nils-herrmann/clean_scirepeval huggingface dataset:
Traffic road line detection based on the vanishing point and contour information

This paper describes an algorithm to detect the road lane boundary for a driver who changes lane as ‘access road’, ‘passing lane’, ‘approach ramp’, etc. The proposed algorithm starts with binarization and labeling for the image. The binary image is generated by adaptive threshold. The binary image is labeled recursively to blobs. From each blob, the algorithm analyses principal axes calculated by Eigen value and vector. Those are based on the covariance matrix of pixel distribution for the blobs. Then it shows intersection points by the combination of each long principal axis. Among intersection points, the highest density cluster is selected to a dominant vanishing point as the parallel road lines. In experiments of road line detection from the urban road image, the proposed method shows the effectiveness of lane boundary detection in the rate of more than 94.3%.
Data fusion - Resolving Data Conflicts for Integration

The amount of information produced in the world increases by 30% every year and this rate will only go up. With advanced network technology, more and more sources are available either over the Internet or in enterprise intranets. Modern data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, often require integrating available data sources and providing a uniform interface for users to access data from different sources; such requirements have been driving fruitful research on data integration over the last two decades [13, 15]. Data integration systems face two folds of challenges. First, data from disparate sources are often heterogeneous. Heterogeneity can exist at the schema level, where different data sources often describe the same domain using different schemas; it can also exist at the instance level, where different sources can represent the same real-world entity in different ways. There has been rich body of work on resolving heterogeneity in data, including, at the schema level, schema mapping and matching [17], model management , answering queries using views , data exchange [10], and at the instance level, record linkage (a.k.a., entity resolution, object matching, reference linkage, etc.) [9, 18], string similarity comparison , etc. Second, different sources can provide conflicting data. Conflicts can arise because of incomplete data, erroneous data, and out-of-date data. Returning incorrect data in a query result can be misleading and even harmful: one may contact a person by an out-of-date phone number, visit a clinic at a wrong address, carry wrong knowledge of the real world, and even make poor business decisions. It is thus critical for data integration systems to resolve conflicts from various sources and identify true values from false ones. This problem becomes especially prominent with the ease of publishing and spreading false information on the Web and has recently received increasing attention. This tutorial focuses on data fusion, which addresses the second challenge by fusing records on the same real-world
Unsupervised Learning of Long-Term Motion Dynamics for Videos

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, depth, and RGB-D videos.
3D printing: Basic concepts mathematics and technologies

3D printing is the process of being able to print any object layer by layer. But if we question this proposition, can we find any three dimensional objects that can't be printed layer by layer? To banish any disbeliefs we walked together through the mathematics that prove 3d printing is feasible for any real life object. 3d printers create three dimensional objects by building them up layer by layer. The current generation of 3d printers typically requires input from a CAD program in the form of an STL file, which defines a shape by a list of triangle vertices. The vast majority of 3d printers use two techniques, FDM (Fused Deposition Modelling) and PBP (Powder Binder Printing). One advanced form of 3d printing that has been an area of increasing scientific interest the recent years is bioprinting. Cell printers utilizing techniques similar to FDM were developed for bioprinting. These printers give us the ability to place cells in positions that mimic their respective positions in organs. Finally through series of case studies we show that 3d printers in medicine have made a massive breakthrough lately.
Architecture Technical Debt: Understanding Causes and a Qualitative Model

A known problem in large software companies is to balance the prioritization of short-term with long-term responsiveness. Specifically, architecture violations (Architecture Technical Debt) taken to deliver fast might hinder future feature development, which would hinder agility. We conducted a multiple-case embedded case study in 7 sites at 5 large companies in order to shed light on the current causes for the accumulation of Architectural Technical Debt that causes effort. We provide a taxonomy of the factors and their influence in the accumulation of debt, and we provide a qualitative model of how the debt is accumulated and recovered over time.
An introduction to hidden Markov models

The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
TraceMixer: Privacy-preserving crowd-sensing sans trusted third party

Crowd-sensing promises cheap and easy large scale data collection by tapping into the sensing and processing capabilities of smart phone users. However, the vast amount of fine-grained location data collected raises serious privacy concerns among potential contributors. In this paper, we argue that crowd-sensing has unique requirements w.r.t. privacy and data utility which renders existing protection mechanisms infeasible. We hence propose TraceMixer, a novel location privacy protection mechanism tailored to the special requirements in crowd-sensing. TraceMixer builds upon the well-studied concept of mix zones to provide trajectory privacy while achieving high spatial accuracy. First in this line of research, TraceMixer applies secure two-party computation technologies to realize a trustless architecture that does not require participants to share locations with anyone in clear. We evaluate TraceMixer on real-world datasets to show the feasibility of our approach in terms of privacy, utility, and performance. Finally, we demonstrate the applicability of TraceMixer in a real-world crowd-sensing campaign.
Nonvolatile memory design based on ferroelectric FETs

