nils-herrmann/clean_scirepeval huggingface dataset:
Top-Down Visual Saliency Guided by Captions

Neural image/video captioning models can generate accurate descriptions, but their internal process of mapping regions to words is a black box and therefore difficult to explain. Top-down neural saliency methods can find important regions given a high-level semantic task such as object classification, but cannot use a natural language sentence as the top-down input for the task. In this paper, we propose Caption-Guided Visual Saliency to expose the region-to-word mapping in modern encoder-decoder networks and demonstrate that it is learned implicitly from caption training data, without any pixel-level annotations. Our approach can produce spatial or spatiotemporal heatmaps for both predicted captions, and for arbitrary query sentences. It recovers saliency without the overhead of introducing explicit attention layers, and can be used to analyze a variety of existing model architectures and improve their design. Evaluation on large-scale video and image datasets demonstrates that our approach achieves comparable captioning performance with existing methods while providing more accurate saliency heatmaps. Our code is available at visionlearninggroup.github.io/caption-guided-saliency/.
DNS Security Introduction and Requirements

Status of This Memo This document specifies an Internet standards track protocol for the Internet community, and requests discussion and suggestions for improvements. Please refer to the current edition of the "Internet Official Protocol Standards" (STD 1) for the standardization state and status of this protocol. Distribution of this memo is unlimited.  The Domain Name System Security Extensions (DNSSEC) add data origin authentication and data integrity to the Domain Name System. This document introduces these extensions and describes their capabilities and limitations. This document also discusses the services that the DNS security extensions do and do not provide. Last, this document describes the interrelationships between the documents that collectively describe DNSSEC.
Securing smart maintenance services: Hardware-security and TLS for MQTT

Increasing the efficiency of production and manufacturing processes is a key goal of initiatives like Industry 4.0. Within the context of the European research project ARROWHEAD, we enable and secure smart maintenance services. An overall goal is to proactively predict and optimize the Maintenance, Repair and Operations (MRO) processes carried out by a device maintainer, for industrial devices deployed at the customer. Therefore it is necessary to centrally acquire maintenance relevant equipment status data from remotely located devices over the Internet. Consequently, security and privacy issues arise from connecting devices to the Internet, and sending data from customer sites to the maintainer's back-end. In this paper we consider an exemplary automotive use case with an AVL Particle Counter (APC) as device. The APC transmits its status information by means of a fingerprint via the publish-subscribe protocol Message Queue Telemetry Transport (MQTT) to an MQTT Information Broker in the remotely located AVL back-end. In a threat analysis we focus on the MQTT routing information asset and identify two elementary security goals in regard to client authentication. Consequently we propose a system architecture incorporating a hardware security controller that processes the Transport Layer Security (TLS) client authentication step. We validate the feasibility of the concept by means of a prototype implementation. Experimental results indicate that no significant performance impact is imposed by the hardware security element. The security evaluation confirms the advanced security of our system, which we believe lays the foundation for security and privacy in future smart service infrastructures.
Bag-of-Entities Representation for Ranking

This paper presents a new bag-of-entities representation for document ranking, with the help of modern knowledge bases and automatic entity linking. Our system represents query and documents by bag-of-entities vectors constructed from their entity annotations, and ranks documents by their matches with the query in the entity space. Our experiments with Freebase on TREC Web Track datasets demonstrate that current entity linking systems can provide sufficient coverage of the general domain search task, and that bag-of-entities representations outperform bag-of-words by as much as 18% in standard document ranking tasks.
Protecting Software through Obfuscation: Can It Keep Pace with Progress in Code Analysis?

Software obfuscation has always been a controversially discussed research area. While theoretical results indicate that provably secure obfuscation in general is impossible, its widespread application in malware and commercial software shows that it is nevertheless popular in practice. Still, it remains largely unexplored to what extent today’s software obfuscations keep up with state-of-the-art code analysis and where we stand in the arms race between software developers and code analysts. The main goal of this survey is to analyze the effectiveness of different classes of software obfuscation against the continuously improving deobfuscation techniques and off-the-shelf code analysis tools.
 The answer very much depends on the goals of the analyst and the available resources. On the one hand, many forms of lightweight static analysis have difficulties with even basic obfuscation schemes, which explains the unbroken popularity of obfuscation among malware writers. On the other hand, more expensive analysis techniques, in particular when used interactively by a human analyst, can easily defeat many obfuscations. As a result, software obfuscation for the purpose of intellectual property protection remains highly challenging.
The anatomy and clinical implications of perioral submuscular fat.

