nils-herrmann/clean_scirepeval huggingface dataset:
Gotcha - Sly Malware!: Scorpion A Metagraph2vec Based Malware Detection System

Due to its severe damages and threats to the security of the Internet and computing devices, malware detection has caught the attention of both anti-malware industry and researchers for decades. To combat the evolving malware attacks, in this paper, we first study how to utilize both content- and relation-based features to characterize sly malware; to model different types of entities (i.e., file, archive, machine, API, DLL ) and the rich semantic relationships among them (i.e., file-archive, file-machine, file-file, API-DLL, file-API relations), we then construct a structural heterogeneous information network (HIN) and present meta-graph based approach to depict the relatedness over files. To measure the relatedness over files on the constructed HIN, since malware detection is a cost-sensitive task, it calls for efficient methods to learn latent representations for HIN. To address this challenge, based on the built meta-graph schemes, we propose a new HIN embedding model metagraph2vec on the first attempt to learn the low-dimensional representations for the nodes in HIN, where both the HIN structures and semantics are maximally preserved for malware detection. A comprehensive experimental study on the real sample collections from Comodo Cloud Security Center is performed to compare various malware detection approaches. The promising experimental results demonstrate that our developed system Scorpion which integrate our proposed method outperforms other alternative malware detection techniques. The developed system has already been incorporated into the scanning tool of Comodo Antivirus product.
Relevance feedback for Content-based Image Retrieval using deep learning

The “semantic gap” issue which exists the low-level image pixels captured by machines and high-level semantic concepts perceived by human has always been a key challenge for lots of applications, such as computer vision, pattern recognition, Content-based Image Retrieval (CBIR). The recent success of deep learning researches bring a hope for bridging the semantic gap. Many researchers have attempted to explore deep learning techniques with applying to CBIR tasks. However, it still has some restrictions for CBIR tasks, because the machine could not well comprehend the semantic concepts from low-level pixels to high-level semantic concepts. In this paper, we introduced the Relevance Feedback (RF) Model , which captures the users' feedback information. When the system returned the initial retrieval results to the user and the initial retrieval results meet the demand of the user, the retrieval process is called off. Generally, relevance feedback is usually added due to the poor results of retrieval. Our CBIR system asks the user to feedback the information related to query image. So the system obtains the information of semantically similar and dissimilar images for the positive and negative feedback samples. Then the initial retrieval results are resorted based the updated user's feedback information. When the resorted results meet the demand of the user, RF model is stop and return the final optimal results to users. the RF is performed iteratively until the user is satisfied with the refined results. Our proposed feedback model is optimized for each user's results. From our empirical studies, deep learning combining with RF model significantly outperforms only applying deep learning for CBIR tasks.
A New Polynomial-Time Algorithm for Linear Programming

We present a new polynomial-time algorithm for linear programming. The running-time of this algorithm is <italic>O</italic>(<italic>n</italic><supscrpt>3-5</supscrpt><italic>L</italic><supscrpt>2</supscrpt>), as compared to <italic>O</italic>(<italic>n</italic><supscrpt>6</supscrpt><italic>L</italic><supscrpt>2</supscrpt>) for the ellipsoid algorithm. We prove that given a polytope <italic>P</italic> and a strictly interior point <italic>a</italic> ε <italic>P</italic>, there is a projective transformation of the space that maps <italic>P</italic>, <italic>a</italic> to <italic>P'</italic>, <italic>a'</italic> having the following property. The ratio of the radius of the smallest sphere with center <italic>a'</italic>, containing <italic>P'</italic> to the radius of the largest sphere with center <italic>a'</italic> contained in <italic>P'</italic> is <italic>O</italic> (<italic>n</italic>). The algorithm consists of repeated application of such projective transformations each followed by optimization over an inscribed sphere to create a sequence of points which converges to the optimal solution in polynomial-time.
Roombots—Towards decentralized reconfiguration with self-reconfiguring modular robotic metamodules

