method, introduced herein. The results of this study strongly suggest that African papionin phylogeny based on molecular systematics, and that based on morphology, are congruent and support a Cercocebus/Mandrillus clade as well as a Papio/Lophocebus/Theropithecus clade. In contrast to previous claims regarding papionin and, more broadly, primate craniodental data, this study finds that such data are a source of valuable phylogenetic information and removes the basis for considering hard tissue anatomy "unreliable" in phylogeny reconstruction. Among highly sexually dimorphic primates such as papionins, male morphologies appear to be particularly good sources of phylogenetic information. In addition, we argue that the male and female morphotypes should be analyzed separately and then added together in a concatenated matrix in future studies of sexually dimorphic taxa. Character transformation analyses identify a series of synapomorphies uniting the various papionin clades that, given a sufficient sample size, should potentially be useful in future morphological analyses, especially those involving fossil taxa.
Sparse Coral Classification Using Deep Convolutional Neural Networks

Autonomous repair of deep-sea coral reefs is a recent proposed idea to support the oceans ecosystem in which is vital for commercial fishing, tourism and other species. This idea can be operated through using many small autonomous underwater vehicles (AUVs) and swarm intelligence techniques to locate and replace chunks of coral which have been broken off, thus enabling re-growth and maintaining the habitat. The aim of this project is developing machine vision algorithms to enable an underwater robot to locate a coral reef and a chunk of coral on the seabed and prompt the robot to pick it up. Although there is no literature on this particular problem, related work on fish counting may give some insight into the problem. The technical challenges are principally due to the potential lack of clarity of the water and platform stabilization as well as spurious artifacts (rocks, fish, and crabs). We present an efficient sparse classification for coral species using supervised deep learning method called Convolutional Neural Networks (CNNs). We compute Weber Local Descriptor (WLD), Phase Congruency (PC), and Zero Component Analysis (ZCA) Whitening to extract shape and texture feature descriptors, which are employed to be supplementary channels (feature-based maps) besides basic spatial color channels (spatial-based maps) of coral input image, we also experiment state-of-art preprocessing underwater algorithms for image enhancement and color normalization and color conversion adjustment. Our proposed coral classification method is developed under MATLAB platform, and evaluated by two different coral datasets (University of California San Diego’s Moorea Labeled Corals, and Heriot-Watt University’s Atlantic Deep Sea). We are part of a living world, not apart from it
Wearability Factors for Skin Interfaces

As interfaces progress beyond wearables and into intrinsic human augmentation, the human body has become an increasingly important topic in the field of HCI. Wearables already act as a new layer of functionality located on the body that leads us to rethink the convergence between technology and fashion, not just in terms of the ability to wear, but also in how devices interact with us. Already, several options for wearable technology have emerged in the form of clothing and accessories. However, by applying sensors and other computing devices directly onto the body surface, wearables could also be designed as skin interfaces. In this paper, we review the wearability factors impacting wearables as clothes and accessories in order to discuss them in the context of skin interfaces. We classify these wearability factors in terms of body aspects (location, body movements and body characteristics) and device aspects (weight, attachment methods, accessibility, interaction, aesthetics, conductors, insulation, device care, connection, communication, battery life). We discuss these factors in the context of two different example skin interfaces: a rigid board embedded into special effects makeup and skin-mounted soft materials connected to devices.
Limiting efficiency of silicon solar cells

The detailed balance method for calculating the radiative recombination limit to the performance of solar cells has been extended to include free carrier absorption and Auger recombination in addition to radiative losses. This method has been applied to crystalline silicon solar cells where the limiting efficiency is found to be 29.8 percent under AM1.5, based on the measured optical absorption spectrum and published values of the Auger and free carrier absorption coefficients. The silicon is assumed to be textured for maximum benefit from light-trapping effects.
Recognizing cited facts and principles in legal judgements

In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, $$\kappa =0.65$$ κ = 0.65 and $$\kappa =0.95$$ κ = 0.95 for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall $$\kappa$$ κ of 0.72 with the human-annotated gold standard.
Wizard of Wikipedia: Knowledge-Powered Conversational agents

