nils-herrmann/clean_scirepeval huggingface dataset:
Detecting stealth software with Strider GhostBuster

Stealth malware programs that silently infect enterprise and consumer machines are becoming a major threat to the future of the Internet. Resource hiding is a powerful stealth technique commonly used by malware to evade detection by computer users and anti-malware scanners. In this paper, we focus on a subclass of malware, termed "ghostware", which hide files, configuration settings, processes, and loaded modules from the operating system's query and enumeration application programming interfaces (APIs). Instead of targeting individual stealth implementations, we describe a systematic framework for detecting multiple types of hidden resources by leveraging the hiding behavior as a detection mechanism. Specifically, we adopt a cross-view diff-based approach to ghostware detection by comparing a high-level infected scan with a low-level clean scan and alternatively comparing an inside-the-box infected scan with an outside-the-box clean scan. We describe the design and implementation of the Strider GhostBuster tool and demonstrate its efficiency and effectiveness in detecting resources hidden by real-world malware such as rootkits, Trojans, and key-loggers.
TeamBeam - Meta-Data Extraction from Scientific Literature

An important aspect of the work of researchers as well as librarians is to manage collections of scientific literature. Social research networks, such as Mendeley and CiteULike, provide services that support this task. Meta-data plays an important role in providing services to retrieve and organise the articles. In such settings, meta-data is rarely explicitly provided, leading to the need for automatically extracting this valuable information. The TeamBeam algorithm analyses a scientific article and extracts out structured meta-data, such as the title, journal name and abstract, as well as information about the article’s authors (e.g. names, e-mail addresses, affiliations). The input of the algorithm is a set of blocks generated from the article text. A classification algorithm, which takes the sequence of the input into account, is then applied in two consecutive phases. In the evaluation the performance of the algorithm is compared against two heuristics and three existing meta-data extraction systems. Three different data sets with varying characteristics are used to assess the quality of the extraction results. TeamBeam performs well under testing and compares favourably with existing approaches.
No free lunch theorems for optimization

A framework is developed to explore the connection between e ective optimization algorithms and the problems they are solving A number of no free lunch NFL theorems are presented that establish that for any algorithm any elevated performance over one class of problems is exactly paid for in performance over another class These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem Applications of the NFL theorems to information theoretic aspects of optimization and benchmark measures of performance are also presented Other issues addressed are time varying optimization problems and a priori head to head minimax distinctions between optimization algorithms distinctions that can obtain despite the NFL theorems enforcing of a type of uniformity over all algorithms
An in vitro evaluation of graphene oxide reduced by Ganoderma spp. in human breast cancer cells (MDA-MB-231)

Recently, graphene and graphene-related materials have attracted much attention due their unique properties, such as their physical, chemical, and biocompatibility properties. This study aimed to determine the cytotoxic effects of graphene oxide (GO) that is reduced biologically using Ganoderma spp. mushroom extracts in MDA-MB-231 human breast cancer cells.



Herein, we describe a facile and green method for the reduction of GO using extracts of Ganoderma spp. as a reducing agent. GO was reduced without any hazardous chemicals in an aqueous solution, and the reduced GO was characterized using a range of analytical procedures. The Ganoderma extract (GE)-reduced GO (GE-rGO) was characterized by ultraviolet-visible absorption spectroscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, dynamic light scattering, scanning electron microscopy, Raman spectroscopy, and atomic force microscopy. Furthermore, the toxicity of GE-rGO was evaluated using a sequence of assays such as cell viability, lactate dehydrogenase leakage, and reactive oxygen species generation in human breast cancer cells (MDA-MB-231).