Ferroelectric FETs (FEFETs) offer intriguing possibilities for the design of low power nonvolatile memories by virtue of their three-terminal structure coupled with the ability of the ferroelectric (FE) material to retain its polarization in the absence of an electric field. Utilizing the distinct features of FEFETs, we propose a 2-transistor (2T) FEFET-based nonvolatile memory with separate read and write paths. With proper co-design at the device, cell and array levels, the proposed design achieves non-destructive read and lower write power at iso-write speed compared to standard FERAM. In addition, the FEFET-based memory exhibits high distinguishability with six orders of magnitude difference in the read currents corresponding to the two states. Comparative analysis based on experimentally calibrated models shows significant improvement of access energy-delay. For example, at a fixed write time of 550ps, the write voltage and energy are 58.5% and 67.7% lower than FERAM, respectively. These benefits are achieved with 2.4 times the area overhead. Further exploration of the proposed FEFET memory in energy harvesting nonvolatile processors shows an average improvement of 27% in forward progress over FERAM.
Autoencoding beyond pixels using a learned similarity metric

We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.
Comparative Study of Cloud Computing Data Security Methods

Cloud computing is the concept implemented to decipher the Daily Computing Problems. Cloud computing is basically virtual pool of resources and it provides these resources to users via internet. Cloud computing is the internet based development and used in computer technology. The prevalent problem associated with cloud computing is data privacy, security, anonymity and reliability etc. But the most important between them is security and how cloud provider assures it. The work plan here is to eliminate the concerns regarding data privacy using encryption algorithms to enhance the security in cloud. Have discussed about cloud computing security mechanisms and presented the comparative study of several algorithms.
Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization

Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.
Efficient Real-Time Auditing and Proof of Violation for Cloud Storage Systems

In this paper we study how to develop an efficient real-time auditing and proof of violation (POV) scheme for cloud storage systems. A POV scheme enables a user or a service provider to produce cryptographic proofs which can be used to prove either the occurrence of the violation of properties or the innocence of the service provider. POV schemes are solutions for obtaining mutual nonrepudiation between users and the service provider in the cloud. After each file operation, a real-time auditing should be performed so that the violation of the service provider can be found instantly. Existing solutions need to cache the hash values of files in client devices and thus the overhead for storing and synchronizing hash values in client devices which share files is huge. We propose a novel scheme in which client devices do not need to cache any hash values of files. A small portion called slice of a binary hash tree is transferred to the client device for real-time auditing and it can support POV whenever the audit does not pass. Experimental results are presented that demonstrate the feasibility of the proposed scheme and show that our scheme outperforms previous work by one to two order of magnitude. Service providers of cloud storage can use the proposed scheme to provide a mutual nonrepudiation guarantee in their service-level agreements.
Partisan Media Exposure and Attitudes Toward the Opposition

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Improving feature-based object recognition for X-ray baggage security screening using primed visualwords

We present a novel Bag-of-Words (BoW) representation scheme for image classification tasks, where the separation of features distinctive of different classes is enforced via class-specific feature-clustering. We investigate the implementation of this approach for the detection of firearms in baggage security X-ray imagery. We implement our novel BoW model using the Speeded-Up Robust Features (SURF) detector and descriptor within a Support Vector Machine (SVM) classifier framework. Experimentation on a large, diverse data set yields a significant improvement in classification performance over previous works with an optimal true positive rate of 99.07% at a false positive rate of 4.31%. Our results indicate that class-specific clustering primes the feature space and ultimately simplifies the classification process. We further demonstrate the importance of using diverse, representative data and efficient training and testing procedures. The excellent performance of the classifier is a strong indication of the potential advantages of this technique in threat object detection in security screening settings.
IMPROVED EXPLAINABILITY OF CAPSULE NETWORKS: RELEVANCE PATH BY AGREEMENT

