nils-herrmann/clean_scirepeval huggingface dataset:
2 Machine Learning Methods for Computer Security 1 Executive Summary

The study of learning in adversarial environments is an emerging discipline at the juncture between machine learning and computer security. The interest in learning-based methods for securityand system-design applications comes from the high degree of complexity of phenomena underlying the security and reliability of computer systems. As it becomes increasingly difficult to reach the desired properties solely using statically designed mechanisms, learning methods are being used more and more to obtain a better understanding of various data collected from these complex systems. However, learning approaches can be evaded by adversaries, who change their behavior in response to the learning methods. To-date, there has been limited research into learning techniques that are resilient to attacks with provable robustness guarantees The Perspectives Workshop, “Machine Learning Methods for Computer Security” was convened to bring together interested researchers from both the computer security and machine learning communities to discuss techniques, challenges, and future research directions for secure learning and learning-based security applications. As a result of the twenty-two invited presentations, workgroup sessions and informal discussion, several priority areas of research were identified. The open problems identified in the field ranged from traditional applications of machine learning in security, such as attack detection and analysis of malicious software, to methodological issues related to secure learning, especially the development of new formal approaches with provable security guarantees. Finally a number of other potential applications were pinpointed outside of the traditional scope of computer security in which security issues may also arise in connection with data-driven methods. Examples of such applications are social media spam, plagiarism detection, authorship identification, copyright enforcement, computer vision (particularly in the context of biometrics), and sentiment analysis.
ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks

We propose to learn a curriculum or a syllabus for deep reinforcement learning and supervised learning with deep neural networks by an attachable deep neural network, called ScreenerNet. Specifically, we learn a weight for each sample by jointly training the ScreenerNet and the main network in an end-to-end selfpaced fashion. The ScreenerNet has neither sampling bias nor memory for the past learning history. We show the networks augmented with the ScreenerNet converge faster with better accuracy than the state-of-the-art curricular learning methods in extensive experiments of a Cart-pole task using Deep Q-learning and supervised visual recognition task using three vision datasets such as Pascal VOC2012, CIFAR10, and MNIST. Moreover, the ScreenerNet can be combined with other curriculum learning methods such as Prioritized Experience Replay (PER) for further accuracy improvement.
Evolution of photosynthesis.

Energy conversion of sunlight by photosynthetic organisms has changed Earth and life on it. Photosynthesis arose early in Earth's history, and the earliest forms of photosynthetic life were almost certainly anoxygenic (non-oxygen evolving). The invention of oxygenic photosynthesis and the subsequent rise of atmospheric oxygen approximately 2.4 billion years ago revolutionized the energetic and enzymatic fundamentals of life. The repercussions of this revolution are manifested in novel biosynthetic pathways of photosynthetic cofactors and the modification of electron carriers, pigments, and existing and alternative modes of photosynthetic carbon fixation. The evolutionary history of photosynthetic organisms is further complicated by lateral gene transfer that involved photosynthetic components as well as by endosymbiotic events. An expanding wealth of genetic information, together with biochemical, biophysical, and physiological data, reveals a mosaic of photosynthetic features. In combination, these data provide an increasingly robust framework to formulate and evaluate hypotheses concerning the origin and evolution of photosynthesis.
Non-Monotonic Sequential Text Generation

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy’s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order, while achieving competitive performance with conventional left-to-right generation.
Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance

The quality of word representations is frequently assessed using correlation with human judgements of word similarity. Here, we question whether such intrinsic evaluation can predict the merits of the representations for downstream tasks. We study the correlation between results on ten word similarity benchmarks and tagger performance on three standard sequence labeling tasks using a variety of word vectors induced from an unannotated corpus of 3.8 billion words, and demonstrate that most intrinsic evaluations are poor predictors of downstream performance. We argue that this issue can be traced in part to a failure to distinguish specific similarity from relatedness in intrinsic evaluation datasets. We make our evaluation tools openly available to facilitate further study.
Automotive E/E-architecture enhancements by usage of ethernet TSN

