nils-herrmann/clean_scirepeval huggingface dataset:
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.

Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.
Distance Metric Learning for Kernel Machines

Recent work in metric learning has significantly improved the state-of-the-art in k-nearest neighbor classification. Support vector machines (SVM), particularly with RBF kernels, are amongst the most popular classification algorithms that uses distance metrics to compare examples. This paper provides an empirical analysis of the efficacy of three of the most popular Mahalanobis metric learning algorithms as pre-processing for SVM training. We show that none of these algorithms generate metrics that lead to particularly satisfying improvements for SVM-RBF classification. As a remedy we introduce support vector metric learning (SVML), a novel algorithm that seamlessly combines the learning of a Mahalanobis metric with the training of the RBF-SVM parameters. We demonstrate the capabilities of SVML on nine benchmark data sets of varying sizes and difficulties. In our study, SVML outperforms all alternative state-of-the-art metric learning algorithms in terms of accuracy and establishes itself as a serious alternative to the standard Euclidean metric with model selection by cross validation.
Real-time bidding algorithms for performance-based display ad allocation

We describe a real-time bidding algorithm for performance-based display ad allocation. A central issue in performance display advertising is matching campaigns to ad impressions, which can be formulated as a constrained optimization problem that maximizes revenue subject to constraints such as budget limits and inventory availability. The current practice is to solve the optimization problem offline at a tractable level of impression granularity (e.g., the page level), and to serve ads online based on the precomputed static delivery scheme. Although this offline approach takes a global view to achieve optimality, it fails to scale to ad allocation at the individual impression level. Therefore, we propose a real-time bidding algorithm that enables fine-grained impression valuation (e.g., targeting users with real-time conversion data), and adjusts value-based bids according to real-time constraint snapshots (e.g., budget consumption levels). Theoretically, we show that under a linear programming (LP) primal-dual formulation, the simple real-time bidding algorithm is indeed an online solver to the original primal problem by taking the optimal solution to the dual problem as input. In other words, the online algorithm guarantees the offline optimality given the same level of knowledge an offline optimization would have. Empirically, we develop and experiment with two real-time bid adjustment approaches to adapting to the non-stationary nature of the marketplace: one adjusts bids against real-time constraint satisfaction levels using control-theoretic methods, and the other adjusts bids also based on the statistically modeled historical bidding landscape. Finally, we show experimental results with real-world ad delivery data that support our theoretical conclusions.
Detection of cracks in nuclear power plant using spatial-temporal grouping of local patches

Robust inspection is important to ensure the safety of nuclear power plant components. An automated approach would require detecting often low contrast cracks that could be surrounded by or even within textures with similar appearances such as welding, scratches and grind marks. We propose a crack detection method for nuclear power plant inspection videos by fine tuning a deep neural network for detecting local patches containing cracks which are then grouped in spatial-temporal space for group-level classification. We evaluate the proposed method on a data set consisting of 17 videos consisting of nearly 150,000 frames of inspection video and provide comparison to prior methods.
Logical Depth and Physical Complexity

Some mathematical and natural objects (a random sequence, a sequence of zeros, a perfect crystal, a gas) are intuitively trivial, while others (e.g. the human body, the digits of π) contain internal evidence of a nontrivial causal history. We formalize this distinction by defining an object’s “logical depth” as the time required by a standard universal Turing machine to generate it from an input that is algorithmically random (i.e. Martin-Löf random). This definition of depth is shown to be reasonably machineindependent, as well as obeying a slow-growth law: deep objects cannot be quickly produced from shallow ones by any deterministic process, nor with much probability by a probabilistic process, but can be produced slowly. Next we apply depth to the physical problem of “self-organization,” inquiring in particular under what conditions (e.g. noise, irreversibility, spatial and other symmetries of the initial conditions and equations of motion) statistical-mechanical model systems can imitate computers well enough to undergo unbounded increase of depth in the limit of infinite space and time.
Comparing In Situ and Multidimensional Relevance Judgments