This paper presents our work towards a decentralized reconfiguration strategy for self-reconfiguring modular robots, assembling furniture-like structures from Roombots (RB) metamodules. We explore how reconfiguration by locomotion from a configuration A to a configuration B can be controlled in a distributed fashion. This is done using Roombots metamodules—two Roombots modules connected serially—that use broadcast signals, lookup tables of their movement space, assumptions about their neighborhood, and connections to a structured surface to collectively build desired structures without the need of a centralized planner.
Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring

I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public.  has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further
Image Segmentation and Identification of Brain Tumor from MRI Image

The image processing is an important aspect of medical science to visualize the different anatomical structures of human body. Sometimes it is very difficult or impossible to detect or visualize such hidden abnormal structures by using simple imaging. For such abnormalitical structures, some planar imaging methods are helpful. One of the significant techniques for examining human body is the Magnetic Resonance Imaging (MRI). It is useful for distinguishing and clarifying the neural architecture of human brain. Magnetic resonance imaging (MRI) technique is one of the many imaging modalities that are available to scan and capture the internal soft tissue structures of the body. This paper describes the proposed strategy to detect & extraction of brain tumor from patient’s MRI scan images of the brain. It includes some noise removal functions, segmentation and morphological operations which are the basic terms of image processing. By using MATLAB software we can detect and extract tumor from MRI scan images of the brain.
Software-defined optical networks (SDONs): a survey

This paper gives an overview of software-defined optical networks (SDONs). It explains the general concepts on software-defined networks (SDNs), their relationship with network function virtualization, and also about OpenFlow, which is a pioneer protocol for SDNs. It then explains the benefits and challenges of extending SDNs to multilayer optical networks, including flexible grid and elastic optical networks, and how it compares to generalized multi-protocol label switching for implementing a unified control plane. An overview on the industry and research efforts on SDON standardization and implementation is given next, to bring the reader up to speed with the current state of the art in this field. Finally, the paper outlines the benefits achieved by SDONs for network operators, and also some of the important and relevant research problems that need to be addressed.
Reducing Test Time of Power Constrained Test by Optimal Selection of Supply Voltage

As supply voltage is reduced, a power constrained test clock can be sped up in spite of the increased delay of the circuit. However, a large reduction in voltage makes the operation structurally constrained, requiring the clock to slow down. We determine an optimum supply voltage that allows fastest clock speed for a given power limit. Examples show that the test time can be reduced by as much as 63% from the nominal voltage test time. In a typical application, this technique can reduce the cost of wafer sort.
Image plane interaction techniques in 3D immersive environments

This paper presents a set of interaction techniques for use in headtracked immersive virtual environments. With these techniques, the user interacts with the 2D projections that 3D objects in the scene make on his image plane. The desktop analog is the use of a mouse to interact with objects in a 3D scene based on their projections on the monitor screen. Participants in an immersive environment can use the techniques we discuss for object selection, object manipulation, and user navigation in virtual environments.
Learning Monocular Depth Estimation with Unsupervised Trinocular Assumptions

Obtaining accurate depth measurements out of a single image represents a fascinating solution to 3D sensing. CNNs led to considerable improvements in this field, and recent trends replaced the need for ground-truth labels with geometry-guided image reconstruction signals enabling unsupervised training. Currently, for this purpose, state-of-the-art techniques rely on images acquired with a binocular stereo rig to predict inverse depth (i.e., disparity) according to the aforementioned supervision principle. However, these methods suffer from well-known problems near occlusions, left image border, etc inherited from the stereo setup. Therefore, in this paper, we tackle these issues by moving to a trinocular domain for training. Assuming the central image as the reference, we train a CNN to infer disparity representations pairing such image with frames on its left and right side. This strategy allows obtaining depth maps not affected by typical stereo artifacts. Moreover, being trinocular datasets seldom available, we introduce a novel interleaved training procedure enabling to enforce the trinocular assumption outlined from current binocular datasets. Exhaustive experimental results on the KITTI dataset confirm that our proposal outperforms state-of-the-art methods for unsupervised monocular depth estimation trained on binocular stereo pairs as well as any known methods relying on other cues.
Foundations of AI: The Big Issues