A huge upheaval emerges from the transition to autonomous vehicles in the domain of road vehicles, ongoing with a change in the vehicle architecture. Many sensors and Electronic Control Units are added to the current vehicle architecture and further safety requirements like reliability become even more necessary. In this paper we present a potential evolution of the Electrical/Electronic-Architecture, including a Zone Architecture, to enable future functionality. We reveal the impact on the communication network concerning these architectures and present a potential communication technology to facilitate such architectures.
Online Credit Card Fraud Detection: A Hybrid Framework with Big Data Technologies

In this paper, we focus on designing an online credit card fraud detection framework with big data technologies, by which we want to achieve three major goals: 1) the ability to fuse multiple detection models to improve accuracy, 2) the ability to process large amount of data and 3) the ability to do the detection in real time. To accomplish that, we propose a general workflow, which satisfies most design ideas of current credit card fraud detection systems. We further implement the workflow with a new framework which consists of four layers: distributed storage layer, batch training layer, key-value sharing layer and streaming detection layer. With the four layers, we are able to support massive trading data storage, fast detection model training, quick model data sharing and real-time online fraud detection, respectively. We implement it with latest big data technologies like Hadoop, Spark, Storm, HBase, etc. A prototype is implemented and tested with a synthetic dataset, which shows great potentials of achieving the above goals.
A research on vulnerability discovering for router protocols based on fuzzing

How to discover router vulnerabilities effectively and automatically is a critical problem to ensure network and information security. Previous research on router security is mostly about the technology of exploiting known flaws of routers. Fuzzing is a famous automated vulnerability finding technology, however, traditional Fuzzing tools are designed for testing network applications or other software. These tools are not or partly not suitable for testing routers. This paper designs a framework of discovering router protocol vulnerabilities, and proposes a mathematical model Two-stage Fuzzing Test Cases Generator (TFTCG) that improves previous methods to generate test cases. We have developed a tool called RPFuzzer based on TFTCG. RPFuzzer monitors routers by sending normal packets, keeping watch on CPU utilization and checking system logs, which can detect DoS, router reboot and so on. RPFuzzer'debugger based on modified Dynamips, which can record register values when an exception occurs. Finally, we experiment on the SNMP protocol, find 8 vulnerabilities,of which there are five unreleased vulnerabilities. The experiment has proved the effectiveness of RPFuzzer.
Journey of discovery: the night journey project as "video/game art"

This paper describes the development of a video/game art project being produced by media artist Bill Viola in collaboration with a team from the USC Game Innovation Lab, which uses a combination of both video and game technologies to explore the universal experience of an individual's journey towards enlightenment. Here, we discuss both the creative and technical approaches to achieving the project's goals of evoking in the player the sense of undertaking a spiritual journey.
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning

This paper presents KeypointNet, an end-to-end geometric reasoning framework to learn an optimal set of category-specific 3D keypoints, along with their detectors. Given a single image, KeypointNet extracts 3D keypoints that are optimized for a downstream task. We demonstrate this framework on 3D pose estimation by proposing a differentiable objective that seeks the optimal set of keypoints for recovering the relative pose between two views of an object. Our model discovers geometrically and semantically consistent keypoints across viewing angles and instances of an object category. Importantly, we find that our end-to-end framework using no ground-truth keypoint annotations outperforms a fully supervised baseline using the same neural network architecture on the task of pose estimation. The discovered 3D keypoints on the car, chair, and plane categories of ShapeNet  are visualized at keypointnet.github.io.
Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs

We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-quality crowd density and count estimation by explicitly incorporating global and local contextual information of crowd images. The proposed CP-CNN consists of four modules: Global Context Estimator (GCE), Local Context Estimator (LCE), Density Map Estimator (DME) and a Fusion-CNN (F-CNN). GCE is a VGG-16 based CNN that encodes global context and it is trained to classify input images into different density classes, whereas LCE is another CNN that encodes local context information and it is trained to perform patch-wise classification of input images into different density classes. DME is a multi-column architecture-based CNN that aims to generate high-dimensional feature maps from the input image which are fused with the contextual information estimated by GCE and LCE using F-CNN. To generate high resolution and high-quality density maps, F-CNN uses a set of convolutional and fractionally-strided convolutional layers and it is trained along with the DME in an end-to-end fashion using a combination of adversarial loss and pixellevel Euclidean loss. Extensive experiments on highly challenging datasets show that the proposed method achieves significant improvements over the state-of-the-art methods.
Radar-based perception for autonomous outdoor vehicles

