nils-herrmann/clean_scirepeval huggingface dataset:
CrySL: Validating Correct Usage of Cryptographic APIs

Various studies have empirically shown that the majority of Java and Android appsmisuse cryptographic libraries, causing devastating breaches of data security. erefore, it is crucial to detect such misuses early in the development process. e fact that insecure usages are not the exception but the norm precludes approaches based on property inference and anomaly detection. In this paper, we present CrySL, a definition language that enables cryptography experts to specify the secure usage of the cryptographic libraries that they provide. CrySL combines the generic concepts of method-call sequences and data-flow constraints with domain-specific constraints related to cryptographic algorithms and their parameters. We have implemented a compiler that translates a CrySL ruleset into a contextand flow-sensitive demanddriven static analysis. e analysis automatically checks a given Java or Android app for violations of the CrySL-encoded rules. We empirically evaluated our ruleset through analyzing 10,001 Android apps. Our results show that misuse of cryptographic APIs is still widespread, with 96% of apps containing at least one misuse. However, we observed fewer of the misuses that were reported in previous work.
PNUTS: Yahoo!'s hosted data serving platform

We describe PNUTS, a massively parallel and geographically distributed database system for Yahoo!’s web applications. PNUTS provides data storage organized as hashed or ordered tables, low latency for large numbers of concurrent requests including updates and queries, and novel per-record consistency guarantees. It is a hosted, centrally managed, and geographically distributed service, and utilizes automated load-balancing and failover to reduce operational complexity. The first version of the system is currently serving in production. We describe the motivation for PNUTS and the design and implementation of its table storage and replication layers, and then present experimental results.
Conceptual Integration of Enterprise Architecture Management and Security Risk Management

Enterprise Architecture Management (EAM) is considered to provide the mechanism for, amongst others, governing enterprise transformations required by changes in the environment. In this paper, we focus on changes that result from the analysis of information security risks and of their impacts on the services delivered by an enterprise. We present how the concepts of an information system security risks management domain can be mapped into the ArchiMate enterprise architecture modeling language. We illustrate the application of the proposed approach through the handling of a lab case.
Towards Lexicalization of DBpedia Ontology with Unsupervised Learning and Semantic Role Labeling

Filling the gap between natural language expressions and ontology concepts or properties is the new trend in Semantic Web. Ontology lexicalization introduces a new layer of lexical information for ontology properties and concepts. We propose a method based on unsupervised learning for the extraction of the potential lexical expressions of DBpedia propertiesfrom Wikipedia text corpus. It is a resource-driven approach that comprises three main steps. The first step consists of the extraction of DBpedia triples for the aimed property followed by the extraction of Wikipedia articles describing the resources from these triples. In the second step, sentences mostly related to the property are extracted from the articles and they are analyzed with a Semantic Role Labeler resulting in a set of SRL annotated trees. In the last step, clusters of expressions are built using spectral clustering based on the distances between the SRL trees. The clusters with the least variance are considered to be relevant for the lexical expressions of the property.
mydNews: Ranked news based on sentiment analysis and recursive cosine similarity

News articles provide a large data set which can be used to classify information such as author's point of view, affiliation and biasedness. Classification on the basis of positive and negative content can help readers to understand the author's sentiments. mydNews application is a proposed a solution to achieve this objective of presenting author's opinion in an article. We performed sentiment analysis using CoreNLP API presented by Stanford and through experimentation we have reached to a conclusion that using 60% sentiment value of text and 40% of headline produces results nearest to human evaluation. We have proposed a new algorithm Recursive Cosine for similarity matching of news. Our algorithm uses classical cosine in a different way. We compared latest news articles with the one's published in same day by different authors and we have achieved 5% better results than classical cosine measure by using the newly constructed algorithm.
Design of Incoherent Frames via Convex Optimization

This paper describes a new procedure for the design of incoherent frames used in the field of sparse representations. We present an efficient algorithm for the design of incoherent frames that works well even when applied to the construction of relatively large frames. The main advantage of the proposed method is that it uses a convex optimization formulation that operates directly on the frame, and not on its Gram matrix. Solving a sequence of convex optimization problems allows for the introduction of constraints on the frame that were previously considered impossible or very hard to include, such as non-negativity. Numerous experimental results validate the approach.
Mapping the lateral extent of human cadaver decomposition with soil chemistry.