To address concerns of TREC-style relevance judgments, we explore two improvements. The first one seeks to make relevance judgments contextual, collecting in situ feedback of users in an interactive search session and embracing usefulness as the primary judgment criterion. The second one collects multidimensional assessments to complement relevance or usefulness judgments, with four distinct alternative aspects examined in this paper - novelty, understandability, reliability, and effort.
 We evaluate different types of judgments by correlating them with six user experience measures collected from a lab user study. Results show that switching from TREC-style relevance criteria to usefulness is fruitful, but in situ judgments do not exhibit clear benefits over the judgments collected without context. In contrast, combining relevance or usefulness with the four alternative judgments consistently improves the correlation with user experience measures, suggesting future IR systems should adopt multi-aspect search result judgments in development and evaluation.
 We further examine implicit feedback techniques for predicting these judgments. We find that click dwell time, a popular indicator of search result quality, is able to predict some but not all dimensions of the judgments. We enrich the current implicit feedback methods using post-click user interaction in a search session and achieve better prediction for all six dimensions of judgments.
3D point cloud segmentation using topological persistence

In this paper, we present an approach to segment 3D point cloud data using ideas from persistent homology theory. The proposed algorithms first generate a simplicial complex representation of the point cloud dataset. Next, we compute the zeroth homology group of the complex which corresponds to the number of connected components. Finally, we extract the clusters of each connected component in the dataset. We show that this technique has several advantages over state of the art methods such as the ability to provide a stable segmentation of point cloud data under noisy or poor sampling conditions and its independence of a fixed distance metric.
Review of hybrid exoskeletons to restore gait following spinal cord injury.

Different approaches are available to compensate gait in persons with spinal cord injury, including passive orthoses, functional electrical stimulation (FES), and robotic exoskeletons. However, several drawbacks arise from each specific approach. Orthotic gait is energy-demanding for the user and functionally ineffective. FES uses the muscles as natural actuators to generate gait, providing not only functional but also psychological benefits to the users. However, disadvantages are also related to the early appearance of muscle fatigue and the control of joint trajectories. Robotic exoskeletons that provide joint moment compensation or substitution to the body during walking have been developed in recent years. Significant advances have been achieved, but the technology itself is not mature yet because of many limitations related to both physical and cognitive interaction as well as portability and energy-management issues. Meanwhile, the combination of FES technology and exoskeletons has emerged as a promising approach to both gait compensation and rehabilitation, bringing together technologies, methods, and rehabilitation principles that can overcome the drawbacks of each individual approach. This article presents an overview of hybrid lower-limb exoskeletons, related technologies, and advances in actuation and control systems. Also, we highlight the functional assessment of individuals with spinal cord injury.
Parallelization and characterization of pattern matching using GPUs

Pattern matching is a highly computationally intensive operation used in a plethora of applications. Unfortunately, due to the ever increasing storage capacity and link speeds, the amount of data that needs to be matched against a given set of patterns is growing rapidly. In this paper, we explore how the highly parallel computational capabilities of commodity graphics processing units (GPUs) can be exploited for high-speed pattern matching. We present the design, implementation, and evaluation of a pattern matching library running on the GPU, which can be used transparently by a wide range of applications to increase their overall performance. The library supports both string searching and regular expression matching on the NVIDIA CUDA architecture. We have also explored the performance impact of different types of memory hierarchies, and present solutions to alleviate memory congestion problems. The results of our performance evaluation using off-the-self graphics processors demonstrate that GPU-based pattern matching can reach tens of gigabits per second on different workloads.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsuper-vised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolu-tional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks-demonstrating their applicability as general image representations .
Culture and Cognitive Development