One of the goals of lip augmentation is to recreate the natural shape and contour of the lips. The literature describes numerous fillers and multiple techniques for this procedure. However, there is a paucity of information regarding the anatomy of the fat layers of the upper and lower lips. While prosecting cadavers, the authors noted the presence of fat deep to the orbicularis muscle of the lip and a separate fat compartment deep to the mentalis muscle. This anatomy has not been previously described.



The authors dissected a total of 10 cadaver specimens. Dissection of the upper and lower lips and chin was performed. Latex injection of the arterial circuit aided the identification of boundaries. Histologic examination was performed on full-thickness specimens using hematoxylin and eosin stains.



Fat was noted deep to the orbicularis oris and mentalis muscles. This fat is distinct from the more superficial fat of the cutaneous lip. The wet-dry border of the lip appears to be the most anterior boundary of the submuscular lip fat. The lateral boundary is the zygomaticus muscle. Histologic examination confirms this macroscopic anatomy.



If volume loss is a significant component of facial aging, it becomes important for the clinician to know the precise anatomy of the subcutaneous tissue. Greater precision is possible in rejuvenating specific characteristics of aging. Augmentation at the vermilion cutaneous junction defines the vermilion border and improves perioral rhytides. Deep fat augmentation of the vermilion restores volume, shape, and lip eversion. Augmentation of submuscular fat beneath the cutaneous lip improves the convex shape. Submentalis fat augmentation specifically reduces the depth of the labiomental hollow. These findings are in agreement with previous observations that volume loss of deep fat--temporal, buccal, and sub-orbicularis oculi fat --contributes to the aging process. Knowledge of this anatomy and site-specific augmentation make it possible for the clinician to approach perioral rejuvenation in an algorithmic fashion.
Bandits with Knapsacks

Multi-armed bandit problems are the predominant theoretical model of exploration-exploitation tradeoffs in learning, and they have countless applications ranging from medical trials, to communication networks, to Web search and advertising. In many of these application domains, the learner may be constrained by one or more supply (or budget) limits, in addition to the customary limitation on the time horizon. The literature lacks a general model encompassing these sorts of problems. We introduce such a model, called bandits with knapsacks, that combines bandit learning with aspects of stochastic integer programming. In particular, a bandit algorithm needs to solve a stochastic version of the well-known knapsack problem, which is concerned with packing items into a limited-size knapsack. A distinctive feature of our problem, in comparison to the existing regret-minimization literature, is that the optimal policy for a given latent distribution may significantly outperform the policy that plays the optimal fixed arm. Consequently, achieving sublinear regret in the bandits-with-knapsacks problem is significantly more challenging than in conventional bandit problems.
 We present two algorithms whose reward is close to the information-theoretic optimum: one is based on a novel “balanced exploration” paradigm, while the other is a primal-dual algorithm that uses multiplicative updates. Further, we prove that the regret achieved by both algorithms is optimal up to polylogarithmic factors. We illustrate the generality of the problem by presenting applications in a number of different domains, including electronic commerce, routing, and scheduling. As one example of a concrete application, we consider the problem of dynamic posted pricing with limited supply and obtain the first algorithm whose regret, with respect to the optimal dynamic policy, is sublinear in the supply.
Scene Recognition by Manifold Regularized Deep Learning Architecture

Scene recognition is an important problem in the field of computer vision, because it helps to narrow the gap between the computer and the human beings on scene understanding. Semantic modeling is a popular technique used to fill the semantic gap in scene recognition. However, most of the semantic modeling approaches learn shallow, one-layer representations for scene recognition, while ignoring the structural information related between images, often resulting in poor performance. Modeled after our own human visual system, as it is intended to inherit humanlike judgment, a manifold regularized deep architecture is proposed for scene recognition. The proposed deep architecture exploits the structural information of the data, making for a mapping between visible layer and hidden layer. By the proposed approach, a deep architecture could be designed to learn the high-level features for scene recognition in an unsupervised fashion. Experiments on standard data sets show that our method outperforms the state-of-the-art used for scene recognition.
Siamese network features for image matching

