nils-herrmann/clean_scirepeval huggingface dataset:
Grounded Situation Models for Robots: Where words and percepts meet

Our long-term objective is to develop robots that engage in natural language-mediated cooperative tasks with humans. To support this goal, we are developing an amodal representation and associated processes which is called a grounded situation model (GSM). We are also developing a modular architecture in which the GSM resides in a centrally located module, around which there are language, perception, and action-related modules. The GSM acts as a sensor-updated "structured blackboard", that serves as a workspace with contents similar to a "theatrical stage" in the robot's "mind", which might be filled in with present, past or imagined situations. Two main desiderata drive the design of the GSM: first, "parsing" situations into ontological types and relations that reflect human language semantics, and second, allowing bidirectional translation between sensory-derived data/expectations and linguistic descriptions. We present an implemented system that allows of a range of conversational and assistive behavior by a manipulator robot. The robot updates beliefs (held in the GSM) about its physical environment, the human user, and itself, based on a mixture of linguistic, visual and proprioceptive evidence. It can answer basic questions about the present or past and also perform actions through verbal interaction. Most importantly, a novel contribution of our approach is the robot's ability for seamless integration of both language- and sensor-derived information about the situation: for example, the system can acquire parts of situations either by seeing them or by "imagining" them through descriptions given by the user: "There is a red ball at the left". These situations can later be used to create mental imagery and sensory expectations, thus enabling the aforementioned bidirectionality
Variational Inference for Policy Search in changing situations

Many policy search algorithms minimize the Kullback-Leibler (KL) divergence to a certain target distribution in order to fit their policy. The commonly used KL-divergence forces the resulting policy to be ’reward-attracted’. The policy tries to reproduce all positively rewarded experience while negative experience is neglected. However, the KL-divergence is not symmetric and we can also minimize the the reversed KL-divergence, which is typically used in variational inference. The policy now becomes ’cost-averse’. It tries to avoid reproducing any negatively-rewarded experience while maximizing exploration. Due to this ’cost-averseness’ of the policy, Variational Inference for Policy Search (VIP) has several interesting properties. It requires no kernelbandwith nor exploration rate, such settings are determined automatically by the inference. The algorithm meets the performance of state-of-theart methods while being applicable to simultaneously learning in multiple situations. We concentrate on using VIP for policy search in robotics. We apply our algorithm to learn dynamic counterbalancing of different kinds of pushes with human-like 2-link and 4-link robots.
Q: Exploit Hardening Made Easy

Prior work has shown that return oriented programming (ROP) can be used to bypass W⊕X, a software defense that stops shellcode, by reusing instructions from large libraries such as libc. Modern operating systems have since enabled address randomization (ASLR), which randomizes the location of libc, making these techniques unusable in practice. However, modern ASLR implementations leave smaller amounts of executable code unrandomized and it has been unclear whether an attacker can use these small code fragments to construct payloads in the general case. In this paper, we show defenses as currently deployed can be bypassed with new techniques for automatically creating ROP payloads from small amounts of unrandomized code. We propose using semantic program verification techniques for identifying the functionality of gadgets, and design a ROP compiler that is resistant to missing gadget types. To demonstrate our techniques, we build Q, an end-to-end system that automatically generates ROP payloads for a given binary. Q can produce payloads for 80% of Linux /usr/bin programs larger than 20KB. We also show that Q can automatically perform exploit hardening: given an exploit that crashes with defenses on, Q outputs an exploit that bypasses both W⊕X and ASLR. We show that Q can harden nine realworld Linux and Windows exploits, enabling an attacker to automatically bypass defenses as deployed by industry for those programs.
Approximating the Minimum Degree Spanning Tree to Within One from the Optimal Degree

We consider the problem of constructing a spanning tree for a graph G = (V,E) with n vertices whose maximal degree is the smallest among all spanning trees of G. This problem is easily shown to be NP-hard. We describe an iterative polynomial time approximation algorithm for this problem. This algorithm computes a spanning tree whose maximal degree is at most O(Δ + log n), where Δ is the degree of some optimal tree. The result is generalized to the case where only some vertices need to be connected (Steiner case) and to the case of directed graphs. It is then shown that our algorithm can be refined to produce a spanning tree of degree at most Δ + 1. Unless P = NP, this is the best bound achievable in polynomial time.
Wideband Microstrip 180 $^{\circ}$ Hybrid Utilizing Ground Slots