In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. The most popular sequence to sequence models typically “generate and hope” generic utterances that can be memorized in the weights of the model when mapping from input utterance(s) to output, rather than employing recalled knowledge as context. Use of knowledge has so far proved difficult, in part because of the lack of a supervised learning benchmark task which exhibits knowledgeable open dialogue with clear grounding. To that end we collect and release a large dataset with conversations directly grounded with knowledge retrieved from Wikipedia. We then design architectures capable of retrieving knowledge, reading and conditioning on it, and finally generating natural responses. Our best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while our new benchmark allows for measuring further improvements in this important research direction.
Beyond Cookie Monster Amnesia: Real World Persistent Online Tracking

Browser fingerprinting is a relatively new method of uniquely identifying browsers that can be used to track web users. In some ways it is more privacy-threatening than tracking via cookies, as users have no direct control over it. A number of authors have considered the wide variety of techniques that can be used to fingerprint browsers; however, relatively little information is available on how widespread browser fingerprinting is, and what information is collected to create these fingerprints in the real world. To help address this gap, we crawled the 10,000 most popular websites; this gave insights into the number of websites that are using the technique, which websites are collecting fingerprinting information, and exactly what information is being retrieved. We found that approximately 69% of websites are, potentially, involved in first-party or third-party browser fingerprinting. We further found that third-party browser fingerprinting, which is potentially more privacy-damaging, appears to be predominant in practice. We also describe FingerprintAlert, a freely available browser add-on we developed that detects and, optionally, blocks fingerprinting attempts by visited websites.
Improved competitive learning neural networks for network intrusion and fraud detection

In this research, we propose two new clustering algorithms, the improved competitive learning network (ICLN) and the supervised improved competitive learning network (SICLN), for fraud detection and network intrusion detection. The ICLN is an unsupervised clustering algorithm, which applies new rules to the standard competitive learning neural network (SCLN). The network neurons in the ICLN are trained to represent the center of the data by a new reward-punishment update rule. This new update rule overcomes the instability of the SCLN. The SICLN is a supervised version of the ICLN. In the SICLN, the new supervised update rule uses the data labels to guide the training process to achieve a better clustering result. The SICLN can be applied to both labeled and unlabeled data and is highly tolerant to missing or delay labels. Furthermore, the SICLN is capable to reconstruct itself, thus is completely independent from the initial number of clusters. To assess the proposed algorithms, we have performed experimental comparisons on both research data and real-world data in fraud detection and network intrusion detection. The results demonstrate that both the ICLN and the SICLN achieve high performance, and the SICLN outperforms traditional unsupervised clustering
BareBox: efficient malware analysis on bare-metal

Present-day malware analysis techniques use both virtualized and emulated environments to analyze malware. The reason is that such environments provide isolation and system restoring capabilities, which facilitate automated analysis of malware samples. However, there exists a class of malware, called VM-aware malware, which is capable of detecting such environments and then hide its malicious behavior to foil the analysis. Because of the artifacts introduced by virtualization or emulation layers, it has always been and will always be possible for malware to detect virtual environments.
 The definitive way to observe the actual behavior of VM-aware malware is to execute them in a system running on real hardware, which is called a "bare-metal" system. However, after each analysis, the system must be restored back to the previous clean state. This is because running a malware program can leave the system in an instable/insecure state and/or interfere with the results of a subsequent analysis run. Most of the available state-of-the-art system restore solutions are based on disk restoring and require a system reboot. This results in a significant downtime between each analysis. Because of this limitation, efficient automation of malware analysis in bare-metal systems has been a challenge.
 This paper presents the design, implementation, and evaluation of a malware analysis framework for bare-metal systems that is based on a fast and rebootless system restore technique. Live system restore is accomplished by restoring the entire physical memory of the analysis operating system from another, small operating system that runs outside of the target OS. By using this technique, we were able to perform a rebootless restore of a live Windows system, running on commodity hardware, within four seconds. We also analyzed 42 malware samples from seven different malware families, that are known to be "silent" in a virtualized or emulated environments, and all of them showed their true malicious behavior within our bare-metal analysis environment.
The Devil of Face Recognition is in the Noise