Recent advancements in signal processing domain have resulted in a surge of interest in deep neural networks (DNNs) due to their unprecedented performance and high accuracy for challenging problems of significant engineering importance. However, when such deep learning architectures are utilized for making critical decisions such as the ones that involve human lives (e.g., in medical applications), it is of paramount importance to understand, trust, and in one word "explain" the rational behind deep models’ decisions. Generally, DNNs are considered as black-box systems, which do not provide any clue on their internal processing actions. Although some recent efforts have been initiated to explain behavior/decisions of deep networks, explainable artificial intelligence (XAI) domain is still in its infancy. In this regard, we consider capsule networks (referred to as CapsNets), which are novel deep structures; recently proposed as an alternative counterpart to convolutional neural networks (CNNs), and posed to change the future of machine intelligence. In this paper, we investigate and analyze structure and behavior of CapsNets and illustrate potential explainability properties of such networks. Furthermore, we show possibility of transforming deep architectures in to transparent networks via incorporation of capsules in different layers instead of convolution layers of the CNNs.
Predicting inter-thread cache contention on a chip multi-processor architecture

This paper studies the impact of L2 cache sharing on threads that simultaneously share the cache, on a chip multi-processor (CMP) architecture. Cache sharing impacts threads nonuniformly, where some threads may be slowed down significantly, while others are not. This may cause severe performance problems such as sub-optimal throughput, cache thrashing, and thread starvation for threads that fail to occupy sufficient cache space to make good progress. Unfortunately, there is no existing model that allows extensive investigation of the impact of cache sharing. To allow such a study, we propose three performance models that predict the impact of cache sharing on co-scheduled threads. The input to our models is the isolated L2 cache stack distance or circular sequence profile of each thread, which can be easily obtained on-line or off-line. The output of the models is the number of extra L2 cache misses for each thread due to cache sharing. The models differ by their complexity and prediction accuracy. We validate the models against a cycle-accurate simulation that implements a dual-core CMP architecture, on fourteen pairs of mostly SPEC benchmarks. The most accurate model, the inductive probability model, achieves an average error of only 3.9%. Finally, to demonstrate the usefulness and practicality of the model, a case study that details the relationship between an application's temporal reuse behavior and its cache sharing impact is presented.
Towards Integrating Plot and Character for Interactive Drama

Interactive drama concerns itself with building dramaticall y interesting virtual worlds inhabited by computer-controlled characters, within which the user (hereafter referred to as the player) experiences a story from a first person perspective (Bates 1992). Over the past decade there has been a fair amount of research into believable agents, that is, autonomous characters exhibiting rich personaliti es, emotions, and social interactions (Mateas 1997; Bates, Loyall and Reill y 1992; Blumberg 1996; Hayes-Roth, van Gent and Huber 1997; Lester and Stone 1997; Stern, Frank, and Resner 1998). There has been comparatively littl e work, however, exploring how the local, reactive behavior of believable agents can be integrated with the more global, deliberative nature of a story plot, so as to build interactive, dramatic worlds (Weyrauch 1997; Blumberg and Galyean 1995). The authors are currently engaged in a two to three year collaboration to build an interactive story world integrating believable agents and interactive plot. This paper provides a brief description of the project goals and design requirements, discusses the problem of autonomy in the context of story-based believable agents, and finall y describes an architecture that uses the dramatic beat as a structural principle to integrate plot and character.
A support vector machine with maximal information coefficient weighted kernel functions for regression

For some time past, support vector machine (SVM) has been generally used in pattern recognition, classification and prediction. However, in traditional SVM arithmetic, various kernels cannot recognize the importance of the feature vector properties, so the prediction accuracy seems to be unsatisfactory. For purpose of optimizing the problem, this paper proposes an modified support vector regression(SVR) method with maximal information coefficient(MIC) weighted kernel function(MWSVR) in this paper. After analyzing the correlation between feature variables and output variables through MIC and giving each feature weight on the radial basis function (RBF) kernel function, and then training the model and predicting the tendency. Adding MIC-weight vector on kernel function can help to identify the importance of the feature vectors and obtain better prediction accuracy. MWSVR has practicability and generalization.
Semantic Wordification of Document Collections

Word clouds have become one of the most widely accepted visual resources for document analysis and visualization, motivating the development of several methods for building layouts of keywords extracted from textual data. Existing methods are effective to demonstrate content, but are not capable of preserving semantic relationships among keywords while still linking the word cloud to the underlying document groups that generated them. Such representation is highly desirable for exploratory analysis of document collections. In this paper we present a novel approach to build document clouds, named ProjCloud that aim at solving both semantical layouts and linking with document sets. ProjCloud generates a semantically consistent layout from a set of documents. Through a multidimensional projection, it is possible to visualize the neighborhood relationship between highly related documents and their corresponding word clouds simultaneously. Additionally, we propose a new algorithm for building word clouds inside polygons, which employs spectral sorting to maintain the semantic relationship among words. The effectiveness and flexibility of our methodology is confirmed when comparisons are made to existing methods. The technique automatically constructs projection based layouts the user may choose to examine in the form of the point clouds or corresponding word clouds, allowing a high degree of control over the exploratory process.