Soil below decomposing cadavers may have a different lateral spatial extent depending upon whether scavengers have access to the human cadaver or not. We examined the lateral spatial extent of decomposition products to a depth of 7cm of soils beneath two decomposing corpses, one in which the subject was autopsied, unclothed and placed under a wire cage to restrict scavenger access and one in which the subject was not autopsied, unclothed and exposed to scavengers. The two bodies had accumulated degree days (ADD) of 5799 and 5469 and post mortem interval (PMI) of 288 and 248d, respectively. The spatial extent for dissolved organic carbon (DOC) and organic nitrogen (DON) for both bodies was large but similar suggesting some movement off site for both compounds. Mean DOC was 1087±727 and 1484±1236μgg(-1) dry soil under the two corpses relative to 150±68μgg(-1) in upslope control soils. Sulfate tended to have 'hot spots' of lower values relative to the control soils indicative of anaerobic respiration. pH was lower and electrical conductivity was higher in the soil under both decomposing cadavers relative to control soils. Some of the nutrients examined downslope of the human remains were significantly higher than control soils upslope suggesting movement of decomposition products off-site which could be an important factor when using human remains detector dogs.
From the neuron doctrine to neural networks

For over a century, the neuron doctrine — which states that the neuron is the structural and functional unit of the nervous system — has provided a conceptual foundation for neuroscience. This viewpoint reflects its origins in a time when the use of single-neuron anatomical and physiological techniques was prominent. However, newer multineuronal recording methods have revealed that ensembles of neurons, rather than individual cells, can form physiological units and generate emergent functional properties and states. As a new paradigm for neuroscience, neural network models have the potential to incorporate knowledge acquired with single-neuron approaches to help us understand how emergent functional states generate behaviour, cognition and mental disease.
ZeroTouch: an optical multi-touch and free-air interaction architecture

ZeroTouch (ZT) is a unique optical sensing technique and architecture that allows precision sensing of hands, fingers, and other objects within a constrained 2-dimensional plane. ZeroTouch provides tracking at 80 Hz, and up to 30 concurrent touch points. Integration with LCDs is trivial. While designed for multi-touch sensing, ZT enables other new modalities, such as pen+touch and free-air interaction. In this paper, we contextualize ZT innovations with a review of other flat-panel sensing technologies. We present the modular sensing architecture behind ZT, and examine early diverse uses of ZT sensing.
Geometric Semantic Genetic Programming

Traditional Genetic Programming (GP) searches the space of functions/programs by using search operators that manipulate their syntactic representation, regardless of their actual semantics/behaviour. Recently, semantically aware search operators have been shown to outperform purely syntactic operators. In this work, using a formal geometric view on search operators and representations, we bring the semantic approach to its extreme consequences and introduce a novel form of GP – Geometric Semantic GP (GSGP) – that searches directly the space of the underlying semantics of the programs. This perspective provides new insights on the relation between program syntax and semantics, search operators and fitness landscape, and allows for principled formal design of semantic search operators for different classes of problems. We derive specific forms of GSGP for a number of classic GP domains and experimentally demonstrate their superiority to conventional operators.
Identification of selected medicinal plant leaves using image features and ANN

Identification of proper medicinal plants is quite challenging and it is the time to protect medicinal plants since several plant species are becoming extinct. Leaves are the key components of a plant. Here we have proposed a method for the extraction of shape, color and texture features from leaf images and training an artificial neural network (ANN) classifier to identify the exact leaf class. The key issue lies in the selection of proper image input features to attain high efficiency with less computational complexity. We tested the accuracy of the network with different combination of image features. The test results on 63 leaf images reveals that this method gives 94.4% accuracy with a minimum of eight input features. This approach is more promising for leaf identification systems that have minimum input and demand less computation time. This work has been implemented using the image processing and neural network toolboxes in MATLAB.
Discriminating IT Governance

T information technology (IT) governance literature predominantly explains firms’ IT governance choices, but not their strategic consequences. We develop the idea that a firm’s IT governance choices induce adeptness at strategically exploiting IT only when they are discriminatingly aligned with its departments’ knowledge outside their specialty. Discriminating means that governing the two undertheorized classes of IT assets—apps and infrastructure—requires “peripheral” knowledge in different departments. Analyses of data from 105 firms support our middle-range theory.
Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography.