The growing scale of face recognition datasets empowers us to train strong convolutional networks for face recognition. While a variety of architectures and loss functions have been devised, we still have a limited understanding of the source and consequence of label noise inherent in existing datasets. We make the following contributions: 1) We contribute cleaned subsets of popular face databases, i.e., MegaFace and MS-Celeb-1M datasets, and build a new large-scale noise-controlled IMDb-Face dataset. 2) With the original datasets and cleaned subsets, we profile and analyze label noise properties of MegaFace and MS-Celeb1M. We show that a few orders more samples are needed to achieve the same accuracy yielded by a clean subset. 3) We study the association between different types of noise, i.e., label flips and outliers, with the accuracy of face recognition models. 4) We investigate ways to improve data cleanliness, including a comprehensive user study on the influence of data labeling strategies to annotation accuracy. The IMDb-Face dataset has been released on https://github.com/fwang91/IMDb-Face.
Prevention and Treatment of the Osmotic Demyelination Syndrome : A Review

The osmotic demyelination syndrome (ODS) is a central nervous system disorder that results from neuronal damage related to abrupt fluctuations of osmolality. In spite of the possibility of full or partial recovery in a considerable proportion of cases, ODS is still categorized as a disorder with poor prognosis that may lead to severe permanent disability or death. Efforts towards the better understanding of the nature of the disorder and the development of effective modes of prevention and treatment continued since Adams, et al., in 1959, first identified the syndrome. Prevention of the ODS that is related to hyponatremia overcorrection starts from the differentiation between chronic and acute hyponatremia, goes through defining a target and method for serum sodium elevation, and ends with re-lowering the unpredicted overly rapid rise of serum sodium. Treatment of the ODS with re-lowering the serum sodium has been evaluated in an animal study and human case reports. Treatment with plasmapheresis and/or intravenous immunoglobulin has been also reported. In the absence of controlled human studies, treatment options for the ODS remain devoid of certainty and validity. Patients who have already developed the syndrome may require longterm intensive supportive therapy looking for a possible complete or partial recovery.
Sentiment Analysis of Twitter Feeds using Machine Learning, Effect of Feature Hash Bit Size

Sentiment Analysis is a way of considering and grouping of opinions or views expressed in a text. In this age when social media technologies are generating vast amounts of data in the form of tweets, Facebook comments, blog posts, and Instagram comments, sentiment analysis of these usergenerated data provides very useful feedback. Since it is undisputable facts that twitter sentiment analysis has become an effective way in determining public sentiment about a certain topic product or issue. Thus, a lot of research have been ongoing in recent years to build efficient models for sentiment classification accuracy and precision. In this work, we analyse twitter data using support vector machine algorithm to classify tweets into positive, negative and neutral sentiments. This research try to find the relationship between feature hash bit size and the accuracy and precision of the model that is generated. We measure the effect of varying the feature has bit size on the accuracy and precision of the model. The research showed that as the feature hash bit size increases at a certain point the accuracy and precision value started decreasing with increase in the feature hash bit size. General Terms Hadoop, Data Processing, Machine learning
Adaptive Preemption of Traffic for Emergency Vehicles

Minimizing travel time is critical for the successful operation of emergency vehicles. Preemption can significantly help emergency vehicles reach the intended destination faster. Majority of the current studies focus on minimizing and/or eliminating delays for EVs and do not consider the negative impacts of preemption on urban traffic. One primary negative impact is extended delays for non-EV traffic due to preemption that is addressed in this paper. We propose an Adaptive Preemption of Traffic (APT) system for Emergency Vehicles in an Intelligent Transportation System. We utilize the knowledge of current traffic conditions in the transportation system to adaptively preempt traffic at signals along the path of EVs so as to minimize, if not eliminate stopped delays for EVs while simultaneously minimizing the delays for non-emergency vehicles in the system. Through extensive simulation results, we show substantial reduction in delays for both EVs.
Live coding youtube: organizing streaming media for an audiovisual performance

Music listening has changed greatly with the emergence of music streaming services, such as Spotify and YouTube. In this paper, we discuss an artistic practice that organizes streaming videos to perform a real-time improvisation via live coding. A live coder uses any available video from YouTube, a video streaming service, as source material to perform an improvised audiovisual piece. The challenge is to manipulate the emerging media that are streamed from a cloud server. The musical gesture can be limited due to the constrained functionalities of the YouTube API. However, the potential sonic and visual space that a musician can explore is practically infinite. The practice embraces the juxtaposition of manipulating emerging media in old-fashioned ways similar to experimental musicians in the 60’s physically manipulating tape loops or scratching vinyl records on a phonograph while exploring the expressiveness enabled by the gigantic repository of all kinds of videos. In this paper, we discuss the challenges of using streaming videos from the platform as musical materials in live music performance and introduce a live coding environment that we developed for real-time improvisation.