Autonomous vehicle operations in outdoor environments challenge robotic perception. Construction, mining, agriculture, and planetary exploration environments are examples in which the presence of dust, fog, rain, changing illumination due to low sun angles, and lack of contrast can dramatically degrade conventional stereo and laser sensing. Nonetheless, environment perception can still succeed under compromised visibility through the use of a millimeter-wave radar. Radar also allows for multiple object detection within a single beam, whereas other range sensors are limited to one target return per emission. However, radar has shortcomings as well, such as a large footprint, specularity effects, and limited range resolution, all of which may result in poor environment survey or difficulty in interpretation. This paper presents a novel method for ground segmentation using a millimeter-wave radar mounted on a ground vehicle. Issues relevant to short-range perception in an outdoor environment are described along with field experiments and a quantitative comparison to laser data. The ability to classify the ground is successfully demonstrated in clear and low-visibility conditions, and significant improvement in range accuracy is shown. Finally, conclusions are drawn on the utility of millimeter-wave radar as a robotic sensor for persistent and accurate perception in natural scenarios.
Should I use a Monad or a Comonad ?

The category theoretic structures of monads and comonads can be used as an abstraction mechanism for simplifying both language semantics and programs. Monads have been used to structure impure computations, whilst comonads have been used to structure context-dependent computations. Interestingly, the class of computations structured by monads and the class of computations structured by comonads are not mutually exclusive. This paper formalises and explores the conditions under which a monad and a comonad can both structure the same notion of computation: when a comonad is left adjoint to a monad. Furthermore, we examine situations where a particular monad/comonad model of computation is deficient in capturing the essence of a computational pattern and provide a technique for calculating an alternative monad or comonad structure which fully captures the essence of the computation. Included is some discussion on how to choose between a monad or comonad structure in the case where either can be used to capture a particular notion of computation.
Design of Tall Buildings Preliminary Design and Optimization

The design of tall buildings essentially involves a conceptual design, approximate analysis, preliminary design and optimization, to safely carry gravity and lateral loads. The design criteria are, strength, serviceability, stability and human comfort. The strength is satisfied by limit stresses, while serviceability is satisfied by drift limits in the range of H/500 to H/1000. Stability is satisfied by sufficient factor of safety against buckling and P-Delta effects. The factor of safety is around 1.67 to 1.92. The human comfort aspects are satisfied by accelerations in the range of 10 to 25 milli-g, where g=acceleration due to gravity, about 981cms/sec^2. The aim of the structural engineer is to arrive at suitable structural schemes, to satisfy these criteria, and assess their structural weights in weight/unit area in square feet or square meters. This initiates structural drawings and specifications to enable construction engineers to proceed with fabrication and erection operations. The weight of steel in lbs/sqft or in kg/sqm is often a parameter the architects and construction managers are looking for from the structural engineer. This includes the weights of floor system, girders, braces and columns. The premium for wind, is optimized to yield drifts in the range of H/500, where H is the height of the tall building. Herein, some aspects of the design of gravity system, and the lateral system, are explored. Preliminary design and optimization steps are illustrated with examples of actual tall buildings designed by CBM Engineers, Houston, Texas, with whom the author has been associated with during the past 3 decades. Dr.Joseph P.Colaco, its President, has been responsible for the tallest buildings in Los Angeles, Houston, St. Louis, Dallas, New Orleans, and Washington, D.C, and with the author in its design staff as a Senior Structural Engineer. Research in the development of approximate methods of analysis, and preliminary design and optimization, has been conducted at WPI, with several of the author’s graduate students. These are also illustrated. Software systems to do approximate analysis of shear-wall frame, framed-tube, out rigger braced tall buildings are illustrated. Advanced Design courses in reinforced and pre-stressed concrete, as well as structural steel design at WPI, use these systems. Research herein, was supported by grants from NSF, Bethlehem Steel, and Army.
Experience-based Co-design and healthcare improvement: realising participatory design in the public sector

Over the last decade, growing attention has been paid to the potential value of design theory and practice in improving public services. Experience-based Co-design (EBCD) is a participatory research approach that draws upon design tools and ways of thinking in order to bring healthcare staff and patients together to improve the quality of care. The co-design process that is integral to EBCD is powerful but also challenging, as it requires both staff and patients to renegotiate their roles and expectations as part of a reconfiguration of the relationships of power between citizens and public services. In this paper, we reflect upon the implementation and adaptation of EBCD in a variety of projects and on the challenges of codesign work within healthcare settings. Our discussion aims to contribute to the growing field of service design and to encourage further research into how co-design processes shape and are shaped by the power relations that characterize contemporary public services.
Surgical anatomy of the midcheek and malar mounds.