The letter describes the design of a wideband microstrip 180° hybrid, which makes use of two sides of a single-layer substrate. The device is formed by the out-of-phase and in-phase dividers which are suitably unified. By making use of ground slots and microstrip to slot transitions all four its ports are of microstrip type. The hybrid exhibits a well-balanced power division between the output ports and good quality return losses of the out-of-phase (difference) and in-phase (summation) ports across the band from 3 to 9 GHz. The isolation between the output ports is better than 10 dB. The isolation between the difference and summation ports is greater than 40 dB and the phase characteristics are close to the ideal 180° and 0°.
Driving Control Algorithm for Maneuverability, Lateral Stability, and Rollover Prevention of 4WD Electric Vehicles With Independently Driven Front and Rear Wheels

This paper describes a driving control algorithm for four-wheel-drive (4WD) electric vehicles equipped with two motors at front and rear driving shafts to improve vehicle maneuverability, lateral stability, and rollover prevention. The driving control algorithm consists of the following three parts: 1) a supervisory controller that determines the control mode, the admissible control region, and the desired dynamics, such as the desired speed and yaw rate; 2) an upper level controller that computes the traction force input and the yaw moment input to track the desired dynamics; and 3) a lower level controller that determines actual actuator commands, such as the front/rear driving motor torques and independent brake torques. The supervisory controller computes the admissible control region, namely, the relationship between the vehicle speed and the maximum curvature of the vehicle considering the maximum steering angle, lateral stability, and rollover prevention. In the lower level controller, a wheel slip controller is designed to keep the slip ratio at each wheel below a limit value. In addition, an optimization-based control allocation strategy is used to map the upper level and wheel slip control inputs to actual actuator commands, taking into account the actuator constraints. Numerical simulation studies have been conducted to evaluate the proposed driving control algorithm. It has been shown from simulation studies that vehicle maneuverability, lateral stability, and rollover mitigation performance can be significantly improved by the proposed driving controller.
Ordinal Regression with Multiple Output CNN for Age Estimation

To address the non-stationary property of aging patterns, age estimation can be cast as an ordinal regression problem. However, the processes of extracting features and learning a regression model are often separated and optimized independently in previous work. In this paper, we propose an End-to-End learning approach to address ordinal regression problems using deep Convolutional Neural Network, which could simultaneously conduct feature learning and regression modeling. In particular, an ordinal regression problem is transformed into a series of binary classification sub-problems. And we propose a multiple output CNN learning algorithm to collectively solve these classification sub-problems, so that the correlation between these tasks could be explored. In addition, we publish an Asian Face Age Dataset (AFAD) containing more than 160K facial images with precise age ground-truths, which is the largest public age dataset to date. To the best of our knowledge, this is the first work to address ordinal regression problems by using CNN, and achieves the state-of-the-art performance on both the MORPH and AFAD datasets.
CMOS and Memristor-Based Neural Network Design for Position Detection

Most hardware neural networks have a basic competitive learning rule on top of a more involved processing algorithm. This work highlights two basic learning rules/behavior: winner-take-all (WTA) and spike-timing-dependent plasticity (STDP). It also gives a design example implementing WTA combined with STDP in a position detector. A complementary metal-oxide-semiconductor (CMOS) and a memristor-MOS technology (MMOST) design simulation results are compared on the bases of power, area, and noise handling capabilities. Design and layout were done in 130-nm IBM process for CMOS, and the HSPICE model files for the process were used to simulate the CMOS part of the MMOST design. CMOS consumes area, 55-W max power, and requires a 3-dB SNR. On the other hand, the MMOST design consumes , 15-W max power, and requires a 4.8-dB SNR. There is a potential to improve upon analog computing with the adoption of MMOST designs.
A 10-Bit LCD Column Driver With Piecewise Linear Digital-to-Analog Converters

A 10-bit LCD column driver, consisting of piecewise linear digital to analog converters (DACs), is proposed. Piecewise linear compensation is utilized to reduce the die area and to increase the effective color depth. The data conversion is carried out by a resistor string type DAC (R-DAC) and a charge sharing DAC, which are used for the most significant bit and least significant bit data conversions, respectively. Gamma correction voltages are applied to the R-DAC to lit the inverse of the liquid crystal trans-mittance-voltage characteristic. The gamma correction can also be digitally fine-tuned in the timing controller or column drivers. A prototype 10-bit LCD column driver implemented in a 0.35-mum CMOS technology demonstrates that the settling time is within 3 mus and the average die size per channel is 0.063 mm2, smaller than those of column drivers based exclusively on R-DACs.
A Position Encoding Convolutional Neural Network Based on Dependency Tree for Relation Classification