Human beings are biologically adapted for culture in ways that other primates are not. The difference can be clearly seen when the social learning skills of humans and their nearest primate relatives are systematically compared. The human adaptation for culture begins to make itself manifest in human ontogeny at around 1 year of age as human infants come to understand other persons as intentional agents like the self and so engage in joint attentional interactions with them. This understanding then enables young children (a) to employ some uniquely powerful forms of cultural learning to acquire the accumulated wisdom of their cultures, especially as embodied in language, and also (b) to comprehend their worlds in some uniquely powerful ways involving perspectivally based symbolic representations. Until fairly recently, the study of children's cognitive development was dominated by the theory of Jean Piaget. Piaget's theory was detailed , elaborate, comprehensive, and, in many important respects, wrong. In attempting to fill the theoretical vacuum created by Piaget's demise, developmental psychologists have sorted themselves into two main groups. In the first group are those theorists who emphasize biology. These neo-nativists believe that organic evolution has provided human beings with some specific domains of knowledge of the world and its workings and that this knowledge is best characterized as " innate. " Such domains include, for example , mathematics, language, biology , and psychology. In the other group are theorists who have focused on the cultural dimension of human cognitive development. These cultural psychologists begin with the fact that human children grow into cogni-tively competent adults in the context of a structured social world full of material and symbolic arti-facts such as tools and language, structured social interactions such as rituals and games, and cultural institutions such as families and religions. The claim is that the cultural context is not just a facilitator or motivator for cognitive development , but rather a unique " ontoge-netic niche " (i.e., a unique context for development) that actually structures human cognition in fundamental ways. There are many thoughtful scientists in each of these theoretical camps. This suggests the possibility that each has identified some aspects of the overall theory that will be needed to go beyond Piaget and incorporate adequately both the cultural and the biological dimensions of human cognitive development. What is needed to achieve this aim, in my opinion, is (a) an evolutionary approach to the human …
Explainable Agency for Intelligent Autonomous Systems

Explainable Agency As intelligent agents become more autonomous, sophisticated, and prevalent, it becomes increasingly important that humans interact with them effectively. Machine learning is now used regularly to acquire expertise, but common techniques produce opaque content whose behavior is difficult to interpret. Before they will be trusted by humans, autonomous agents must be able to explain their decisions and the reasoning that produced their choices. We will refer to this general ability as explainable agency. This capacity for explaining decisions is not an academic exercise. When a self-driving vehicle takes an unfamiliar turn, its passenger may desire to know its reasons. When a synthetic ally in a computer game blocks a player’s path, he may want to understand its purpose. When an autonomous military robot has abandoned a high-priority goal to pursue another one, its commander may request justification. As robots, vehicles, and synthetic characters become more selfreliant, people will require that they explain their behaviors on demand. The more impressive these agents’ abilities, the more essential that we be able to understand them.
Retrospective Mutiple Change-Point Estimation with Kernels

This contribution proposes an extension of the classic dynamic programming algorithm for detecting jumps in noisily observed piecewise-constant signals. The proposed algorithm operates (virtually) in a reproducing kernel Hilbert space through the use of an arbitrary kernel mapping. The resulting approach provides a computationally efficient an versatile tool for segmenting complex signals whose structure is not appropriately captured by standard parametric models.
On the zero-crossing distortion in single-phase PFC converters

Input current distortion in the vicinity of input voltage zero crossings of boost single-phase power factor corrected (PFC) ac-dc converters is studied in this paper. Previously known causes for the zero-crossing distortion are reviewed and are shown to be inadequate in explaining the observed input current distortion, especially under high ac line frequencies. A simple linear model is then presented which reveals two previously unknown causes for zero-crossing distortion, namely, the leading phase of the input current and the lack of critical damping in the current loop. Theoretical and practical limitations in reducing the phase lead and increasing the damping factor are discussed. A simple phase compensation technique to reduce the zero-crossing distortion is also presented. Numerical simulation and experimental results are presented to validate the theory.
ScratchJr demo: a coding language for kindergarten