Kirsh, D.,  the big issues, Artificial Intelligence 47 (1991) 3-30. The objective of research in the foundations of AI is to explore such basic questions as: What is a theory in AI? What are the most abstract assumptions underlying the competing visions of intelligence? What are the basic arguments for and against each assumption? In this essay I discuss five foundational issues: (1) Core AI is the study of conceptualization and should begin with knowledge level theories. (2) Cognition can be studied as a disembodied process without solving the symbol grounding problem. (3) Cognition is nicely described in propositional terms. (4) We can study cognition separately from learning. (5) There is a single architecture underlying virtually all cognition. I explain what each of these implies and present arguments from both outside and inside AI why each has been seen as right or wrong.
Biogeography-Based Optimization: A 10-Year Review

Biogeography-based optimization (BBO) is an evolutionary algorithm which is inspired by the migration of species between habitats. Almost 10 years have passed since the first BBO paper was published in 2008. BBO has successfully solved optimization problems in many different domains and has reached a relatively mature state. Considering the significant and expanding research on BBO and its applications, we find that the time is right to provide a 10-year anniversary review of the published literature, and also to point out some important avenues of future research. The purpose of this paper is to summarize and organize the literature related to the past 10 years of BBO research. Beginning with a foundation of basic BBO, we review the family of BBO algorithms and discuss BBO modifications, hybridizations, applications in science and engineering, and mathematical theory. Finally, the paper presents some interesting open problems and future research directions for BBO.
Subgraph-centric Large-Scale Graph Analytics on Spark

Over the past several years, large-scale graph analytics has become a quickly growing research area. Practical computing problems often deal with large graphs such as social networks and the Web graph, and as these graphs have grown to billions of vertices and trillions of edges, there has been an increased focus on executing graph computations in a distributed fashion. Vertex-centric distributed graph processing platforms like Pregel were some of the first systems meant specifically for distributed graph processing. Although these systems have vastly simplified the implementation of certain distributed graph processing algorithms, they are not well-suited for graph processing tasks that need to consider vertices in the context of their neighbors rather than in isolation. As such, one emerging paradigm is the use of a subgraph-centric view of graph computation in which users write programs at the level of subgraphs rather than at the level of vertices. This paper elucidates the fundamental differences between the vertex-centric and subgraph-centric paradigms, outlines an implementation of a subgraph-centric graph processing system called NScale on Apache Spark, a widely used distributed dataflow system, and introduces a novel interface for subgraph specification unseen in prior subgraph-centric graph processing frameworks.
Improvement of pixel electrode circuit for active-matrix OLED by application of reversed-biased voltage

In this paper, an improved ac pixel electrode circuit for active-matrix organic light-emitting display (AMOLED) has been proposed by adding a thin-film transistor. This circuit can provide an ac driving mode for AMOLED and makes the OLED in a reversed-biased voltage during the reverse cycle. And a circuit design for understanding ac driving mode was presented. The circuit simulation results indicate that this circuit is feasible. The circuit structure is practical for the AMOLED pixel driver; it can improve the performance of OLED.
(Invited) Nanoimprinted perovskite for optoelectronics

Organic-inorganic hybrid perovskites have recently emerged as promising materials for optoelectronics. Here we show successful patterning of hybrid perovskite into nanostructures with cost-effective nanoimprint technology. Photodetectors are fabricated on nanoimprinted perovskite with improved responsivity. Nanoimprinted perovskite metasurface forms with significantly enhanced photoluminescence. Lasing is expected on nanoimprinted perovskite with optimized cavity design and process.
Designed Interdependence, Growing Social Capital and Knowledge Sharing in IS Project Teams

This study brings in the task interdependence as the antecedent for growing team social capital and increasing knowledge sharing among team members in IS project context in which heterogeneous members need to interact very closely. Data collected from 203 IS projects largely confirm the proposed research model. Between the task interdependence and team performance, the team social capital as well as the knowledge sharing among members fully mediates the relationship. Interestingly, direct path from task interdependence onto performance is found to be insignificant, rebutting the suggestions of the work design theory which says well designed interdependence directly increase performance in heterogeneous teams. Findings of our study signify the criticality of the mediating roles of social capital and knowledge sharing. Additionally, it is also confirmed that the social capital grows with time. Also, longer term projects revealed a bit different mechanism than shorter ones. Implications are discussed with limitations and further studies.
Medical image processing on the GPU - Past, present and future

Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.
CODA: high dimensional copula discriminant analysis

We propose a high dimensional classification method, named the Copula Discriminant Analysis (CODA). The CODA generalizes the normal-based linear discriminant analysis to the larger Gaussian Copula models (or the nonparanormal) as proposed by Liu et al. (2009). To simultaneously achieve estimation efficiency and robustness, the nonparametric rank-based methods including the Spearman’s rho and Kendall’s tau are exploited in estimating the covariance matrix. In high dimensional settings, we prove that the sparsity pattern of the discriminant features can be consistently recovered with the parametric rate, and the expected misclassification error is consistent to the Bayes risk. Our theory is backed up by careful numerical experiments, which show that the extra flexibility gained by the CODA method incurs little efficiency loss even when the data are truly Gaussian. These results suggest that the CODA method can be an alternative choice besides the normal-based high dimensional linear discriminant analysis.
Closed-Loop Analysis and Cascade Control of a Nonminimum Phase Boost Converter

In this paper, a cascade controller is designed and analyzed for a boost converter. The fast inner current loop uses sliding-mode control. The slow outer voltage loop uses the proportional-integral (PI) control. Stability analysis and selection of PI gains are based on the nonlinear closed-loop error dynamics. It is proven that the closed-loop system has a nonminimum phase behavior. The voltage transients and reference voltage are predictable. The current ripple and system sensitivity are studied. The controller is validated by a simulation circuit with nonideal circuit parameters, different circuit parameters, and various maximum switching frequencies. The simulation results show that the reference output voltage is well tracked under parametric changes, system uncertainties, or external disturbances with fast dynamic transients, confirming the validity of the proposed controller.
Rethinking Connection Security Indicators

We propose a new set of browser security indicators, based on user research and an understanding of the design challenges faced by browsers. To motivate the need for new security indicators, we critique existing browser security indicators and survey 1,329 people about Google Chrome’s indicators. We then evaluate forty icons and seven complementary strings by surveying thousands of respondents about their perceptions of the candidates. Ultimately, we select and propose three indicators. Our proposed indicators have been adopted by Google Chrome, and we hope to motivate others to update their security indicators as well.
An image encryption scheme based on elliptic curve pseudo random and Advanced Encryption System

Elliptic curve cryptography (ECC) has proven to be an effective cryptography. ECC has its own advantages such as efficient key size compared to other public key infrastructures. This paper exploits the Elliptic curve random generator defined by National Institute of Standards and Technology (NIST) to generate a sequence of arbitrary numbers based on curves. The random generation phase is based on public shared key and a changing point G, which is a generator of a curve to obtain random sequences. Then, Advanced Encryption System is applied to these sequences acquiring arbitrary keys for encrypting image. Using AES alongside well distributed randoms provides a prominent encryption technique. Our experiments show that the proposed method fulfills the basics of cryptography including simpleness and correctness. Moreover, the results of the evaluation prove the effectiveness and security of the proposed method.
Memristor-Based Cellular Nonlinear/Neural Network: Design, Analysis, and Applications

Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively parallel architecture capable of solving complex engineering problems by performing trillions of analog operations per second. The memristor was theoretically predicted in the late seventies, but it garnered nascent research interest due to the recent much-acclaimed discovery of nanocrossbar memories by engineers at the Hewlett-Packard Laboratory. The memristor is expected to be co-integrated with nanoscale CMOS technology to revolutionize conventional von Neumann as well as neuromorphic computing. In this paper, a compact CNN model based on memristors is presented along with its performance analysis and applications. In the new CNN design, the memristor bridge circuit acts as the synaptic circuit element and substitutes the complex multiplication circuit used in traditional CNN architectures. In addition, the negative differential resistance and nonlinear current-voltage characteristics of the memristor have been leveraged to replace the linear resistor in conventional CNNs. The proposed CNN design has several merits, for example, high density, nonvolatility, and programmability of synaptic weights. The proposed memristor-based CNN design operations for implementing several image processing functions are illustrated through simulation and contrasted with conventional CNNs. Monte-Carlo simulation has been used to demonstrate the behavior of the proposed CNN due to the variations in memristor synaptic weights.