With the renaissance of neural network in recent years, relation classification has again become a research hotspot in natural language processing, and leveraging parse trees is a common and effective method of tackling this problem. In this work, we offer a new perspective on utilizing syntactic information of dependency parse tree and present a position encoding convolutional neural network (PECNN) based on dependency parse tree for relation classification. First, treebased position features are proposed to encode the relative positions of words in dependency trees and help enhance the word representations. Then, based on a redefinition of “context”, we design two kinds of tree-based convolution kernels for capturing the semantic and structural information provided by dependency trees. Finally, the features extracted by convolution module are fed to a classifier for labelling the semantic relations. Experiments on the benchmark dataset show that PECNN outperforms state-of-the-art approaches. We also compare the effect of different position features and visualize the influence of treebased position feature by tracing back the convolution process.
Approaches to Legacy System Evolution

The approach that one chooses to evolve software-intensive systems depends on the organization, the system, and the technology. We believe that significant progress in system architecture, system understanding, object technology, and net-centric computing make it possible to economically evolve software systems to a state in which they exhibit greater functionality and maintainability. In particular, interface technology, wrapping technology, and network technology are opening many opportunities to leverage existing software assets instead of scrapping them and starting over. But these promising technologies cannot be applied in a vacuum or without management understanding and control. There must be a framework in which to motivate the organization to understand its business opportunities, its application systems, and its road to an improved target system. This report outlines a comprehensive system evolution approach that incorporates an enterprise framework for the application of the promising technologies in the context of legacy systems.
Self-Disclosure in Social Media : Extending the Functional Approach to Disclosure Motivations and Characteristics on Social Network Sites 1

This article introduces the functional model of self-disclosure on social network sites by integrating a functional theory of self-disclosure and research on audience representations as situational cues for activating interpersonal goals. According to this model, people pursue strategic goals and disclose differently depending on social media affordances, and self-disclosure goals mediate between media affordances and disclosure intimacy. The results of the empirical study examining self-disclosure motivations and characteristics in Facebook status updates, wall posts, and private messaging lend support to this model and provide insights into the motivational drivers of self-disclosure on SNSs, helping to reconcile traditional views on self-disclosure and self-disclosing behaviors in new media contexts.
Guided Optical Flow Learning

We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data. Supervised CNNs, due to their immense learning capacity, have shown superior performance on a range of computer vision problems including optical flow prediction. They however require the ground truth flow which is usually not accessible except on limited synthetic data. Without the guidance of ground truth optical flow, unsupervised CNNs often perform worse as they are naturally ill-conditioned. We therefore propose a novel framework in which proxy ground truth data generated from classical approaches is used to guide the CNN learning. The models are further refined in an unsupervised fashion using an image reconstruction loss. Our guided learning approach is competitive with or superior to state-of-the-art approaches on three standard benchmark datasets yet is completely unsupervised and can run in real time.
Versatile and robust 3D walking with a simulated humanoid robot (Atlas): A model predictive control approach

In this paper, we propose a novel walking method for torque controlled robots. The method is able to produce a wide range of speeds without requiring off-line optimizations and re-tuning of parameters. We use a quadratic whole-body optimization method running online which generates joint torques, given desired Cartesian accelerations of center of mass and feet. Using a dynamics model of the robot inside this optimizer, we ensure both compliance and tracking, required for fast locomotion. We have designed a foot-step planner that uses a linear inverted pendulum as simplified robot internal model. This planner is formulated as a quadratic convex problem which optimizes future steps of the robot. Fast libraries help us performing these calculations online. With very few parameters to tune and no perception, our method shows notable robustness against strong external pushes, relatively large terrain variations, internal noises, model errors and also delayed communication.
3D-MAM: 3D morphable appearance model for efficient fine head pose estimation from still images

Identity-invariant estimation of head pose from still images is a challenging task due to the high variability of facial appearance. We present a novel 3D head pose estimation approach, which utilizes the flexibility and expressibility of a dense generative 3D facial model in combination with a very fast fitting algorithm. The efficiency of the head pose estimation is obtained by a 2D synthesis of the facial input image. This optimization procedure drives the appearance and pose of the 3D facial model. In contrast to many other approaches we are specifically interested in the more difficult task of head pose estimation from still images, instead of tracking faces in image sequences. We evaluate our approach on two publicly available databases (FacePix and USF HumanID) and compare our method to the 3D morphable model and other state of the art approaches in terms of accuracy and speed.
Efficiency evaluation of single-phase solutions for AC-DC PFC boost converters for plug-in-hybrid electric vehicle battery chargers

In this paper, the bridgeless interleaved boost topology is proposed for plug-in hybrid electric vehicle and electric vehicle battery chargers to achieve high efficiency, which is critical to minimize the charger size, charging time and the amount and cost of electricity drawn from the utility. An analytical model for this topology is developed, enabling the calculation of power losses and efficiency. Experimental and simulation results of prototype units converting the universal AC input voltage to 400 V DC at 3.4 kW are given to verify the proof of concept, and analytical work reported in this paper.
Simulation of a Smart Grid City with Software Agents