Understanding the long association pathways that convey cortical connections is a critical step in exploring the anatomic substrates of cognition in health and disease. Diffusion tensor imaging (DTI) is able to demonstrate fibre tracts non-invasively, but present approaches have been hampered by the inability to visualize fibres that have intersecting trajectories (crossing fibres), and by the lack of a detailed map of the origins, course and terminations of the white matter pathways. We therefore used diffusion spectrum imaging (DSI) that has the ability to resolve crossing fibres at the scale of single MRI voxels, and identified the long association tracts in the monkey brain. We then compared the results with available expositions of white matter pathways in the monkey using autoradiographic histological tract tracing. We identified 10 long association fibre bundles with DSI that match the observations in the isotope material: emanating from the parietal lobe, the superior longitudinal fasciculus subcomponents I, II and III; from the occipital-parietal region, the fronto-occipital fasciculus; from the temporal lobe, the middle longitudinal fasciculus and from rostral to caudal, the uncinate fasciculus, extreme capsule and arcuate fasciculus; from the occipital-temporal region, the inferior longitudinal fasciculus; and from the cingulate gyrus, the cingulum bundle. We suggest new interpretations of the putative functions of these fibre bundles based on the cortical areas that they link. These findings using DSI and validated with reference to autoradiographic tract tracing in the monkey represent a considerable advance in the understanding of the fibre pathways in the cerebral white matter. By replicating the major features of these tracts identified by histological techniques in monkey, we show that DSI has the potential to cast new light on the organization of the human brain in the normal state and in clinical disorders.
COGENT: Certified Compilation for a Functional Systems Language

We present a self-certifying compiler for the Cogent systems language. Cogent is a restricted, polymorphic, higher-order, and purely functional language with linear types and without the need for a trusted runtime or garbage collector. It compiles to efficient C code that is designed to interoperate with existing C functions. The language is suited for layered systems code with minimal sharing such as file systems or network protocol control code. For a well-typed Cogent program, the compiler produces C code, a high-level shallow embedding of its semantics in Isabelle/HOL, and a proof that the C code correctly implements this embedding. The aim is for proof engineers to reason about the full semantics of real-world systems code productively and equationally, while retaining the interoperability and leanness of C. We describe the formal verification stages of the compiler, which include automated formal refinement calculi, a switch from imperative update semantics to functional value semantics formally justified by the linear type system, and a number of standard compiler phases such as type checking and monomorphisation. The compiler certificate is a series of language-level meta proofs and per-program translation validation phases, combined into one coherent top-level theorem in Isabelle/HOL.
Indoor smartphone localization via fingerprint crowdsourcing: challenges and approaches

Nowadays, smartphones have become indispensable to everyone, with more and more built-in location-based applications to enrich our daily life. In the last decade, fingerprinting based on RSS has become a research focus in indoor localization, due to its minimum hardware requirement and satisfiable positioning accuracy. However, its time-consuming and labor-intensive site survey is a big hurdle for practical deployments. Fingerprint crowdsourcing has recently been promoted to relieve the burden of site survey by allowing common users to contribute to fingerprint collection in a participatory sensing manner. For its promising commitment, new challenges arise to practice fingerprint crowdsourcing. This article first identifies two main challenging issues, fingerprint annotation and device diversity, and then reviews the state of the art of fingerprint crowdsourcing-based indoor localization systems, comparing their approaches to cope with the two challenges. We then propose a new indoor subarea localization scheme via fingerprint crowdsourcing, clustering, and matching, which first constructs subarea fingerprints from crowdsourced RSS measurements and relates them to indoor layouts. We also propose a new online localization algorithm to deal with the device diversity issue. Our experiment results show that in a typical indoor scenario, the proposed scheme can achieve a 95 percent hit rate to correctly locate a smartphone in its subarea.
Management challenges to implementing agile processes in traditional development organizations

Discussions with traditional developers and managers concerning agile software development practices nearly always contain two somewhat contradictory ideas. They find that on small, stand-alone projects, agile practices are less burdensome and more in tune with the software industry's increasing needs for rapid development and coping with continuous change. Managers face several barriers, real and perceived, when they try to bring agile approaches into traditional organizations. They categorized the barriers either as problems only in terms of scope or scale, or as significant general issues needing resolution. From these two categories, we've identified three areas - development process conflicts, business process conflicts, and people conflicts - that we believe are the critical challenges to software managers of large organizations in bringing agile approaches to bear in their projects.
Avoiding Confusing Features in Place Recognition