Finding matching images across large datasets plays a key role in many computer vision applications such as structure-from-motion (SfM), multi-view 3D reconstruction, image retrieval, and image-based localisation. In this paper, we propose finding matching and non-matching pairs of images by representing them with neural network based feature vectors, whose similarity is measured by Euclidean distance. The feature vectors are obtained with convolutional neural networks which are learnt from labeled examples of matching and non-matching image pairs by using a contrastive loss function in a Siamese network architecture. Previously Siamese architecture has been utilised in facial image verification and in matching local image patches, but not yet in generic image retrieval or whole-image matching. Our experimental results show that the proposed features improve matching performance compared to baseline features obtained with networks which are trained for image classification task. The features generalize well and improve matching of images of new landmarks which are not seen at training time. This is despite the fact that the labeling of matching and non-matching pairs is imperfect in our training data. The results are promising considering image retrieval applications, and there is potential for further improvement by utilising more training image pairs with more accurate ground truth labels.
Pipelined Decision Tree Classification Accelerator Implementation in FPGA (DT-CAIF)

Decision tree classification (DTC) is a widely used technique in data mining algorithms known for its high accuracy in forecasting. As technology has progressed and available storage capacity in modern computers increased, the amount of data available to be processed has also increased substantially, resulting in much slower induction and classification times. Many parallel implementations of DTC algorithms have already addressed the issues of reliability and accuracy in the induction process. In the classification process, larger amounts of data require proportionately more execution time, thus hindering the performance of legacy systems. We have devised a pipelined architecture for the implementation of axis parallel binary DTC that dramatically improves the execution time of the algorithm while consuming minimal resources in terms of area. Scalability is achieved when connected to a high-speed communication unit capable of performing data transfers at a rate similar to that of the DTC engine. We propose a hardware accelerated solution composed of parallel processing nodes capable of independently processing data from a streaming source. Each engine processes the data in a pipelined fashion to use resources more efficiently and increase the achievable throughput. The results show that this system is 3.5 times faster than the existing hardware implementation of classification.
Pathological laughter and crying: a link to the cerebellum.

Patients with pathological laughter and crying (PLC) are subject to relatively uncontrollable episodes of laughter, crying or both. The episodes occur either without an apparent triggering stimulus or following a stimulus that would not have led the subject to laugh or cry prior to the onset of the condition. PLC is a disorder of emotional expression rather than a primary disturbance of feelings, and is thus distinct from mood disorders in which laughter and crying are associated with feelings of happiness or sadness. The traditional and currently accepted view is that PLC is due to the damage of pathways that arise in the motor areas of the cerebral cortex and descend to the brainstem to inhibit a putative centre for laughter and crying. In that view, the lesions 'disinhibit' or 'release' the laughter and crying centre. The neuroanatomical findings in a recently studied patient with PLC, along with new knowledge on the neurobiology of emotion and feeling, gave us an opportunity to revisit the traditional view and propose an alternative. Here we suggest that the critical PLC lesions occur in the cerebro-ponto-cerebellar pathways and that, as a consequence, the cerebellar structures that automatically adjust the execution of laughter or crying to the cognitive and situational context of a potential stimulus, operate on the basis of incomplete information about that context, resulting in inadequate and even chaotic behaviour.
A review of WBG power semiconductor devices

It is worldwide accepted that a real breakthrough in the Power Electronics field mainly comes from the development and use of Wide Band Gap (WBG) semiconductor devices. WBG semiconductors such as SiC, GaN, and diamond show superior material properties, which allow operation at high switching speed, high voltage and high temperature. These unique performances provide a qualitative change in their applications for energy processing. From energy generation to the end-user, the electric energy undergoes a number of conversions, which are currently highly inefficient to the point that it is estimated that only 20% of the whole energy involved in energy generation reaches the end-user. WGB semiconductors increase the conversion efficiency thanks to their outstanding material properties. The recent progress in the development of high voltage WBG power semiconductor devices, especially SiC and GaN, is reviewed. Future trends in device development and industrialization are also addressed.
Generating Coherent Patterns of Activity from Chaotic Neural Networks

Neural circuits display complex activity patterns both spontaneously and when responding to a stimulus or generating a motor output. How are these two forms of activity related? We develop a procedure called FORCE learning for modifying synaptic strengths either external to or within a model neural network to change chaotic spontaneous activity into a wide variety of desired activity patterns. FORCE learning works even though the networks we train are spontaneously chaotic and we leave feedback loops intact and unclamped during learning. Using this approach, we construct networks that produce a wide variety of complex output patterns, input-output transformations that require memory, multiple outputs that can be switched by control inputs, and motor patterns matching human motion capture data. Our results reproduce data on premovement activity in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid and powerful modulator of network activity than generally appreciated.
High Accuracy Monocular SFM and Scale Correction for Autonomous Driving