In the future smart city, new information and communication technologies will enable a better management of the available resources. The future smart grid infrastructure is emerging as a complex system where fine-grained monitoring and control of energy generating and/or consuming entities within the electricity network is possible. This will result to better approaches that will boost energy efficiency. A simulation of a dynamic ecosystem such as the smart city, will enable us to test new concepts and resource-optimization approaches. Therefore we have analyzed, designed, and build a simulator based on software agents that attempts to create the dynamic behavior of a smart city. It simulates discrete heterogeneous devices that consume and/or produce energy, that are able to act autonomously and collaborate. The behavior of these devices and their groupings e.g. smart houses, has been modeled in order to map as near as possible the real behavior patterns of the respective physical objects.
Jointly Extracting Relations with Class Ties via Effective Deep Ranking

Connections between relations in relation extraction, which we call class ties, are common. In distantly supervised scenario, one entity tuple may have multiple relation facts. Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction. However, previous models are not effective or ignore to model this property. In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network with a general pairwise ranking framework, in which two novel ranking loss functions are introduced. Additionally, an effective method is presented to relieve the impact of NR (not relation) for model training, which significantly boosts our model performance. Experiments on a widely used dataset show that leveraging class ties will enhance extraction and demonstrate that our model is effective to learn class ties. Our model outperforms baselines significantly, achieving state-of-the-art performance. The source code of this paper can be obtained from https://github.com/ yehaibuaa/DS_RE_DeepRanking.
A Background Layer Model for Object Tracking Through Occlusion

Motion layer estimation has recently emerged as a promising object tracking method. In this paper, we extend previous research on layer-based tracker by introducing the concept of background occluding layers and explicitly inferring depth ordering of foreground layers. The background occluding layers lie in front of, behind, and in between foreground layers. Each pixel in the background regions belongs to one of these layers and occludes all the foreground layers behind it. Together with the foreground ordering, the complete information necessary for reliably tracking objects through occlusion is included in our representation. An MAP estimation framework is developed to simultaneously update the motion layer parameters, the ordering parameters, and the background occluding layers. Experimental results show that under various conditions with occlusion, including situations with moving objects undergoing complex motions or having complex interactions, our tracking algorithm is able to handle many difficult tracking tasks reliably.
SR-IOV Networking in Xen: Architecture, Design and Implementation

SR-IOV capable network devices offer the benefits of direct I/O throughput and reduced CPU utilization while greatly increasing the scalability and sharing capabilities of the device. SR-IOV allows the benefits of the paravirtualized driver’s throughput increase and additional CPU usage reductions in HVMs (Hardware Virtual Machines). SR-IOV uses direct I/O assignment of a network device to multiple VMs, maximizing the potential for using the full bandwidth capabilities of the network device, as well as enabling unmodified guest OS based device drivers which will work for different underlying VMMs. Drawing on our recent experience in developing an SR-IOV capable networking solution for the Xen hypervisor we discuss the system level requirements and techniques for SR-IOV enablement on the platform. We discuss PCI configuration considerations, direct MMIO, interrupt handling and DMA into an HVM using an IOMMU (I/O Memory Management Unit). We then explain the architectural, design and implementation considerations for SR-IOV networking in Xen in which the Physical Function has a driver running in the driver domain that serves as a “master” and each Virtual Function exposed to a guest VM has its own virtual driver.
PushTrust: An Efficient Recommendation Algorithm by Leveraging Trust and Distrust Relations

The significance of social-enhanced recommender systems is increasing, along with its practicality, as online reviews, ratings, friendship links, and follower relationships are increasingly becoming available. In recent years, there has been an upsurge of interest in exploiting social information, such as trust and distrust relations in recommendation algorithms. The goal is to improve the quality of suggestions and mitigate the data sparsity and the cold-start users problems in existing systems. In this paper, we introduce a general collaborative social ranking model to rank the latent features of users extracted from rating data based on the social context of users. In contrast to existing social regularization methods, the proposed framework is able to simultaneously leverage trust, distrust, and neutral relations, and has a linear dependency on the social network size. By integrating the ranking based social regularization idea into the matrix factorization algorithm, we propose a novel recommendation algorithm, dubbed PushTrust. Our experiments on the Epinions dataset demonstrate that collaboratively ranking the latent features of users by exploiting trust and distrust relations leads to a substantial increase in performance, and to effectively deal with cold-start users problem.