This paper describes the ScratchJr research project, a collaboration between Tufts University's Developmental Technologies Research Group, MIT's Lifelong Kindergarten Group, and the Playful Invention Company. Over the past five years, dozens of ScratchJr prototypes have been designed and studied with over 300 K-2nd grade students, teachers and parents. ScratchJr allows children ages 5 to 7 years to explore concepts of computer programming and digital content creation in a safe and fun environment. This paper describes the progression of major prototypes leading to the current public version, as well as the educational resources developed for use with ScratchJr. Future directions and educational implications are also discussed.
Denial-of-Service in Wireless Sensor Networks: Attacks and Defenses

This survey of denial-of-service threats and countermeasures considers wireless sensor platforms' resource constraints as well as the denial-of-sleep attack, which targets a battery-powered device's energy supply. Here, we update the survey of denial-of-service threats with current threats and countermeasures.In particular, we more thoroughly explore the denial-of-sleep attack, which specifically targets the energy-efficient protocols unique to sensor network deployments. We start by exploring such networks' characteristics and then discuss how researchers have adapted general security mechanisms to account for these characteristics.
Ultra-fast programming of silicided polysilicon fuses based on new insights in the programming physics

New insights in the programming physics of silicided polysilicon fuses integrated in 90 nm CMOS have led to a programming time of 100 ns, while achieving a resistance increase of 107. This is an order of magnitude better than any previously published result for the programming time and resistance increase individually. Simple calculations and TEM-analyses substantiate the proposed programming mechanism. The advantage of a rectangular fuse head over a tapered fuse head is shown and explained
Code injection attacks on harvard-architecture devices

Harvard architecture CPU design is common in the embedded world. Examples of Harvard-based architecture devices are the Mica family of wireless sensors. Mica motes have limited memory and can process only very small packets. Stack-based buffer overflow techniques that inject code into the stack and then execute it are therefore not applicable. It has been a common belief that code injection is impossible on Harvard architectures. This paper presents a remote code injection attack for Mica sensors. We show how to exploit program vulnerabilities to permanently inject any piece of code into the program memory of an Atmel AVR-based sensor. To our knowledge, this is the first result that presents a code injection technique for such devices. Previous work only succeeded in injecting data or performing transient attacks. Injecting permanent code is more powerful since the attacker can gain full control of the target sensor. We also show that this attack can be used to inject a worm that can propagate through the wireless sensor network and possibly create a sensor botnet. Our attack combines different techniques such as return oriented programming and fake stack injection. We present implementation details and suggest some counter-measures.
Design and Validation of a Mobile Robot for Power Line Inspection and Maintenance

This paper presents the LineScout Technology, a mobile teleoperated robot for power line inspection and maintenance. Optimizing several geometric parameters achieved a compact design that was successfully tested over many line configurations and obstacle sequences. An overview of the technology is presented, including a description of the control strategy, followed by a section focusing on key aspects of the prototype thorough validation. Working on live lines, up to 735 kV and 1,000 A, means that the technology must be robust to electromagnetic interference. The third generation prototype, tested in laboratory and in field conditions, is now ready to undertake inspection pilot projects.
Dynamic programming and influence diagrams

A hstruct -The concept of a super value node is developed to estend the theor? of influence diagrams to allow dynamic programming to be performed within this graphical modeling framework. The operations necessa? to exploit the presence of these nodes and efficiently analyze the models are developed. The key result is that by reprewnting value function separability in the structure of the graph of the influence diagram. formulation is simplified and operations on the model can take advantage of the wparability. Froni the decision analysis perspective. this allows simple exploitation of separabilih in the value function of a decision problem which can significantly reduce memory and computation requirements. Importantly. this allows algorithms to be designed to solve influence diagrams that automatically recognize the opportunih for applying dynamic programming. From the decision processes perspective, influence diagrams with super value nodes allow efficient formulation and solution of nonstandard decision process structures. They a h allow the exploitation of conditional independence between state variables. Examples are provided that demonstrate these advantages.