The preliminary characterization of reduction of GO was confirmed by the red-shifting of the absorption peak for GE-rGO to 265 nm from 230 nm. The size of GO and GE-rGO was found to be 1,880 and 3,200 nm, respectively. X-ray diffraction results confirmed that reduction processes of GO and the processes of removing intercalated water molecules and the oxide groups. The surface functionalities and chemical natures of GO and GE-rGO were confirmed using Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy. The surface morphologies of the synthesized graphene were analyzed using high-resolution scanning electron microscopy. Raman spectroscopy revealed single- and multilayer properties of GE-rGO. Atomic force microscopy images provided evidence for the formation of graphene. Furthermore, the effect of GO and GE-rGO was examined using a series of assays, such as cell viability, membrane integrity, and reactive oxygen species generation, which are key molecules involved in apoptosis. The results obtained from cell viability and lactate dehydrogenase assay suggest that GO and GE-rGO cause dose-dependent toxicity in the cells. Interestingly, it was found that biologically derived GE-rGO is more toxic to cancer cells than GO.



We describe a simple, green, nontoxic, and cost-effective approach to producing graphene using mushroom extract as a reducing and stabilizing agent. The proposed method could enable synthesis of graphene with potential biological and biomedical applications such as in cancer and angiogenic disorders. To our knowledge, this is the first report using mushroom extract as a reducing agent for the synthesis of graphene. Mushroom extract can be used as a biocatalyst for the production of graphene.
Feature Based Sentiment Analysis for Service Reviews

Sentiment Analysis deals with the analysis of emotions, opinions and facts in the sentences which are expressed by the people. It allows us to track attitudes and feelings of the people by analyzing blogs, comments, reviews and tweets about all the aspects. The development of Internet has strong influence in all types of industries like tourism, healthcare and any business. The availability of Internet has changed the way of accessing the information and sharing their experience among users. Social media provide this information and these comments are trusted by other users. This paper recognizes the use and impact of social media on healthcare industry by analyzing the users’ feelings expressed in the form of free text, thereby gives the quality indicators of services or features related with them. In this paper, a sentiment classifier model using improved Term Frequency Inverse Document Frequency (TFIDF) method and linear regression model has been proposed to classify online reviews, tweets or customer feedback for various features. The model involves the process of gathering online user reviews about hospitals and analyzes those reviews in terms of sentiments expressed. Information Extraction process filters irrelevant reviews, extracts sentimental words of features identified and quantifies the sentiment of features using sentiment dictionary. Emotionally expressed positive or negative words are assigned weights using the classification prescribed in the dictionary. The sentiment analysis on tweets/reviews is done for various features using Natural Language Processing (NLP) and Information Retrieval (IR) techniques. The proposed linear regression model using the senti-score predicts the star rating of the feature of service. The statistical results show that improved TF-IDF method gives better accuracy when compared with TF and TF-IDF methods, used for representing the text. The senti-score obtained as a result of text analysis (user feedback) on features gives not only the opinion summarization but also the comparative results on various features of different competitors. This information can be used by business to focus on the low scored features so as to improve their business and ensure a very high level of user satisfaction.
Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics.

Electronics that are capable of intimate, non-invasive integration with the soft, curvilinear surfaces of biological tissues offer important opportunities for diagnosing and treating disease and for improving brain/machine interfaces. This article describes a material strategy for a type of bio-interfaced system that relies on ultrathin electronics supported by bioresorbable substrates of silk fibroin. Mounting such devices on tissue and then allowing the silk to dissolve and resorb initiates a spontaneous, conformal wrapping process driven by capillary forces at the biotic/abiotic interface. Specialized mesh designs and ultrathin forms for the electronics ensure minimal stresses on the tissue and highly conformal coverage, even for complex curvilinear surfaces, as confirmed by experimental and theoretical studies. In vivo, neural mapping experiments on feline animal models illustrate one mode of use for this class of technology. These concepts provide new capabilities for implantable and surgical devices.
Video Super-Resolution via Bidirectional Recurrent Convolutional Networks

Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.
Multi-sensor multi-object tracking of vehicles using high-resolution radars

Recent advances in automotive radar technology have led to increasing sensor resolution and hence a more detailed image of the environment with multiple measurements per object. This poses several challenges for tracking systems: new algorithms are necessary to fully exploit the additional information and algorithms need to resolve measurement-to-object association ambiguities in cluttered multi-object scenarios. Also, the information has to be fused if multi-sensor setups are used to obtain redundancy and increased fields of view. In this paper, a Labeled Multi-Bernoulli filter for tracking multiple vehicles using multiple high-resolution radars is presented. This finite-set-statistics-based filter tackles all three challenges in a fully probabilistic fashion and is the first Monte Carlo implementation of its kind. The filter performance is evaluated using radar data from an experimental vehicle.
Face Recognition: a Summary of 1995 - 1997