We present a real-time monocular visual odometry system that achieves high accuracy in real-world autonomous driving applications. First, we demonstrate robust monocular SFM that exploits multithreading to handle driving scenes with large motions and rapidly changing imagery. To correct for scale drift, we use known height of the camera from the ground plane. Our second contribution is a novel data-driven mechanism for cue combination that allows highly accurate ground plane estimation by adapting observation covariances of multiple cues, such as sparse feature matching and dense inter-frame stereo, based on their relative confidences inferred from visual data on a per-frame basis. Finally, we demonstrate extensive benchmark performance and comparisons on the challenging KITTI dataset, achieving accuracy comparable to stereo and exceeding prior monocular systems. Our SFM system is optimized to output pose within 50 ms in the worst case, while average case operation is over 30 fps. Our framework also significantly boosts the accuracy of applications like object localization that rely on the ground plane.
Virtual interference study for FMCW and PMCW radar

Mutual interference of radar systems has been identified as one of the major challenges for future automotive radar systems. In this work the interference of frequency (FMCW) and phase modulated continuous wave (PMCW) systems is investigated by means of simulations. All twofold combinations of the aforementioned systems are considered. The interference scenario follows a typical use-case from the well-known MOre Safety for All by Radar Interference Mitigation (MOSARIM) study. The investigated radar systems operate with similar system parameters to guarantee a certain comparability, but with different waveform durations, and chirps with different slopes and different phase code sequences, respectively. Since the effects in perfect synchrony are well understood, we focus on the cases where both systems exhibit a certain asynchrony. It is shown that the energy received from interferers can cluster in certain Doppler bins in the range-Doppler plane when systems exhibit a slight asynchrony.
Occupancy grid computation from dense stereo and sparse structure and motion points for automotive applications

We present a complete processing chain for computing 2D occupancy grids from image sequences. A multi layer grid is introduced which serves several purposes. First the 3D points reconstructed from the images are distributed onto the underlying grid. Thereafter a virtual measurement is computed for each cell thus reducing computational complexity and rejecting potential outliers. Subsequently a height profile is updated from which the current measurement is partitioned into ground and obstacle pixels. Different height profile update strategies are tested and compared yielding a stable height profile estimation. Lastly the occupancy layer of the grid is updated. To asses the algorithm we evaluate it quantitatively by comparing the output of it to ground truth data illustrating its accuracy. We show the applicability of the algorithm by using both, dense stereo reconstructed and sparse structure and motion points. The algorithm was implemented and run online on one of our test vehicles in real time.
Multi-Material Anisotropic Friction Wheels for Omnidirectional Ground Vehicles

In this paper, a novel omnidirectional vehicle with anisotropic friction wheels is presented. The proposed wheel has a series of bendable “nodes” on its circumference, each of which is made of two materials with differing friction properties: one material exhibits high friction, and the other exhibits low friction. The high friction section of the node generates a high traction force, while the low friction section enables the wheel to passively slide. The wheels are arranged such that the robot wheel exhibits high traction in its driving direction (much like a conventional tire), but low traction when sliding laterally. Due to this “anisotropic friction” property, the proposed wheel enables a vehicle to realize omnidirectional motion (i.e. the vehicle can move any direction within the plane—forward, back, or laterally). While many other omnidirectional wheel drives exist, the proposed wheel is simpler than any other existing design because the wheel is composed of a single, moldable element. This paper summarizes the design of the proposed wheel and presents a comparison between a small omnidirectional vehicle that uses the proposed wheel and an omnidirectional vehicle that uses conventional wheels.
A field study of refactoring challenges and benefits

It is widely believed that refactoring improves software quality and developer productivity. However, few empirical studies quantitatively assess refactoring benefits or investigate developers' perception towards these benefits. This paper presents a field study of refactoring benefits and challenges at Microsoft through three complementary study methods: a survey, semi-structured interviews with professional software engineers, and quantitative analysis of version history data. Our survey finds that the refactoring definition in practice is not confined to a rigorous definition of semantics-preserving code transformations and that developers perceive that refactoring involves substantial cost and risks. We also report on interviews with a designated refactoring team that has led a multi-year, centralized effort on refactoring Windows. The quantitative analysis of Windows 7 version history finds that the binary modules refactored by this team experienced significant reduction in the number of inter-module dependencies and post-release defects, indicating a visible benefit of refactoring.