We seek to recognize the place depicted in a query image using a database of “street side” images annotated with geolocation information. This is a challenging task due to changes in scale, viewpoint and lighting between the query and the images in the database. One of the key problems in place recognition is the presence of objects such as trees or road markings, which frequently occur in the database and hence cause significant confusion between different places. As the main contribution, we show how to avoid features leading to confusion of particular places by using geotags attached to database images as a form of supervision. We develop a method for automatic detection of image-specific and spatially-localized groups of confusing features, and demonstrate that suppressing them significantly improves place recognition performance while reducing the database size. We show the method combines well with the state of the art bag-of-features model including query expansion, and demonstrate place recognition that generalizes over wide range of viewpoints and lighting conditions. Results are shown on a geotagged database of over 17K images of Paris downloaded from Google Street View.
Automated Generation of Attack Trees

Attack trees are widely used to represent threat scenarios in a succinct and intuitive manner, suitable for conveying security information to non-experts. The manual construction of such objects relies on the creativity and experience of specialists, and therefore it is error-prone and impracticable for large systems. Nonetheless, the automated generation of attack trees has only been explored in connection to computer networks and levering rich models, whose analysis typically leads to an exponential blow-up of the state space. We propose a static analysis approach where attack trees are automatically inferred from a process algebraic specification in a syntax-directed fashion, encompassing a great many application domains and avoiding incurring systematically an exponential explosion. Moreover, we show how the standard propositional denotation of an attack tree can be used to phrase interesting quantitative problems, that can be solved through an encoding into Satisfiability Modulo Theories. The flexibility and effectiveness of the approach is demonstrated on the study of a national-scale authentication system, whose attack tree is computed thanks to a Java implementation of the framework.
Object detection on panoramic images based on deep learning

Panoramic image can be widely used in many applications, such as virtual reality, visual surveillance and autonomous vehicle, because of its large field of view. However, the inherent distortion for panorama causes object detection to be a challenging task. This paper focuses on the multi-class objects detection in panoramic images using deep learning method. The proposed system uses three fisheye cameras to efficiently create panoramas and build a large dataset. A region based convolutional neutral network (R-CNN) is implemented to train and test on an indoor panoramic image dataset. Experiments show great improvement performance on ten categories of distorted indoor objects with a mean average precision of 68.7%.
Iterative point matching for registration of free-form curves and surfaces

A heuristic method has been developed for registering two sets of 3-D curves obtained by using an edge-based stereo system, or two dense 3-D maps obtained by using a correlation-based stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually approximately known. From this initial estimate, our algorithm computes observer motion with very good precision, which is required for environment modeling (e.g., building a Digital Elevation Map). Objects are represented by a set of 3-D points, which are considered as the samples of a surface. No constraint is imposed on the form of the objects. The proposed algorithm is based on iteratively matching points in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subset-subset matching. A least-squares technique is used to estimate 3-D motion from the point correspondences, which reduces the average distance between points in the two sets. Both synthetic and real data have been used to test the algorithm, and the results show that it is efficient and robust, and yields an accurate motion estimate.
Deep Parametric Continuous Convolutional Neural Networks

Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This limits their applicability to many real-world applications. In this paper we propose Parametric Continuous Convolution, a new learnable operator that operates over non-grid structured data. The key idea is to exploit parameterized kernel functions that span the full continuous vector space. This generalization allows us to learn over arbitrary data structures as long as their support relationship is computable. Our experiments show significant improvement over the state-of-the-art in point cloud segmentation of indoor and outdoor scenes, and lidar motion estimation of driving scenes.
Millimeter-wave mobile broadband with large scale spatial processing for 5G mobile communication

The phenomenal growth in mobile data traffic calls for a drastic increase in mobile network capacity beyond current 3G/4G networks. In this paper, we propose a millimeter wave mobile broadband (MMB) system for the next generation mobile communication system (5G). MMB taps into the vast spectrum in 3-300 GHz range to meet this growing demand. We reason why the millimeter wave spectrum is suitable for mobile broadband applications. We discuss the unique advantages of millimeter waves such as spectrum availability and large beamforming gain in small form factors. We also describe a practical MMB system design capable of providing Gb/s data rates at distances up to 500 meters and supports mobility up to 350 km/h. By means of system simulations, we show that a basic MMB system is capable of delivering an average cell throughput and cell-edge throughput performances that is 10-100 times better than the current 20MHz LTE-Advanced systems.