The anatomy of the midcheek has not been satisfactorily described to adequately explain midcheek aging and malar mounds, nor has it suggested a logical approach to their correction or provided sufficient detail for safe surgery in this area. This cadaver study, which was complemented by many operative dissections, located a missing link: a glide plane space overlying the body of the zygoma. The space functions to allow mobility of the orbicularis oculi, where it overlies the zygoma and the origins of the elevator muscles to the upper lip. The space is a cleft between the sub-orbicularis oculi fat and the preperiosteal fat and is lined by a fine membrane. The anatomic boundaries are clearly defined by retaining ligaments, which correlate with the triangularity of the space. Several anatomic features provide the functional characteristics of the prezygomatic space, including the (1) absence of direct attachments between the orbicularis in the roof to the floor, (2) more rigid inferior boundary formed by the zygomatic ligaments, and (3) more mobile upper ligamentous boundary formed by the orbicularis retaining ligament (separating from the preseptal space of the lower lid). These components determine the characteristic aging changes that occur in this region and explain much about malar mounds. An appreciation of this anatomy has several surgical implications. The prezygomatic space is a junction area that can be approached from the temple, lower lid, and cheek. The zygomatic branches of the facial nerve to the orbicularis do not cross the space; rather, they course in the walls and in the sub-orbicularis fat within the roof of the space.
An Approach for Detection and Classification of Fruit Disease : A Survey

Agriculture is the mother of all cultures. It has played an important role in the development of human civilization. Using image Processing can detect degradation in plant/fruit. Plant fruit disease and leaf disease are crucial causes that which reduce quantity and can degrade the quality of the agricultural products. Currently chemicals are applied to the plants periodically without knowing the requirement of plants. There are some common symptoms of plant like color distortion, abnormal leaf growth, shriveled and damaged pods, stunted growth. Although diseases and insect pests can cause considerable yield losses or bring death to plants and it’s also directly affect to human health. These require careful diagnosis and timely handling to protect the crops from heavy losses. In plant, diseases can be found in various parts such as fruit, stem and leaves. This paper represents the survey of various approaches for segmentation method along with feature extraction and classifiers for detection of diseases in fruit and leaf.
Factorized Attention: Self-Attention with Linear Complexities

Recent works have been applying self-attention to various fields in computer vision and natural language processing. However, the memory and computational demands of existing self-attention operations grow quadratically with the spatiotemporal size of the input. This prohibits the application of self-attention on large inputs, e.g., long sequences, high-definition images, or large videos. To remedy this, this paper proposes a novel factorized attention (FA) module, which achieves the same expressive power as previous approaches with substantially less memory and computational consumption. The resource-efficiency allows more widespread and flexible application of it. Empirical evaluations on object recognition demonstrate the effectiveness of these advantages. FA-augmented models achieved state-ofthe-art performance for object detection and instance segmentation on MS-COCO. Further, the resource-efficiency of FA democratizes self-attention to fields where the prohibitively high costs currently prevent its application. The state-of-the-art result for stereo depth estimation on the Scene Flow dataset exemplifies this.
A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks

Many inverse problems are formulated as optimization problems over certain appropriate input distributions. Recently, there has been a growing interest in understanding the computational hardness of these optimization problems, not only in the worst case, but in an average-complexity sense under this same input distribution. In this note, we are interested in studying another aspect of hardness, related to the ability to learn how to solve a problem by simply observing a collection of previously solved instances. These are used to supervise the training of an appropriate predictive model that parametrizes a broad class of algorithms, with the hope that the resulting “algorithm” will provide good accuracy-complexity tradeoffs in the average sense. We illustrate this setup on the Quadratic Assignment Problem, a fundamental problem in Network Science. We observe that data-driven models based on Graph Neural Networks offer intriguingly good performance, even in regimes where standard relaxation based techniques appear to suffer.