The development of face recognition over the past years allows an organization into three types of recognition algorithms, namely frontal, profile, and view-tolerant recognition, depending on the kind of imagery and the according recognition algorithms. While frontal recognition certainly is the classical approach, view-tolerant algorithms usually perform recognition in a more sophisticated fashion by taking into consideration some of the underlying physics, geometry, and statistics. Profile schemes as stand-alone systems have a rather marginal significance for identification. However, they are very practical either for fast coarse pre-searches of large face databases to reduce the computational load for a subsequent sophisticated algorithm, or as part of a hybrid recognition scheme. Such hybrid approaches have a special status among face recognition systems as they combine different recognition approaches in an either serial or parallel order to overcome the shortcomings of the individual components.
Affective image classification using features inspired by psychology and art theory

Images can affect people on an emotional level. Since the emotions that arise in the viewer of an image are highly subjective, they are rarely indexed. However there are situations when it would be helpful if images could be retrieved based on their emotional content. We investigate and develop methods to extract and combine low-level features that represent the emotional content of an image, and use these for image emotion classification. Specifically, we exploit theoretical and empirical concepts from psychology and art theory to extract image features that are specific to the domain of artworks with emotional expression. For testing and training, we use three data sets: the International Affective Picture System (IAPS); a set of artistic photography from a photo sharing site (to investigate whether the conscious use of colors and textures displayed by the artists improves the classification); and a set of peer rated abstract paintings to investigate the influence of the features and ratings on pictures without contextual content. Improved classification results are obtained on the International Affective Picture System (IAPS), compared to state of the art work.
Learning Structured Output Representation using Deep Conditional Generative Models

Supervised deep learning has been successfully applied to many recognition problems. Although it can approximate a complex many-to-one function well when a large amount of training data is provided, it is still challenging to model complex structured output representations that effectively perform probabilistic inference and make diverse predictions. In this work, we develop a deep conditional generative model for structured output prediction using Gaussian latent variables. The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows for fast prediction using stochastic feed-forward inference. In addition, we provide novel strategies to build robust structured prediction algorithms, such as input noise-injection and multi-scale prediction objective at training. In experiments, we demonstrate the effectiveness of our proposed algorithm in comparison to the deterministic deep neural network counterparts in generating diverse but realistic structured output predictions using stochastic inference. Furthermore, the proposed training methods are complimentary, which leads to strong pixel-level object segmentation and semantic labeling performance on Caltech-UCSD Birds 200 and the subset of Labeled Faces in the Wild dataset.
Competing Cognitive Tactical Networks

Tactical communication networks generally operate in highly congested and contested environments. Traditionally, tactical communication radios and devices are statically configured to operate within a preallocated portions of the electromagnetic (EM) radio-frequency (RF) spectrum prior to deployment. This approach requires minimal coordination among the radios and reduces interference by other devices; however, it limits the radios' ability to take advantage of other unused spectrum resources and to avoid unintentional jamming by friendly forces or intentional jamming by adversaries. The capability of radios to dynamically search for, learn, and access available spectrum resources can greatly improve network performance in a congested environment. Independently from communication radios, friendly electronic warfare (EW) devices have to continuously search for and suppress adversaries' communications and jamming elements while avoiding unintentional jamming of friendly radios. The ability of friendly jamming devices to dynamically sense, learn, and observe spectral activities can lead to more effective jamming strategies and can help minimize self-jamming of friendly radios, especially during missions in which both assets have to coexist. Thus, it is imperative that future communication radios and EW devices be able to dynamically sense, classify, and coordinate access to the EM spectrum for more improved performance under congestion and more robust operations under contention . Before expanding upon strategies for spectrum sharing in the tactical domain, we would like to highlight ongoing spectrum-sharing efforts in the commercial sector. A cognitive-based strategy for transmission channel access addresses the need for an approach that is spectrally agile and efficient under scarce resources and that is resilient to adversarial threats. The strategy's five key components—sensing, learning, optimizing, transmitting, and jamming—are combined to model a tactical communications network, and game-theoretic algorithms and a performance metric are employed in a hypothetical blue-force versus red-force competition for spectrum resources.
An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages

This paper investigates neural characterbased morphological tagging for languages with complex morphology and large tag sets. Character-based approaches are attractive as they can handle rarelyand unseen words gracefully. We evaluate on 14 languages and observe consistent gains over a state-of-the-art morphological tagger across all languages except for English and French, where we match the state-of-the-art. We compare two architectures for computing characterbased word vectors using recurrent (RNN) and convolutional (CNN) nets. We show that the CNN based approach performs slightly worse and less consistently than the RNN based approach. Small but systematic gains are observed when combining the two architectures by ensembling.
Accessing Structured Health Information through English Queries and Automatic Deduction

While much health data is available online, patients who are not technically astute may be unable to access it because they may not know the relevant resources, they may be reluctant to confront an unfamiliar interface, and they may not know how to compose an answer from information provided by multiple heterogeneous resources. We describe ongoing research in using natural English text queries and automated deduction to obtain answers based on multiple structured data sources in a specific subject domain. Each English query is transformed using natural language technology into an unambiguous logical form; this is submitted to a theorem prover that operates over an axiomatic theory of the subject domain. Symbols in the theory are linked to relations in external databases known to the system. An answer is obtained from the proof, along with an English language explanation of how the answer was obtained. Answers need not be present explicitly in any of the databases, but rather may be deduced or computed from the information they provide. Although English is highly ambiguous, the natural language technology is informed by subject domain knowledge, so that readings of the query that are syntactically plausible but semantically impossible are discarded. When a question is still ambiguous, the system can interrogate the patient to determine what meaning was intended. Additional queries can clarify earlier ones or ask questions referring to previously computed answers. We describe a prototype system, Quadri, which answers questions about HIV treatment using the Stanford HIV Drug Resistance Database and other resources. Natural language processing is provided by PARC’s Bridge, and the deductive mechanism is SRI’s SNARK theorem prover. We discuss some of the problems that must be faced to make this approach work, and some of our solutions.
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition

Offline handwriting recognition systems require cropped text line images for both training and recognition. On the one hand, the annotation of position and transcript at line level is costly to obtain. On the other hand, automatic line segmentation algorithms are prone to errors, compromising the subsequent recognition. In this paper, we propose a modification of the popular and efficient Multi-Dimensional Long Short-Term Memory Recurrent Neural Networks (MDLSTM-RNNs) to enable end-to-end processing of handwritten paragraphs. More particularly, we replace the collapse layer transforming the two-dimensional representation into a sequence of predictions by a recurrent version which can select one line at a time. In the proposed model, a neural network performs a kind of implicit line segmentation by computing attention weights on the image representation. The experiments on paragraphs of Rimes and IAM databases yield results that are competitive with those of networks trained at line level, and constitute a significant step towards end-to-end transcription of full documents.
Managing Risk of Bidding in Display Advertising

In this paper, we deal with the uncertainty of bidding for display advertising. Similar to the financial market trading, real-time bidding (RTB) based display advertising employs an auction mechanism to automate the impression level media buying; and running a campaign is no different than an investment of acquiring new customers in return for obtaining additional converted sales. Thus, how to optimally bid on an ad impression to drive the profit and return-on-investment becomes essential. However, the large randomness of the user behaviors and the cost uncertainty caused by the auction competition may result in a significant risk from the campaign performance estimation. In this paper, we explicitly model the uncertainty of user click-through rate estimation and auction competition to capture the risk. We borrow an idea from finance and derive the value at risk for each ad display opportunity. Our formulation results in two risk-aware bidding strategies that penalize risky ad impressions and focus more on the ones with higher expected return and lower risk. The empirical study on real-world data demonstrates the effectiveness of our proposed risk-aware bidding strategies: yielding profit gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on a commercial RTB platform over the widely applied bidding strategies.